WorldWideScience

Sample records for network response identification

  1. Identification and network-enabled characterization of auxin response factor genes in Medicago truncatula

    Directory of Open Access Journals (Sweden)

    David J. Burks

    2016-12-01

    Full Text Available The Auxin Response Factor (ARF family of transcription factors is an important regulator of environmental response and symbiotic nodulation in the legume Medicago truncatula. While previous studies have identified members of this family, a recent spurt in gene expression data coupled with genome update and reannotation calls for a reassessment of the prevalence of ARF genes and their interaction networks in M. truncatula. We performed a comprehensive analysis of the M. truncatula genome and transcriptome that entailed search for novel ARF genes and the co-expression networks. Our investigation revealed 8 novel M. truncatula ARF (MtARF genes, of the total 22 identified, and uncovered novel gene co-expression networks as well. Furthermore, the topological clustering and single enrichment analysis of several network models revealed the roles of individual members of the MtARF family in nitrogen regulation, nodule initiation, and post-embryonic development through a specialized protein packaging and secretory pathway. In summary, this study not just shines new light on an important gene family, but also provides a guideline for identification of new members of gene families and their functional characterization through network analyses.

  2. Identification of conserved drought stress responsive gene-network across tissues and developmental stages in rice.

    Science.gov (United States)

    Smita, Shuchi; Katiyar, Amit; Pandey, Dev Mani; Chinnusamy, Viswanathan; Archak, Sunil; Bansal, Kailash Chander

    2013-01-01

    Identification of genes that are coexpressed across various tissues and environmental stresses is biologically interesting, since they may play coordinated role in similar biological processes. Genes with correlated expression patterns can be best identified by using coexpression network analysis of transcriptome data. In the present study, we analyzed the temporal-spatial coordination of gene expression in root, leaf and panicle of rice under drought stress and constructed network using WGCNA and Cytoscape. Total of 2199 differentially expressed genes (DEGs) were identified in at least three or more tissues, wherein 88 genes have coordinated expression profile among all the six tissues under drought stress. These 88 highly coordinated genes were further subjected to module identification in the coexpression network. Based on chief topological properties we identified 18 hub genes such as ABC transporter, ATP-binding protein, dehydrin, protein phosphatase 2C, LTPL153 - Protease inhibitor, phosphatidylethanolaminebinding protein, lactose permease-related, NADP-dependent malic enzyme, etc. Motif enrichment analysis showed the presence of ABRE cis-elements in the promoters of > 62% of the coordinately expressed genes. Our results suggest that drought stress mediated upregulated gene expression was coordinated through an ABA-dependent signaling pathway across tissues, at least for the subset of genes identified in this study, while down regulation appears to be regulated by tissue specific pathways in rice.

  3. Identification of Gene Modules Associated with Low Temperatures Response in Bambara Groundnut by Network-Based Analysis.

    Directory of Open Access Journals (Sweden)

    Venkata Suresh Bonthala

    Full Text Available Bambara groundnut (Vigna subterranea (L. Verdc. is an African legume and is a promising underutilized crop with good seed nutritional values. Low temperature stress in a number of African countries at night, such as Botswana, can effect the growth and development of bambara groundnut, leading to losses in potential crop yield. Therefore, in this study we developed a computational pipeline to identify and analyze the genes and gene modules associated with low temperature stress responses in bambara groundnut using the cross-species microarray technique (as bambara groundnut has no microarray chip coupled with network-based analysis. Analyses of the bambara groundnut transcriptome using cross-species gene expression data resulted in the identification of 375 and 659 differentially expressed genes (p<0.01 under the sub-optimal (23°C and very sub-optimal (18°C temperatures, respectively, of which 110 genes are commonly shared between the two stress conditions. The construction of a Highest Reciprocal Rank-based gene co-expression network, followed by its partition using a Heuristic Cluster Chiseling Algorithm resulted in 6 and 7 gene modules in sub-optimal and very sub-optimal temperature stresses being identified, respectively. Modules of sub-optimal temperature stress are principally enriched with carbohydrate and lipid metabolic processes, while most of the modules of very sub-optimal temperature stress are significantly enriched with responses to stimuli and various metabolic processes. Several transcription factors (from MYB, NAC, WRKY, WHIRLY & GATA classes that may regulate the downstream genes involved in response to stimulus in order for the plant to withstand very sub-optimal temperature stress were highlighted. The identified gene modules could be useful in breeding for low-temperature stress tolerant bambara groundnut varieties.

  4. Delayed Cardiomyocyte Response to Total Body Particle Radiation Exposure - Identification of Regulatory Gene Network [proton

    Data.gov (United States)

    National Aeronautics and Space Administration — We examined molecular responses using transcriptome profiling in isolated left ventricular murine cardiomyocytes to 90 cGy 1 GeV proton (1H) and 15 cGy 1 GeV/nucleon...

  5. Delayed Cardiomyocyte Response to Total Body Particle Radiation Exposure - Identification of Regulatory Gene Network [iron

    Data.gov (United States)

    National Aeronautics and Space Administration — We examined molecular responses using transcriptome profiling in isolated left ventricular murine cardiomyocytes to 90 cGy 1 GeV proton (1H) and 15 cGy 1 GeV/nucleon...

  6. Identification of PEG-induced water stress responsive transcripts using co-expression network in Eucalyptus grandis.

    Science.gov (United States)

    Ghosh Dasgupta, Modhumita; Dharanishanthi, Veeramuthu

    2017-09-05

    Ecophysiological studies in Eucalyptus have shown that water is the principal factor limiting stem growth. Effect of water deficit conditions on physiological and biochemical parameters has been extensively reported in Eucalyptus. The present study was conducted to identify major polyethylene glycol induced water stress responsive transcripts in Eucalyptus grandis using gene co-expression network. A customized array representing 3359 water stress responsive genes was designed to document their expression in leaves of E. grandis cuttings subjected to -0.225MPa of PEG treatment. The differentially expressed transcripts were documented and significantly co-expressed transcripts were used for construction of network. The co-expression network was constructed with 915 nodes and 3454 edges with degree ranging from 2 to 45. Ninety four GO categories and 117 functional pathways were identified in the network. MCODE analysis generated 27 modules and module 6 with 479 nodes and 1005 edges was identified as the biologically relevant network. The major water responsive transcripts represented in the module included dehydrin, osmotin, LEA protein, expansin, arabinogalactans, heat shock proteins, major facilitator proteins, ARM repeat proteins, raffinose synthase, tonoplast intrinsic protein and transcription factors like DREB2A, ARF9, AGL24, UNE12, WLIM1 and MYB66, MYB70, MYB 55, MYB 16 and MYB 103. The coordinated analysis of gene expression patterns and coexpression networks developed in this study identified an array of transcripts that may regulate PEG induced water stress responses in E. grandis. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Entrepreneurial Idea Identification through Online Social Networks

    Science.gov (United States)

    Lang, Matthew C.

    2010-01-01

    The increasing use of social network websites may signal a change in the way the next generation of entrepreneurs identify entrepreneurial ideas. An important part of the entrepreneurship literature emphasizes how vital the use of social networks is to entrepreneurial idea identification, opportunity recognition, and ultimately new venture…

  8. Distribution network topology identification based on synchrophasor

    Directory of Open Access Journals (Sweden)

    Stefania Conti

    2018-03-01

    Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.

  9. Identification of generalized state transfer matrix using neural networks

    International Nuclear Information System (INIS)

    Zhu Changchun

    2001-01-01

    The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible

  10. A modular structure to accident identification using neural networks

    International Nuclear Information System (INIS)

    Duque Estrada, Cassius Rodrigo

    2005-01-01

    This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

  11. Crack identification by artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Hwu, C.B.; Liang, Y.C. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Inst. of Aeronaut. and Astronaut.

    1998-04-01

    In this paper, a most popular artificial neural network called the back propagation neural network (BPN) is employed to achieve an ideal on-line identification of the crack embedded in a composite plate. Different from the usual dynamic estimate, the parameters used for the present crack identification are the strains of static deformation. It is known that the crack effects are localized which may not be clearly reflected from the boundary information especially when the data is from static deformation only. To remedy this, we use data from multiple-loading modes in which the loading modes may include the opening, shearing and tearing modes. The results show that our method for crack identification is always stable and accurate no matter how far-away of the test data from its training set. (orig.) 8 refs.

  12. Parameter Identification by Bayes Decision and Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1994-01-01

    The problem of parameter identification by Bayes point estimation using neural networks is investigated.......The problem of parameter identification by Bayes point estimation using neural networks is investigated....

  13. Network identification of hormonal regulation.

    Directory of Open Access Journals (Sweden)

    Daniel J Vis

    Full Text Available Relations among hormone serum concentrations are complex and depend on various factors, including gender, age, body mass index, diurnal rhythms and secretion stochastics. Therefore, endocrine deviations from healthy homeostasis are not easily detected or understood. A generic method is presented for detecting regulatory relations between hormones. This is demonstrated with a cohort of obese women, who underwent blood sampling at 10 minute intervals for 24-hours. The cohort was treated with bromocriptine in an attempt to clarify how hormone relations change by treatment. The detected regulatory relations are summarized in a network graph and treatment-induced changes in the relations are determined. The proposed method identifies many relations, including well-known ones. Ultimately, the method provides ways to improve the description and understanding of normal hormonal relations and deviations caused by disease or treatment.

  14. 23 CFR 658.21 - Identification of National Network.

    Science.gov (United States)

    2010-04-01

    ... 23 Highways 1 2010-04-01 2010-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

  15. Response to ``Comment on `Adaptive Q-S (lag, anticipated, and complete) time-varying synchronization and parameters identification of uncertain delayed neural networks''' [Chaos 17, 038101 (2007)

    Science.gov (United States)

    Yu, Wenwu; Cao, Jinde

    2007-09-01

    Parameter identification of dynamical systems from time series has received increasing interest due to its wide applications in secure communication, pattern recognition, neural networks, and so on. Given the driving system, parameters can be estimated from the time series by using an adaptive control algorithm. Recently, it has been reported that for some stable systems, in which parameters are difficult to be identified [Li et al., Phys Lett. A 333, 269-270 (2004); Remark 5 in Yu and Cao, Physica A 375, 467-482 (2007); and Li et al., Chaos 17, 038101 (2007)], and in this paper, a brief discussion about whether parameters can be identified from time series is investigated. From some detailed analyses, the problem of why parameters of stable systems can be hardly estimated is discussed. Some interesting examples are drawn to verify the proposed analysis.

  16. Nonlinear identification of process dynamics using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Atiya, A.F.; Chong, K.T.

    1992-01-01

    In this paper the nonlinear identification of process dynamics encountered in nuclear power plant components is addressed, in an input-output sense, using artificial neural systems. A hybrid feedforward/feedback neural network, namely, a recurrent multilayer perceptron, is used as the model structure to be identified. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard backpropagation learning algorithm is modified, and it is used for the supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying process dynamics is investigated via the case study of a U-tube steam generator. The response of representative steam generator is predicted using a neural network, and it is compared to the response obtained from a sophisticated computer model based on first principles. The transient responses compare well, although further research is warranted to determine the predictive capabilities of these networks during more severe operational transients and accident scenarios

  17. Network-based identification of biomarkers coexpressed with multiple pathways.

    Science.gov (United States)

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

  18. [Terahertz Spectroscopic Identification with Deep Belief Network].

    Science.gov (United States)

    Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

    2015-12-01

    Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

  19. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  20. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  1. ISINA: INTEGRAL Source Identification Network Algorithm

    Science.gov (United States)

    Scaringi, S.; Bird, A. J.; Clark, D. J.; Dean, A. J.; Hill, A. B.; McBride, V. A.; Shaw, S. E.

    2008-11-01

    We give an overview of ISINA: INTEGRAL Source Identification Network Algorithm. This machine learning algorithm, using random forests, is applied to the IBIS/ISGRI data set in order to ease the production of unbiased future soft gamma-ray source catalogues. First, we introduce the data set and the problems encountered when dealing with images obtained using the coded mask technique. The initial step of source candidate searching is introduced and an initial candidate list is created. A description of the feature extraction on the initial candidate list is then performed together with feature merging for these candidates. Three training and testing sets are created in order to deal with the diverse time-scales encountered when dealing with the gamma-ray sky. Three independent random forests are built: one dealing with faint persistent source recognition, one dealing with strong persistent sources and a final one dealing with transients. For the latter, a new transient detection technique is introduced and described: the transient matrix. Finally the performance of the network is assessed and discussed using the testing set and some illustrative source examples. Based on observations with INTEGRAL, an ESA project with instruments and science data centre funded by ESA member states (especially the PI countries: Denmark, France, Germany, Italy, Spain), Czech Republic and Poland, and the participation of Russia and the USA. E-mail: simo@astro.soton.ac.uk

  2. Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

    Science.gov (United States)

    Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

    2018-06-01

    A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

  3. Identification of the Bacterial Community Responsible for ...

    African Journals Online (AJOL)

    Identification of bacteria community responsible for decontaminating Eleme petrochemical industrial effluent using 16S PCR denaturing gradient gel electrophoresis (DGGE) was determined. Gene profiles were determined by extracting DNA from bacterial isolates and amplified by polymerase chain reaction (PCR) using ...

  4. Particle identification using artificial neural networks at BESIII

    International Nuclear Information System (INIS)

    Qin Gang; Lv Junguang; Bian Jianming; Chinese Academy of Sciences, Beijing

    2008-01-01

    A multilayered perceptrons' neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. (authors)

  5. Transient identification system with noising data and 'don't know' response

    International Nuclear Information System (INIS)

    Mol, Antonio C. de A.; Martinez, Aquilino S.; Schirru, Roberto

    2002-01-01

    In the last years, many different approaches based on neural network (NN) has been proposed for transient identification in nuclear power plants (NPP). Some of them focus the dynamic identification using recurrent neural networks however, they are not able to deal with unrecognized transients. Other kind of solution uses competitive learning in order to allow the 'don't know' response. In this case dynamic, dynamic features are not well represented. This work presents a new approach for neural network based transient identification which allows either dynamic identification and 'don't know'response. Such approach uses two multilayer neural networks trained with backpropagation algorithm. The first one is responsible for the dynamic identification. This NN uses, a short set (in a movable time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide 'don't know' response. In order to validate the method a NPP transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in other to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. In order to validate the method, a NPP transient identification problem comprising 15 postulated accidents simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. (author)

  6. Boosted jet identification using particle candidates and deep neural networks

    CERN Document Server

    CMS Collaboration

    2017-01-01

    This note presents developments for the identification of hadronically decaying top quarks using deep neural networks in CMS. A new method that utilizes one dimensional convolutional neural networks based on jet constituent particles is proposed. Alternative methods using boosted decision trees based on jet observables are compared. The new method shows significant improvement in performance.

  7. FUZZY NEURAL NETWORK FOR OBJECT IDENTIFICATION ON INTEGRATED CIRCUIT LAYOUTS

    Directory of Open Access Journals (Sweden)

    A. A. Doudkin

    2015-01-01

    Full Text Available Fuzzy neural network model based on neocognitron is proposed to identify layout objects on images of topological layers of integrated circuits. Testing of the model on images of real chip layouts was showed a highеr degree of identification of the proposed neural network in comparison to base neocognitron.

  8. Topology Identification of General Dynamical Network with Distributed Time Delays

    International Nuclear Information System (INIS)

    Zhao-Yan, Wu; Xin-Chu, Fu

    2009-01-01

    General dynamical networks with distributed time delays are studied. The topology of the networks are viewed as unknown parameters, which need to be identified. Some auxiliary systems (also called the network estimators) are designed to achieve this goal. Both linear feedback control and adaptive strategy are applied in designing these network estimators. Based on linear matrix inequalities and the Lyapunov function method, the sufficient condition for the achievement of topology identification is obtained. This method can also better monitor the switching topology of dynamical networks. Illustrative examples are provided to show the effectiveness of this method. (general)

  9. Identification of host response signatures of infection.

    Energy Technology Data Exchange (ETDEWEB)

    Branda, Steven S.; Sinha, Anupama; Bent, Zachary

    2013-02-01

    Biological weapons of mass destruction and emerging infectious diseases represent a serious and growing threat to our national security. Effective response to a bioattack or disease outbreak critically depends upon efficient and reliable distinguishing between infected vs healthy individuals, to enable rational use of scarce, invasive, and/or costly countermeasures (diagnostics, therapies, quarantine). Screening based on direct detection of the causative pathogen can be problematic, because culture- and probe-based assays are confounded by unanticipated pathogens (e.g., deeply diverged, engineered), and readily-accessible specimens (e.g., blood) often contain little or no pathogen, particularly at pre-symptomatic stages of disease. Thus, in addition to the pathogen itself, one would like to detect infection-specific host response signatures in the specimen, preferably ones comprised of nucleic acids (NA), which can be recovered and amplified from tiny specimens (e.g., fingerstick draws). Proof-of-concept studies have not been definitive, however, largely due to use of sub-optimal sample preparation and detection technologies. For purposes of pathogen detection, Sandia has developed novel molecular biology methods that enable selective isolation of NA unique to, or shared between, complex samples, followed by identification and quantitation via Second Generation Sequencing (SGS). The central hypothesis of the current study is that variations on this approach will support efficient identification and verification of NA-based host response signatures of infectious disease. To test this hypothesis, we re-engineered Sandia's sophisticated sample preparation pipelines, and developed new SGS data analysis tools and strategies, in order to pioneer use of SGS for identification of host NA correlating with infection. Proof-of-concept studies were carried out using specimens drawn from pathogen-infected non-human primates (NHP). This work provides a strong foundation for

  10. Elucidation of time-dependent systems biology cell response patterns with time course network enrichment

    DEFF Research Database (Denmark)

    Wiwie, Christian; Rauch, Alexander; Haakonsson, Anders

    2018-01-01

    , no methods exist to integrate time series data with networks, thus preventing the identification of time-dependent systems biology responses. We close this gap with Time Course Network Enrichment (TiCoNE). It combines a new kind of human-augmented clustering with a novel approach to network enrichment...

  11. Optimization-based topology identification of complex networks

    International Nuclear Information System (INIS)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

  12. Neural network based electron identification in the ZEUS calorimeter

    International Nuclear Information System (INIS)

    Abramowicz, H.; Caldwell, A.; Sinkus, R.

    1995-01-01

    We present an electron identification algorithm based on a neural network approach applied to the ZEUS uranium calorimeter. The study is motivated by the need to select deep inelastic, neutral current, electron proton interactions characterized by the presence of a scattered electron in the final state. The performance of the algorithm is compared to an electron identification method based on a classical probabilistic approach. By means of a principle component analysis the improvement in the performance is traced back to the number of variables used in the neural network approach. (orig.)

  13. A neural model for transient identification in dynamic processes with 'don't know' response

    International Nuclear Information System (INIS)

    Mol, Antonio C. de A.; Martinez, Aquilino S.; Schirru, Roberto

    2003-01-01

    This work presents an approach for neural network based transient identification which allows either dynamic identification or a 'don't know' response. The approach uses two 'jump' multilayer neural networks (NN) trained with the backpropagation algorithm. The 'jump' network is used because it is useful to dealing with very complex patterns, which is the case of the space of the state variables during some abnormal events. The first one is responsible for the dynamic identification. This NN uses, as input, a short set (in a moving time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide a 'don't know' response. In order to validate the method, a Nuclear Power Plant (NPP) transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor (PWR), was proposed in the validation process it has been considered noisy data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. Another important point studied in this work is that the system has shown to be independent of a trigger signal which indicates the beginning of the transient, thus making it robust in relation to this limitation

  14. Two-component network model in voice identification technologies

    Directory of Open Access Journals (Sweden)

    Edita K. Kuular

    2018-03-01

    Full Text Available Among the most important parameters of biometric systems with voice modalities that determine their effectiveness, along with reliability and noise immunity, a speed of identification and verification of a person has been accentuated. This parameter is especially sensitive while processing large-scale voice databases in real time regime. Many research studies in this area are aimed at developing new and improving existing algorithms for presentation and processing voice records to ensure high performance of voice biometric systems. Here, it seems promising to apply a modern approach, which is based on complex network platform for solving complex massive problems with a large number of elements and taking into account their interrelationships. Thus, there are known some works which while solving problems of analysis and recognition of faces from photographs, transform images into complex networks for their subsequent processing by standard techniques. One of the first applications of complex networks to sound series (musical and speech analysis are description of frequency characteristics by constructing network models - converting the series into networks. On the network ontology platform a previously proposed technique of audio information representation aimed on its automatic analysis and speaker recognition has been developed. This implies converting information into the form of associative semantic (cognitive network structure with amplitude and frequency components both. Two speaker exemplars have been recorded and transformed into pertinent networks with consequent comparison of their topological metrics. The set of topological metrics for each of network models (amplitude and frequency one is a vector, and together  those combine a matrix, as a digital "network" voiceprint. The proposed network approach, with its sensitivity to personal conditions-physiological, psychological, emotional, might be useful not only for person identification

  15. Neural network application for illicit substances identification

    International Nuclear Information System (INIS)

    Nunes, Wallace V.; Silva, Ademir X. da; Crispim, Verginia R.; Schirru, Roberto

    2000-01-01

    Thermal neutron activation analysis is based on neutron capture prompt gamma-ray analysis and has been used in wide variety of fields, for examples, for inspection of checked airline baggage and for detection of buried land mines. In all of these applications, the detected γ-ray intensities from the elements present are used to estimate their concentrations. A study about application using a trained neutral network is presented to determine the presence of illicit substances, such as explosives and drugs, carried out in the luggages. The illicit substances emit characteristic detected γ-ray which are the fingerprint of each isotope. The fingerprint data-base of the gamma-ray spectrum of substances is obtained via Monte Carlo N-Particle Transport code, MCNP, version 4B. It was possible to train the neural network to determine the presence of explosives and narcotics even hidden by several materials. (author)

  16. Plant Species Identification by Bi-channel Deep Convolutional Networks

    Science.gov (United States)

    He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

    2018-04-01

    Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

  17. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  18. Identification of important nodes in directed biological networks: a network motif approach.

    Directory of Open Access Journals (Sweden)

    Pei Wang

    Full Text Available Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA, this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  19. White blood cells identification system based on convolutional deep neural learning networks.

    Science.gov (United States)

    Shahin, A I; Guo, Yanhui; Amin, K M; Sharawi, Amr A

    2017-11-16

    White blood cells (WBCs) differential counting yields valued information about human health and disease. The current developed automated cell morphology equipments perform differential count which is based on blood smear image analysis. Previous identification systems for WBCs consist of successive dependent stages; pre-processing, segmentation, feature extraction, feature selection, and classification. There is a real need to employ deep learning methodologies so that the performance of previous WBCs identification systems can be increased. Classifying small limited datasets through deep learning systems is a major challenge and should be investigated. In this paper, we propose a novel identification system for WBCs based on deep convolutional neural networks. Two methodologies based on transfer learning are followed: transfer learning based on deep activation features and fine-tuning of existed deep networks. Deep acrivation featues are extracted from several pre-trained networks and employed in a traditional identification system. Moreover, a novel end-to-end convolutional deep architecture called "WBCsNet" is proposed and built from scratch. Finally, a limited balanced WBCs dataset classification is performed through the WBCsNet as a pre-trained network. During our experiments, three different public WBCs datasets (2551 images) have been used which contain 5 healthy WBCs types. The overall system accuracy achieved by the proposed WBCsNet is (96.1%) which is more than different transfer learning approaches or even the previous traditional identification system. We also present features visualization for the WBCsNet activation which reflects higher response than the pre-trained activated one. a novel WBCs identification system based on deep learning theory is proposed and a high performance WBCsNet can be employed as a pre-trained network. Copyright © 2017. Published by Elsevier B.V.

  20. A Gamma Memory Neural Network for System Identification

    Science.gov (United States)

    Motter, Mark A.; Principe, Jose C.

    1992-01-01

    A gamma neural network topology is investigated for a system identification application. A discrete gamma memory structure is used in the input layer, providing delayed values of both the control inputs and the network output to the input layer. The discrete gamma memory structure implements a tapped dispersive delay line, with the amount of dispersion regulated by a single, adaptable parameter. The network is trained using static back propagation, but captures significant features of the system dynamics. The system dynamics identified with the network are the Mach number dynamics of the 16 Foot Transonic Tunnel at NASA Langley Research Center, Hampton, Virginia. The training data spans an operating range of Mach numbers from 0.4 to 1.3.

  1. Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems

    CERN Document Server

    Patan, Maciej

    2012-01-01

    Sensor networks have recently come into prominence because they hold the potential to revolutionize a wide spectrum of both civilian and military applications. An ingenious characteristic of sensor networks is the distributed nature of data acquisition. Therefore they seem to be ideally prepared for the task of monitoring processes with spatio-temporal dynamics which constitute one of most general and important classes of systems in modelling of the real-world phenomena. It is clear that careful deployment and activation of sensor nodes are critical for collecting the most valuable information from the observed environment. Optimal Sensor Network Scheduling in Identification of Distributed Parameter Systems discusses the characteristic features of the sensor scheduling problem, analyzes classical and recent approaches, and proposes a wide range of original solutions, especially dedicated for networks with mobile and scanning nodes. Both researchers and practitioners will find the case studies, the proposed al...

  2. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  3. Network Culture, Performance & Corporate Responsibility

    OpenAIRE

    Silvio M. Brondoni

    2003-01-01

    The growth and sustainability of free market economies highlights the need to define rules more suited to the current condition of market globalisation and also encourages firms to adopt more transparent and accountable corporate responsibility (and corporate social responsibility, namely the relationship between the company, environment and social setting). From a managerial perspective, corporate responsibility is linked to ensure the lasting pursuit of the company mission, seeking increasi...

  4. On the identification of instabilities with neural networks on JET

    International Nuclear Information System (INIS)

    Murari, A.; Arena, P.; Buscarino, A.; Fortuna, L.; Iachello, M.

    2013-01-01

    JET plasmas are affected by various instabilities, which can be particularly dangerous in high performance discharges. An identification method, based on the use of advanced neural networks, called Recurrent Neural Networks (RNNs), has been applied to ELMs. The potential of the recurrent networks to identify the dynamics of the instabilities has been first tested using synthetic data. The networks have then been applied to JET experimental signals. An appropriate selection of the networks topology allows identifying quite well the time evolution of the edge temperature and of the magnetic fields, considered the best indicators of the ELMs. A quite limited number of periodic oscillations are used to train the networks, which then manage to follow quite well the dynamics of the instabilities, in a recurrent configuration on one of the inputs. The time evolution of the aforementioned signals, also during intervals not used in the training and never seen by the networks, are properly reproduced. A careful analysis of the various terms in the RNNs has the potential to give clear indications about the nature of these instabilities and their dynamical behaviour

  5. Data identification for improving gene network inference using computational algebra.

    Science.gov (United States)

    Dimitrova, Elena; Stigler, Brandilyn

    2014-11-01

    Identification of models of gene regulatory networks is sensitive to the amount of data used as input. Considering the substantial costs in conducting experiments, it is of value to have an estimate of the amount of data required to infer the network structure. To minimize wasted resources, it is also beneficial to know which data are necessary to identify the network. Knowledge of the data and knowledge of the terms in polynomial models are often required a priori in model identification. In applications, it is unlikely that the structure of a polynomial model will be known, which may force data sets to be unnecessarily large in order to identify a model. Furthermore, none of the known results provides any strategy for constructing data sets to uniquely identify a model. We provide a specialization of an existing criterion for deciding when a set of data points identifies a minimal polynomial model when its monomial terms have been specified. Then, we relax the requirement of the knowledge of the monomials and present results for model identification given only the data. Finally, we present a method for constructing data sets that identify minimal polynomial models.

  6. Distribution network fault section identification and fault location using artificial neural network

    DEFF Research Database (Denmark)

    Dashtdar, Masoud; Dashti, Rahman; Shaker, Hamid Reza

    2018-01-01

    In this paper, a method for fault location in power distribution network is presented. The proposed method uses artificial neural network. In order to train the neural network, a series of specific characteristic are extracted from the recorded fault signals in relay. These characteristics...... components of the sequences as well as three-phase signals could be obtained using statistics to extract the hidden features inside them and present them separately to train the neural network. Also, since the obtained inputs for the training of the neural network strongly depend on the fault angle, fault...... resistance, and fault location, the training data should be selected such that these differences are properly presented so that the neural network does not face any issues for identification. Therefore, selecting the signal processing function, data spectrum and subsequently, statistical parameters...

  7. Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

    Science.gov (United States)

    Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

    2016-01-01

    Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

  8. User Identification Framework in Social Network Services Environment

    Directory of Open Access Journals (Sweden)

    Brijesh BAKARIYA

    2014-01-01

    Full Text Available Social Network Service is a one of the service where people may communicate with one an-other; and may also exchange messages even of any type of audio or video communication. Social Network Service as name suggests a type of network. Such type of web application plays a dominant role in internet technology. In such type of online community, people may share their common interest. Facebook LinkedIn, orkut and many more are the Social Network Service and it is good medium of making link with people having unique or common interest and goals. But the problem of privacy protection is a big issue in today’s world. As social networking sites allows anonymous users to share information of other stuffs. Due to which cybercrime is also increasing to a rapid extent. In this article we preprocessed the web log data of Social Network Services and assemble that data on the basis of image file format like jpg, jpeg, gif, png, bmp etc. and also propose a framework for victim’s identification.

  9. Identification of influential users by neighbors in online social networks

    Science.gov (United States)

    Sheikhahmadi, Amir; Nematbakhsh, Mohammad Ali; Zareie, Ahmad

    2017-11-01

    Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors' role for each user, a two-level method is proposed for both detecting users' influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects.

  10. ISOLATION AND IDENTIFICATION OF FUNGI RESPONSIBLE FOR ...

    African Journals Online (AJOL)

    User

    technique. The diseased plant leaves were taken to the laboratory for culture, isolation, and identification ... The tree is native to Asia particularly eastern India, Burma, and the Andaman ..... in Southeastern Nigeria and Biological. Control with ...

  11. Stability Analysis of Neural Networks-Based System Identification

    Directory of Open Access Journals (Sweden)

    Talel Korkobi

    2008-01-01

    Full Text Available This paper treats some problems related to nonlinear systems identification. A stability analysis neural network model for identifying nonlinear dynamic systems is presented. A constrained adaptive stable backpropagation updating law is presented and used in the proposed identification approach. The proposed backpropagation training algorithm is modified to obtain an adaptive learning rate guarantying convergence stability. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condition, unstable phenomena during the learning process are avoided. A Lyapunov analysis leads to the computation of the expression of a convenient adaptive learning rate verifying the convergence stability criteria. Finally, the elaborated training algorithm is applied in several simulations. The results confirm the effectiveness of the CSBP algorithm.

  12. Sensor network based vehicle classification and license plate identification system

    Energy Technology Data Exchange (ETDEWEB)

    Frigo, Janette Rose [Los Alamos National Laboratory; Brennan, Sean M [Los Alamos National Laboratory; Rosten, Edward J [Los Alamos National Laboratory; Raby, Eric Y [Los Alamos National Laboratory; Kulathumani, Vinod K [WEST VIRGINIA UNIV.

    2009-01-01

    Typically, for energy efficiency and scalability purposes, sensor networks have been used in the context of environmental and traffic monitoring applications in which operations at the sensor level are not computationally intensive. But increasingly, sensor network applications require data and compute intensive sensors such video cameras and microphones. In this paper, we describe the design and implementation of two such systems: a vehicle classifier based on acoustic signals and a license plate identification system using a camera. The systems are implemented in an energy-efficient manner to the extent possible using commercially available hardware, the Mica motes and the Stargate platform. Our experience in designing these systems leads us to consider an alternate more flexible, modular, low-power mote architecture that uses a combination of FPGAs, specialized embedded processing units and sensor data acquisition systems.

  13. Mechanical response of biopolymer double networks

    Science.gov (United States)

    Carroll, Joshua; Das, Moumita

    We investigate a double network model of articular cartilage (AC) and characterize its equilibrium mechanical response. AC has very few cells and the extracellular matrix mainly determines its mechanical response. This matrix can be thought of as a double polymer network made of collagen and aggrecan. The collagen fibers are stiff and resist tension and compression forces, while aggrecans are flexible and control swelling and hydration. We construct a microscopic model made of two interconnected disordered polymer networks, with fiber elasticity chosen to qualitatively mimic the experimental system. We study the collective mechanical response of this double network as a function of the concentration and stiffness of the individual components as well as the strength of the connection between them using rigidity percolation theory. Our results may provide a better understanding of mechanisms underlying the mechanical resilience of AC, and more broadly may also lead to new perspectives on the mechanical response of multicomponent soft materials. This work was partially supported by a Cottrell College Science Award.

  14. Identification of neutral biochemical network models from time series data

    Directory of Open Access Journals (Sweden)

    Maia Marco

    2009-05-01

    Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  15. Identification of neutral biochemical network models from time series data.

    Science.gov (United States)

    Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S

    2009-05-05

    The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.

  16. A neural network model for non invasive subsurface stratigraphic identification

    International Nuclear Information System (INIS)

    Sullivan, John M. Jr.; Ludwig, Reinhold; Lai Qiang

    2000-01-01

    Ground-Penetrating Radar (GRP) is a powerful tool to examine the stratigraphy below ground surface for remote sensing. Increasingly GPR has also found applications in microwave NDE as an interrogation tool to assess dielectric layers. Unfortunately, GPR data is characterized by a high degree of uncertainty and natural physical ambiguity. Robust decomposition routines are sparse for this application. We have developed a hierarchical set of neural network modules which split the task of layer profiling into consecutive stages. Successful GPR profiling of the subsurface stratigraphy is of key importance for many remote sensing applications including microwave NDE. Neural network modules were designed to accomplish the two main processing goals of recognizing the 'subsurface pattern' followed by the identification of the depths of the subsurface layers like permafrost, groundwater table, and bedrock. We used an adaptive transform technique to transform raw GPR data into a small feature vector containing the most representative and discriminative features of the signal. This information formed the input for the neural network processing units. This strategy reduced the number of required training samples for the neural network by orders of magnitude. The entire processing system was trained using the adaptive transformed feature vector inputs and tested with real measured GPR data. The successful results of this system establishes the feasibility the feasibility of delineating subsurface layering nondestructively

  17. RBF Neural Network Approach for Identification and Control of DC Motors

    Directory of Open Access Journals (Sweden)

    EA Feilat

    2012-12-01

    Full Text Available In this paper, a neural network approach for the identification and control of a separately excited direct (DC motor (SEDCM driving a centrifugal pump load is applied. In this application, two radial basis function neural networks (RBFNN are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model control schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The performance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN. The simulation results show that both of the BPNN and RBFNN controllers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical.

  18. Simulation of Stimuli-Responsive Polymer Networks

    Directory of Open Access Journals (Sweden)

    Thomas Gruhn

    2013-11-01

    Full Text Available The structure and material properties of polymer networks can depend sensitively on changes in the environment. There is a great deal of progress in the development of stimuli-responsive hydrogels for applications like sensors, self-repairing materials or actuators. Biocompatible, smart hydrogels can be used for applications, such as controlled drug delivery and release, or for artificial muscles. Numerical studies have been performed on different length scales and levels of details. Macroscopic theories that describe the network systems with the help of continuous fields are suited to study effects like the stimuli-induced deformation of hydrogels on large scales. In this article, we discuss various macroscopic approaches and describe, in more detail, our phase field model, which allows the calculation of the hydrogel dynamics with the help of a free energy that considers physical and chemical impacts. On a mesoscopic level, polymer systems can be modeled with the help of the self-consistent field theory, which includes the interactions, connectivity, and the entropy of the polymer chains, and does not depend on constitutive equations. We present our recent extension of the method that allows the study of the formation of nano domains in reversibly crosslinked block copolymer networks. Molecular simulations of polymer networks allow the investigation of the behavior of specific systems on a microscopic scale. As an example for microscopic modeling of stimuli sensitive polymer networks, we present our Monte Carlo simulations of a filament network system with crosslinkers.

  19. Village Building Identification Based on Ensemble Convolutional Neural Networks

    Science.gov (United States)

    Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

    2017-01-01

    In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

  20. A lithology identification method for continental shale oil reservoir based on BP neural network

    Science.gov (United States)

    Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

    2018-06-01

    The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

  1. Disease candidate gene identification and prioritization using protein interaction networks

    Directory of Open Access Journals (Sweden)

    Aronow Bruce J

    2009-02-01

    Full Text Available Abstract Background Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN analyses. Results For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds", and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance. Conclusion Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.

  2. Energy Efficient Distributed Fault Identification Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Meenakshi Panda

    2014-01-01

    Full Text Available A distributed fault identification algorithm is proposed here to find both hard and soft faulty sensor nodes present in wireless sensor networks. The algorithm is distributed, self-detectable, and can detect the most common byzantine faults such as stuck at zero, stuck at one, and random data. In the proposed approach, each sensor node gathered the observed data from the neighbors and computed the mean to check whether faulty sensor node is present or not. If a node found the presence of faulty sensor node, then compares observed data with the data of the neighbors and predict probable fault status. The final fault status is determined by diffusing the fault information from the neighbors. The accuracy and completeness of the algorithm are verified with the help of statistical model of the sensors data. The performance is evaluated in terms of detection accuracy, false alarm rate, detection latency and message complexity.

  3. System Identification Using Multilayer Differential Neural Networks: A New Result

    Directory of Open Access Journals (Sweden)

    J. Humberto Pérez-Cruz

    2012-01-01

    Full Text Available In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.

  4. Orientation selective neural network for cosmic muon identification

    International Nuclear Information System (INIS)

    Abramowicz, H.; Tel Aviv Univ.; Horn, D.; Naftaly, U.; Sahar-Pikielny, C.

    1997-01-01

    We discuss a novel method for identification of a linear pattern of pixels on a two-dimensional grid. Motivated by principles employed by the visual cortex, we construct orientation selective neurons in a neural network that performs this task. The method is then applied to a sample of data collected with the ZEUS detector at HERA in order to identify cosmic muons that leave a linear pattern of signals in the segmented uranium-scintillator calorimeter. A two dimensional representation of the relevant part of the detector is used. The algorithm performs well in the presence of noise and pixels with limited efficiency. Given its architecture, this system becomes a good candidate for fast pattern recognition in parallel processing devices. (orig.)

  5. Multi-level damage identification with response reconstruction

    Science.gov (United States)

    Zhang, Chao-Dong; Xu, You-Lin

    2017-10-01

    Damage identification through finite element (FE) model updating usually forms an inverse problem. Solving the inverse identification problem for complex civil structures is very challenging since the dimension of potential damage parameters in a complex civil structure is often very large. Aside from enormous computation efforts needed in iterative updating, the ill-condition and non-global identifiability features of the inverse problem probably hinder the realization of model updating based damage identification for large civil structures. Following a divide-and-conquer strategy, a multi-level damage identification method is proposed in this paper. The entire structure is decomposed into several manageable substructures and each substructure is further condensed as a macro element using the component mode synthesis (CMS) technique. The damage identification is performed at two levels: the first is at macro element level to locate the potentially damaged region and the second is over the suspicious substructures to further locate as well as quantify the damage severity. In each level's identification, the damage searching space over which model updating is performed is notably narrowed down, not only reducing the computation amount but also increasing the damage identifiability. Besides, the Kalman filter-based response reconstruction is performed at the second level to reconstruct the response of the suspicious substructure for exact damage quantification. Numerical studies and laboratory tests are both conducted on a simply supported overhanging steel beam for conceptual verification. The results demonstrate that the proposed multi-level damage identification via response reconstruction does improve the identification accuracy of damage localization and quantization considerably.

  6. A neural network model of lateralization during letter identification.

    Science.gov (United States)

    Shevtsova, N; Reggia, J A

    1999-03-01

    The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.

  7. Modal Parameter Identification from Responses of General Unknown Random Inputs

    DEFF Research Database (Denmark)

    Ibrahim, S. R.; Asmussen, J. C.; Brincker, Rune

    1996-01-01

    Modal parameter identification from ambient responses due to a general unknown random inputs is investigated. Existing identification techniques which are based on assumptions of white noise and or stationary random inputs are utilized even though the inputs conditions are not satisfied....... This is accomplished via adding. In cascade. A force cascade conversion to the structures system under consideration. The input to the force conversion system is white noise and the output of which is the actual force(s) applied to the structure. The white noise input(s) and the structures responses are then used...

  8. Horizontal two phase flow pattern identification by neural networks

    International Nuclear Information System (INIS)

    Crivelaro, Kelen Cristina Oliveira; Seleghim Junior, Paulo; Hervieu, Eric

    1999-01-01

    A multiphase fluid can flow according to several flow regimes. The problem associated with multiphase systems are basically related to the behavior of macroscopic parameters, such as pressure drop, thermal exchanges and so on, and their strong correlation to the flow regime. From the industrial applications point of view, the safety and longevity of equipment and systems can only be assured when they work according to the flow regimes for which they were designed to. This implies in the need to diagnose flow regimes in real time. The automatic diagnosis of flow regimes represents an objective of extreme importance, mainly for applications on nuclear and petrochemical industries. In this work, a neural network is used in association to a probe of direct visualization for the identification of a gas-liquid flow horizontal regimes, developed in an experimental circuit. More specifically, the signals produced by the probe are used to compose a qualitative image of the flow, which is promptly sent to the network for the recognition of the regimes. Results are presented for different transitions among the flow regimes, which demonstrate the extremely satisfactory performance of the diagnosis system. (author)

  9. Pilot Ionosonde Network for Identification of Traveling Ionospheric Disturbances

    Science.gov (United States)

    Reinisch, Bodo; Galkin, Ivan; Belehaki, Anna; Paznukhov, Vadym; Huang, Xueqin; Altadill, David; Buresova, Dalia; Mielich, Jens; Verhulst, Tobias; Stankov, Stanimir; Blanch, Estefania; Kouba, Daniel; Hamel, Ryan; Kozlov, Alexander; Tsagouri, Ioanna; Mouzakis, Angelos; Messerotti, Mauro; Parkinson, Murray; Ishii, Mamoru

    2018-03-01

    Traveling ionospheric disturbances (TIDs) are the ionospheric signatures of atmospheric gravity waves. Their identification and tracking is important because the TIDs affect all services that rely on predictable ionospheric radio wave propagation. Although various techniques have been proposed to measure TID characteristics, their real-time implementation still has several difficulties. In this contribution, we present a new technique, based on the analysis of oblique Digisonde-to-Digisonde "skymap" observations, to directly identify TIDs and specify the TID wave parameters based on the measurement of angle of arrival, Doppler frequency, and time of flight of ionospherically reflected high-frequency radio pulses. The technique has been implemented for the first time for the Network for TID Exploration project with data streaming from the network of European Digisonde DPS4D observatories. The performance is demonstrated during a period of moderate auroral activity, assessing its consistency with independent measurements such as data from auroral magnetometers and electron density perturbations from Digisondes and Global Navigation Satellite System stations. Given that the different types of measurements used for this assessment were not made at exactly the same time and location, and that there was insufficient coverage in the area between the atmospheric gravity wave sources and the measurement locations, we can only consider our interpretation as plausible and indicative for the reliability of the extracted TID characteristics. In the framework of the new TechTIDE project (European Commission H2020), a retrospective analysis of the Network for TID Exploration results in comparison with those extracted from Global Navigation Satellite System total electron content-based methodologies is currently being attempted, and the results will be the objective of a follow-up paper.

  10. Bistable responses in bacterial genetic networks: Designs and dynamical consequences

    Science.gov (United States)

    Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.

    2011-01-01

    A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588

  11. Modal Identification from Ambient Responses using Frequency Domain Decomposition

    DEFF Research Database (Denmark)

    Brincker, Rune; Zhang, L.; Andersen, P.

    2000-01-01

    In this paper a new frequency domain technique is introduced for the modal identification from ambient responses, ie. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical ...

  12. Modal Identification from Ambient Responses Using Frequency Domain Decomposition

    DEFF Research Database (Denmark)

    Brincker, Rune; Zhang, Lingmi; Andersen, Palle

    2000-01-01

    In this paper a new frequency domain technique is introduced for the modal identification from ambient responses, i.e. in the case where the modal parameters must be estimated without knowing the input exciting the system. By its user friendliness the technique is closely related to the classical...

  13. A Kalman-filter based approach to identification of time-varying gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jie Xiong

    Full Text Available MOTIVATION: Conventional identification methods for gene regulatory networks (GRNs have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs. RESULTS: It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.

  14. Identification of the IGF1/PI3K/NF κB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancer

    International Nuclear Information System (INIS)

    Koti, Madhuri; Evans, Kenneth; Feilotter, Harriet E; Park, Paul C; Squire, Jeremy A; Gooding, Robert J; Nuin, Paulo; Haslehurst, Alexandria; Crane, Colleen; Weberpals, Johanne; Childs, Timothy; Bryson, Peter; Dharsee, Moyez

    2013-01-01

    Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with standard platinum-based chemotherapy. Twelve patient tumours demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumours from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using an Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumours from the resistant group and the sensitive group. Microarray data analysis revealed a set of 204 discriminating genes possessing expression levels which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NF κB/ERK gene signalling networks. This study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. In addition, our results provide a pathway context for

  15. Reverse engineering biological networks :applications in immune responses to bio-toxins.

    Energy Technology Data Exchange (ETDEWEB)

    Martino, Anthony A.; Sinclair, Michael B.; Davidson, George S.; Haaland, David Michael; Timlin, Jerilyn Ann; Thomas, Edward Victor; Slepoy, Alexander; Zhang, Zhaoduo; May, Elebeoba Eni; Martin, Shawn Bryan; Faulon, Jean-Loup Michel

    2005-12-01

    Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.

  16. Dynamic neural networks based on-line identification and control of high performance motor drives

    Science.gov (United States)

    Rubaai, Ahmed; Kotaru, Raj

    1995-01-01

    In the automated and high-tech industries of the future, there wil be a need for high performance motor drives both in the low-power range and in the high-power range. To meet very straight demands of tracking and regulation in the two quadrants of operation, advanced control technologies are of a considerable interest and need to be developed. In response a dynamics learning control architecture is developed with simultaneous on-line identification and control. the feature of the proposed approach, to efficiently combine the dual task of system identification (learning) and adaptive control of nonlinear motor drives into a single operation is presented. This approach, therefore, not only adapts to uncertainties of the dynamic parameters of the motor drives but also learns about their inherent nonlinearities. In fact, most of the neural networks based adaptive control approaches in use have an identification phase entirely separate from the control phase. Because these approaches separate the identification and control modes, it is not possible to cope with dynamic changes in a controlled process. Extensive simulation studies have been conducted and good performance was observed. The robustness characteristics of neuro-controllers to perform efficiently in a noisy environment is also demonstrated. With this initial success, the principal investigator believes that the proposed approach with the suggested neural structure can be used successfully for the control of high performance motor drives. Two identification and control topologies based on the model reference adaptive control technique are used in this present analysis. No prior knowledge of load dynamics is assumed in either topology while the second topology also assumes no knowledge of the motor parameters.

  17. Guest editorial - Networked collaboration, sharing and response

    Directory of Open Access Journals (Sweden)

    Olav Skundberg

    2008-11-01

    Full Text Available  This issue of Seminar.net contains three articles that were written in connection with a Norwegian e-learning conference titled “Networked collaboration, sharing and response”. The conference was held in Mars 2008 in Trondheim, and the presentations from the conference is available (in norwegian language at http://www.nvu.no. Networked collaboration was chosen as a theme because collaboration is important to achieve learning, according to the social-constructivistic pedagogy that has a strong standing in Norway, but how should this occur on the net? Sharing of content, as in digital learning resources, is a phenomenon with increasing popularity as described in the OECD-report “Giving Knowledge for Free”. But to achieve reuse of content, not only publishing it, it is important with a networked community where the plethora of information can be sorted with relevance to specific topics. Response is about guiding, coaching and tutoring. In what ways may resources and tools be used to move in the direction of solving Bloom’s two sigma problem/challenge? The first article, by Morten Flate Paulsen, shows how cooperative learning can be implemented successfully so that students have optimal individual freedom within online learning communities. The second article, by Carl F. Dons, shows how student teachers can be prepared to deal with pupils who have a wide range of experiences of the digital world. The third and last article, by Kristin Dale, is sharing experiences with multiple choice-tests to give midterm responses to students. In addition, this issue has a commentary article by Rune Krumsvik discussing the need to develop new practices for teachers and students on the background of the digital developments. The conference and articles covers three big themes. It may be difficult to find more important issues, apart from finding money and time to support its development. Olav Skundberg, guest editorAssociate professor

  18. Neural Network Substorm Identification: Enabling TREx Sensor Web Modes

    Science.gov (United States)

    Chaddock, D.; Spanswick, E.; Arnason, K. M.; Donovan, E.; Liang, J.; Ahmad, S.; Jackel, B. J.

    2017-12-01

    Transition Region Explorer (TREx) is a ground-based sensor web of optical and radio instruments that is presently being deployed across central Canada. The project consists of an array of co-located blue-line, full-colour, and near-infrared all-sky imagers, imaging riometers, proton aurora spectrographs, and GNSS systems. A key goal of the TREx project is to create the world's first (artificial) intelligent sensor web for remote sensing space weather. The sensor web will autonomously control and coordinate instrument operations in real-time. To accomplish this, we will use real-time in-line analytics of TREx and other data to dynamically switch between operational modes. An operating mode could be, for example, to have a blue-line imager gather data at a one or two orders of magnitude higher cadence than it operates for its `baseline' mode. The software decision to increase the imaging cadence would be in response to an anticipated increase in auroral activity or other programmatic requirements. Our first test for TREx's sensor web technologies is to develop the capacity to autonomously alter the TREx operating mode prior to a substorm expansion phase onset. In this paper, we present our neural network analysis of historical optical and riometer data and our ability to predict an optical onset. We explore the preliminary insights into using a neural network to pick out trends and features which it deems are similar among substorms.

  19. Feature-Augmented Neural Networks for Patient Note De-identification

    OpenAIRE

    Lee, Ji Young; Dernoncourt, Franck; Uzuner, Ozlem; Szolovits, Peter

    2016-01-01

    Patient notes contain a wealth of information of potentially great interest to medical investigators. However, to protect patients' privacy, Protected Health Information (PHI) must be removed from the patient notes before they can be legally released, a process known as patient note de-identification. The main objective for a de-identification system is to have the highest possible recall. Recently, the first neural-network-based de-identification system has been proposed, yielding state-of-t...

  20. An Explication and Test of Communication Network Content and Multiplexity as Predictors of Organizational Identification.

    Science.gov (United States)

    Bullis, Connie; Bach, Betsy Wackernagel

    1991-01-01

    Examines the relationship between identification and communication using organizational identification (OI) as a theoretical framework for studying communication networks among incoming graduate students in three university departments of communication. Concludes that, irrespective of initial OI, stronger initial multiplexity predicts the growth…

  1. Anomalous Anticipatory Responses in Networked Random Data

    International Nuclear Information System (INIS)

    Nelson, Roger D.; Bancel, Peter A.

    2006-01-01

    We examine an 8-year archive of synchronized, parallel time series of random data from a world spanning network of physical random event generators (REGs). The archive is a publicly accessible matrix of normally distributed 200-bit sums recorded at 1 Hz which extends from August 1998 to the present. The primary question is whether these data show non-random structure associated with major events such as natural or man-made disasters, terrible accidents, or grand celebrations. Secondarily, we examine the time course of apparently correlated responses. Statistical analyses of the data reveal consistent evidence that events which strongly affect people engender small but significant effects. These include suggestions of anticipatory responses in some cases, leading to a series of specialized analyses to assess possible non-random structure preceding precisely timed events. A focused examination of data collected around the time of earthquakes with Richter magnitude 6 and greater reveals non-random structure with a number of intriguing, potentially important features. Anomalous effects in the REG data are seen only when the corresponding earthquakes occur in populated areas. No structure is found if they occur in the oceans. We infer that an important contributor to the effect is the relevance of the earthquake to humans. Epoch averaging reveals evidence for changes in the data some hours prior to the main temblor, suggestive of reverse causation

  2. A neural model for transient identification in dynamic processes with 'don't know' response

    Energy Technology Data Exchange (ETDEWEB)

    Mol, Antonio C. de A. E-mail: mol@ien.gov.br; Martinez, Aquilino S. E-mail: aquilino@lmp.ufrj.br; Schirru, Roberto E-mail: schirru@lmp.ufrj.br

    2003-09-01

    This work presents an approach for neural network based transient identification which allows either dynamic identification or a 'don't know' response. The approach uses two 'jump' multilayer neural networks (NN) trained with the backpropagation algorithm. The 'jump' network is used because it is useful to dealing with very complex patterns, which is the case of the space of the state variables during some abnormal events. The first one is responsible for the dynamic identification. This NN uses, as input, a short set (in a moving time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide a 'don't know' response. In order to validate the method, a Nuclear Power Plant (NPP) transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor (PWR), was proposed in the validation process it has been considered noisy data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. Another important point studied in this work is that the system has shown to be independent of a trigger signal which indicates the beginning of the transient, thus making it robust in relation to this limitation.

  3. Target identification using Zernike moments and neural networks

    Science.gov (United States)

    Azimi-Sadjadi, Mahmood R.; Jamshidi, Arta A.; Nevis, Andrew J.

    2001-10-01

    The development of an underwater target identification algorithm capable of identifying various types of underwater targets, such as mines, under different environmental conditions pose many technical problems. Some of the contributing factors are: targets have diverse sizes, shapes and reflectivity properties. Target emplacement environment is variable; targets may be proud or partially buried. Environmental properties vary significantly from one location to another. Bottom features such as sand, rocks, corals, and vegetation can conceal a target whether it is partially buried or proud. Competing clutter with responses that closely resemble those of the targets may lead to false positives. All the problems mentioned above contribute to overly difficult and challenging conditions that could lead to unreliable algorithm performance with existing methods. In this paper, we developed and tested a shape-dependent feature extraction scheme that provides features invariant to rotation, size scaling and translation; properties that are extremely useful for any target classification problem. The developed schemes were tested on an electro-optical imagery data set collected under different environmental conditions with variable background, range and target types. The electro-optic data set was collected using a Laser Line Scan (LLS) sensor by the Coastal Systems Station (CSS), located in Panama City, Florida. The performance of the developed scheme and its robustness to distortion, rotation, scaling and translation was also studied.

  4. Recurrent neural network for non-smooth convex optimization problems with application to the identification of genetic regulatory networks.

    Science.gov (United States)

    Cheng, Long; Hou, Zeng-Guang; Lin, Yingzi; Tan, Min; Zhang, Wenjun Chris; Wu, Fang-Xiang

    2011-05-01

    A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

  5. Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

    Science.gov (United States)

    Kuznetsov, A. V.; Makaryants, G. M.

    2018-01-01

    There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

  6. Identification of Resting State Networks Involved in Executive Function.

    Science.gov (United States)

    Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W

    2016-06-01

    The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.

  7. Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

    Directory of Open Access Journals (Sweden)

    Liu Heng

    Full Text Available This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method.

  8. Topology identification of the complex networks with non-delayed and delayed coupling

    International Nuclear Information System (INIS)

    Guo Wanli; Chen Shihua; Sun Wen

    2009-01-01

    In practical situation, there exists many uncertain information in complex networks, such as the topological structures. So the topology identification is an important issue in the research of the complex networks. Based on LaSalle's invariance principle, in this Letter, an adaptive controlling method is proposed to identify the topology of a weighted general complex network model with non-delayed and delayed coupling. Finally, simulation results show that the method is effective.

  9. Identification and management of distributed data NGN, content-centric networks and the web

    CERN Document Server

    Bartolomeo, Giovanni

    2013-01-01

    Although several books and academic courses discuss data management and networking, few of them focus on the convergence of networking and software technologies for identifying, addressing, and managing distributed data. Focusing on this convergence, Identification and Management of Distributed Data: NGN, Content-Centric Networks and the Web collates and describes the various distributed data management technologies to help readers from various backgrounds understand the common aspects that govern distributed data management. With a focus on the primary problems in identifying, addressing, and

  10. Identification of hybrid node and link communities in complex networks.

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  11. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  12. Identification of Tobacco Topping Responsive Proteins in Roots

    Directory of Open Access Journals (Sweden)

    Hongxiang eGuo

    2016-04-01

    Full Text Available Tobacco plant has many responses to topping, such as the increase in ability of nicotine synthesis and secondary growth of roots. Some topping responsive miRNAs and genes had been identified in our previous work, but it is not enough to elaborate mechanism of tobacco response to topping. Here, topping responsive proteins were screened from tobacco roots with two-dimensional electrophoresis. Of these proteins, calretulin (CRT and Auxin-responsive protein IAA9 were related to the secondary growth of roots, LRR disease resistance, heat shock protein 70 and farnesyl pyrophosphate synthase 1(FPPS)were involved in wounding stress response, and F-box protein played an important role in promoting the ability of nicotine synthesis after topping. In addition, there were five tobacco bHLH proteins (NtbHLH, NtMYC1a, NtMYC1b, NtMYC2a and NtMYC2b related to nicotine synthesis. It was suggested that NtMYC2 might be the main positive transcription factor and NtbHLH protein is a negative regulator in the JA-mediating activation of nicotine synthesis after topping. Tobacco topping activates some comprehensive biology processes involving IAA and JA signaling pathway, and the identification of these proteins will be helpful to understand the process of topping response.

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

    OpenAIRE

    Atar, Rami; Dupuis, Paul

    1999-01-01

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

  14. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  15. Friend or Foe? Fake Profile Identification in Online Social Networks

    OpenAIRE

    Fire, Michael; Kagan, Dima; Elyashar, Aviad; Elovici, Yuval

    2013-01-01

    The amount of personal information unwillingly exposed by users on online social networks is staggering, as shown in recent research. Moreover, recent reports indicate that these networks are infested with tens of millions of fake users profiles, which may jeopardize the users' security and privacy. To identify fake users in such networks and to improve users' security and privacy, we developed the Social Privacy Protector software for Facebook. This software contains three protection layers,...

  16. Identification of illicit drugs by using SOM neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Liang Meiyan; Shen Jingling; Wang Guangqin [Beijing Key Lab for Terahertz Spectroscopy and Imaging, Key Laboratory of Terahertz Optoelectronics, Ministry of Education, Department of Physics, Capital Normal University, Beijing 100037 (China)], E-mail: liangyan661982@163.com, E-mail: jinglingshen@gmail.com, E-mail: pywgq2004@163.com

    2008-07-07

    Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with.

  17. Identification of illicit drugs by using SOM neural networks

    International Nuclear Information System (INIS)

    Liang Meiyan; Shen Jingling; Wang Guangqin

    2008-01-01

    Absorption spectra of six illicit drugs were measured by using the terahertz time-domain spectroscopy technique in the range 0.2-2.6 THz and then clustered with self-organization feature map (SOM) artificial neural network. After the network training process, the spectra collected at another time were identified successfully by the well-trained SOM network. An effective distance was introduced as a quantitative criterion to decide which cluster the new spectra were affiliated with

  18. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  19. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  20. Using Web2.0 social network technology for sampling framework identification and respondent recruitment: experiences with a small-scale experiment

    NARCIS (Netherlands)

    Grigolon, A.B.; Kemperman, A.D.A.M.; Timmermans, H.J.P.

    2011-01-01

    In this paper, we report the results of a small–scale experiment to explore the potential of using social network technology for respondent recruitment. Of particular interest are the following questions (i) can social media be used for the identification of sampling frames, (ii) what response rates

  1. Home Network Technologies and Automating Demand Response

    Energy Technology Data Exchange (ETDEWEB)

    McParland, Charles

    2009-12-01

    Over the past several years, interest in large-scale control of peak energy demand and total consumption has increased. While motivated by a number of factors, this interest has primarily been spurred on the demand side by the increasing cost of energy and, on the supply side by the limited ability of utilities to build sufficient electricity generation capacity to meet unrestrained future demand. To address peak electricity use Demand Response (DR) systems are being proposed to motivate reductions in electricity use through the use of price incentives. DR systems are also be design to shift or curtail energy demand at critical times when the generation, transmission, and distribution systems (i.e. the 'grid') are threatened with instabilities. To be effectively deployed on a large-scale, these proposed DR systems need to be automated. Automation will require robust and efficient data communications infrastructures across geographically dispersed markets. The present availability of widespread Internet connectivity and inexpensive, reliable computing hardware combined with the growing confidence in the capabilities of distributed, application-level communications protocols suggests that now is the time for designing and deploying practical systems. Centralized computer systems that are capable of providing continuous signals to automate customers reduction of power demand, are known as Demand Response Automation Servers (DRAS). The deployment of prototype DRAS systems has already begun - with most initial deployments targeting large commercial and industrial (C & I) customers. An examination of the current overall energy consumption by economic sector shows that the C & I market is responsible for roughly half of all energy consumption in the US. On a per customer basis, large C & I customers clearly have the most to offer - and to gain - by participating in DR programs to reduce peak demand. And, by concentrating on a small number of relatively

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

    International Nuclear Information System (INIS)

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

    1991-01-01

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

  3. An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

    OpenAIRE

    Wang, Weidong; Wang, Liqiang; Lu, Wei

    2016-01-01

    QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful techniqu...

  4. Multimodal Neural Network for Overhead Person Re-identification

    DEFF Research Database (Denmark)

    Lejbølle, Aske Rasch; Nasrollahi, Kamal; Krogh, Benjamin

    2017-01-01

    Person re-identification is a topic which has potential to be used for applications within forensics, flow analysis and queue monitoring. It is the process of matching persons across two or more camera views, most often by extracting colour and texture based hand-crafted features, to identify...

  5. Rotor Resistance Online Identification of Vector Controlled Induction Motor Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Bo Fan

    2014-01-01

    Full Text Available Rotor resistance identification has been well recognized as one of the most critical factors affecting the theoretical study and applications of AC motor’s control for high performance variable frequency speed adjustment. This paper proposes a novel model for rotor resistance parameters identification based on Elman neural networks. Elman recurrent neural network is capable of performing nonlinear function approximation and possesses the ability of time-variable characteristic adaptation. Those influencing factors of specified parameter are analyzed, respectively, and various work states are covered to ensure the completeness of the training samples. Through signal preprocessing on samples and training dataset, different input parameters identifications with one network are compared and analyzed. The trained Elman neural network, applied in the identification model, is able to efficiently predict the rotor resistance in high accuracy. The simulation and experimental results show that the proposed method owns extensive adaptability and performs very well in its application to vector controlled induction motor. This identification method is able to enhance the performance of induction motor’s variable-frequency speed regulation.

  6. Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics.

    Science.gov (United States)

    Prescott, Aaron M; McCollough, Forest W; Eldreth, Bryan L; Binder, Brad M; Abel, Steven M

    2016-01-01

    Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene

  7. Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics

    Directory of Open Access Journals (Sweden)

    Aaron M. Prescott

    2016-08-01

    Full Text Available Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. However, the dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB. In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB. Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms

  8. Boosted decision trees as an alternative to artificial neural networks for particle identification

    International Nuclear Information System (INIS)

    Roe, Byron P.; Yang Haijun; Zhu Ji; Liu Yong; Stancu, Ion; McGregor, Gordon

    2005-01-01

    The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at Fermilab searching for neutrino oscillations. Based on studies of Monte Carlo samples of simulated data, particle identification with boosting algorithms has better performance than that with artificial neural networks for the MiniBooNE experiment. Although the tests in this paper were for one experiment, it is expected that boosting algorithms will find wide application in physics

  9. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

  10. The NNSYSID Toolbox - A MATLAB Toolbox for System Identification with Neural Networks

    DEFF Research Database (Denmark)

    Nørgård, Peter Magnus; Ravn, Ole; Hansen, Lars Kai

    1996-01-01

    To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms for determi......To assist the identification of nonlinear dynamic systems, a set of tools has been developed for the MATLAB(R) environment. The tools include a number of different model structures, highly effective training algorithms, functions for validating trained networks, and pruning algorithms...

  11. Identification of serial number on bank card using recurrent neural network

    Science.gov (United States)

    Liu, Li; Huang, Linlin; Xue, Jian

    2018-04-01

    Identification of serial number on bank card has many applications. Due to the different number printing mode, complex background, distortion in shape, etc., it is quite challenging to achieve high identification accuracy. In this paper, we propose a method using Normalization-Cooperated Gradient Feature (NCGF) and Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM) for serial number identification. The NCGF maps the gradient direction elements of original image to direction planes such that the RNN with direction planes as input can recognize numbers more accurately. Taking the advantages of NCGF and RNN, we get 90%digit string recognition accuracy.

  12. Identification of Complex Dynamical Systems with Neural Networks (2/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  13. Identification of Complex Dynamical Systems with Neural Networks (1/2)

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    The identification and analysis of high dimensional nonlinear systems is obviously a challenging task. Neural networks have been proven to be universal approximators but this still leaves the identification task a hard one. To do it efficiently, we have to violate some of the rules of classical regression theory. Furthermore we should focus on the interpretation of the resulting model to overcome its black box character. First, we will discuss function approximation with 3 layer feedforward neural networks up to new developments in deep neural networks and deep learning. These nets are not only of interest in connection with image analysis but are a center point of the current artificial intelligence developments. Second, we will focus on the analysis of complex dynamical system in the form of state space models realized as recurrent neural networks. After the introduction of small open dynamical systems we will study dynamical systems on manifolds. Here manifold and dynamics have to be identified in parall...

  14. Feedback Models for Collaboration and Trust in Crisis Response Networks

    National Research Council Canada - National Science Library

    Hudgens, Bryan J; Bordetsky, Alex

    2008-01-01

    .... Coordination within disaster response networks is difficult for several reasons, including the chaotic nature of the crisis, a need for the various organizations to balance shared goals (crisis amelioration...

  15. Response variability in balanced cortical networks

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ursta, C.; Hertz, J.

    2006-01-01

    We study the spike statistics of neurons in a network with dynamically balanced excitation and inhibition. Our model, intended to represent a generic cortical column, comprises randomly connected excitatory and inhibitory leaky integrate-and-fire neurons, driven by excitatory input from an external...

  16. Identification of control targets in Boolean molecular network models via computational algebra.

    Science.gov (United States)

    Murrugarra, David; Veliz-Cuba, Alan; Aguilar, Boris; Laubenbacher, Reinhard

    2016-09-23

    Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or otherwise undesirable state of a biological system into another, more desirable, state through an intervention, such as a drug or other therapeutic treatment. The identification of such strategies is typically based on a mathematical model of the process to be altered through targeted control inputs. This paper focuses on processes at the molecular level that determine the state of an individual cell, involving signaling or gene regulation. The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system. This paper presents a method for the identification of potential intervention targets in Boolean molecular network models using algebraic techniques. The approach exploits an algebraic representation of Boolean networks to encode the control candidates in the network wiring diagram as the solutions of a system of polynomials equations, and then uses computational algebra techniques to find such controllers. The control methods in this paper are validated through the identification of combinatorial interventions in the signaling pathways of previously reported control targets in two well studied systems, a p53-mdm2 network and a blood T cell lymphocyte granular leukemia survival signaling network. Supplementary data is available online and our code in Macaulay2 and Matlab are available via http://www.ms.uky.edu/~dmu228/ControlAlg . This paper presents a novel method for the identification of intervention targets in Boolean network models. The results in this paper show that the proposed methods are useful and efficient for moderately large networks.

  17. Radioactivity nuclide identification based on BP and LM algorithm neural network

    International Nuclear Information System (INIS)

    Wang Jihong; Sun Jian; Wang Lianghou

    2012-01-01

    The paper provides the method which can identify radioactive nuclide based on the BP and LM algorithm neural network. Then, this paper compares the above-mentioned method with FR algorithm. Through the result of the Matlab simulation, the method of radioactivity nuclide identification based on the BP and LM algorithm neural network is superior to the FR algorithm. With the better effect and the higher accuracy, it will be the best choice. (authors)

  18. A network identity authentication system based on Fingerprint identification technology

    Science.gov (United States)

    Xia, Hong-Bin; Xu, Wen-Bo; Liu, Yuan

    2005-10-01

    Fingerprint verification is one of the most reliable personal identification methods. However, most of the automatic fingerprint identification system (AFIS) is not run via Internet/Intranet environment to meet today's increasing Electric commerce requirements. This paper describes the design and implementation of the archetype system of identity authentication based on fingerprint biometrics technology, and the system can run via Internet environment. And in our system the COM and ASP technology are used to integrate Fingerprint technology with Web database technology, The Fingerprint image preprocessing algorithms are programmed into COM, which deployed on the internet information server. The system's design and structure are proposed, and the key points are discussed. The prototype system of identity authentication based on Fingerprint have been successfully tested and evaluated on our university's distant education applications in an internet environment.

  19. Natural semantic networks in the Social Representations of Responsibility

    Directory of Open Access Journals (Sweden)

    Humberto Emilio Aguilera Arévalo

    2010-07-01

    Full Text Available The study of social representations of responsibility is a fundamental construct of the present democratic societies. Different empirical techniques such as natural semantic networks can significantly improve the approach to the object of study than the traditional associationist techniques. The present study examines natural semantic networks of six stimulus words with respect to responsibility and irresponsibility at the individual, in group and out group level in a sample of Guatemalan students.

  20. Social networks : Effects on Identification, Performance and Satisfaction Effects on identification, performance and satisfaction

    NARCIS (Netherlands)

    Stormbroek-Burgers, van R.G.B.M.; Montfort, van K.; Sluis, van der E.C. (Lidewey)

    2011-01-01

    This study contributes to research on the impact of social networks on organizational outcomes in the context of the increasing number of professionals in the Netherlands. The aim of this study was to get insight into the characteristics of professionals’ social networks and to examine the effect of

  1. Particle Identification in Cherenkov Detectors using Convolutional Neural Networks

    CERN Document Server

    Theodore, Tomalty

    2016-01-01

    Cherenkov detectors are used for charged particle identification. When a charged particle moves through a medium faster than light can propagate in that medium, Cherenkov radiation is released in the shape of a cone in the direction of movement. The interior of the Cherenkov detector is instrumented with PMTs to detect this Cherenkov light. Particles, then, can be identified by the shapes of the images on the detector walls.

  2. Damage identification in beams by a response surface based technique

    Directory of Open Access Journals (Sweden)

    Teidj S.

    2014-01-01

    Full Text Available In this work, identification of damage in uniform homogeneous metallic beams was considered through the propagation of non dispersive elastic torsional waves. The proposed damage detection procedure consisted of the following sequence. Giving a localized torque excitation, having the form of a short half-sine pulse, the first step was calculating the transient solution of the resulting torsional wave. This torque could be generated in practice by means of asymmetric laser irradiation of the beam surface. Then, a localized defect assumed to be characterized by an abrupt reduction of beam section area with a given height and extent was placed at a known location of the beam. Next, the response in terms of transverse section rotation rate was obtained for a point situated afterwards the defect, where the sensor was positioned. This last could utilize in practice the concept of laser vibrometry. A parametric study has been conducted after that by using a full factorial design of experiments table and numerical simulations based on a finite difference characteristic scheme. This has enabled the derivation of a response surface model that was shown to represent adequately the response of the system in terms of the following factors: defect extent and severity. The final step was performing the inverse problem solution in order to identify the defect characteristics by using measurement.

  3. Identification of gene networks underlying dystocia in dairy cattle

    Science.gov (United States)

    Dystocia is a trait with a high impact in the dairy industry. Among its risk factors are calf weight, gestation length, breed and conformation. Biological networks have been proposed to capture the genetic architecture of complex traits, where GWAS show limitations. The objective of this study was t...

  4. Neural network based system for script identification in Indian ...

    Indian Academy of Sciences (India)

    2016-08-26

    Aug 26, 2016 ... The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. Script identification is a basic requirement in automation of document processing, in multi-script, multi-lingual ...

  5. Inverse parameter identification for a branching 1 D arterial network

    CSIR Research Space (South Africa)

    Bogaers, Alfred EJ

    2012-07-01

    Full Text Available In this paper we investigate the invertability of a branching 1 D arterial blood flow network. We limit our investigation to a single bifurcating vessel, where the material properties, unloaded areas and variables characterizing the input and output...

  6. Topologically determined optimal stochastic resonance responses of spatially embedded networks

    International Nuclear Information System (INIS)

    Gosak, Marko; Marhl, Marko; Korosak, Dean

    2011-01-01

    We have analyzed the stochastic resonance phenomenon on spatial networks of bistable and excitable oscillators, which are connected according to their location and the amplitude of external forcing. By smoothly altering the network topology from a scale-free (SF) network with dominating long-range connections to a network where principally only adjacent oscillators are connected, we reveal that besides an optimal noise intensity, there is also a most favorable interaction topology at which the best correlation between the response of the network and the imposed weak external forcing is achieved. For various distributions of the amplitudes of external forcing, the optimal topology is always found in the intermediate regime between the highly heterogeneous SF network and the strong geometric regime. Our findings thus indicate that a suitable number of hubs and with that an optimal ratio between short- and long-range connections is necessary in order to obtain the best global response of a spatial network. Furthermore, we link the existence of the optimal interaction topology to a critical point indicating the transition from a long-range interactions-dominated network to a more lattice-like network structure.

  7. System Identification of Mistuned Bladed Disks from Traveling Wave Response Measurements

    Science.gov (United States)

    Feiner, D. M.; Griffin, J. H.; Jones, K. W.; Kenyon, J. A.; Mehmed, O.; Kurkov, A. P.

    2003-01-01

    A new approach to modal analysis is presented. By applying this technique to bladed disk system identification methods, one can determine the mistuning in a rotor based on its response to a traveling wave excitation. This allows system identification to be performed under rotating conditions, and thus expands the applicability of existing mistuning identification techniques from integrally bladed rotors to conventional bladed disks.

  8. IAEA Response and Assistance Network. Date Effective: 1 January 2011

    International Nuclear Information System (INIS)

    2010-01-01

    This publication is a tool for (1) supporting the provision of international assistance in the event of a nuclear or radiological incident or emergency, (2) cooperation between States, their competent authorities and the IAEA, and (3) harmonization of response capabilities of States offering assistance under the Response and Assistance Network (RANET). The publication may also assist competent authorities and other response organizations in their efforts to establish and/or maintain their own response capabilities.

  9. People identification for domestic non-overlapping RGB-D camera networks

    NARCIS (Netherlands)

    Takac, B.; Rauterberg, G.W.M.; Català, A.; Chen, W.

    2015-01-01

    The ability to identify the specific person in a home camera network is very relevant for healthcare applications where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem of

  10. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, M.; Stensballe, A.; Rasmussen, T.E.

    2004-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  11. A network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption

    Science.gov (United States)

    Zhu, Lijuan; Liu, Jingao

    2013-07-01

    This paper describes a network identity authentication protocol of bank account system based on fingerprint identification and mixed encryption. This protocol can provide every bank user a safe and effective way to manage his own bank account, and also can effectively prevent the hacker attacks and bank clerk crime, so that it is absolute to guarantee the legitimate rights and interests of bank users.

  12. Identification of phosphorylation sites in protein kinase A substrates using artificial neural networks and mass spectrometry

    DEFF Research Database (Denmark)

    Hjerrild, Majbrit; Stensballe, Allan; Rasmussen, Thomas E

    2011-01-01

    Protein phosphorylation plays a key role in cell regulation and identification of phosphorylation sites is important for understanding their functional significance. Here, we present an artificial neural network algorithm: NetPhosK (http://www.cbs.dtu.dk/services/NetPhosK/) that predicts protein...

  13. Attention in Multimodal Neural Networks for Person Re-identification

    DEFF Research Database (Denmark)

    Lejbølle, Aske Rasch; Krogh, Benjamin; Nasrollahi, Kamal

    2018-01-01

    In spite of increasing interest from the research commu- nity, person re-identification remains an unsolved problem. Correctly deciding on a true match by comparing images of a person, captured by several cameras, requires extrac- tion of discriminative features to counter challenges...... such as changes in lighting, viewpoint and occlusion. Besides de- vising novel feature descriptors, the setup can be changed to capture persons from an overhead viewpoint rather than a horizontal. Furthermore, additional modalities can be considered that are not affected by similar environmental changes as RGB...

  14. Robust network topologies for generating switch-like cellular responses.

    Directory of Open Access Journals (Sweden)

    Najaf A Shah

    2011-06-01

    Full Text Available Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity and extent of memory (bistability. Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability. Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28% and bistability (up to 18%; strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3% or bistable (up to 1% responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

  15. Functional neural networks underlying response inhibition in adolescents and adults.

    Science.gov (United States)

    Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

    2007-07-19

    This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

  16. Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

    Science.gov (United States)

    Yan, Xiaofei; Cheng, Hong; Zhao, Yandong; Yu, Wenhua; Huang, Huan; Zheng, Xiaoliang

    2016-08-04

    Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO₂, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO₂ and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO₂; smoke and temperature; smoke, CO₂ and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%-92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.

  17. Robust synchronization of delayed neural networks based on adaptive control and parameters identification

    International Nuclear Information System (INIS)

    Zhou Jin; Chen Tianping; Xiang Lan

    2006-01-01

    This paper investigates synchronization dynamics of delayed neural networks with all the parameters unknown. By combining the adaptive control and linear feedback with the updated law, some simple yet generic criteria for determining the robust synchronization based on the parameters identification of uncertain chaotic delayed neural networks are derived by using the invariance principle of functional differential equations. It is shown that the approaches developed here further extend the ideas and techniques presented in recent literature, and they are also simple to implement in practice. Furthermore, the theoretical results are applied to a typical chaotic delayed Hopfied neural networks, and numerical simulation also demonstrate the effectiveness and feasibility of the proposed technique

  18. The Convolutional Visual Network for Identification and Reconstruction of NOvA Events

    Energy Technology Data Exchange (ETDEWEB)

    Psihas, Fernanda [Indiana U.

    2017-11-22

    In 2016 the NOvA experiment released results for the observation of oscillations in the vμ and ve channels as well as ve cross section measurements using neutrinos from Fermilab’s NuMI beam. These and other measurements in progress rely on the accurate identification and reconstruction of the neutrino flavor and energy recorded by our detectors. This presentation describes the first application of convolutional neural network technology for event identification and reconstruction in particle detectors like NOvA. The Convolutional Visual Network (CVN) Algorithm was developed for identification, categorization, and reconstruction of NOvA events. It increased the selection efficiency of the ve appearance signal by 40% and studies show potential impact to the vμ disappearance analysis.

  19. Identification of Functional Information Subgraphs in Complex Networks

    International Nuclear Information System (INIS)

    Bettencourt, Luis M. A.; Gintautas, Vadas; Ham, Michael I.

    2008-01-01

    We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells

  20. The application of neural networks to flow regime identification

    International Nuclear Information System (INIS)

    Embrechts, M.; Yapo, T.C.; Lahey, R.T. Jr.

    1993-01-01

    This paper deals with the application of a Kohonen map for the identification of two-phase flow regimes where a mixture of gas and fluid flows through a horizontal tube. Depending on the relative flow velocities of the gas and the liquid phase, four distinct flow regimes can be identified: Wavy flow, plug flow, slug flow and annular flow. A schematic of these flow regimes is presented. The objective identification of two-phase flow regimes constitutes an important and challenging problem for the design of safe and reliable nuclear power plants. Previous attempts to classify these flow regimes are reviewed by Franca and Lahey. The authors describe how a Kohonen map can be applied to distinguish between flow regimes based on the Fourier power spectra and wavelet transforms of pressure drop fluctuations. The Fourier power spectra allowed the Kohonen map to identify the flow regimes successfully. In contrast, the Kohonen maps based on a wavelet transform could only distinguish between wavy and annular flows. An analysis of typical two-phase pressure drop data for an air/water mixture in a horizontal pipe is presented. Use of the wavelet transform and the Kohonen feature map are discussed

  1. Frequency Response Function Based Damage Identification for Aerospace Structures

    Science.gov (United States)

    Oliver, Joseph Acton

    Structural health monitoring technologies continue to be pursued for aerospace structures in the interests of increased safety and, when combined with health prognosis, efficiency in life-cycle management. The current dissertation develops and validates damage identification technology as a critical component for structural health monitoring of aerospace structures and, in particular, composite unmanned aerial vehicles. The primary innovation is a statistical least-squares damage identification algorithm based in concepts of parameter estimation and model update. The algorithm uses frequency response function based residual force vectors derived from distributed vibration measurements to update a structural finite element model through statistically weighted least-squares minimization producing location and quantification of the damage, estimation uncertainty, and an updated model. Advantages compared to other approaches include robust applicability to systems which are heavily damped, large, and noisy, with a relatively low number of distributed measurement points compared to the number of analytical degrees-of-freedom of an associated analytical structural model (e.g., modal finite element model). Motivation, research objectives, and a dissertation summary are discussed in Chapter 1 followed by a literature review in Chapter 2. Chapter 3 gives background theory and the damage identification algorithm derivation followed by a study of fundamental algorithm behavior on a two degree-of-freedom mass-spring system with generalized damping. Chapter 4 investigates the impact of noise then successfully proves the algorithm against competing methods using an analytical eight degree-of-freedom mass-spring system with non-proportional structural damping. Chapter 5 extends use of the algorithm to finite element models, including solutions for numerical issues, approaches for modeling damping approximately in reduced coordinates, and analytical validation using a composite

  2. Role of centrality for the identification of influential spreaders in complex networks.

    Science.gov (United States)

    de Arruda, Guilherme Ferraz; Barbieri, André Luiz; Rodríguez, Pablo Martín; Rodrigues, Francisco A; Moreno, Yamir; Costa, Luciano da Fontoura

    2014-09-01

    The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumorlike dynamics. Recent works have suggested that the identification of influential spreaders is not independent of the dynamics being studied. For instance, the key disease spreaders might not necessarily be so important when it comes to analyzing social contagion or rumor propagation. Additionally, it has been shown that different metrics (degree, coreness, etc.) might identify different influential nodes even for the same dynamical processes with diverse degrees of accuracy. In this paper, we investigate how nine centrality measures correlate with the disease and rumor spreading capabilities of the nodes in different synthetic and real-world (both spatial and nonspatial) networks. We also propose a generalization of the random walk accessibility as a new centrality measure and derive analytical expressions for the latter measure for simple network configurations. Our results show that for nonspatial networks, the k-core and degree centralities are the most correlated to epidemic spreading, whereas the average neighborhood degree, the closeness centrality, and accessibility are the most related to rumor dynamics. On the contrary, for spatial networks, the accessibility measure outperforms the rest of the centrality metrics in almost all cases regardless of the kind of dynamics considered. Therefore, an important consequence of our analysis is that previous studies performed in synthetic random networks cannot be generalized to the case of spatial networks.

  3. Corporate Social Responsibility in Online Social Networks

    DEFF Research Database (Denmark)

    Horn, Christian; Brem, Alexander; Wölfl, S.

    2014-01-01

    Considering growing public awareness of social, ethical and ecological responsibility, companies have constantly been increasing their efforts in CSR communications. Social Media as tools of brand communication receive increasing attention and it is expected that the marketing sector...

  4. An integer optimization algorithm for robust identification of non-linear gene regulatory networks

    Directory of Open Access Journals (Sweden)

    Chemmangattuvalappil Nishanth

    2012-09-01

    Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters

  5. Faba bean drought responsive gene identification and validation

    Directory of Open Access Journals (Sweden)

    Megahed H. Ammar

    2017-01-01

    Full Text Available This study was carried out to identify drought-responsive genes in a drought tolerant faba bean variety (Hassawi 2 using a suppressive subtraction hybridization approach (SSH. A total of 913 differentially expressed clones were sequenced from a differential cDNA library that resulted in a total of 225 differentially expressed ESTs. The genes of mitochondrial and chloroplast origin were removed, and the remaining 137 EST sequences were submitted to the gene bank EST database (LIBEST_028448. A sequence analysis identified 35 potentially drought stress-related ESTs that regulate ion channels, kinases, and energy production and utilization and transcription factors. Quantitative PCR on Hassawi 2 genotype confirmed that more than 65% of selected drought-responsive genes were drought-related. Among these induced genes, the expression levels of eight highly up-regulated unigenes were further analyzed across 38 selected faba bean genotypes that differ in their drought tolerance levels. These unigenes included ribulose 1,5-bisphosphate carboxylase (rbcL gene, non-LTR retroelement reverse related, probable cyclic nucleotide-gated ion channel, polyubiquitin, potassium channel, calcium-dependent protein kinase and putative respiratory burst oxidase-like protein C and a novel unigene. The expression patterns of these unigenes were variable across 38 genotypes however, it was found to be very high in tolerant genotype. The up-regulation of these unigenes in majority of tolerant genotypes suggests their possible role in drought tolerance. The identification of possible drought responsive candidate genes in Vicia faba reported here is an important step toward the development of drought-tolerant genotypes that can cope with arid environments.

  6. Identification, characterization and expression analysis of pigeonpea miRNAs in response to Fusarium wilt.

    Science.gov (United States)

    Hussain, Khalid; Mungikar, Kanak; Kulkarni, Abhijeet; Kamble, Avinash

    2018-05-05

    Upon confrontation with unfavourable conditions, plants invoke a very complex set of biochemical and physiological reactions and alter gene expression patterns to combat the situations. MicroRNAs (miRNAs), a class of small non-coding RNA, contribute extensively in regulation of gene expression through translation inhibition or degradation of their target mRNAs during such conditions. Therefore, identification of miRNAs and their targets holds importance in understanding the regulatory networks triggered during stress. Structure and sequence similarity based in silico prediction of miRNAs in Cajanus cajan L. (Pigeonpea) draft genome sequence has been carried out earlier. These annotations also appear in related GenBank genome sequence entries. However, there are no reports available on context dependent miRNA expression and their targets in pigeonpea. Therefore, in the present study we addressed these questions computationally, using pigeonpea EST sequence information. We identified five novel pigeonpea miRNA precursors, their mature forms and targets. Interestingly, only one of these miRNAs (miR169i-3p) was identified earlier in draft genome sequence. We then validated expression of these miRNAs, experimentally. It was also observed that these miRNAs show differential expression patterns in response to Fusarium inoculation indicating their biotic stress responsive nature. Overall these results will help towards better understanding the regulatory network of defense during pigeonpea -pathogen interactions and role of miRNAs in the process. Copyright © 2018 Elsevier B.V. All rights reserved.

  7. Identification of Multiple-Mode Linear Models Based on Particle Swarm Optimizer with Cyclic Network Mechanism

    Directory of Open Access Journals (Sweden)

    Tae-Hyoung Kim

    2017-01-01

    Full Text Available This paper studies the metaheuristic optimizer-based direct identification of a multiple-mode system consisting of a finite set of linear regression representations of subsystems. To this end, the concept of a multiple-mode linear regression model is first introduced, and its identification issues are established. A method for reducing the identification problem for multiple-mode models to an optimization problem is also described in detail. Then, to overcome the difficulties that arise because the formulated optimization problem is inherently ill-conditioned and nonconvex, the cyclic-network-topology-based constrained particle swarm optimizer (CNT-CPSO is introduced, and a concrete procedure for the CNT-CPSO-based identification methodology is developed. This scheme requires no prior knowledge of the mode transitions between subsystems and, unlike some conventional methods, can handle a large amount of data without difficulty during the identification process. This is one of the distinguishing features of the proposed method. The paper also considers an extension of the CNT-CPSO-based identification scheme that makes it possible to simultaneously obtain both the optimal parameters of the multiple submodels and a certain decision parameter involved in the mode transition criteria. Finally, an experimental setup using a DC motor system is established to demonstrate the practical usability of the proposed metaheuristic optimizer-based identification scheme for developing a multiple-mode linear regression model.

  8. Neural Network Target Identification System for False Alarm Reduction

    Science.gov (United States)

    Ye, David; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin

    2009-01-01

    A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feed forward back propagation neural network (NN) is then trained to classify each feature vector and remove false positives. This paper discusses the test of the system performance and parameter optimizations process which adapts the system to various targets and datasets. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar image dataset.

  9. Surrogate-assisted identification of influences of network construction on evolving weighted functional networks

    Science.gov (United States)

    Stahn, Kirsten; Lehnertz, Klaus

    2017-12-01

    We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.

  10. Identification and adaptive neural network control of a DC motor system with dead-zone characteristics.

    Science.gov (United States)

    Peng, Jinzhu; Dubay, Rickey

    2011-10-01

    In this paper, an adaptive control approach based on the neural networks is presented to control a DC motor system with dead-zone characteristics (DZC), where two neural networks are proposed to formulate the traditional identification and control approaches. First, a Wiener-type neural network (WNN) is proposed to identify the motor DZC, which formulates the Wiener model with a linear dynamic block in cascade with a nonlinear static gain. Second, a feedforward neural network is proposed to formulate the traditional PID controller, termed as PID-type neural network (PIDNN), which is then used to control and compensate for the DZC. In this way, the DC motor system with DZC is identified by the WNN identifier, which provides model information to the PIDNN controller in order to make it adaptive. Back-propagation algorithms are used to train both neural networks. Also, stability and convergence analysis are conducted using the Lyapunov theorem. Finally, experiments on the DC motor system demonstrated accurate identification and good compensation for dead-zone with improved control performance over the conventional PID control. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

  11. IMPLICATIONS OF SOCIAL RESPONSIBILITY DISCLOSURE ON GLOBAL PRODUCTION NETWORK

    OpenAIRE

    Le Bo; Dan Shen; Jin Jun Bo

    2014-01-01

    This paper aims to discuss effectiveness of social responsibility disclosure in promoting global production network. Through a critical review on the theoretical development from supply chain to global production network, the global supply chain management of Apple Inc., as a case, is investigated, with focus on corporate and NGOs’ social disclosure on the environmental and labor rights' issues of its suppliers in China. The paper concludes that effectiveness of corporate social disclosure on...

  12. Real-time radionuclide identification in γ-emitter mixtures based on spiking neural network

    International Nuclear Information System (INIS)

    Bobin, C.; Bichler, O.; Lourenço, V.; Thiam, C.; Thévenin, M.

    2016-01-01

    Portal radiation monitors dedicated to the prevention of illegal traffic of nuclear materials at international borders need to deliver as fast as possible a radionuclide identification of a potential radiological threat. Spectrometry techniques applied to identify the radionuclides contributing to γ-emitter mixtures are usually performed using off-line spectrum analysis. As an alternative to these usual methods, a real-time processing based on an artificial neural network and Bayes’ rule is proposed for fast radionuclide identification. The validation of this real-time approach was carried out using γ-emitter spectra ( 241 Am, 133 Ba, 207 Bi, 60 Co, 137 Cs) obtained with a high-efficiency well-type NaI(Tl). The first tests showed that the proposed algorithm enables a fast identification of each γ-emitting radionuclide using the information given by the whole spectrum. Based on an iterative process, the on-line analysis only needs low-statistics spectra without energy calibration to identify the nature of a radiological threat. - Highlights: • A fast radionuclide identification algorithm applicable in spectroscopic portal monitors is presented. • The proposed algorithm combines a Bayesian sequential approach and a spiking neural network. • The algorithm was validated using the mixture of γ-emitter spectra provided by a well-type NaI(Tl) detector. • The radionuclide identification process is implemented using the whole γ-spectrum without energy calibration.

  13. 5-HTTLPR differentially predicts brain network responses to emotional faces

    DEFF Research Database (Denmark)

    Fisher, Patrick M; Grady, Cheryl L; Madsen, Martin K

    2015-01-01

    The effects of the 5-HTTLPR polymorphism on neural responses to emotionally salient faces have been studied extensively, focusing on amygdala reactivity and amygdala-prefrontal interactions. Despite compelling evidence that emotional face paradigms engage a distributed network of brain regions...... to fearful faces was significantly greater in S' carriers compared to LA LA individuals. These findings provide novel evidence for emotion-specific 5-HTTLPR effects on the response of a distributed set of brain regions including areas responsive to emotionally salient stimuli and critical components...... involved in emotion, cognitive and visual processing, less is known about 5-HTTLPR effects on broader network responses. To address this, we evaluated 5-HTTLPR differences in the whole-brain response to an emotional faces paradigm including neutral, angry and fearful faces using functional magnetic...

  14. Enhancing response coordination through the assessment of response network structural dynamics.

    Directory of Open Access Journals (Sweden)

    Alireza Abbasi

    Full Text Available Preparing for intensifying threats of emergencies in unexpected, dangerous, and serious natural or man-made events, and consequent management of the situation, is highly demanding in terms of coordinating the personnel and resources to support human lives and the environment. This necessitates prompt action to manage the uncertainties and risks imposed by such extreme events, which requires collaborative operation among different stakeholders (i.e., the personnel from both the state and local communities. This research aims to find a way to enhance the coordination of multi-organizational response operations. To do so, this manuscript investigates the role of participants in the formed coordination response network and also the emergence and temporal dynamics of the network. By analyzing an inter-personal response coordination operation to an extreme bushfire event, the networks' and participants' structural change is evaluated during the evolution of the operation network over four time durations. The results reveal that the coordination response network becomes more decentralized over time due to the high volume of communication required to exchange information. New emerging communication structures often do not fit the developed plans, which stress the need for coordination by feedback in addition to by plan. In addition, we find that the participant's brokering role in the response operation network identifies a formal and informal coordination role. This is useful for comparison of network structures to examine whether what really happens during response operations complies with the initial policy.

  15. A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

    Science.gov (United States)

    Hong, Xia

    2006-07-01

    In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

  16. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  17. Identification of literary movements using complex networks to represent texts

    International Nuclear Information System (INIS)

    Amancio, Diego Raphael; Oliveira, Osvaldo N Jr; Fontoura Costa, Luciano da

    2012-01-01

    The use of statistical methods to analyze large databases of text has been useful in unveiling patterns of human behavior and establishing historical links between cultures and languages. In this study, we identified literary movements by treating books published from 1590 to 1922 as complex networks, whose metrics were analyzed with multivariate techniques to generate six clusters of books. The latter correspond to time periods coinciding with relevant literary movements over the last five centuries. The most important factor contributing to the distinctions between different literary styles was the average shortest path length, in particular the asymmetry of its distribution. Furthermore, over time there has emerged a trend toward larger average shortest path lengths, which is correlated with increased syntactic complexity, and a more uniform use of the words reflected in a smaller power-law coefficient for the distribution of word frequency. Changes in literary style were also found to be driven by opposition to earlier writing styles, as revealed by the analysis performed with geometrical concepts. The approaches adopted here are generic and may be extended to analyze a number of features of languages and cultures. (paper)

  18. Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

    Science.gov (United States)

    Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

    2000-01-01

    Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

  19. Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

    International Nuclear Information System (INIS)

    Hiroshi Goda; Seungjin Kim; Ye Mi; Finch, Joshua P.; Mamoru Ishii; Jennifer Uhle

    2002-01-01

    Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated. (authors)

  20. Individual Identification Using Functional Brain Fingerprint Detected by Recurrent Neural Network.

    Science.gov (United States)

    Chen, Shiyang; Hu, Xiaoping P

    2018-03-20

    Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network based model for identifying individuals based on only a short segment of resting state functional MRI data. In addition, we demonstrate how the global signal and differences in atlases affect the individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.

  1. Topological derivatives of eigenvalues and neural networks in identification of imperfections

    International Nuclear Information System (INIS)

    Grzanek, M; Nowakowski, A; Sokolowski, J

    2008-01-01

    Numerical method for identification of imperfections is devised for elliptic spectral problems. The neural networks are employed for numerical solution. The topological derivatives of eigenvalues are used in the learning procedure of the neural networks. The topological derivatives of eigenvalues are determined by the methods of asymptotic analysis in singularly perturbed geometrical domains. The convergence of the numerical method in a probabilistic setting is analysed. The method is presented for the identification of small singular perturbations of the boundary of geometrical domain, however the framework is general and can be used for numerical solutions of inverse problems in the presence of small imperfections in the interior of the domain. Some numerical results are given for elliptic spectral problem in two spatial dimensions.

  2. Identification of chaotic systems by neural network with hybrid learning algorithm

    International Nuclear Information System (INIS)

    Pan, S.-T.; Lai, C.-C.

    2008-01-01

    Based on the genetic algorithm (GA) and steepest descent method (SDM), this paper proposes a hybrid algorithm for the learning of neural networks to identify chaotic systems. The systems in question are the logistic map and the Duffing equation. Different identification schemes are used to identify both the logistic map and the Duffing equation, respectively. Simulation results show that our hybrid algorithm is more efficient than that of other methods

  3. Transient identification system with noising data and 'don't know' response; Sistema de identificacao de transientes com inclusao de ruidos e indicacao de eventos desconhecidos

    Energy Technology Data Exchange (ETDEWEB)

    Mol, Antonio C. de A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil). Div. de Confiabilidade Humana; Martinez, Aquilino S.; Schirru, Roberto [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia. Programa de Engenharia Nuclear

    2002-07-01

    In the last years, many different approaches based on neural network (NN) has been proposed for transient identification in nuclear power plants (NPP). Some of them focus the dynamic identification using recurrent neural networks however, they are not able to deal with unrecognized transients. Other kind of solution uses competitive learning in order to allow the 'don't know' response. In this case dynamic, dynamic features are not well represented. This work presents a new approach for neural network based transient identification which allows either dynamic identification and 'don't know'response. Such approach uses two multilayer neural networks trained with backpropagation algorithm. The first one is responsible for the dynamic identification. This NN uses, a short set (in a movable time window) of recent measurements of each variable avoiding the necessity of using starting events. The other one is used to validate the instantaneous identification (from the first net) through the validation of each variable. This net is responsible for allowing the system to provide 'don't know' response. In order to validate the method a NPP transient identification problem comprising 15 postulated accidents, simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in other to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know' response. In order to validate the method, a NPP transient identification problem comprising 15 postulated accidents simulated for a pressurized water reactor, was proposed in the validation process it has been considered noising data in order to evaluate the method robustness. Obtained results reveal the ability of the method in dealing with both dynamic identification of transients and correct 'don't know

  4. Characterizing root response phenotypes by neural network analysis

    OpenAIRE

    Hatzig, Sarah V.; Schiessl, Sarah; Stahl, Andreas; Snowdon, Rod J.

    2015-01-01

    Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root architecture in response to stress can be difficult to quantify. Here, the Sholl method, an established technique from neurobiology used for the characterization of neural network anatomy, was adapted to more adequately describe root responses to osm...

  5. Local and global responses in complex gene regulation networks

    Science.gov (United States)

    Tsuchiya, Masa; Selvarajoo, Kumar; Piras, Vincent; Tomita, Masaru; Giuliani, Alessandro

    2009-04-01

    An exacerbated sensitivity to apparently minor stimuli and a general resilience of the entire system stay together side-by-side in biological systems. This apparent paradox can be explained by the consideration of biological systems as very strongly interconnected network systems. Some nodes of these networks, thanks to their peculiar location in the network architecture, are responsible for the sensitivity aspects, while the large degree of interconnection is at the basis of the resilience properties of the system. One relevant feature of the high degree of connectivity of gene regulation networks is the emergence of collective ordered phenomena influencing the entire genome and not only a specific portion of transcripts. The great majority of existing gene regulation models give the impression of purely local ‘hard-wired’ mechanisms disregarding the emergence of global ordered behavior encompassing thousands of genes while the general, genome wide, aspects are less known. Here we address, on a data analysis perspective, the discrimination between local and global scale regulations, this goal was achieved by means of the examination of two biological systems: innate immune response in macrophages and oscillating growth dynamics in yeast. Our aim was to reconcile the ‘hard-wired’ local view of gene regulation with a global continuous and scalable one borrowed from statistical physics. This reconciliation is based on the network paradigm in which the local ‘hard-wired’ activities correspond to the activation of specific crucial nodes in the regulation network, while the scalable continuous responses can be equated to the collective oscillations of the network after a perturbation.

  6. Response to Intervention for Specific Learning Disabilities Identification: The Impact of Graduate Preparation and Experience on Identification Consistency

    Science.gov (United States)

    Maki, Kathrin E.

    2018-01-01

    Response to intervention (RTI) is increasingly being implemented in schools as a means to identify students with specific learning disabilities (SLD). Despite its wide use, there is limited research regarding school psychologists' graduate preparation in and familiarity with RTI for SLD identification. This study examined how school psychologists'…

  7. Transient response of nonlinear polymer networks: A kinetic theory

    Science.gov (United States)

    Vernerey, Franck J.

    2018-06-01

    Dynamic networks are found in a majority of natural materials, but also in engineering materials, such as entangled polymers and physically cross-linked gels. Owing to their transient bond dynamics, these networks display a rich class of behaviors, from elasticity, rheology, self-healing, or growth. Although classical theories in rheology and mechanics have enabled us to characterize these materials, there is still a gap in our understanding on how individuals (i.e., the mechanics of each building blocks and its connection with others) affect the emerging response of the network. In this work, we introduce an alternative way to think about these networks from a statistical point of view. More specifically, a network is seen as a collection of individual polymer chains connected by weak bonds that can associate and dissociate over time. From the knowledge of these individual chains (elasticity, transient attachment, and detachment events), we construct a statistical description of the population and derive an evolution equation of their distribution based on applied deformation and their local interactions. We specifically concentrate on nonlinear elastic response that follows from the strain stiffening response of individual chains of finite size. Upon appropriate averaging operations and using a mean field approximation, we show that the distribution can be replaced by a so-called chain distribution tensor that is used to determine important macroscopic measures such as stress, energy storage and dissipation in the network. Prediction of the kinetic theory are then explored against known experimental measurement of polymer responses under uniaxial loading. It is found that even under the simplest assumptions of force-independent chain kinetics, the model is able to reproduce complex time-dependent behaviors of rubber and self-healing supramolecular polymers.

  8. Identification of Pavement Distress Types and Pavement Condition Evaluation Based on Network Level Inspection for Jazan City Road Network

    Directory of Open Access Journals (Sweden)

    M Mubaraki

    2014-06-01

    Full Text Available The first step in establishing a pavement management system (PMS is road network identification. An important feature of a PMS is the ability to determine the current condition of a road network and predict its future condition. Pavement condition evaluation may involve structure, roughness, surface distress, and safety evaluation. In this study, a pavement distress condition rating procedure was used to achieve the objectives of this study. The main objectives of this study were to identify the common types of distress that exist on the Jazan road network (JRN, either on main roads or secondary roads, and to evaluate the pavement condition based on network level inspection. The study was conducted by collecting pavement distress types from 227 sample units on main roads and 500 sample units from secondary roads. Data were examined through analysis of common types of distress identified in both main and secondary roads. Through these data, pavement condition index (PCI for each sample unit was then calculated. Through these calculations, average PCIs for the main and secondary roads were determined. Results indicated that the most common pavement distress types on main roads were patching and utility cut patching, longitudinal and transverse cracking, polished aggregate, weathering and raveling, and alligator cracking. The most common pavement distress types on secondary roads were weathering and raveling, patching and utility cut patching, longitudinal and transverse cracking, potholes, and alligator cracking. The results also indicated that 65% of Jazan's main road network has an average pavement condition rating of very good while only 30% of Jazan's secondary roads network has an average pavement condition.

  9. Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network.

    Science.gov (United States)

    Lin, Yang-Yin; Chang, Jyh-Yeong; Lin, Chin-Teng

    2013-02-01

    This paper presents a novel recurrent fuzzy neural network, called an interactively recurrent self-evolving fuzzy neural network (IRSFNN), for prediction and identification of dynamic systems. The recurrent structure in an IRSFNN is formed as an external loops and internal feedback by feeding the rule firing strength of each rule to others rules and itself. The consequent part in the IRSFNN is composed of a Takagi-Sugeno-Kang (TSK) or functional-link-based type. The proposed IRSFNN employs a functional link neural network (FLNN) to the consequent part of fuzzy rules for promoting the mapping ability. Unlike a TSK-type fuzzy neural network, the FLNN in the consequent part is a nonlinear function of input variables. An IRSFNNs learning starts with an empty rule base and all of the rules are generated and learned online through a simultaneous structure and parameter learning. An on-line clustering algorithm is effective in generating fuzzy rules. The consequent update parameters are derived by a variable-dimensional Kalman filter algorithm. The premise and recurrent parameters are learned through a gradient descent algorithm. We test the IRSFNN for the prediction and identification of dynamic plants and compare it to other well-known recurrent FNNs. The proposed model obtains enhanced performance results.

  10. Integration of Online Parameter Identification and Neural Network for In-Flight Adaptive Control

    Science.gov (United States)

    Hageman, Jacob J.; Smith, Mark S.; Stachowiak, Susan

    2003-01-01

    An indirect adaptive system has been constructed for robust control of an aircraft with uncertain aerodynamic characteristics. This system consists of a multilayer perceptron pre-trained neural network, online stability and control derivative identification, a dynamic cell structure online learning neural network, and a model following control system based on the stochastic optimal feedforward and feedback technique. The pre-trained neural network and model following control system have been flight-tested, but the online parameter identification and online learning neural network are new additions used for in-flight adaptation of the control system model. A description of the modification and integration of these two stand-alone software packages into the complete system in preparation for initial flight tests is presented. Open-loop results using both simulation and flight data, as well as closed-loop performance of the complete system in a nonlinear, six-degree-of-freedom, flight validated simulation, are analyzed. Results show that this online learning system, in contrast to the nonlearning system, has the ability to adapt to changes in aerodynamic characteristics in a real-time, closed-loop, piloted simulation, resulting in improved flying qualities.

  11. Reconstructing a Network of Stress-Response Regulators via Dynamic System Modeling of Gene Regulation

    Directory of Open Access Journals (Sweden)

    Wei-Sheng Wu

    2008-01-01

    Full Text Available Unicellular organisms such as yeasts have evolved mechanisms to respond to environmental stresses by rapidly reorganizing the gene expression program. Although many stress-response genes in yeast have been discovered by DNA microarrays, the stress-response transcription factors (TFs that regulate these stress-response genes remain to be investigated. In this study, we use a dynamic system model of gene regulation to describe the mechanism of how TFs may control a gene’s expression. Then, based on the dynamic system model, we develop the Stress Regulator Identification Algorithm (SRIA to identify stress-response TFs for six kinds of stresses. We identified some general stress-response TFs that respond to various stresses and some specific stress-response TFs that respond to one specifi c stress. The biological significance of our findings is validated by the literature. We found that a small number of TFs is probably suffi cient to control a wide variety of expression patterns in yeast under different stresses. Two implications can be inferred from this observation. First, the response mechanisms to different stresses may have a bow-tie structure. Second, there may be regulatory cross-talks among different stress responses. In conclusion, this study proposes a network of stress-response regulators and the details of their actions.

  12. Jimena: efficient computing and system state identification for genetic regulatory networks.

    Science.gov (United States)

    Karl, Stefan; Dandekar, Thomas

    2013-10-11

    Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

  13. Identification from the Natural Response of Vasco Da Gama Bridge

    DEFF Research Database (Denmark)

    Cunha, A.; Caetano, E.; Brincker, Rune

    2004-01-01

    This paper describes the reanalysis of the ambient vibration data of Vasco da Gama cable-stayed bridge with the purpose of testing the efficiency and accuracy of two recent and promising identification methods in a large application: the Frequency Domain Decomposition (FDD) and the Stochastic...... Subspace Identification (SSI) methods. The modal estimates obtained using these alternative approaches are compared, taking also into account the estimates previously obtained with the conventional Peak Picking technique from the free vibration test of the bridge, performed at the end of construction....

  14. Identification and expression analysis of cold and freezing stress responsive genes of Brassica oleracea.

    Science.gov (United States)

    Ahmed, Nasar Uddin; Jung, Hee-Jeong; Park, Jong-In; Cho, Yong-Gu; Hur, Yoonkang; Nou, Ill-Sup

    2015-01-10

    Cold and freezing stress is a major environmental constraint to the production of Brassica crops. Enhancement of tolerance by exploiting cold and freezing tolerance related genes offers the most efficient approach to address this problem. Cold-induced transcriptional profiling is a promising approach to the identification of potential genes related to cold and freezing stress tolerance. In this study, 99 highly expressed genes were identified from a whole genome microarray dataset of Brassica rapa. Blast search analysis of the Brassica oleracea database revealed the corresponding homologous genes. To validate their expression, pre-selected cold tolerant and susceptible cabbage lines were analyzed. Out of 99 BoCRGs, 43 were differentially expressed in response to varying degrees of cold and freezing stress in the contrasting cabbage lines. Among the differentially expressed genes, 18 were highly up-regulated in the tolerant lines, which is consistent with their microarray expression. Additionally, 12 BoCRGs were expressed differentially after cold stress treatment in two contrasting cabbage lines, and BoCRG54, 56, 59, 62, 70, 72 and 99 were predicted to be involved in cold regulatory pathways. Taken together, the cold-responsive genes identified in this study provide additional direction for elucidating the regulatory network of low temperature stress tolerance and developing cold and freezing stress resistant Brassica crops. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Corporate social responsibility and the identification of stakeholders

    NARCIS (Netherlands)

    Vos, Janita F.J.

    2002-01-01

    As a management problem the identification of stakeholders is not easily solved. It comprises a modelling and a normative issue, which need to be solved in connection with each other. In stakeholder literature knowledge can be found, e.g. on various stakeholder categorizations, that could be useful

  16. Identification of tipping elements of the Indian Summer Monsoon using climate network approach

    Science.gov (United States)

    Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

    2015-04-01

    Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a

  17. The impact of measurement errors in the identification of regulatory networks

    Directory of Open Access Journals (Sweden)

    Sato João R

    2009-12-01

    Full Text Available Abstract Background There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent and non-time series (independent data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models and dependent (autoregressive models data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error. The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

  18. Identification of hadronic tau decays at the ATLAS detector using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Duschinger, Dirk; Hanisch, Stefanie; Mader, Wolfgang; Madysa, Nico; Straessner, Arno [Institut fuer Kern- und Teilchenphysik, TU Dresden (Germany)

    2016-07-01

    One of the primary goals of the ATLAS experiment at the LHC is the search for physics beyond the Standard Model. The efficient identification of hadronically decaying tau leptons is crucial for this as they comprise the final states of several decay channels sensitive to new physics. (e. g. Higgs boson decays H → τ{sub had} τ{sub had}) The identification algorithm currently applied at ATLAS utilizes multi-variate methods and reconstructed particle properties to discriminate against QCD jets, which constitute an important background. This talk presents a new neural-network-based approach to hadronic tau decay identification and investigates its dependence on hyperparameters such as the network topology or number of training cycles. Ensembling is presented as a technique to improve classifier performance and robustness against overtraining. The resulting classifier is compared to the current approach based on Boosted Decision Trees. The study is based on 2012 data taken at the ATLAS detector at a center-of-mass energy of √(s)=8 TeV.

  19. Dynamical Response of Networks Under External Perturbations: Exact Results

    Science.gov (United States)

    Chinellato, David D.; Epstein, Irving R.; Braha, Dan; Bar-Yam, Yaneer; de Aguiar, Marcus A. M.

    2015-04-01

    We give exact statistical distributions for the dynamic response of influence networks subjected to external perturbations. We consider networks whose nodes have two internal states labeled 0 and 1. We let nodes be frozen in state 0, in state 1, and the remaining nodes change by adopting the state of a connected node with a fixed probability per time step. The frozen nodes can be interpreted as external perturbations to the subnetwork of free nodes. Analytically extending and to be smaller than 1 enables modeling the case of weak coupling. We solve the dynamical equations exactly for fully connected networks, obtaining the equilibrium distribution, transition probabilities between any two states and the characteristic time to equilibration. Our exact results are excellent approximations for other topologies, including random, regular lattice, scale-free and small world networks, when the numbers of fixed nodes are adjusted to take account of the effect of topology on coupling to the environment. This model can describe a variety of complex systems, from magnetic spins to social networks to population genetics, and was recently applied as a framework for early warning signals for real-world self-organized economic market crises.

  20. Identification of complex systems by artificial neural networks. Applications to mechanical frictions

    International Nuclear Information System (INIS)

    Dominguez, Manuel

    1998-01-01

    In the frame of complex systems modelization, we describe in this report the contribution of neural networks to mechanical friction modelization. This thesis is divided in three parts, each one corresponding to every stage of the realized work. The first part takes stock of the properties of neural networks by replacing them in the statistic frame of learning theory (particularly: non-linear and non-parametric regression models) and by showing the existing links with other more 'classic' techniques from automatics. We show then how identification models can be integrated in the neural networks description as a larger nonlinear model class. A methodology of neural networks use have been developed. We focused on validation techniques using correlation functions for non-linear systems, and on the use of regularization methods. The second part deals with the problematic of friction in mechanical systems. Particularly, we present the main current identified physical phenomena, which are integrated in advanced friction modelization. Characterization of these phenomena allows us to state a priori knowledge to be used in the identification stage. We expose some of the most well-known friction models: Dahl's model, Reset Integrator and Canuda's dynamical model, which are then used in simulation studies. The last part links the former one by illustrating a real-world application: an electric jack from SFIM-Industries, used in the Very Large Telescope (VLT) control scheme. This part begins with physical system presentation. The results are compared with more 'classic' methods. We finish using neural networks compensation scheme in closed-loop control. (author) [fr

  1. A neural network device for on-line particle identification in cosmic ray experiments

    International Nuclear Information System (INIS)

    Scrimaglio, R.; Finetti, N.; D'Altorio, L.; Rantucci, E.; Raso, M.; Segreto, E.; Tassoni, A.; Cardarilli, G.C.

    2004-01-01

    On-line particle identification is one of the main goals of many experiments in space both for rare event studies and for optimizing measurements along the orbital trajectory. Neural networks can be a useful tool for signal processing and real time data analysis in such experiments. In this document we report on the performances of a programmable neural device which was developed in VLSI analog/digital technology. Neurons and synapses were accomplished by making use of Operational Transconductance Amplifier (OTA) structures. In this paper we report on the results of measurements performed in order to verify the agreement of the characteristic curves of each elementary cell with simulations and on the device performances obtained by implementing simple neural structures on the VLSI chip. A feed-forward neural network (Multi-Layer Perceptron, MLP) was implemented on the VLSI chip and trained to identify particles by processing the signals of two-dimensional position-sensitive Si detectors. The radiation monitoring device consisted of three double-sided silicon strip detectors. From the analysis of a set of simulated data it was found that the MLP implemented on the neural device gave results comparable with those obtained with the standard method of analysis confirming that the implemented neural network could be employed for real time particle identification

  2. Performance of wavelet analysis and neural networks for pathological voices identification

    Science.gov (United States)

    Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

    2011-09-01

    Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

  3. Identification of driving network of cellular differentiation from single sample time course gene expression data

    Science.gov (United States)

    Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

    Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

  4. A Heterogeneous Wireless Identification Network for the Localization of Animals Based on Stochastic Movements

    Directory of Open Access Journals (Sweden)

    Ivana Raos

    2009-05-01

    Full Text Available The improvement in the transmission range in wireless applications without the use of batteries remains a significant challenge in identification applications. In this paper, we describe a heterogeneous wireless identification network mostly powered by kinetic energy, which allows the localization of animals in open environments. The system relies on radio communications and a global positioning system. It is made up of primary and secondary nodes. Secondary nodes are kinetic-powered and take advantage of animal movements to activate the node and transmit a specific identifier, reducing the number of batteries of the system. Primary nodes are battery-powered and gather secondary-node transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. The system allows tracking based on contextual information obtained from statistical data.

  5. Resting State EEG-based biometrics for individual identification using convolutional neural networks.

    Science.gov (United States)

    Lan Ma; Minett, James W; Blu, Thierry; Wang, William S-Y

    2015-08-01

    Biometrics is a growing field, which permits identification of individuals by means of unique physical features. Electroencephalography (EEG)-based biometrics utilizes the small intra-personal differences and large inter-personal differences between individuals' brainwave patterns. In the past, such methods have used features derived from manually-designed procedures for this purpose. Another possibility is to use convolutional neural networks (CNN) to automatically extract an individual's best and most unique neural features and conduct classification, using EEG data derived from both Resting State with Open Eyes (REO) and Resting State with Closed Eyes (REC). Results indicate that this CNN-based joint-optimized EEG-based Biometric System yields a high degree of accuracy of identification (88%) for 10-class classification. Furthermore, rich inter-personal difference can be found using a very low frequency band (0-2Hz). Additionally, results suggest that the temporal portions over which subjects can be individualized is less than 200 ms.

  6. NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification

    Directory of Open Access Journals (Sweden)

    Min Peng

    2016-10-01

    Full Text Available Near-infrared (NIR face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN for NIR face recognition (specifically face identification in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.

  7. Identification of human disease genes from interactome network using graphlet interaction.

    Directory of Open Access Journals (Sweden)

    Xiao-Dong Wang

    Full Text Available Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes.

  8. Identification of Human Disease Genes from Interactome Network Using Graphlet Interaction

    Science.gov (United States)

    Yang, Lun; Wei, Dong-Qing; Qi, Ying-Xin; Jiang, Zong-Lai

    2014-01-01

    Identifying genes related to human diseases, such as cancer and cardiovascular disease, etc., is an important task in biomedical research because of its applications in disease diagnosis and treatment. Interactome networks, especially protein-protein interaction networks, had been used to disease genes identification based on the hypothesis that strong candidate genes tend to closely relate to each other in some kinds of measure on the network. We proposed a new measure to analyze the relationship between network nodes which was called graphlet interaction. The graphlet interaction contained 28 different isomers. The results showed that the numbers of the graphlet interaction isomers between disease genes in interactome networks were significantly larger than random picked genes, while graphlet signatures were not. Then, we designed a new type of score, based on the network properties, to identify disease genes using graphlet interaction. The genes with higher scores were more likely to be disease genes, and all candidate genes were ranked according to their scores. Then the approach was evaluated by leave-one-out cross-validation. The precision of the current approach achieved 90% at about 10% recall, which was apparently higher than the previous three predominant algorithms, random walk, Endeavour and neighborhood based method. Finally, the approach was applied to predict new disease genes related to 4 common diseases, most of which were identified by other independent experimental researches. In conclusion, we demonstrate that the graphlet interaction is an effective tool to analyze the network properties of disease genes, and the scores calculated by graphlet interaction is more precise in identifying disease genes. PMID:24465923

  9. Development of objective flow regime identification method using self-organizing neural network

    International Nuclear Information System (INIS)

    Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

    2004-01-01

    Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

  10. "Everybody Identifies with Smokey the Bear": Employee Responses to Newsletter Identification Inducements at the U.S. Forest Service.

    Science.gov (United States)

    DiSanza, James R.; Bullis, Connie

    1999-01-01

    Contributes to scholarship on organizational identification (linked to decision making) by examining the identification rhetoric of an in-house newsletter at the U. S. Forest Service and by examining employee responses to newsletter content. Discusses the four responses to the newsletter that were identified: non identification, textual…

  11. Time response of temperature sensors using neural networks

    International Nuclear Information System (INIS)

    Santos, Roberto Carlos dos

    2010-01-01

    In a PWR nuclear power plant, the primary coolant temperature and feedwater temperature are measured using RTDs (Resistance Temperature Detectors). These RTDs typically feed the plant's control and safety systems and must, therefore, be very accurate and have good dynamic performance. The response time of RTDs is characterized by a single parameter called the Plunge Time Constant defined as the time it takes the sensor output to achieve 63.2 percent of its final value after a step change in temperature. Nuclear reactor service conditions are difficult to reproduce in the laboratory, and an in-situ test method called LCSR (Loop Current Step Response) test was developed to measure remotely the response time of RTDs. >From this test, the time constant of the sensor is identified by means of the LCSR transformation that involves the dynamic response modal time constants determination using a nodal heat-transfer model. This calculation is not simple and requires specialized personnel. For this reason an Artificial Neural Network has been developed to predict the time constant of RTD from LCSR test transient. It eliminates the transformations involved in the LCSR application. A series of LCSR tests on RTDs generates the response transients of the sensors, the input data of the networks. Plunge tests are used to determine the time constants of the RTDs, the desired output of the ANN, trained using these sets of input/output data. This methodology was firstly applied to theoretical data simulating 10 RTDs with different time constant values, resulting in an average error of about 0.74 %. Experimental data from three different RTDs was used to predict time constant resulting in a maximum error of 3,34 %. The time constants values predicted from ANN were compared with those obtained from traditional way resulting in an average error of about 18 % and that shows the network is able to predict accurately the sensor time constant. (author)

  12. Signalling network construction for modelling plant defence response.

    Directory of Open Access Journals (Sweden)

    Dragana Miljkovic

    Full Text Available Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2 triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be

  13. System identification and adaptive control theory and applications of the neurofuzzy and fuzzy cognitive network models

    CERN Document Server

    Boutalis, Yiannis; Kottas, Theodore; Christodoulou, Manolis A

    2014-01-01

    Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented.  Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model  stems  from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering s...

  14. Identification of Jets Containing b-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    A novel b-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS Experiment. This talk presents the expected performance of the RNN based b-tagging in simulated $t \\bar t$ events. The RNN based b-tagging processes properties of tracks associated to jets which are represented in sequences. In contrast to traditional impact-parameter-based b-tagging algorithms which assume the tracks of jets are independent from each other, RNN based b-tagging can exploit the spatial and kinematic correlations of tracks which are initiated from the same b-hadrons. The neural network nature of the tagging algorithm also allows the flexibility of extending input features to include more track properties than can be effectively used in traditional algorithms.

  15. Differential Neural Networks for Identification and Filtering in Nonlinear Dynamic Games

    Directory of Open Access Journals (Sweden)

    Emmanuel García

    2014-01-01

    Full Text Available This paper deals with the problem of identifying and filtering a class of continuous-time nonlinear dynamic games (nonlinear differential games subject to additive and undesired deterministic perturbations. Moreover, the mathematical model of this class is completely unknown with the exception of the control actions of each player, and even though the deterministic noises are known, their power (or their effect is not. Therefore, two differential neural networks are designed in order to obtain a feedback (perfect state information pattern for the mentioned class of games. In this way, the stability conditions for two state identification errors and for a filtering error are established, the upper bounds of these errors are obtained, and two new learning laws for each neural network are suggested. Finally, an illustrating example shows the applicability of this approach.

  16. IAEA Response and Assistance Network. Date Effective: 1 September 2013

    International Nuclear Information System (INIS)

    2013-01-01

    The Parties to the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency (the 'Assistance Convention') have undertaken to cooperate between themselves and with the IAEA to facilitate the timely provision of assistance in the case of a nuclear accident or radiological emergency, in order to mitigate its consequences. In September 2000, the General Conference of the IAEA, in resolution GC(44)/RES/16, encouraged Member States ''to implement instruments for improving their response, in particular their contribution to international response, to nuclear and radiological emergencies'' as well as ''to participate actively in the process of strengthening international, national and regional capabilities for responding to nuclear and radiological emergencies and to make those capabilities more consistent and coherent''. As part of the IAEA's strategy for supporting the practical implementation of the Assistance Convention, in 2000 the IAEA Secretariat established a global Emergency Response Network (ERNET) of teams suitably qualified to respond to nuclear or radiological emergencies rapidly and, in principle, on a regional basis. The IAEA Secretariat published IAEA Emergency Response Network - ERNET (EPR-ERNET) in 2000, which set out the criteria and requirements to be met by members of the network. An updated edition was published in 2002. The Second Meeting of the Representatives of Competent Authorities Identified under the Convention on Early Notification of a Nuclear Accident and the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency, held in Vienna in June 2003, recommended that the IAEA Secretariat convene a Technical Meeting to formulate recommendations aimed at improving participation in the network. Participants in a Technical Meeting held in March 2004 developed a new concept for the network and a completely new draft of the publication. In July 2005, the Third Meeting of Competent Authorities

  17. Discovering functional interdependence relationship in PPI networks for protein complex identification.

    Science.gov (United States)

    Lam, Winnie W M; Chan, Keith C C

    2012-04-01

    Protein molecules interact with each other in protein complexes to perform many vital functions, and different computational techniques have been developed to identify protein complexes in protein-protein interaction (PPI) networks. These techniques are developed to search for subgraphs of high connectivity in PPI networks under the assumption that the proteins in a protein complex are highly interconnected. While these techniques have been shown to be quite effective, it is also possible that the matching rate between the protein complexes they discover and those that are previously determined experimentally be relatively low and the "false-alarm" rate can be relatively high. This is especially the case when the assumption of proteins in protein complexes being more highly interconnected be relatively invalid. To increase the matching rate and reduce the false-alarm rate, we have developed a technique that can work effectively without having to make this assumption. The name of the technique called protein complex identification by discovering functional interdependence (PCIFI) searches for protein complexes in PPI networks by taking into consideration both the functional interdependence relationship between protein molecules and the network topology of the network. The PCIFI works in several steps. The first step is to construct a multiple-function protein network graph by labeling each vertex with one or more of the molecular functions it performs. The second step is to filter out protein interactions between protein pairs that are not functionally interdependent of each other in the statistical sense. The third step is to make use of an information-theoretic measure to determine the strength of the functional interdependence between all remaining interacting protein pairs. Finally, the last step is to try to form protein complexes based on the measure of the strength of functional interdependence and the connectivity between proteins. For performance evaluation

  18. Phase-response curves and synchronized neural networks.

    Science.gov (United States)

    Smeal, Roy M; Ermentrout, G Bard; White, John A

    2010-08-12

    We review the principal assumptions underlying the application of phase-response curves (PRCs) to synchronization in neuronal networks. The PRC measures how much a given synaptic input perturbs spike timing in a neural oscillator. Among other applications, PRCs make explicit predictions about whether a given network of interconnected neurons will synchronize, as is often observed in cortical structures. Regarding the assumptions of the PRC theory, we conclude: (i) The assumption of noise-tolerant cellular oscillations at or near the network frequency holds in some but not all cases. (ii) Reduced models for PRC-based analysis can be formally related to more realistic models. (iii) Spike-rate adaptation limits PRC-based analysis but does not invalidate it. (iv) The dependence of PRCs on synaptic location emphasizes the importance of improving methods of synaptic stimulation. (v) New methods can distinguish between oscillations that derive from mutual connections and those arising from common drive. (vi) It is helpful to assume linear summation of effects of synaptic inputs; experiments with trains of inputs call this assumption into question. (vii) Relatively subtle changes in network structure can invalidate PRC-based predictions. (viii) Heterogeneity in the preferred frequencies of component neurons does not invalidate PRC analysis, but can annihilate synchronous activity.

  19. Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles

    Directory of Open Access Journals (Sweden)

    Shah Imran

    2011-07-01

    Full Text Available Abstract Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our

  20. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2018-01-01

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Temporal neural network for the identification of nuclear power plant transients

    International Nuclear Information System (INIS)

    Uluyol, O.; Ragheb, M.

    1993-01-01

    In this paper a layered spatiotemporal neural network is proposed for the identification of nuclear power plant transients. The developed layered spatiotemporal network is inspired by the formal avalanche structure developed by S. Grossberg and offers advantages compared with the stationary pattern approach using the perceptron paradigm. Each layer in the network is trained to recognize a separate time-dependent accident scenario. Within each scenario, the temporal behavior of the relevant parameters such as pressurizer pressure, pressurizer water volume, cold and hot legs temperatures, vessel flow, and power, are considered. Numerical cases are considered where the proposed methodology is applied to two nuclear power plant anticipated transient scenarios: the Station Blackout and the Anticipated Transient without Scram transients in a pressurized water reactor . The transient signatures used were generated by modeling the accidents using RELAP5/MOD2, a best-estimate thermal-hydraulics numerical code. The ability of the proposed layered spatiotemporal network to operate at different noise levels is investigated. Its incorporation within an Insightful Algorithm and Anticipatory Systems context for identifying and in predicting the course of nuclear transients is discussed

  2. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-05-24

    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.

  3. Discriminating response groups in metabolic and regulatory pathway networks.

    Science.gov (United States)

    Van Hemert, John L; Dickerson, Julie A

    2012-04-01

    Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

  4. Brain network response underlying decisions about abstract reinforcers.

    Science.gov (United States)

    Mills-Finnerty, Colleen; Hanson, Catherine; Hanson, Stephen Jose

    2014-12-01

    Decision making studies typically use tasks that involve concrete action-outcome contingencies, in which subjects do something and get something. No studies have addressed decision making involving abstract reinforcers, where there are no action-outcome contingencies and choices are entirely hypothetical. The present study examines these kinds of choices, as well as whether the same biases that exist for concrete reinforcer decisions, specifically framing effects, also apply during abstract reinforcer decisions. We use both General Linear Model as well as Bayes network connectivity analysis using the Independent Multi-sample Greedy Equivalence Search (IMaGES) algorithm to examine network response underlying choices for abstract reinforcers under positive and negative framing. We find for the first time that abstract reinforcer decisions activate the same network of brain regions as concrete reinforcer decisions, including the striatum, insula, anterior cingulate, and VMPFC, results that are further supported via comparison to a meta-analysis of decision making studies. Positive and negative framing activated different parts of this network, with stronger activation in VMPFC during negative framing and in DLPFC during positive, suggesting different decision making pathways depending on frame. These results were further clarified using connectivity analysis, which revealed stronger connections between anterior cingulate, insula, and accumbens during negative framing compared to positive. Taken together, these results suggest that not only do abstract reinforcer decisions rely on the same brain substrates as concrete reinforcers, but that the response underlying framing effects on abstract reinforcers also resemble those for concrete reinforcers, specifically increased limbic system connectivity during negative frames. Copyright © 2014 Elsevier Inc. All rights reserved.

  5. An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

    Science.gov (United States)

    Zheng, Jing; Lu, Jiren; Peng, Suping; Jiang, Tianqi

    2018-02-01

    The conventional arrival pick-up algorithms cannot avoid the manual modification of the parameters for the simultaneous identification of multiple events under different signal-to-noise ratios (SNRs). Therefore, in order to automatically obtain the arrivals of multiple events with high precision under different SNRs, in this study an algorithm was proposed which had the ability to pick up the arrival of microseismic or acoustic emission events based on deep recurrent neural networks. The arrival identification was performed using two important steps, which included a training phase and a testing phase. The training process was mathematically modelled by deep recurrent neural networks using Long Short-Term Memory architecture. During the testing phase, the learned weights were utilized to identify the arrivals through the microseismic/acoustic emission data sets. The data sets were obtained by rock physics experiments of the acoustic emission. In order to obtain the data sets under different SNRs, this study added random noise to the raw experiments' data sets. The results showed that the outcome of the proposed method was able to attain an above 80 per cent hit-rate at SNR 0 dB, and an approximately 70 per cent hit-rate at SNR -5 dB, with an absolute error in 10 sampling points. These results indicated that the proposed method had high selection precision and robustness.

  6. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.

    Science.gov (United States)

    Khan, Faisal Nadeem; Zhong, Kangping; Zhou, Xian; Al-Arashi, Waled Hussein; Yu, Changyuan; Lu, Chao; Lau, Alan Pak Tao

    2017-07-24

    We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification (MFI) in digital coherent receivers. The proposed technique automatically extracts OSNR and modulation format dependent features of AHs, obtained after constant modulus algorithm (CMA) equalization, and exploits them for the joint estimation of these parameters. Experimental results for 112 Gbps polarization-multiplexed (PM) quadrature phase-shift keying (QPSK), 112 Gbps PM 16 quadrature amplitude modulation (16-QAM), and 240 Gbps PM 64-QAM signals demonstrate OSNR monitoring with mean estimation errors of 1.2 dB, 0.4 dB, and 1 dB, respectively. Similarly, the results for MFI show 100% identification accuracy for all three modulation formats. The proposed technique applies deep machine learning algorithms inside standard digital coherent receiver and does not require any additional hardware. Therefore, it is attractive for cost-effective multi-parameter estimation in next-generation elastic optical networks (EONs).

  7. Metastable Features of Economic Networks and Responses to Exogenous Shocks.

    Directory of Open Access Journals (Sweden)

    Ali Hosseiny

    Full Text Available It is well known that a network structure plays an important role in addressing a collective behavior. In this paper we study a network of firms and corporations for addressing metastable features in an Ising based model. In our model we observe that if in a recession the government imposes a demand shock to stimulate the network, metastable features shape its response. Actually we find that there exists a minimum bound where any demand shock with a size below it is unable to trigger the market out of recession. We then investigate the impact of network characteristics on this minimum bound. We surprisingly observe that in a Watts-Strogatz network, although the minimum bound depends on the average of the degrees, when translated into the language of economics, such a bound is independent of the average degrees. This bound is about 0.44ΔGDP, where ΔGDP is the gap of GDP between recession and expansion. We examine our suggestions for the cases of the United States and the European Union in the recent recession, and compare them with the imposed stimulations. While the stimulation in the US has been above our threshold, in the EU it has been far below our threshold. Beside providing a minimum bound for a successful stimulation, our study on the metastable features suggests that in the time of crisis there is a "golden time passage" in which the minimum bound for successful stimulation can be much lower. Hence, our study strongly suggests stimulations to arise within this time passage.

  8. Identification of Hadronic Tau Lepton Decays at the ATLAS Detector Using Artificial Neural Networks

    CERN Document Server

    AUTHOR|(CDS)2093068; Zuber, Kai

    Tau leptons play an important role in a wide range of physics analyses at the LHC, such as the verification of the Standard Model at the TeV scale or the determination of Higgs boson properties. For the identification of hadronically decaying tau leptons with the ATLAS detector, a sophisticated, multi-variate algorithm is required. This is due to the high production cross section for QCD jets, the dominant background. Artificial neural networks (ANNs) have gained much attention in recent years by winning several pattern recognition contests. In this thesis, a survey of ANNs is given with a focus on developments of the past 20 years. Based on this work, a novel, ANN-based tau identification is presented which is competitive to the current BDT-based approach. The influence of various hyperparameters on the identification is studied and optimized. Both stability and performance are enhanced through formation of ANN ensembles. Additionally, a score-flattening algorithm is presented that is beneficial to physics a...

  9. Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic.

    Science.gov (United States)

    Gu, Jinghua; Xuan, Jianhua; Riggins, Rebecca B; Chen, Li; Wang, Yue; Clarke, Robert

    2012-08-01

    Identification of transcriptional regulatory networks (TRNs) is of significant importance in computational biology for cancer research, providing a critical building block to unravel disease pathways. However, existing methods for TRN identification suffer from the inclusion of excessive 'noise' in microarray data and false-positives in binding data, especially when applied to human tumor-derived cell line studies. More robust methods that can counteract the imperfection of data sources are therefore needed for reliable identification of TRNs in this context. In this article, we propose to establish a link between the quality of one target gene to represent its regulator and the uncertainty of its expression to represent other target genes. Specifically, an outlier sum statistic was used to measure the aggregated evidence for regulation events between target genes and their corresponding transcription factors. A Gibbs sampling method was then developed to estimate the marginal distribution of the outlier sum statistic, hence, to uncover underlying regulatory relationships. To evaluate the effectiveness of our proposed method, we compared its performance with that of an existing sampling-based method using both simulation data and yeast cell cycle data. The experimental results show that our method consistently outperforms the competing method in different settings of signal-to-noise ratio and network topology, indicating its robustness for biological applications. Finally, we applied our method to breast cancer cell line data and demonstrated its ability to extract biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The Gibbs sampler MATLAB package is freely available at http://www.cbil.ece.vt.edu/software.htm. xuan@vt.edu Supplementary data are available at Bioinformatics online.

  10. The cell envelope stress response of Bacillus subtilis: from static signaling devices to dynamic regulatory network.

    Science.gov (United States)

    Radeck, Jara; Fritz, Georg; Mascher, Thorsten

    2017-02-01

    The cell envelope stress response (CESR) encompasses all regulatory events that enable a cell to protect the integrity of its envelope, an essential structure of any bacterial cell. The underlying signaling network is particularly well understood in the Gram-positive model organism Bacillus subtilis. It consists of a number of two-component systems (2CS) and extracytoplasmic function σ factors that together regulate the production of both specific resistance determinants and general mechanisms to protect the envelope against antimicrobial peptides targeting the biogenesis of the cell wall. Here, we summarize the current picture of the B. subtilis CESR network, from the initial identification of the corresponding signaling devices to unraveling their interdependence and the underlying regulatory hierarchy within the network. In the course of detailed mechanistic studies, a number of novel signaling features could be described for the 2CSs involved in mediating CESR. This includes a novel class of so-called intramembrane-sensing histidine kinases (IM-HKs), which-instead of acting as stress sensors themselves-are activated via interprotein signal transfer. Some of these IM-HKs are involved in sensing the flux of antibiotic resistance transporters, a unique mechanism of responding to extracellular antibiotic challenge.

  11. DC Motor Parameter Identification Using Speed Step Responses

    Directory of Open Access Journals (Sweden)

    Wei Wu

    2012-01-01

    Full Text Available Based on the DC motor speed response measurement under a step voltage input, important motor parameters such as the electrical time constant, the mechanical time constant, and the friction can be estimated. A power series expansion of the motor speed response is presented, whose coefficients are related to the motor parameters. These coefficients can be easily computed using existing curve fitting methods. Experimental results are presented to demonstrate the application of this approach. In these experiments, the approach was readily implemented and gave more accurate estimates than conventional methods.

  12. Response-only modal identification using random decrement algorithm with time-varying threshold level

    International Nuclear Information System (INIS)

    Lin, Chang Sheng; Tseng, Tse Chuan

    2014-01-01

    Modal Identification from response data only is studied for structural systems under nonstationary ambient vibration. The topic of this paper is the estimation of modal parameters from nonstationary ambient vibration data by applying the random decrement algorithm with time-varying threshold level. In the conventional random decrement algorithm, the threshold level for evaluating random dec signatures is defined as the standard deviation value of response data of the reference channel. The distortion of random dec signatures may be, however, induced by the error involved in noise from the original response data in practice. To improve the accuracy of identification, a modification of the sampling procedure in random decrement algorithm is proposed for modal-parameter identification from the nonstationary ambient response data. The time-varying threshold level is presented for the acquisition of available sample time history to perform averaging analysis, and defined as the temporal root-mean-square function of structural response, which can appropriately describe a wide variety of nonstationary behaviors in reality, such as the time-varying amplitude (variance) of a nonstationary process in a seismic record. Numerical simulations confirm the validity and robustness of the proposed modal-identification method from nonstationary ambient response data under noisy conditions.

  13. Efficient identification of critical residues based only on protein structure by network analysis.

    Directory of Open Access Journals (Sweden)

    Michael P Cusack

    2007-05-01

    Full Text Available Despite the increasing number of published protein structures, and the fact that each protein's function relies on its three-dimensional structure, there is limited access to automatic programs used for the identification of critical residues from the protein structure, compared with those based on protein sequence. Here we present a new algorithm based on network analysis applied exclusively on protein structures to identify critical residues. Our results show that this method identifies critical residues for protein function with high reliability and improves automatic sequence-based approaches and previous network-based approaches. The reliability of the method depends on the conformational diversity screened for the protein of interest. We have designed a web site to give access to this software at http://bis.ifc.unam.mx/jamming/. In summary, a new method is presented that relates critical residues for protein function with the most traversed residues in networks derived from protein structures. A unique feature of the method is the inclusion of the conformational diversity of proteins in the prediction, thus reproducing a basic feature of the structure/function relationship of proteins.

  14. Neural networks based identification and compensation of rate-dependent hysteresis in piezoelectric actuators

    International Nuclear Information System (INIS)

    Zhang, Xinliang; Tan, Yonghong; Su, Miyong; Xie, Yangqiu

    2010-01-01

    This paper presents a method of the identification for the rate-dependent hysteresis in the piezoelectric actuator (PEA) by use of neural networks. In this method, a special hysteretic operator is constructed from the Prandtl-Ishlinskii (PI) model to extract the changing tendency of the static hysteresis. Then, an expanded input space is constructed by introducing the proposed hysteretic operator to transform the multi-valued mapping of the hysteresis into a one-to-one mapping. Thus, a feedforward neural network is applied to the approximation of the rate-independent hysteresis on the constructed expanded input space. Moreover, in order to describe the rate-dependent performance of the hysteresis, a special hybrid model, which is constructed by a linear auto-regressive exogenous input (ARX) sub-model preceded with the previously obtained neural network based rate-independent hysteresis sub-model, is proposed. For the compensation of the effect of the hysteresis in PEA, the PID feedback controller with a feedforward hysteresis compensator is developed for the tracking control of the PEA. Thus, a corresponding inverse model based on the proposed modeling method is developed for the feedforward hysteresis compensator. Finally, both simulations and experimental results on piezoelectric actuator are presented to verify the effectiveness of the proposed approach for the rate-dependent hysteresis.

  15. Identification and control of plasma vertical position using neural network in Damavand tokamak

    International Nuclear Information System (INIS)

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-01-01

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  16. Identification and control of plasma vertical position using neural network in Damavand tokamak

    Energy Technology Data Exchange (ETDEWEB)

    Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

    2013-02-15

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  17. EEG signal classification using PSO trained RBF neural network for epilepsy identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

  18. Identification of Lactic Acid Bacteria and Propionic Acid Bacteria using FTIR Spectroscopy and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Beata Nalepa

    2012-01-01

    Full Text Available In the present study, lactic acid bacteria and propionic acid bacteria have been identified at the genus level with the use of artificial neural networks (ANNs and Fourier transform infrared spectroscopy (FTIR. Bacterial strains of the genera Lactobacillus, Lactococcus, Leuconostoc, Streptococcus and Propionibacterium were analyzed since they deliver health benefits and are routinely used in the food processing industry. The correctness of bacterial identification by ANNs and FTIR was evaluated at two stages. At first stage, ANNs were tested based on the spectra of 66 reference bacterial strains. At second stage, the evaluation involved 286 spectra of bacterial strains isolated from food products, deposited in our laboratory collection, and identified by genus-specific PCR. ANNs were developed based on the spectra and their first derivatives. The most satisfactory results were reported for the probabilistic neural network, which was built using a combination of W5W4W3 spectral ranges. This network correctly identified the genus of 95 % of the lactic acid bacteria and propionic acid bacteria strains analyzed.

  19. [Analysis of different pipe corrosion by ESEM and bacteria identification by API in pilot distribution network].

    Science.gov (United States)

    Wu, Qing; Zhao, Xinhua; Yu, Qing; Li, Jun

    2008-07-01

    To understand the corrosion of different material water supply pipelines and bacterium in drinking water and biofilms. A pilot distribution network was built and water quality detection was made on popular pipelines of galvanized iron pipe, PPR and ABS plastic pipes by ESEM (environmental scanning electron microscopy). Bacterium in drinking water and biofilms were identified by API Bacteria Identification System 10s and 20E (Biomerieux, France), and pathogenicity of bacterium were estimated. Galvanized zinc pipes were seriously corroded; there were thin layers on inner face of PPR and ABS plastic pipes. 10 bacterium (got from water samples) were identified by API10S, in which 7 bacterium were opportunistic pathogens. 21 bacterium (got from water and biofilms samples) were identified by API20E, in which 5 bacterium were pathogens and 11 bacterium were opportunistic pathogens and 5 bacteria were not reported for their pathogenicities to human beings. The bacterial water quality of drinking water distribution networks were not good. Most bacterium in drinking water and biofilms on the inner face of pipeline of the drinking water distribution network were opportunistic pathogens, it could cause serious water supply accident, if bacteria spread in suitable conditions. In the aspect of pipe material, old pipelines should be changed by new material pipes.

  20. Identification of differentially expressed proteins in response to Pb ...

    African Journals Online (AJOL)

    In response to Pb, a total of 76 proteins, out of the 95 differentially expressed proteins, were subjected to MALDI-TOF-MS Of these, 46 identities were identified by PMF and 19 identities were identified by microsequencing. Basic metabolisms such as photosynthesis, photorespiration and protein biosynthesis in C. roseus ...

  1. Local Identification of Voltage Instability from Load Tap Changer Response

    DEFF Research Database (Denmark)

    Weckesser, Johannes Tilman Gabriel; Papangelis, Lampros; Vournas, Costas D.

    2017-01-01

    . The new method is not bound to assessing system response over a predefined LTC tapping period. This allows handling LTCs with variable delays, as well as events taking place during the tapping sequence impacting the distribution voltages. For that purpose, eLIVES applies recursive least square fitting...

  2. Response of moose to a high‐density road network

    Science.gov (United States)

    Wattles, David W.; Zeller, Katherine A.; DeStefano, Stephen

    2018-01-01

    Road networks and the disturbance associated with vehicle traffic alter animal behavior, movements, and habitat selection. The response of moose (Alces americanus) to roads has been documented in relatively rural areas, but less is known about moose response to roads in more highly roaded landscapes. We examined road‐crossing frequencies and habitat use of global positioning system (GPS)‐collared moose in Massachusetts, USA, where moose home ranges have road densities approximately twice that of previous studies. We compared seasonal road‐crossing frequencies of moose with a null movement model. We estimated moose travel speeds during road‐crossing events and compared them with speeds during other home range movements. To estimate the extent of the road effect zone and determine how roads influenced moose habitat use, we fit a third‐order resource selection function. With the exception of the lowest use road class (roads less than expected based on the null movement model and frequency decreased with increasing road size and traffic. Moose crossed roads faster than they traveled during other times. This effect increased with increasing road use intensity. Overall, roads were a major factor determining what portions of Massachusetts moose used and how they moved among habitat patches. Our results suggest that moose in Massachusetts can adapt to a high‐density road network, but the road effect is still strongly negative and, in some cases, is more pronounced than in study areas with lower road densities. Future road construction and the expansion of road networks may have a large effect on moose and other wildlife.

  3. Wavelet-based blind identification of the UCLA Factor building using ambient and earthquake responses

    International Nuclear Information System (INIS)

    Hazra, B; Narasimhan, S

    2010-01-01

    Blind source separation using second-order blind identification (SOBI) has been successfully applied to the problem of output-only identification, popularly known as ambient system identification. In this paper, the basic principles of SOBI for the static mixtures case is extended using the stationary wavelet transform (SWT) in order to improve the separability of sources, thereby improving the quality of identification. Whereas SOBI operates on the covariance matrices constructed directly from measurements, the method presented in this paper, known as the wavelet-based modified cross-correlation method, operates on multiple covariance matrices constructed from the correlation of the responses. The SWT is selected because of its time-invariance property, which means that the transform of a time-shifted signal can be obtained as a shifted version of the transform of the original signal. This important property is exploited in the construction of several time-lagged covariance matrices. The issue of non-stationary sources is addressed through the formation of several time-shifted, windowed covariance matrices. Modal identification results are presented for the UCLA Factor building using ambient vibration data and for recorded responses from the Parkfield earthquake, and compared with published results for this building. Additionally, the effect of sensor density on the identification results is also investigated

  4. Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

    OpenAIRE

    Naikwad, S. N.; Dudul, S. V.

    2009-01-01

    A focused time lagged recurrent neural network (FTLR NN) with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes tempora...

  5. Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

    Directory of Open Access Journals (Sweden)

    Minjia Lu

    2018-01-01

    Full Text Available RNAs may act as competing endogenous RNAs (ceRNAs, a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA–messenger RNA (mRNAlong non-coding RNA (lncRNA networks from miRcode database and weighted correlation network analysis (WGCNA, based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB. We applied the pipeline into The Cancer Genome Atlas (TCGA thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA–mRNA–lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients’ survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

  6. A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms

    Directory of Open Access Journals (Sweden)

    Sajad Sabzi

    2018-03-01

    Full Text Available Accurate classification of fruit varieties in processing factories and during post-harvesting applications is a challenge that has been widely studied. This paper presents a novel approach to automatic fruit identification applied to three common varieties of oranges (Citrus sinensis L., namely Bam, Payvandi and Thomson. A total of 300 color images were used for the experiments, 100 samples for each orange variety, which are publicly available. After segmentation, 263 parameters, including texture, color and shape features, were extracted from each sample using image processing. Among them, the 6 most effective features were automatically selected by using a hybrid approach consisting of an artificial neural network and particle swarm optimization algorithm (ANN-PSO. Then, three different classifiers were applied and compared: hybrid artificial neural network – artificial bee colony (ANN-ABC; hybrid artificial neural network – harmony search (ANN-HS; and k-nearest neighbors (kNN. The experimental results show that the hybrid approaches outperform the results of kNN. The average correct classification rate of ANN-HS was 94.28%, while ANN-ABS achieved 96.70% accuracy with the available data, contrasting with the 70.9% baseline accuracy of kNN. Thus, this new proposed methodology provides a fast and accurate way to classify multiple fruits varieties, which can be easily implemented in processing factories. The main contribution of this work is that the method can be directly adapted to other use cases, since the selection of the optimal features and the configuration of the neural network are performed automatically using metaheuristic algorithms.

  7. Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks.

    Science.gov (United States)

    Joshi, Vinayak S; Reinhardt, Joseph M; Garvin, Mona K; Abramoff, Michael D

    2014-01-01

    The separation of the retinal vessel network into distinct arterial and venous vessel trees is of high interest. We propose an automated method for identification and separation of retinal vessel trees in a retinal color image by converting a vessel segmentation image into a vessel segment map and identifying the individual vessel trees by graph search. Orientation, width, and intensity of each vessel segment are utilized to find the optimal graph of vessel segments. The separated vessel trees are labeled as primary vessel or branches. We utilize the separated vessel trees for arterial-venous (AV) classification, based on the color properties of the vessels in each tree graph. We applied our approach to a dataset of 50 fundus images from 50 subjects. The proposed method resulted in an accuracy of 91.44% correctly classified vessel pixels as either artery or vein. The accuracy of correctly classified major vessel segments was 96.42%.

  8. [Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

    Science.gov (United States)

    Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er

    2012-04-01

    In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

  9. Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification.

    Science.gov (United States)

    Pereira, Clayton R; Pereira, Danilo R; Rosa, Gustavo H; Albuquerque, Victor H C; Weber, Silke A T; Hook, Christian; Papa, João P

    2018-04-16

    Parkinson's disease (PD) is considered a degenerative disorder that affects the motor system, which may cause tremors, micrography, and the freezing of gait. Although PD is related to the lack of dopamine, the triggering process of its development is not fully understood yet. In this work, we introduce convolutional neural networks to learn features from images produced by handwritten dynamics, which capture different information during the individual's assessment. Additionally, we make available a dataset composed of images and signal-based data to foster the research related to computer-aided PD diagnosis. The proposed approach was compared against raw data and texture-based descriptors, showing suitable results, mainly in the context of early stage detection, with results nearly to 95%. The analysis of handwritten dynamics using deep learning techniques showed to be useful for automatic Parkinson's disease identification, as well as it can outperform handcrafted features. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Preliminary Results of Ocular Artefacts Identification in EEC Series by Neural Network

    Directory of Open Access Journals (Sweden)

    M. Kofronova

    1996-06-01

    Full Text Available The human electroencephalogram (EEG, is record of the electrical activity of the brain and contains useful diagnostic information on a variety of neurological disorders. Normal EEG signal are usually registered from electrodes placed on the scalp, and are often very small in amplitude, of 20 µV. The EEG, like all biomedical signals, is very susceptible to a variety of large signal contamination or artefacts (signals of other than brain activity which reduce its clinical usefulness. For example, blinking or moving eyes produces large electrical potentials around the eyes called the electrooculogram (EOG. The EOG spreads across the scalp to contaminate the EEG, when it is referred to as an ocular artefact (OA. This paper includes method of identification portion of the EEG record where ocular artefact appears and classification its type by neural network.

  11. Identification of Jets Containing $b$-Hadrons with Recurrent Neural Networks at the ATLAS Experiment

    CERN Document Server

    The ATLAS collaboration

    2017-01-01

    A novel $b$-jet identification algorithm is constructed with a Recurrent Neural Network (RNN) at the ATLAS experiment at the CERN Large Hadron Collider. The RNN based $b$-tagging algorithm processes charged particle tracks associated to jets without reliance on secondary vertex finding, and can augment existing secondary-vertex based taggers. In contrast to traditional impact-parameter-based $b$-tagging algorithms which assume that tracks associated to jets are independent from each other, the RNN based $b$-tagging algorithm can exploit the spatial and kinematic correlations between tracks which are initiated from the same $b$-hadrons. This new approach also accommodates an extended set of input variables. This note presents the expected performance of the RNN based $b$-tagging algorithm in simulated $t \\bar t$ events at $\\sqrt{s}=13$ TeV.

  12. Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity.

    Science.gov (United States)

    Leistritz, L; Suesse, T; Haueisen, J; Hilgenfeld, B; Witte, H

    2006-01-01

    Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

  13. Identification and discrimination of oral asaccharolytic Eubacterium spp. by pyrolysis mass spectrometry and artificial neural networks.

    Science.gov (United States)

    Goodacre, R; Hiom, S J; Cheeseman, S L; Murdoch, D; Weightman, A J; Wade, W G

    1996-02-01

    Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNS) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C2 and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.

  14. Identification of pulse echo impulse responses for multi source transmission

    DEFF Research Database (Denmark)

    Gran, Fredrik; Jensen, Jørgen Arendt

    2004-01-01

    is a mixture of the information corresponding to several transmitters. There is, thus, no direct way of determining which information corresponds to which transmitter, preventing proper focusing. In this paper we decode the received signal by estimating the pulse echo impulse responses between every....... The method is evaluated using the simulation tool Field II. Three point spread functions are simulated where axial movement of 1 m/s is present. The axial resolution for the moving scatterer is 0.249 mm (-3dB) and 0.291 mm (-6dB), which is compared to a standard STA transmission scheme with sequential...

  15. De-identification of clinical notes via recurrent neural network and conditional random field.

    Science.gov (United States)

    Liu, Zengjian; Tang, Buzhou; Wang, Xiaolong; Chen, Qingcai

    2017-11-01

    De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set. We develop a hybrid system for the de-identification task on the training set. Firstly, four individual subsystems, that is, a subsystem based on bidirectional LSTM (long-short term memory, a variant of recurrent neural network), a subsystem-based on bidirectional LSTM with features, a subsystem based on conditional random field (CRF) and a rule-based subsystem, are used to identify PHI instances. Then, an ensemble learning-based classifiers is deployed to combine all PHI instances predicted by above three machine learning-based subsystems. Finally, the results of the ensemble learning-based classifier and the rule-based subsystem are merged together. Experiments conducted on the official test set show that our system achieves the highest micro F1-scores of 93.07%, 91.43% and 95.23% under the "token", "strict" and "binary token" criteria respectively, ranking first in the 2016 CEGS N-GRID NLP challenge. In addition, on the dataset of 2014 i2b2 NLP challenge, our system achieves the highest micro F1-scores of 96.98%, 95.11% and 98.28% under the "token", "strict" and "binary token" criteria respectively, outperforming other state-of-the-art systems. All these experiments prove the effectiveness of our proposed method. Copyright © 2017. Published by Elsevier Inc.

  16. Flow regime identification methodology with MCNP-X code and artificial neural network

    International Nuclear Information System (INIS)

    Salgado, Cesar M.; Instituto de Engenharia Nuclear; Schirru, Roberto; Brandao, Luis E.B.; Pereira, Claudio M.N.A.

    2009-01-01

    This paper presents flow regimes identification methodology in multiphase system in annular, stratified and homogeneous oil-water-gas regimes. The principle is based on recognition of the pulse height distributions (PHD) from gamma-ray with supervised artificial neural network (ANN) systems. The detection geometry simulation comprises of two NaI(Tl) detectors and a dual-energy gamma-ray source. The measurement of scattered radiation enables the dual modality densitometry (DMD) measurement principle to be explored. Its basic principle is to combine the measurement of scattered and transmitted radiation in order to acquire information about the different flow regimes. The PHDs obtained by the detectors were used as input to ANN. The data sets required for training and testing the ANN were generated by the MCNP-X code from static and ideal theoretical models of multiphase systems. The ANN correctly identified the three different flow regimes for all data set evaluated. The results presented show that PHDs examined by ANN may be applied in the successfully flow regime identification. (author)

  17. Hypertensive retinopathy identification through retinal fundus image using backpropagation neural network

    Science.gov (United States)

    Syahputra, M. F.; Amalia, C.; Rahmat, R. F.; Abdullah, D.; Napitupulu, D.; Setiawan, M. I.; Albra, W.; Nurdin; Andayani, U.

    2018-03-01

    Hypertension or high blood pressure can cause damage of blood vessels in the retina of eye called hypertensive retinopathy (HR). In the event Hypertension, it will cause swelling blood vessels and a decrese in retina performance. To detect HR in patients body, it is usually performed through physical examination of opthalmoscope which is still conducted manually by an ophthalmologist. Certainly, in such a manual manner, takes a ong time for a doctor to detetct HR on aa patient based on retina fundus iamge. To overcome ths problem, a method is needed to identify the image of retinal fundus automatically. In this research, backpropagation neural network was used as a method for retinal fundus identification. The steps performed prior to identification were pre-processing (green channel, contrast limited adapative histogram qualization (CLAHE), morphological close, background exclusion, thresholding and connected component analysis), feature extraction using zoning. The results show that the proposed method is able to identify retinal fundus with an accuracy of 95% with maximum epoch of 1500.

  18. Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

    Science.gov (United States)

    Tigges, P; Kathmann, N; Engel, R R

    1997-07-01

    Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

  19. Automated species-level identification and segmentation of planktonic foraminifera using convolutional neural networks

    Science.gov (United States)

    Marchitto, T. M., Jr.; Mitra, R.; Zhong, B.; Ge, Q.; Kanakiya, B.; Lobaton, E.

    2017-12-01

    Identification and picking of foraminifera from sediment samples is often a laborious and repetitive task. Previous attempts to automate this process have met with limited success, but we show that recent advances in machine learning can be brought to bear on the problem. As a `proof of concept' we have developed a system that is capable of recognizing six species of extant planktonic foraminifera that are commonly used in paleoceanographic studies. Our pipeline begins with digital photographs taken under 16 different illuminations using an LED ring, which are then fused into a single 3D image. Labeled image sets were used to train various types of image classification algorithms, and performance on unlabeled image sets was measured in terms of precision (whether IDs are correct) and recall (what fraction of the target species are found). We find that Convolutional Neural Network (CNN) approaches achieve precision and recall values between 80 and 90%, which is similar precision and better recall than human expert performance using the same type of photographs. We have also trained a CNN to segment the 3D images into individual chambers and apertures, which can not only improve identification performance but also automate the measurement of foraminifera for morphometric studies. Given that there are only 35 species of extant planktonic foraminifera larger than 150 μm, we suggest that a fully automated characterization of this assemblage is attainable. This is the first step toward the realization of a foram picking robot.

  20. Identification of crystalline structures using Moessbauer parameters and artificial neural network

    International Nuclear Information System (INIS)

    Salles, E.O.T.; Souza Junior, P.A. De; Garg, V.K.

    1995-01-01

    Moessbauer spectroscopy is a useful technique for characterizing the valences, electronic and magnetic states, coordination symmetric and site occupancies of Fe cations. The Moessbauer parameters of Isomer Shift (I.S.) and Quadrupole Splitting (Q.S.) are useful to distinguish paramagnetic ferrous and ferric ions in several substances, while the internal magnetic field provides information on the crystallinity. A correlation is being sought between Moessbauer parameters and several structure properties of some iron-containing minerals using Artificial Neural Networks (ANN). Distinct regions of crystalline structures are defined when any two parameters are plotted, but in several cases superposition of these regions leads to erroneous conclusions. We have tried to eliminate this difficulty by using convenient axes. These axes form n-dimensional vectors as input to our ANN. In recent years ANN has shown to be a powerful technique to solve problems as pattern recognition, optimization, preview ups and downs in stock market, automatic control and identification of a mineral from a Moessbauer spectrum of Moessbauer data bank. Using ANN we have been successful in identification of crystalline structures from plots of Moessbauer spectral parameters of I.S., Q.S., and structure using Moessbauer parameters of I.S., Q.S., and polyhedral volume of a coordination site are presented. (author) 28 refs.; 4 figs.; 2 tabs

  1. Atomoxetine restores the response inhibition network in Parkinson's disease.

    Science.gov (United States)

    Rae, Charlotte L; Nombela, Cristina; Rodríguez, Patricia Vázquez; Ye, Zheng; Hughes, Laura E; Jones, P Simon; Ham, Timothy; Rittman, Timothy; Coyle-Gilchrist, Ian; Regenthal, Ralf; Sahakian, Barbara J; Barker, Roger A; Robbins, Trevor W; Rowe, James B

    2016-08-01

    Parkinson's disease impairs the inhibition of responses, and whilst impulsivity is mild for some patients, severe impulse control disorders affect ∼10% of cases. Based on preclinical models we proposed that noradrenergic denervation contributes to the impairment of response inhibition, via changes in the prefrontal cortex and its subcortical connections. Previous work in Parkinson's disease found that the selective noradrenaline reuptake inhibitor atomoxetine could improve response inhibition, gambling decisions and reflection impulsivity. Here we tested the hypotheses that atomoxetine can restore functional brain networks for response inhibition in Parkinson's disease, and that both structural and functional connectivity determine the behavioural effect. In a randomized, double-blind placebo-controlled crossover study, 19 patients with mild-to-moderate idiopathic Parkinson's disease underwent functional magnetic resonance imaging during a stop-signal task, while on their usual dopaminergic therapy. Patients received 40 mg atomoxetine or placebo, orally. This regimen anticipates that noradrenergic therapies for behavioural symptoms would be adjunctive to, not a replacement for, dopaminergic therapy. Twenty matched control participants provided normative data. Arterial spin labelling identified no significant changes in regional perfusion. We assessed functional interactions between key frontal and subcortical brain areas for response inhibition, by comparing 20 dynamic causal models of the response inhibition network, inverted to the functional magnetic resonance imaging data and compared using random effects model selection. We found that the normal interaction between pre-supplementary motor cortex and the inferior frontal gyrus was absent in Parkinson's disease patients on placebo (despite dopaminergic therapy), but this connection was restored by atomoxetine. The behavioural change in response inhibition (improvement indicated by reduced stop-signal reaction

  2. Dose response relationship in anti-stress gene regulatory networks.

    Science.gov (United States)

    Zhang, Qiang; Andersen, Melvin E

    2007-03-02

    To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products) in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear) depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on

  3. Dose response relationship in anti-stress gene regulatory networks.

    Directory of Open Access Journals (Sweden)

    Qiang Zhang

    2007-03-01

    Full Text Available To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O2, is first detected by specialized sensor molecules, then the signal is transduced to specific transcription factors. Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species (controlled variables, transcription factors, and gene products in these gene regulatory networks. Our work indicated that the shape of dose response curves (linear, superlinear, or sublinear depends on changes in the specific values of local response coefficients (gains distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear

  4. Contact parameter identification for vibrational response variability prediction

    DEFF Research Database (Denmark)

    Creixell Mediante, Ester; Brunskog, Jonas; Jensen, Jakob Søndergaard

    2018-01-01

    industry, where the vibrational behavior of the structures within the hearing frequency range is critical for the performance of the devices. A procedure to localize the most probable contact areas and determine the most sensitive contact points with respect to variations in the modes of vibration......Variability in the dynamic response of assembled structures can arise due to variations in the contact conditions between the parts that conform them. Contact conditions are difficult to model accurately due to randomness in physical properties such as contact surface, load distribution...... or geometric details. Those properties can vary for a given structure due to the assembly and disassembly process, and also across nominally equal items that are produced in series. This work focuses on modeling the contact between small light-weight plastic pieces such as those used in the hearing aid...

  5. A characterization of scale invariant responses in enzymatic networks.

    Directory of Open Access Journals (Sweden)

    Maja Skataric

    Full Text Available An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO, whose validity we show is both necessary and sufficient for scale invariance of three-node enzymatic networks (and sufficient for any number of nodes. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions.

  6. Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method

    Science.gov (United States)

    Al-garadi, Mohammed Ali; Varathan, Kasturi Dewi; Ravana, Sri Devi

    2017-02-01

    Online social networks (OSNs) have become a vital part of everyday living. OSNs provide researchers and scientists with unique prospects to comprehend individuals on a scale and to analyze human behavioral patterns. Influential spreaders identification is an important subject in understanding the dynamics of information diffusion in OSNs. Targeting these influential spreaders is significant in planning the techniques for accelerating the propagation of information that is useful for various applications, such as viral marketing applications or blocking the diffusion of annoying information (spreading of viruses, rumors, online negative behaviors, and cyberbullying). Existing K-core decomposition methods consider links equally when calculating the influential spreaders for unweighted networks. Alternatively, the proposed link weights are based only on the degree of nodes. Thus, if a node is linked to high-degree nodes, then this node will receive high weight and is treated as an important node. Conversely, the degree of nodes in OSN context does not always provide accurate influence of users. In the present study, we improve the K-core method for OSNs by proposing a novel link-weighting method based on the interaction among users. The proposed method is based on the observation that the interaction of users is a significant factor in quantifying the spreading capability of user in OSNs. The tracking of diffusion links in the real spreading dynamics of information verifies the effectiveness of our proposed method for identifying influential spreaders in OSNs as compared with degree centrality, PageRank, and original K-core.

  7. Language Identification in Short Utterances Using Long Short-Term Memory (LSTM) Recurrent Neural Networks.

    Science.gov (United States)

    Zazo, Ruben; Lozano-Diez, Alicia; Gonzalez-Dominguez, Javier; Toledano, Doroteo T; Gonzalez-Rodriguez, Joaquin

    2016-01-01

    Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system running on limited computational resources (a single GPU) that outperforms a reference i-vector system on a subset of the NIST Language Recognition Evaluation (8 target languages, 3s task) by up to a 26%. This result is in line with previously published research using proprietary LSTM implementations and huge computational resources, which made these former results hardly reproducible. Further, we extend those previous experiments modeling unseen languages (out of set, OOS, modeling), which is crucial in real applications. Results show that a LSTM RNN with OOS modeling is able to detect these languages and generalizes robustly to unseen OOS languages. Finally, we also analyze the effect of even more limited test data (from 2.25s to 0.1s) proving that with as little as 0.5s an accuracy of over 50% can be achieved.

  8. BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species

    Directory of Open Access Journals (Sweden)

    Limin Jiang

    2016-01-01

    Full Text Available MicroRNAs (miRNAs are a set of short (21–24 nt noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs’ essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.

  9. GPS on Every Roof, GPS Sensor Network for Post-Seismic Building-Wise Damage Identification

    Directory of Open Access Journals (Sweden)

    Kenji Oguni

    2013-12-01

    Full Text Available Development of wireless sensor network equipped with GPS for post-seismic building-wise damage identification is presented in this paper. This system is called GPS on Every Roof. Sensor node equipped with GPS antenna and receiver is installed on the top of the roof of each and every building. The position of this sensor node is measured before and after earthquake. The final goal of this system is to i identify the displacement of the roof of each house and ii collect the information of displacement of the roof of the houses through wireless communication. Superposing this information on GIS, building-wise damage distribution due to earthquake can be obtained. The system overview, hardware and some of the key components of the system such as on-board GPS relative positioning algorithm to achieve the accuracy in the order of several centimeters are described in detail. Also, the results from a field experiment using a wireless sensor network with 39 sensor nodes are presented.

  10. Tooth labeling in cone-beam CT using deep convolutional neural network for forensic identification

    Science.gov (United States)

    Miki, Yuma; Muramatsu, Chisako; Hayashi, Tatsuro; Zhou, Xiangrong; Hara, Takeshi; Katsumata, Akitoshi; Fujita, Hiroshi

    2017-03-01

    In large disasters, dental record plays an important role in forensic identification. However, filing dental charts for corpses is not an easy task for general dentists. Moreover, it is laborious and time-consuming work in cases of large scale disasters. We have been investigating a tooth labeling method on dental cone-beam CT images for the purpose of automatic filing of dental charts. In our method, individual tooth in CT images are detected and classified into seven tooth types using deep convolutional neural network. We employed the fully convolutional network using AlexNet architecture for detecting each tooth and applied our previous method using regular AlexNet for classifying the detected teeth into 7 tooth types. From 52 CT volumes obtained by two imaging systems, five images each were randomly selected as test data, and the remaining 42 cases were used as training data. The result showed the tooth detection accuracy of 77.4% with the average false detection of 5.8 per image. The result indicates the potential utility of the proposed method for automatic recording of dental information.

  11. Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

    Directory of Open Access Journals (Sweden)

    Leandro L. S. Linhares

    2015-01-01

    Full Text Available Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS. In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE cost function is replaced by the Maximum Correntropy Criterion (MCC in the traditional error backpropagation (BP algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

  12. Identification of formaldehyde-responsive genes by suppression subtractive hybridization

    International Nuclear Information System (INIS)

    Lee, Min-Ho; Kim, Young-Ae; Na, Tae-Young; Kim, Sung-Hye; Shin, Young Kee; Lee, Byung-Hoon; Shin, Ho-Sang; Lee, Mi-Ock

    2008-01-01

    Formaldehyde is frequently used in indoor household and occupational environments. Inhalation of formaldehyde invokes an inflammatory response, including a variety of allergic signs and symptoms. Therefore, formaldehyde has been considered as the most prevalent cause of sick building syndrome, which has become a major social problem, especially in developing urban areas. Further formaldehyde is classified as a genotoxicant in the respiratory tract of rats and humans. To better understand the molecular mechanisms involved in formaldehyde intoxication, we sought differentially regulated genes by formaldehyde exposure to Hs 680.Tr human trachea cells, using polymerase chain reaction (PCR)-based suppression subtractive hybridization. We identified 27 different formaldehyde-inducible genes, including those coding for the major histocompatibility complex, class IA, calcyclin, glutathione S-transferase pi, mouse double minute 2 (MDM2), platelet-derived growth factor receptor alpha, and which are known to be associated with cell proliferation and differentiation, immunity and inflammation, and detoxification. Induction of these genes by formaldehyde treatment was confirmed by reverse transcription PCR and western blot analysis. Further, the expression of calcyclin, glutathione S-transferase pi, PDGFRA and MDM2 were significantly induced in the tracheal epithelium of Sprague Dawley rats after formaldehyde inhalation. Our results suggest that the elevated levels of these genes may be associated with the formaldehyde-induced toxicity, and that they deserve evaluation as potential biomarkers for formaldehyde intoxication

  13. A modified random decrement technique for modal identification from nonstationary ambient response data only

    International Nuclear Information System (INIS)

    Lin, Chang Sheng; Chiang, Dar Yun

    2012-01-01

    Modal identification is considered from response data of structural system under nonstationary ambient vibration. In a previous paper, we showed that by assuming the ambient excitation to be nonstationary white noise in the form of a product model, the nonstationary response signals can be converted into free-vibration data via the correlation technique. In the present paper, if the ambient excitation can be modeled as a nonstationary white noise in the form of a product model, then the nonstationary cross random decrement signatures of structural response evaluated at any fixed time instant are shown theoretically to be proportional to the nonstationary cross-correlation functions. The practical problem of insufficient data samples available for evaluating nonstationary random decrement signatures can be approximately resolved by first extracting the amplitude-modulating function from the response and then transforming the nonstationary responses into stationary ones. Modal-parameter identification can then be performed using the Ibrahim time-domain technique, which is effective at identifying closely spaced modes. The theory proposed can be further extended by using the filtering concept to cover the case of nonstationary color excitations. Numerical simulations confirm the validity of the proposed method for identification of modal parameters from nonstationary ambient response data

  14. Network of siren, public address and display system to preparedness and response for nuclear emergencies

    International Nuclear Information System (INIS)

    Joshi, G.H.; Padmanabhan, N.; Raman, N.; Pradeepkumar, K.S.; Sharma, D.N.; Abani, M.C.

    2003-01-01

    For an effective emergency response and implementation of counter measures, communication during a nuclear emergency is a very important aspect. The declaration of a nuclear emergency must be immediately conveyed to all those working in the plant and around the nuclear site. Besides this, the nature of emergency also needs to be conveyed unambiguously along with corresponding counter measures, such as stay in, evacuation or all clear signal for the relevant plants. This requirement has necessitated the need for a networked signaling system. Based on this requirement, a microcontroller based signaling and a telephone/wireless based communication and display system has been designed at Bhabha Atomic Research Centre. It is proposed to be used as a part of emergency preparedness and response programme at the nuclear facility sites. As per the design made for Bhabha Atomic Research Centre, Trombay site, each plant or area in the site is identified by a unique identification code. The main Site Emergency Control Centre/Emergency Response Centre at Mod. Labs. selectively calls the various plants and declares the nature of emergency to be followed In that plant/area through different siren signals along with display and announcement of instructions. This paper describes the details of the system that is designed for Bhabha Atomic Research Centre, Trombay site and proposed for other nuclear power plant sites. (author)

  15. Acute LSD effects on response inhibition neural networks.

    Science.gov (United States)

    Schmidt, A; Müller, F; Lenz, C; Dolder, P C; Schmid, Y; Zanchi, D; Lang, U E; Liechti, M E; Borgwardt, S

    2017-10-02

    Recent evidence shows that the serotonin 2A receptor (5-hydroxytryptamine2A receptor, 5-HT2AR) is critically involved in the formation of visual hallucinations and cognitive impairments in lysergic acid diethylamide (LSD)-induced states and neuropsychiatric diseases. However, the interaction between 5-HT2AR activation, cognitive impairments and visual hallucinations is still poorly understood. This study explored the effect of 5-HT2AR activation on response inhibition neural networks in healthy subjects by using LSD and further tested whether brain activation during response inhibition under LSD exposure was related to LSD-induced visual hallucinations. In a double-blind, randomized, placebo-controlled, cross-over study, LSD (100 µg) and placebo were administered to 18 healthy subjects. Response inhibition was assessed using a functional magnetic resonance imaging Go/No-Go task. LSD-induced visual hallucinations were measured using the 5 Dimensions of Altered States of Consciousness (5D-ASC) questionnaire. Relative to placebo, LSD administration impaired inhibitory performance and reduced brain activation in the right middle temporal gyrus, superior/middle/inferior frontal gyrus and anterior cingulate cortex and in the left superior frontal and postcentral gyrus and cerebellum. Parahippocampal activation during response inhibition was differently related to inhibitory performance after placebo and LSD administration. Finally, activation in the left superior frontal gyrus under LSD exposure was negatively related to LSD-induced cognitive impairments and visual imagery. Our findings show that 5-HT2AR activation by LSD leads to a hippocampal-prefrontal cortex-mediated breakdown of inhibitory processing, which might subsequently promote the formation of LSD-induced visual imageries. These findings help to better understand the neuropsychopharmacological mechanisms of visual hallucinations in LSD-induced states and neuropsychiatric disorders.

  16. The National Response System: The Need to Leverage Networks and Knowledge

    National Research Council Canada - National Science Library

    Compagnoni, Barry A

    2006-01-01

    .... When viewing our national response from the perspective of network theory and knowledge management, specific gaps are identified in doctrine, organizational composition and technological capability...

  17. Employees' responses to an organizational merger: Intraindividual change in organizational identification, attachment, and turnover.

    Science.gov (United States)

    Sung, Wookje; Woehler, Meredith L; Fagan, Jesse M; Grosser, Travis J; Floyd, Theresa M; Labianca, Giuseppe Joe

    2017-06-01

    The authors used pre-post merger data from 599 employees experiencing a major corporate merger to compare 3 conceptual models based on the logic of social identity theory (SIT) and exchange theory to explain employees' merger responses. At issue is how perceived change in employees' own jobs and roles (i.e., personal valence) and perceived change in their organization's status and merger appropriateness (i.e., organizational valence) affect their changing organizational identification, attachment attitudes, and voluntary turnover. The first model suggests that organizational identification and organizational attachment develop independently and have distinct antecedents. The second model posits that organizational identification mediates the relationships between change in organizational and personal valence and change in attachment and turnover. The third model posits that change in personal valence moderates the relationship between changes in organizational valence and in organizational identification and attachment. Using latent difference score (LDS) modeling in an SEM framework and survival analysis, the results suggest an emergent fourth model that integrates the first and second models: Although change in organizational identification during the merger mediates the relationship between change in personal status and organizational valence and change in attachment, there is a direct and unmediated relationship between change in personal valence and attachment. This integrated model has implications for M&A theory and practice. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  18. Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

    Science.gov (United States)

    Jamal, Salma; Goyal, Sukriti; Shanker, Asheesh; Grover, Abhinav

    2016-10-18

    Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

  19. Application of artificial intelligence to electrofacies identification: neural networks versus discriminant analysis; Aplicacao de inteligencia artificial na identificacao de eletrofacies redes neuroniais versus analise discriminante

    Energy Technology Data Exchange (ETDEWEB)

    Silva Rodrigues, F da [PETROBRAS, Rio de Janeiro, RJ (Brazil); Queiroz Neto, I.A. de [PETROBRAS, Rio de Janeiro, RJ (Brazil). Centro de Pesquisas

    1992-07-01

    Electro-facies are identified by neural network trained with well log and core data. Differences between neural network and expert system are discussed. According the author, the combination of neural network computing and traditional computing methods, like discriminant analysis, can help in the solution of many problems in electro-facies identification. 5 figs., 1 tab., 11 refs.

  20. Shared beliefs enhance shared feelings: religious/irreligious identifications modulate empathic neural responses.

    Science.gov (United States)

    Huang, Siyuan; Han, Shihui

    2014-01-01

    Recent neuroimaging research has revealed stronger empathic neural responses to same-race compared to other-race individuals. Is the in-group favouritism in empathic neural responses specific to race identification or a more general effect of social identification-including those based on religious/irreligious beliefs? The present study investigated whether and how intergroup relationships based on religious/irreligious identifications modulate empathic neural responses to others' pain expressions. We recorded event-related brain potentials from Chinese Christian and atheist participants while they perceived pain or neutral expressions of Chinese faces that were marked as being Christians or atheists. We found that both Christian and atheist participants showed stronger neural activity to pain (versus neutral) expressions at 132-168 ms and 200-320 ms over the frontal region to those with the same (versus different) religious/irreligious beliefs. The in-group favouritism in empathic neural responses was also evident in a later time window (412-612 ms) over the central/parietal regions in Christian but not in atheist participants. Our results indicate that the intergroup relationship based on shared beliefs, either religious or irreligious, can lead to in-group favouritism in empathy for others' suffering.

  1. Multilevel Bloom Filters for P2P Flows Identification Based on Cluster Analysis in Wireless Mesh Network

    Directory of Open Access Journals (Sweden)

    Xia-an Bi

    2015-01-01

    Full Text Available With the development of wireless mesh networks and distributed computing, lots of new P2P services have been deployed and enrich the Internet contents and applications. The rapid growth of P2P flows brings great pressure to the regular network operation. So the effective flow identification and management of P2P applications become increasingly urgent. In this paper, we build a multilevel bloom filters data structure to identify the P2P flows through researches on the locality characteristics of P2P flows. Different level structure stores different numbers of P2P flow rules. According to the characteristics values of the P2P flows, we adjust the parameters of the data structure of bloom filters. The searching steps of the scheme traverse from the first level to the last level. Compared with the traditional algorithms, our method solves the drawbacks of previous schemes. The simulation results demonstrate that our algorithm effectively enhances the performance of P2P flows identification. Then we deploy our flow identification algorithm in the traffic monitoring sensors which belong to the network traffic monitoring system at the export link in the campus network. In the real environment, the experiment results demonstrate that our algorithm has a fast speed and high accuracy to identify the P2P flows; therefore, it is suitable for actual deployment.

  2. On the structure of Bayesian network for Indonesian text document paraphrase identification

    Science.gov (United States)

    Prayogo, Ario Harry; Syahrul Mubarok, Mohamad; Adiwijaya

    2018-03-01

    Paraphrase identification is an important process within natural language processing. The idea is to automatically recognize phrases that have different forms but contain same meanings. For examples if we input query “causing fire hazard”, then the computer has to recognize this query that this query has same meaning as “the cause of fire hazard. Paraphrasing is an activity that reveals the meaning of an expression, writing, or speech using different words or forms, especially to achieve greater clarity. In this research we will focus on classifying two Indonesian sentences whether it is a paraphrase to each other or not. There are four steps in this research, first is preprocessing, second is feature extraction, third is classifier building, and the last is performance evaluation. Preprocessing consists of tokenization, non-alphanumerical removal, and stemming. After preprocessing we will conduct feature extraction in order to build new features from given dataset. There are two kinds of features in the research, syntactic features and semantic features. Syntactic features consist of normalized levenshtein distance feature, term-frequency based cosine similarity feature, and LCS (Longest Common Subsequence) feature. Semantic features consist of Wu and Palmer feature and Shortest Path Feature. We use Bayesian Networks as the method of training the classifier. Parameter estimation that we use is called MAP (Maximum A Posteriori). For structure learning of Bayesian Networks DAG (Directed Acyclic Graph), we use BDeu (Bayesian Dirichlet equivalent uniform) scoring function and for finding DAG with the best BDeu score, we use K2 algorithm. In evaluation step we perform cross-validation. The average result that we get from testing the classifier as follows: Precision 75.2%, Recall 76.5%, F1-Measure 75.8% and Accuracy 75.6%.

  3. Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response.

    Science.gov (United States)

    Watson, Emma; MacNeil, Lesley T; Arda, H Efsun; Zhu, Lihua Julie; Walhout, Albertha J M

    2013-03-28

    Expression profiles are tailored according to dietary input. However, the networks that control dietary responses remain largely uncharacterized. Here, we combine forward and reverse genetic screens to delineate a network of 184 genes that affect the C. elegans dietary response to Comamonas DA1877 bacteria. We find that perturbation of a mitochondrial network composed of enzymes involved in amino acid metabolism and the TCA cycle affects the dietary response. In humans, mutations in the corresponding genes cause inborn diseases of amino acid metabolism, most of which are treated by dietary intervention. We identify several transcription factors (TFs) that mediate the changes in gene expression upon metabolic network perturbations. Altogether, our findings unveil a transcriptional response system that is poised to sense dietary cues and metabolic imbalances, illustrating extensive communication between metabolic networks in the mitochondria and gene regulatory networks in the nucleus. Copyright © 2013 Elsevier Inc. All rights reserved.

  4. Effects of corporate social responsibility on brand reputation and brand identification with children

    OpenAIRE

    Pais, Madalena Sofia Sarmento de Figueiroa-Rêgo

    2012-01-01

    A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics This study aims to understand children‟s perceptions of Corporate Social Responsibility (CSR) initiatives and its effect on the brand, namely Reputation and Identification. Moreover, it analyzes if the use of Cartoons helps to increase these effects. Differences among gender, age and social class, will also be considered. 292 children fro...

  5. Real-time identification of vehicle motion-modes using neural networks

    Science.gov (United States)

    Wang, Lifu; Zhang, Nong; Du, Haiping

    2015-01-01

    A four-wheel ground vehicle has three body-dominated motion-modes, that is, bounce, roll, and pitch motion-modes. Real-time identification of these motion-modes can make vehicle suspensions, in particular, active suspensions, target on the dominant motion-mode and apply appropriate control strategies to improve its performance with less power consumption. Recently, a motion-mode energy method (MEM) was developed to identify the vehicle body motion-modes. However, this method requires the measurement of full vehicle states and road inputs, which are not always available in practice. This paper proposes an alternative approach to identify vehicle primary motion-modes with acceptable accuracy by employing neural networks (NNs). The effectiveness of the trained NNs is verified on a 10-DOF full-car model under various types of excitation inputs. The results confirm that the proposed method is effective in determining vehicle primary motion-modes with comparable accuracy to the MEM method. Experimental data is further used to validate the proposed method.

  6. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Science.gov (United States)

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  7. Network Understanding of Herb Medicine via Rapid Identification of Ingredient-Target Interactions

    Science.gov (United States)

    Zhang, Hai-Ping; Pan, Jian-Bo; Zhang, Chi; Ji, Nan; Wang, Hao; Ji, Zhi-Liang

    2014-01-01

    Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.

  8. Incipient fault detection and identification in process systems using accelerating neural network learning

    International Nuclear Information System (INIS)

    Parlos, A.G.; Muthusami, J.; Atiya, A.F.

    1994-01-01

    The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methods becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary

  9. Automated bony region identification using artificial neural networks: reliability and validation measurements

    Energy Technology Data Exchange (ETDEWEB)

    Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); Powell, Stephanie M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Magnotta, Vincent A. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Ramme, Austin J. [University of Iowa Hospitals and Clinics, The University of Iowa, Department of Radiology, Iowa City, IA (United States); Adams, Brian D. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); Grosland, Nicole M. [The University of Iowa, Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, Iowa City, IA (United States); University of Iowa Hospitals and Clinics, The University of Iowa, Department of Orthopaedics and Rehabilitation, Iowa City, IA (United States); The University of Iowa, Center for Computer-Aided Design, Iowa City, IA (United States)

    2008-04-15

    The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

  10. Automated bony region identification using artificial neural networks: reliability and validation measurements

    International Nuclear Information System (INIS)

    Gassman, Esther E.; Kallemeyn, Nicole A.; DeVries, Nicole A.; Shivanna, Kiran H.; Powell, Stephanie M.; Magnotta, Vincent A.; Ramme, Austin J.; Adams, Brian D.; Grosland, Nicole M.

    2008-01-01

    The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen. Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand. The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater. The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality. (orig.)

  11. Functional identification of interneurons responsible for left-right coordination of hindlimbs in mammals

    DEFF Research Database (Denmark)

    Butt, Simon J.B.; Kiehn, Ole

    2003-01-01

    Local neuronal networks that are responsible for walking are poorly characterized in mammals. Using an innovative approach to identify interneuron inputs onto motorneuron populations in a neonatal rodent spinal cord preparation, we have investigated the network responsible for left-right coordina......Local neuronal networks that are responsible for walking are poorly characterized in mammals. Using an innovative approach to identify interneuron inputs onto motorneuron populations in a neonatal rodent spinal cord preparation, we have investigated the network responsible for left......-right coordination of the hindlimbs. We demonstrate how commissural interneurons (CINs), whose axons traverse the midline to innervate contralateral neurons, are organized such that distinct flexor and extensor centers in the rostral lumbar spinal cord define activity in both flexor and extensor caudal motor pools....... In addition, the nature of some connections are reconfigured on switching from rest to locomotion via a mechanism that might be associated with synaptic plasticity in the spinal cord. These results from identified pattern-generating interneurons demonstrate how interneuron populations create an effective...

  12. Progressively expanded neural network for automatic material identification in hyperspectral imagery

    Science.gov (United States)

    Paheding, Sidike

    The science of hyperspectral remote sensing focuses on the exploitation of the spectral signatures of various materials to enhance capabilities including object detection, recognition, and material characterization. Hyperspectral imagery (HSI) has been extensively used for object detection and identification applications since it provides plenty of spectral information to uniquely identify materials by their reflectance spectra. HSI-based object detection algorithms can be generally classified into stochastic and deterministic approaches. Deterministic approaches are comparatively simple to apply since it is usually based on direct spectral similarity such as spectral angles or spectral correlation. In contrast, stochastic algorithms require statistical modeling and estimation for target class and non-target class. Over the decades, many single class object detection methods have been proposed in the literature, however, deterministic multiclass object detection in HSI has not been explored. In this work, we propose a deterministic multiclass object detection scheme, named class-associative spectral fringe-adjusted joint transform correlation. Human brain is capable of simultaneously processing high volumes of multi-modal data received every second of the day. In contrast, a machine sees input data simply as random binary numbers. Although machines are computationally efficient, they are inferior when comes to data abstraction and interpretation. Thus, mimicking the learning strength of human brain has been current trend in artificial intelligence. In this work, we present a biological inspired neural network, named progressively expanded neural network (PEN Net), based on nonlinear transformation of input neurons to a feature space for better pattern differentiation. In PEN Net, discrete fixed excitations are disassembled and scattered in the feature space as a nonlinear line. Each disassembled element on the line corresponds to a pattern with similar features

  13. Inferring a Drive-Response Network from Time Series of Topological Measures in Complex Networks with Transfer Entropy

    Directory of Open Access Journals (Sweden)

    Xinbo Ai

    2014-11-01

    Full Text Available Topological measures are crucial to describe, classify and understand complex networks. Lots of measures are proposed to characterize specific features of specific networks, but the relationships among these measures remain unclear. Taking into account that pulling networks from different domains together for statistical analysis might provide incorrect conclusions, we conduct our investigation with data observed from the same network in the form of simultaneously measured time series. We synthesize a transfer entropy-based framework to quantify the relationships among topological measures, and then to provide a holistic scenario of these measures by inferring a drive-response network. Techniques from Symbolic Transfer Entropy, Effective Transfer Entropy, and Partial Transfer Entropy are synthesized to deal with challenges such as time series being non-stationary, finite sample effects and indirect effects. We resort to kernel density estimation to assess significance of the results based on surrogate data. The framework is applied to study 20 measures across 2779 records in the Technology Exchange Network, and the results are consistent with some existing knowledge. With the drive-response network, we evaluate the influence of each measure by calculating its strength, and cluster them into three classes, i.e., driving measures, responding measures and standalone measures, according to the network communities.

  14. Artificial Neural Networks for Nonlinear Dynamic Response Simulation in Mechanical Systems

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Høgsberg, Jan Becker; Winther, Ole

    2011-01-01

    It is shown how artificial neural networks can be trained to predict dynamic response of a simple nonlinear structure. Data generated using a nonlinear finite element model of a simplified wind turbine is used to train a one layer artificial neural network. When trained properly the network is ab...... to perform accurate response prediction much faster than the corresponding finite element model. Initial result indicate a reduction in cpu time by two orders of magnitude....

  15. Structure identification of an uncertain network coupled with complex-variable chaotic systems via adaptive impulsive control

    International Nuclear Information System (INIS)

    Liu Dan-Feng; Wu Zhao-Yan; Ye Qing-Ling

    2014-01-01

    In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is investigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results. (general)

  16. Development of the Real Time Situation Identification Model for Adaptive Service Support in Vehicular Communication Networks Domain

    Directory of Open Access Journals (Sweden)

    Mindaugas Kurmis

    2013-01-01

    Full Text Available The article discusses analyses and assesses the key proposals how to deal with the situation identification for the heterogeneous service support in vehicular cooperation environment. This is one of the most important topics of the pervasive computing. Without the solution it is impossible to adequately respond to the user's needs and to provide needed services in the right place at the right moment and in the right way. In this work we present our developed real time situation identification model for adaptive service support in vehicular communication networks domain. Our solution is different from the others as it uses additional virtual context information source - information from other vehicles which for our knowledge is not addressed in the past. The simulation results show the promising context exchange rate between vehicles. The other vehicles provided additional context source in our developed model helps to increase situations identification level.

  17. Impulse response identification with deterministic inputs using non-parametric methods

    International Nuclear Information System (INIS)

    Bhargava, U.K.; Kashyap, R.L.; Goodman, D.M.

    1985-01-01

    This paper addresses the problem of impulse response identification using non-parametric methods. Although the techniques developed herein apply to the truncated, untruncated, and the circulant models, we focus on the truncated model which is useful in certain applications. Two methods of impulse response identification will be presented. The first is based on the minimization of the C/sub L/ Statistic, which is an estimate of the mean-square prediction error; the second is a Bayesian approach. For both of these methods, we consider the effects of using both the identity matrix and the Laplacian matrix as weights on the energy in the impulse response. In addition, we present a method for estimating the effective length of the impulse response. Estimating the length is particularly important in the truncated case. Finally, we develop a method for estimating the noise variance at the output. Often, prior information on the noise variance is not available, and a good estimate is crucial to the success of estimating the impulse response with a nonparametric technique

  18. Identification of hand motion using background subtraction method and extraction of image binary with backpropagation neural network on skeleton model

    Science.gov (United States)

    Fauziah; Wibowo, E. P.; Madenda, S.; Hustinawati

    2018-03-01

    Capturing and recording motion in human is mostly done with the aim for sports, health, animation films, criminality, and robotic applications. In this study combined background subtraction and back propagation neural network. This purpose to produce, find similarity movement. The acquisition process using 8 MP resolution camera MP4 format, duration 48 seconds, 30frame/rate. video extracted produced 1444 pieces and results hand motion identification process. Phase of image processing performed is segmentation process, feature extraction, identification. Segmentation using bakground subtraction, extracted feature basically used to distinguish between one object to another object. Feature extraction performed by using motion based morfology analysis based on 7 invariant moment producing four different classes motion: no object, hand down, hand-to-side and hands-up. Identification process used to recognize of hand movement using seven inputs. Testing and training with a variety of parameters tested, it appears that architecture provides the highest accuracy in one hundred hidden neural network. The architecture is used propagate the input value of the system implementation process into the user interface. The result of the identification of the type of the human movement has been clone to produce the highest acuracy of 98.5447%. The training process is done to get the best results.

  19. Frequency response function-based explicit framework for dynamic identification in human-structure systems

    Science.gov (United States)

    Wei, Xiaojun; Živanović, Stana

    2018-05-01

    The aim of this paper is to propose a novel theoretical framework for dynamic identification in a structure occupied by a single human. The framework enables the prediction of the dynamics of the human-structure system from the known properties of the individual system components, the identification of human body dynamics from the known dynamics of the empty structure and the human-structure system and the identification of the properties of the structure from the known dynamics of the human and the human-structure system. The novelty of the proposed framework is the provision of closed-form solutions in terms of frequency response functions obtained by curve fitting measured data. The advantages of the framework over existing methods are that there is neither need for nonlinear optimisation nor need for spatial/modal models of the empty structure and the human-structure system. In addition, the second-order perturbation method is employed to quantify the effect of uncertainties in human body dynamics on the dynamic identification of the empty structure and the human-structure system. The explicit formulation makes the method computationally efficient and straightforward to use. A series of numerical examples and experiments are provided to illustrate the working of the method.

  20. Identification of a Typical CSTR Using Optimal Focused Time Lagged Recurrent Neural Network Model with Gamma Memory Filter

    Directory of Open Access Journals (Sweden)

    S. N. Naikwad

    2009-01-01

    Full Text Available A focused time lagged recurrent neural network (FTLR NN with gamma memory filter is designed to learn the subtle complex dynamics of a typical CSTR process. Continuous stirred tank reactor exhibits complex nonlinear operations where reaction is exothermic. It is noticed from literature review that process control of CSTR using neuro-fuzzy systems was attempted by many, but optimal neural network model for identification of CSTR process is not yet available. As CSTR process includes temporal relationship in the input-output mappings, time lagged recurrent neural network is particularly used for identification purpose. The standard back propagation algorithm with momentum term has been proposed in this model. The various parameters like number of processing elements, number of hidden layers, training and testing percentage, learning rule and transfer function in hidden and output layer are investigated on the basis of performance measures like MSE, NMSE, and correlation coefficient on testing data set. Finally effects of different norms are tested along with variation in gamma memory filter. It is demonstrated that dynamic NN model has a remarkable system identification capability for the problems considered in this paper. Thus FTLR NN with gamma memory filter can be used to learn underlying highly nonlinear dynamics of the system, which is a major contribution of this paper.

  1. Environmental Learning in Online Social Networks: Adopting Environmentally Responsible Behaviors

    Science.gov (United States)

    Robelia, Beth A.; Greenhow, Christine; Burton, Lisa

    2011-01-01

    Online social networks are increasingly important information and communication tools for young people and for the environmental movement. Networks may provide the motivation for young adults to increase environmental behaviors by increasing their knowledge of environmental issues and of the specific actions they can take to reduce greenhouse gas…

  2. Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

    Science.gov (United States)

    Gao, Zhongke; Jin, Ningde

    2009-06-01

    The identification of flow pattern is a basic and important issue in multiphase systems. Because of the complexity of phase interaction in gas-liquid two-phase flow, it is difficult to discern its flow pattern objectively. In this paper, we make a systematic study on the vertical upward gas-liquid two-phase flow using complex network. Three unique network construction methods are proposed to build three types of networks, i.e., flow pattern complex network (FPCN), fluid dynamic complex network (FDCN), and fluid structure complex network (FSCN). Through detecting the community structure of FPCN by the community-detection algorithm based on K -mean clustering, useful and interesting results are found which can be used for identifying five vertical upward gas-liquid two-phase flow patterns. To investigate the dynamic characteristics of gas-liquid two-phase flow, we construct 50 FDCNs under different flow conditions, and find that the power-law exponent and the network information entropy, which are sensitive to the flow pattern transition, can both characterize the nonlinear dynamics of gas-liquid two-phase flow. Furthermore, we construct FSCN and demonstrate how network statistic can be used to reveal the fluid structure of gas-liquid two-phase flow. In this paper, from a different perspective, we not only introduce complex network theory to the study of gas-liquid two-phase flow but also indicate that complex network may be a powerful tool for exploring nonlinear time series in practice.

  3. Cognitive radio-aided wireless sensor networks for emergency response

    International Nuclear Information System (INIS)

    Arkoulis, Stamatios; Spanos, Dimitrios-Emmanuel; Barbounakis, Socrates; Zafeiropoulos, Anastasios; Mitrou, Nikolas

    2010-01-01

    A lot of research effort has been put into wireless sensor networks (WSNs) and several methods have been proposed to minimize the energy consumption and maximize the network's lifetime. However, little work has been carried out regarding WSNs deployed for emergency situations. We argue that such WSNs should function under a flexible channel allocation scheme when needed and be able to operate and adapt in dynamic, ever-changing environments coexisting with other interfering networks (IEEE 802.11b/g, 802.15.4, 802.15.1). In this paper, a simple and efficient method for the detection of a single operational frequency channel that guarantees satisfactory communication among all network nodes is proposed. Experimental measurements carried out in a real environment reveal the coexistence problem among networks in close proximity that operate in the same frequency band and prove the validity and efficiency of our approach

  4. Identification of Homogeneous Stations for Quality Monitoring Network of Mashhad Aquifer Based on Nitrate Pollution

    Directory of Open Access Journals (Sweden)

    Moslem Akbarzadeh

    2017-01-01

    Full Text Available Introduction: For water resources monitoring, Evaluation of groundwater quality obtained via detailed analysis of pollution data. The most fundamental analysis is to identify the exact measurement of dangerous zones and homogenous station identification in terms of pollution. In case of quality evaluation, the monitoring improvement could be achieved via identifying homogenous wells in terms of pollution. Presenting a method for clustering is essential in large amounts of quality data for aquifer monitoring and quality evaluation, including identification of homogeneous stations of monitoring network and their clustering based on pollution. In this study, with the purpose of Mashhad aquifer quality evaluation, clustering have been studied based on Euclidean distance and Entropy criteria. Cluster analysis is the task of grouping a set of objects in such a way that objects in the same group (called a cluster are more similar (in some sense or another to each other than to those in other groups (clusters. SNI as a combined entropy measure for clustering calculated from dividing mutual information of two values (pollution index values to the joint entropy. These measures apply as similar distance criteria for monitoring stations clustering. Materials and Methods: First, nitrate data (as pollution index and electrical conductivity (EC (as covariate collected from the related locational situation of 287 wells in statistical period 2002 to 2011. Having identified the outlying data and estimating non-observed points by spatial-temporal Kriging method and then standardizes them, the clustering process was carried out. A similar distance of wells calculated through a clustering process based on Euclidean distance and Entropy (SNI criteria. This difference explained by characteristics such as the location of wells (longitude & latitude and the pollution index (nitrate. Having obtained a similar distance of each well to others, the hierarchical clustering

  5. Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

    Science.gov (United States)

    Cui, Jian; Liu, Jinghua; Li, Yuhua; Shi, Tieliu

    2011-01-01

    Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome. PMID:21297957

  6. IAEA emergency response network ERNET. Emergency preparedness and response. Date effective: 1 December 2002

    International Nuclear Information System (INIS)

    2003-04-01

    The Parties to the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency have undertaken to co-operate among themselves and with the IAEA in facilitating the prompt provision of assistance in the event of a nuclear accident or radiological emergency, and in minimizing the consequences and in protecting life, property and the environment from the effects of any radioactive releases. As part of the IAEA strategy for supporting such co-operation, the Secretariat of the IAEA is establishing a global Emergency Response Network (ERNET) of teams suitably qualified to respond rapidly, on a regional basis, to nuclear accidents or radiological emergencies. This manual sets out the criteria and requirements to be met by ERNET teams. It is intended for use by institutions in Member States in developing, applying and maintaining their emergency response capabilities and in implementing quality assurance programmes within the context of ERNET. The manual is worded on the assumption that a State Competent Authority designated as the body responsible for reacting to nuclear accidents or radiological emergencies which occur outside the jurisdiction of that State will be the State Contact Point for receiving requests for assistance from the IAEA under the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency

  7. IAEA emergency response network ERNET. Emergency preparedness and response. Date effective: 1 December 2000

    International Nuclear Information System (INIS)

    2000-12-01

    The Parties to the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency have undertaken to co-operate among themselves and with the IAEA in facilitating the prompt provision of assistance in the event of a nuclear accident or radiological emergency, and in minimizing the consequences and in protecting life, property and the environment from the effects of any radioactive releases. As part of the IAEA strategy for supporting such co-operation, the Secretariat of the IAEA is establishing a global Emergency Response Network (ERNET) of teams suitably qualified to respond rapidly, on a regional basis, to nuclear accidents or radiological emergencies. This manual sets out the criteria and requirements to be met by ERNET teams. It is intended for use by institutions in Member States in developing, applying and maintaining their emergency response capabilities and in implementing quality assurance programmes within the context of ERNET. The manual is worded on the assumption that a State Competent Authority designated as the body responsible for reacting to nuclear accidents or radiological emergencies which occur outside the jurisdiction of that State will be the State Contact Point for receiving requests for assistance from the IAEA under the Convention on Assistance in the Case of a Nuclear Accident or Radiological Emergency

  8. Blind identification of the number of sub-carriers for orthogonal frequency division multiplexing-based elastic optical networking

    Science.gov (United States)

    Zhao, Lei; Xu, Hengying; Bai, Chenglin

    2018-03-01

    In orthogonal frequency division multiplexing (OFDM)-based elastic optical networking (EON), it is imperative to identify unknown parameters of OFDM-based EON signals quickly, intelligently and robustly. Because the number of sub-carriers determines the size of the sub-carriers spacing and then affects the symbol period of the OFDM and the anti-dispersion capability of the system, the identification of the number of sub-carriers has a profound effect on the identification of other key parameters of the system. In this paper, we proposed a method of number identification for sub-carriers of OFDM-based EON signals with help of high-order cyclic cumulant. The specific fourth-order cyclic cumulant exists only at the location of its sub-carriers frequencies. So the identification of the number of sub-carriers can be implemented by detecting the cyclic-frequencies. The proposed scheme in our study can be divided into three sub-stages, i.e. estimating the spectral range, calculating the high-order cyclic cumulant and identifying the number of sub-carriers. When the optical signal-to-noise ratios (OSNR) varied from 16dB to 22dB, the number of sub-carriers (64-512) was successfully identified in the experiment, and from the statistical point of view, the average identification absolute accuracy (IAAs) exceeded 94%.

  9. Application of NARX neural networks in thermal dynamics identification of a pulsating heat pipe

    International Nuclear Information System (INIS)

    Lee Yawei; Chang Tienli

    2009-01-01

    The pulsating heat pipe (PHP) receiving much attention in industries is a novel type of cooling device. The distinguishing feature of PHPs is the unsteady flow oscillations formed by the passing non-uniform distributions of vapour plugs and liquid slugs. This study introduces a methodology of a non-linear auto-regressive with exogenous (NARX) neural network to analyze the thermal dynamics of a PHP in both the time and frequency domains. Three heating powers: 30, 70, and 110 W are tested, and all the predicted results are presented in quite good agreement with the measured results. Herein, the harmonic analysis of the non-linear structure can be equivalently conducted with generalized frequency response functions (GFRFs). Based on the non-linear coupling between the various input spectral components, the interpretations of the higher order GFRFs have been extensively presented for demonstrating the non-linear effects on the heat transfer of a PHP at different operating conditions

  10. Identification, Response, and Referral of Suicidal Youth Following Applied Suicide Intervention Skills Training.

    Science.gov (United States)

    Ewell Foster, Cynthia J; Burnside, Amanda N; Smith, Patricia K; Kramer, Anne C; Wills, Allie; A King, Cheryl

    2017-06-01

    Gatekeeper training is a public health approach to suicide prevention that encourages community members to identify those at risk for suicide, respond appropriately, and refer for clinical services. Despite widespread use, few studies have examined whether training results in behavior change in participants. This study employed a naturalistic pre-post design to follow 434 participants in Applied Suicide Intervention Skills Training, finding small but significant increases in self-reported identification of at-risk youth, some helpful responses to youth, and numbers of youth referred to treatment from pre-test to 6- to 9-month follow-up. Changes in active listening and helping behaviors meant to support treatment referrals (such as convincing a youth to seek treatment) were not observed over time. Additional analyses explored predictors of self-reported skill utilization including identification as a "natural helper" and attitudes about suicide prevention. © 2016 The American Association of Suicidology.

  11. Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

    Science.gov (United States)

    Chiu, Yu-Chiao; Wang, Li-Ju; Hsiao, Tzu-Hung; Chuang, Eric Y; Chen, Yidong

    2017-10-03

    With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

  12. Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network.

    Science.gov (United States)

    Liu, Chenglin; Cui, Peng; Huang, Tao

    2017-01-01

    The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype are analyzed. Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight to the cell cycle mechanisms. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  13. Synchronization of chaotic systems and identification of nonlinear systems by using recurrent hierarchical type-2 fuzzy neural networks.

    Science.gov (United States)

    Mohammadzadeh, Ardashir; Ghaemi, Sehraneh

    2015-09-01

    This paper proposes a novel approach for training of proposed recurrent hierarchical interval type-2 fuzzy neural networks (RHT2FNN) based on the square-root cubature Kalman filters (SCKF). The SCKF algorithm is used to adjust the premise part of the type-2 FNN and the weights of defuzzification and the feedback weights. The recurrence property in the proposed network is the output feeding of each membership function to itself. The proposed RHT2FNN is employed in the sliding mode control scheme for the synchronization of chaotic systems. Unknown functions in the sliding mode control approach are estimated by RHT2FNN. Another application of the proposed RHT2FNN is the identification of dynamic nonlinear systems. The effectiveness of the proposed network and its learning algorithm is verified by several simulation examples. Furthermore, the universal approximation of RHT2FNNs is also shown. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  14. Identification of T1D susceptibility genes within the MHC region by combining protein interaction networks and SNP genotyping data

    DEFF Research Database (Denmark)

    Brorsson, C.; Hansen, Niclas Tue; Hansen, Kasper Lage

    2009-01-01

    genes. We have developed a novel method that combines single nucleotide polymorphism (SNP) genotyping data with protein-protein interaction (ppi) networks to identify disease-associated network modules enriched for proteins encoded from the MHC region. Approximately 2500 SNPs located in the 4 Mb MHC......To develop novel methods for identifying new genes that contribute to the risk of developing type 1 diabetes within the Major Histocompatibility Complex (MHC) region on chromosome 6, independently of the known linkage disequilibrium (LD) between human leucocyte antigen (HLA)-DRB1, -DQA1, -DQB1...... region were analysed in 1000 affected offspring trios generated by the Type 1 Diabetes Genetics Consortium (T1DGC). The most associated SNP in each gene was chosen and genes were mapped to ppi networks for identification of interaction partners. The association testing and resulting interacting protein...

  15. Identification of Plasma Parameters and Optimization of Magnetic Sensors in the Superconducting Steady-State Tokamak-1 Using Neural Networks

    International Nuclear Information System (INIS)

    Sengupta, A.; Ranjan, P.

    2001-01-01

    In this paper, we examine the possibility of using a multilayered feedforward neural network to extract tokamak plasma parameters from magnetic measurements as an improvement over the traditional methodology of function parametrization. It is also used to optimize the number and locations of the magnetic diagnostics designed for the tokamak. This work has been undertaken with the specific purpose of application of the neural network technique to the newly designed (and currently under fabrication) Superconducting Steady-State Tokamak-1 (SST-1). The magnetic measurements will be utilized to achieve real-time control of plasma shape, position, and some global profiles. A trained neural network is tested, and the results of parameter identification are compared with function parametrization. Both techniques appear well suited for the purpose, but a definite improvement with neural networks is observed. Although simulated measurements are used in this work, confidence regarding the network performance with actual experimental data is ensured by testing the network's noise tolerance with Gaussian noise of up to 10%. Finally, three possible methods of ranking the diagnostics in decreasing order of importance are suggested, and the neural network is used to optimize the number and locations of the magnetic sensors designed for SST-1. The results from the three methods are compared with one another and also with function parametrization. Magnetic probes within the plasma-facing side of the outboard limiter have been ranked high. Function parametrization and one of the neural network methods show a distinct tendency to favor the probes in the remote regions of the vacuum vessel, proving the importance of redundancy. Fault tolerance of the optimized network is tested. The results obtained should, in the long run, help in the decision regarding the final effective set of magnetic diagnostics to be used in SST-1 for reconstruction of the control parameters

  16. Effective Response to Attacks On Department of Defense Computer Networks

    National Research Council Canada - National Science Library

    Shaha, Patrick

    2001-01-01

    .... For the Commanders-in-Chief (CINCs), computer networking has proven especially useful in maintaining contact and sharing data with elements forward deployed as well as with host nation governments and agencies...

  17. Identification of shareholder ethics and responsibilities in online reverse auctions for construction projects.

    Science.gov (United States)

    Hatipkarasulu, Yilmaz; Gill, James H

    2004-04-01

    The increasing number of companies providing internet services and auction tools helped popularize the online reverse auction trend for purchasing commodities and services in the last decade. As a result, a number of owners, both public and private, accepted the online reverse auctions as the bidding technique for their construction projects. Owners, while trying to minimize their costs for construction projects, are also required to address their ethical responsibilities to the shareholders. In the case of online reverse auctions for construction projects, the ethical issues involved in the bidding technique directly reflects on the owner's ethical and social responsibilities to their shareholders. The goal of this paper is to identify the shareholder ethics and responsibilities in online reverse auctions for construction projects by analyzing the ethical issues for the parties involved in the process. The identification of the ethical issues and responsibilities requires clear definition and understanding of professional ethics and the roles of the involved parties. In this paper, first, the concept of professional ethics and social responsibility is described in a general form. To illustrate the ethical issues and responsibilities, a sample case of bidding for a construction project using online reverse auction techniques is presented in which the shareholders were actively involved in questioning the ethical issues. The issues involved in the bidding process and their reflection on the shareholder responsibilities are described and analyzed for each stage of the process. A brief discussion of the overall process is also included to address the general ethical issues involved in online reverse auctions.

  18. Dissociation of rapid response learning and facilitation in perceptual and conceptual networks of person recognition.

    Science.gov (United States)

    Valt, Christian; Klein, Christoph; Boehm, Stephan G

    2015-08-01

    Repetition priming is a prominent example of non-declarative memory, and it increases the accuracy and speed of responses to repeatedly processed stimuli. Major long-hold memory theories posit that repetition priming results from facilitation within perceptual and conceptual networks for stimulus recognition and categorization. Stimuli can also be bound to particular responses, and it has recently been suggested that this rapid response learning, not network facilitation, provides a sound theory of priming of object recognition. Here, we addressed the relevance of network facilitation and rapid response learning for priming of person recognition with a view to advance general theories of priming. In four experiments, participants performed conceptual decisions like occupation or nationality judgments for famous faces. The magnitude of rapid response learning varied across experiments, and rapid response learning co-occurred and interacted with facilitation in perceptual and conceptual networks. These findings indicate that rapid response learning and facilitation in perceptual and conceptual networks are complementary rather than competing theories of priming. Thus, future memory theories need to incorporate both rapid response learning and network facilitation as individual facets of priming. © 2014 The British Psychological Society.

  19. Hybrid Polymer-Network Hydrogels with Tunable Mechanical Response

    Directory of Open Access Journals (Sweden)

    Sebastian Czarnecki

    2016-03-01

    Full Text Available Hybrid polymer-network gels built by both physical and covalent polymer crosslinking combine the advantages of both these crosslinking types: they exhibit high mechanical strength along with excellent fracture toughness and extensibility. If these materials are extensively deformed, their physical crosslinks can break such that strain energy is dissipated and irreversible fracturing is restricted to high strain only. This mechanism of energy dissipation is determined by the kinetics and thermodynamics of the physical crosslinking contribution. In this paper, we present a poly(ethylene glycol (PEG based material toolkit to control these contributions in a rational and custom fashion. We form well-defined covalent polymer-network gels with regularly distributed additional supramolecular mechanical fuse links, whose strength of connectivity can be tuned without affecting the primary polymer-network composition. This is possible because the supramolecular fuse links are based on terpyridine–metal complexation, such that the mere choice of the fuse-linking metal ion adjusts their kinetics and thermodynamics of complexation–decomplexation, which directly affects the mechanical properties of the hybrid gels. We use oscillatory shear rheology to demonstrate this rational control and enhancement of the mechanical properties of the hybrid gels. In addition, static light scattering reveals their highly regular and well-defined polymer-network structures. As a result of both, the present approach provides an easy and reliable concept for preparing hybrid polymer-network gels with rationally designed properties.

  20. Identification of material properties of orthotropic composite plate using experimental frequency response function data

    Science.gov (United States)

    Tam, Jun Hui; Ong, Zhi Chao; Ismail, Zubaidah; Ang, Bee Chin; Khoo, Shin Yee

    2018-05-01

    The demand for composite materials is increasing due to their great superiority in material properties, e.g., lightweight, high strength and high corrosion resistance. As a result, the invention of composite materials of diverse properties is becoming prevalent, and thus, leading to the development of material identification methods for composite materials. Conventional identification methods are destructive, time-consuming and costly. Therefore, an accurate identification approach is proposed to circumvent these drawbacks, involving the use of Frequency Response Function (FRF) error function defined by the correlation discrepancy between experimental and Finite-Element generated FRFs. A square E-glass epoxy composite plate is investigated under several different configurations of boundary conditions. It is notable that the experimental FRFs are used as the correlation reference, such that, during computation, the predicted FRFs are continuously updated with reference to the experimental FRFs until achieving a solution. The final identified elastic properties, namely in-plane elastic moduli, Ex and Ey, in-plane shear modulus, Gxy, and major Poisson's ratio, vxy of the composite plate are subsequently compared to the benchmark parameters as well as with those obtained using modal-based approach. As compared to the modal-based approach, the proposed method is found to have yielded relatively better results. This can be explained by the direct employment of raw data in the proposed method that avoids errors that might incur during the stage of modal extraction.

  1. A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    KAUST Repository

    Werfelmann, Robert

    2018-01-01

    around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions

  2. Genetic dissection of acute ethanol responsive gene networks in prefrontal cortex: functional and mechanistic implications.

    Directory of Open Access Journals (Sweden)

    Aaron R Wolen

    Full Text Available Individual differences in initial sensitivity to ethanol are strongly related to the heritable risk of alcoholism in humans. To elucidate key molecular networks that modulate ethanol sensitivity we performed the first systems genetics analysis of ethanol-responsive gene expression in brain regions of the mesocorticolimbic reward circuit (prefrontal cortex, nucleus accumbens, and ventral midbrain across a highly diverse family of 27 isogenic mouse strains (BXD panel before and after treatment with ethanol.Acute ethanol altered the expression of ~2,750 genes in one or more regions and 400 transcripts were jointly modulated in all three. Ethanol-responsive gene networks were extracted with a powerful graph theoretical method that efficiently summarized ethanol's effects. These networks correlated with acute behavioral responses to ethanol and other drugs of abuse. As predicted, networks were heavily populated by genes controlling synaptic transmission and neuroplasticity. Several of the most densely interconnected network hubs, including Kcnma1 and Gsk3β, are known to influence behavioral or physiological responses to ethanol, validating our overall approach. Other major hub genes like Grm3, Pten and Nrg3 represent novel targets of ethanol effects. Networks were under strong genetic control by variants that we mapped to a small number of chromosomal loci. Using a novel combination of genetic, bioinformatic and network-based approaches, we identified high priority cis-regulatory candidate genes, including Scn1b, Gria1, Sncb and Nell2.The ethanol-responsive gene networks identified here represent a previously uncharacterized intermediate phenotype between DNA variation and ethanol sensitivity in mice. Networks involved in synaptic transmission were strongly regulated by ethanol and could contribute to behavioral plasticity seen with chronic ethanol. Our novel finding that hub genes and a small number of loci exert major influence over the ethanol

  3. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    Energy Technology Data Exchange (ETDEWEB)

    Xu Yuhua, E-mail: yuhuaxu2004@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China) and Department of Maths, Yunyang Teacher' s College, Hubei 442000 (China); Zhou Wuneng, E-mail: wnzhou@163.co [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang Jian' an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Lu Hongqian [Shandong Institute of Light Industry, Shandong Jinan 250353 (China)

    2009-12-28

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  4. Structure identification and adaptive synchronization of uncertain general complex dynamical networks

    International Nuclear Information System (INIS)

    Xu Yuhua; Zhou Wuneng; Fang Jian'an; Lu Hongqian

    2009-01-01

    This Letter proposes an approach to identify the topological structure and unknown parameters for uncertain general complex networks simultaneously. By designing effective adaptive controllers, we achieve synchronization between two complex networks. The unknown network topological structure and system parameters of uncertain general complex dynamical networks are identified simultaneously in the process of synchronization. Several useful criteria for synchronization are given. Finally, an illustrative example is presented to demonstrate the application of the theoretical results.

  5. Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae

    Directory of Open Access Journals (Sweden)

    Araabi Babak N

    2010-12-01

    Full Text Available Abstract Background It has been understood that biological networks have modular organizations which are the sources of their observed complexity. Analysis of networks and motifs has shown that two types of hubs, party hubs and date hubs, are responsible for this complexity. Party hubs are local coordinators because of their high co-expressions with their partners, whereas date hubs display low co-expressions and are assumed as global connectors. However there is no mutual agreement on these concepts in related literature with different studies reporting their results on different data sets. We investigated whether there is a relation between the biological features of Saccharomyces Cerevisiae's proteins and their roles as non-hubs, intermediately connected, party hubs, and date hubs. We propose a classifier that separates these four classes. Results We extracted different biological characteristics including amino acid sequences, domain contents, repeated domains, functional categories, biological processes, cellular compartments, disordered regions, and position specific scoring matrix from various sources. Several classifiers are examined and the best feature-sets based on average correct classification rate and correlation coefficients of the results are selected. We show that fusion of five feature-sets including domains, Position Specific Scoring Matrix-400, cellular compartments level one, and composition pairs with two and one gaps provide the best discrimination with an average correct classification rate of 77%. Conclusions We study a variety of known biological feature-sets of the proteins and show that there is a relation between domains, Position Specific Scoring Matrix-400, cellular compartments level one, composition pairs with two and one gaps of Saccharomyces Cerevisiae's proteins, and their roles in the protein interaction network as non-hubs, intermediately connected, party hubs and date hubs. This study also confirms the

  6. Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods

    Directory of Open Access Journals (Sweden)

    Antonella Iuliano

    2018-06-01

    Full Text Available Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of ad-hoc statistical methods. In this work, we propose novel screening-network methods that predict patient survival outcome by screening key survival-related genes and we assess the capability of the proposed approaches using METABRIC dataset. In particular, we first identify a subset of genes by using variable screening techniques on gene expression data. Then, we perform Cox regression analysis by incorporating network information associated with the selected subset of genes. The novelty of this work consists in the improved prediction of survival responses due to the different types of screenings (i.e., a biomedical-driven, data-driven and a combination of the two before building the network-penalized model. Indeed, the combination of the two screening approaches allows us to use the available biological knowledge on breast cancer and complement it with additional information emerging from the data used for the analysis. Moreover, we also illustrate how to extend the proposed approaches to integrate an additional omic layer, such as copy number

  7. The national response plan and radioactive incident monitoring network (RIMNET)

    International Nuclear Information System (INIS)

    Jones, M.W.

    1989-01-01

    The Department of the Environment is responsible through Her Majesty's Inspectorate of Pollution for co-ordination of the Government's response to overseas nuclear incidents. This paper describes the contingency arrangements that have been set up for this purpose. (author)

  8. Response of pressurized water reactor (PWR) to network power generation demands

    International Nuclear Information System (INIS)

    Schreiner, L.A.

    1991-01-01

    The flexibility of the PWR type reactor in terms of response to the variations of the network power demands, is demonstrated. The factors that affect the transitory flexibility and some design prospects that allow the reactor fits the requirements of the network power demands, are also discussed. (M.J.A.)

  9. [Rapid Identification of Epicarpium Citri Grandis via Infrared Spectroscopy and Fluorescence Spectrum Imaging Technology Combined with Neural Network].

    Science.gov (United States)

    Pan, Sha-sha; Huang, Fu-rong; Xiao, Chi; Xian, Rui-yi; Ma, Zhi-guo

    2015-10-01

    To explore rapid reliable methods for detection of Epicarpium citri grandis (ECG), the experiment using Fourier Transform Attenuated Total Reflection Infrared Spectroscopy (FTIR/ATR) and Fluorescence Spectrum Imaging Technology combined with Multilayer Perceptron (MLP) Neural Network pattern recognition, for the identification of ECG, and the two methods are compared. Infrared spectra and fluorescence spectral images of 118 samples, 81 ECG and 37 other kinds of ECG, are collected. According to the differences in tspectrum, the spectra data in the 550-1 800 cm(-1) wavenumber range and 400-720 nm wavelength are regarded as the study objects of discriminant analysis. Then principal component analysis (PCA) is applied to reduce the dimension of spectroscopic data of ECG and MLP Neural Network is used in combination to classify them. During the experiment were compared the effects of different methods of data preprocessing on the model: multiplicative scatter correction (MSC), standard normal variable correction (SNV), first-order derivative(FD), second-order derivative(SD) and Savitzky-Golay (SG). The results showed that: after the infrared spectra data via the Savitzky-Golay (SG) pretreatment through the MLP Neural Network with the hidden layer function as sigmoid, we can get the best discrimination of ECG, the correct percent of training set and testing set are both 100%. Using fluorescence spectral imaging technology, corrected by the multiple scattering (MSC) results in the pretreatment is the most ideal. After data preprocessing, the three layers of the MLP Neural Network of the hidden layer function as sigmoid function can get 100% correct percent of training set and 96.7% correct percent of testing set. It was shown that the FTIR/ATR and fluorescent spectral imaging technology combined with MLP Neural Network can be used for the identification study of ECG and has the advantages of rapid, reliable effect.

  10. MicroRadarNet: A network of weather micro radars for the identification of local high resolution precipitation patterns

    Science.gov (United States)

    Turso, S.; Paolella, S.; Gabella, M.; Perona, G.

    2013-01-01

    In this paper, MicroRadarNet, a novel micro radar network for continuous, unattended meteorological monitoring is presented. Key aspects and constraints are introduced. Specific design strategies are highlighted, leading to the technological implementations of this wireless, low-cost, low power consumption sensor network. Raw spatial and temporal datasets are processed on-board in real-time, featuring a consistent evaluation of the signals from the sensors and optimizing the data loads to be transmitted. Network servers perform the final post-elaboration steps on the data streams coming from each unit. Final network products are meteorological mappings of weather events, monitored with high spatial and temporal resolution, and lastly served to the end user through any Web browser. This networked approach is shown to imply a sensible reduction of the overall operational costs, including management and maintenance aspects, if compared to the traditional long range monitoring strategy. Adoption of the TITAN storm identification and nowcasting engine is also here evaluated for in-loop integration within the MicroRadarNet data processing chain. A brief description of the engine workflow is provided, to present preliminary feasibility results and performance estimates. The outcomes were not so predictable, taking into account relevant operational differences between a Western Alps micro radar scenario and the long range radar context in the Denver region of Colorado. Finally, positive results from a set of case studies are discussed, motivating further refinements and integration activities.

  11. Identifying the relevant dependencies of the neural network response on characteristics of the input space

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    This talk presents an approach to identify those characteristics of the neural network inputs that are most relevant for the response and therefore provides essential information to determine the systematic uncertainties.

  12. The construction of corporate social responsibility in network societies: A communication view

    NARCIS (Netherlands)

    Schultz, F.; Castello, I.; Morsing, M.

    2013-01-01

    The paper introduces the communication view on Corporate Social Responsibility (CSR), which regards CSR as communicatively constructed in dynamic interaction processes in today's networked societies. Building on the idea that communication constitutes organizations we discuss the potentially

  13. Fast demand response in support of the active distribution network

    NARCIS (Netherlands)

    MacDougall, P.; Heskes, P.; Crolla, P.; Burt, G.; Warmer, C.

    2013-01-01

    Demand side management has traditionally been investigated for "normal" operation services such as balancing and congestion management. However they potentially could be utilized for Distributed Network Operator (DNO) services. This paper investigates and validates the use of a supply and demand

  14. Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

    Directory of Open Access Journals (Sweden)

    Chen Jiun-Ching

    2007-05-01

    Full Text Available Abstract Background Genome-wide identification of specific oligonucleotides (oligos is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos. Results We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes. Conclusion The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through

  15. Network-based Type-2 Fuzzy System with Water Flow Like Algorithm for System Identification and Signal Processing

    Directory of Open Access Journals (Sweden)

    Che-Ting Kuo

    2015-02-01

    Full Text Available This paper introduces a network-based interval type-2 fuzzy inference system (NT2FIS with a dynamic solution agent algorithm water flow like algorithm (WFA, for nonlinear system identification and blind source separation (BSS problem. The NT2FIS consists of interval type-2 asymmetric fuzzy membership functions and TSK-type consequent parts to enhance the performance. The proposed scheme is optimized by a new heuristic learning algorithm, WFA, with dynamic solution agents. The proposed WFA is inspired by the natural behavior of water flow. Splitting, moving, merging, evaporation, and precipitation have all been introduced for optimization. Some modifications, including new moving strategies, such as the application of tabu searching and gradient-descent techniques, are proposed to enhance the performance of the WFA in training the NT2FIS systems. Simulation and comparison results for nonlinear system identification and blind signal separation are presented to illustrate the performance and effectiveness of the proposed approach.

  16. Identification of neuronal network properties from the spectral analysis of calcium imaging signals in neuronal cultures.

    Science.gov (United States)

    Tibau, Elisenda; Valencia, Miguel; Soriano, Jordi

    2013-01-01

    Neuronal networks in vitro are prominent systems to study the development of connections in living neuronal networks and the interplay between connectivity, activity and function. These cultured networks show a rich spontaneous activity that evolves concurrently with the connectivity of the underlying network. In this work we monitor the development of neuronal cultures, and record their activity using calcium fluorescence imaging. We use spectral analysis to characterize global dynamical and structural traits of the neuronal cultures. We first observe that the power spectrum can be used as a signature of the state of the network, for instance when inhibition is active or silent, as well as a measure of the network's connectivity strength. Second, the power spectrum identifies prominent developmental changes in the network such as GABAA switch. And third, the analysis of the spatial distribution of the spectral density, in experiments with a controlled disintegration of the network through CNQX, an AMPA-glutamate receptor antagonist in excitatory neurons, reveals the existence of communities of strongly connected, highly active neurons that display synchronous oscillations. Our work illustrates the interest of spectral analysis for the study of in vitro networks, and its potential use as a network-state indicator, for instance to compare healthy and diseased neuronal networks.

  17. Complex Projective Synchronization in Drive-Response Stochastic Complex Networks by Impulsive Pinning Control

    Directory of Open Access Journals (Sweden)

    Xuefei Wu

    2014-01-01

    Full Text Available The complex projective synchronization in drive-response stochastic coupled networks with complex-variable systems is considered. The impulsive pinning control scheme is adopted to achieve complex projective synchronization and several simple and practical sufficient conditions are obtained in a general drive-response network. In addition, the adaptive feedback algorithms are proposed to adjust the control strength. Several numerical simulations are provided to show the effectiveness and feasibility of the proposed methods.

  18. Effects of the network structure and coupling strength on the noise-induced response delay of a neuronal network

    International Nuclear Information System (INIS)

    Ozer, Mahmut; Uzuntarla, Muhammet

    2008-01-01

    The Hodgkin-Huxley (H-H) neuron model driven by stimuli just above threshold shows a noise-induced response delay with respect to time to the first spike for a certain range of noise strengths, an effect called 'noise delayed decay' (NDD). We study the response time of a network of coupled H-H neurons, and investigate how the NDD can be affected by the connection topology of the network and the coupling strength. We show that the NDD effect exists for weak and intermediate coupling strengths, whereas it disappears for strong coupling strength regardless of the connection topology. We also show that although the network structure has very little effect on the NDD for a weak coupling strength, the network structure plays a key role for an intermediate coupling strength by decreasing the NDD effect with the increasing number of random shortcuts, and thus provides an additional operating regime, that is absent in the regular network, in which the neurons may also exploit a spike time code

  19. Identification of Lactobacillus plantarum genes modulating the cytokine response of human peripheral blood mononuclear cells

    Directory of Open Access Journals (Sweden)

    Molenaar Douwe

    2010-11-01

    Full Text Available Abstract Background Modulation of the immune system is one of the most plausible mechanisms underlying the beneficial effects of probiotic bacteria on human health. Presently, the specific probiotic cell products responsible for immunomodulation are largely unknown. In this study, the genetic and phenotypic diversity of strains of the Lactobacillus plantarum species were investigated to identify genes of L. plantarum with the potential to influence the amounts of cytokines interleukin 10 (IL-10 and IL-12 and the ratio of IL-10/IL-12 produced by peripheral blood mononuclear cells (PBMCs. Results A total of 42 Lactobacillus plantarum strains isolated from diverse environmental and human sources were evaluated for their capacity to stimulate cytokine production in PBMCs. The L. plantarum strains induced the secretion of the anti-inflammatory cytokine IL-10 over an average 14-fold range and secretion of the pro-inflammatory cytokine IL-12 over an average 16-fold range. Comparisons of the strain-specific cytokine responses of PBMCs to comparative genome hybridization profiles obtained with L. plantarum WCFS1 DNA microarrays (also termed gene-trait matching resulted in the identification of 6 candidate genetic loci with immunomodulatory capacities. These loci included genes encoding an N-acetyl-glucosamine/galactosamine phosphotransferase system, the LamBDCA quorum sensing system, and components of the plantaricin (bacteriocin biosynthesis and transport pathway. Deletion of these genes in L. plantarum WCFS1 resulted in growth phase-dependent changes in the PBMC IL-10 and IL-12 cytokine profiles compared with wild-type cells. Conclusions The altered PBMC cytokine profiles obtained with the L. plantarum WCFS1 mutants were in good agreement with the predictions made by gene-trait matching for the 42 L. plantarum strains. This study therefore resulted in the identification of genes present in certain strains of L. plantarum which might be responsible for

  20. Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic

    Science.gov (United States)

    Lu, Thomas; Pham, Timothy; Liao, Jason

    2011-01-01

    This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.

  1. Proteomic identification of early salicylate- and flg22-responsive redox-sensitive proteins in Arabidopsis

    KAUST Repository

    Liu, Peng

    2015-02-27

    Accumulation of reactive oxygen species (ROS) is one of the early defense responses against pathogen infection in plants. The mechanism about the initial and direct regulation of the defense signaling pathway by ROS remains elusive. Perturbation of cellular redox homeostasis by ROS is believed to alter functions of redox-sensitive proteins through their oxidative modifications. Here we report an OxiTRAQ-based proteomic study in identifying proteins whose cysteines underwent oxidative modifications in Arabidopsis cells during the early response to salicylate or flg22, two defense pathway elicitors that are known to disturb cellular redox homeostasis. Among the salicylate- and/or flg22-responsive redox-sensitive proteins are those involved in transcriptional regulation, chromatin remodeling, RNA processing, post-translational modifications, and nucleocytoplasmic shuttling. The identification of the salicylate-/flg22-responsive redox-sensitive proteins provides a foundation from which further study can be conducted toward understanding biological significance of their oxidative modifications during the plant defense response.

  2. Identification of key residues for protein conformational transition using elastic network model.

    Science.gov (United States)

    Su, Ji Guo; Xu, Xian Jin; Li, Chun Hua; Chen, Wei Zu; Wang, Cun Xin

    2011-11-07

    Proteins usually undergo conformational transitions between structurally disparate states to fulfill their functions. The large-scale allosteric conformational transitions are believed to involve some key residues that mediate the conformational movements between different regions of the protein. In the present work, a thermodynamic method based on the elastic network model is proposed to predict the key residues involved in protein conformational transitions. In our method, the key functional sites are identified as the residues whose perturbations largely influence the free energy difference between the protein states before and after transition. Two proteins, nucleotide binding domain of the heat shock protein 70 and human/rat DNA polymerase β, are used as case studies to identify the critical residues responsible for their open-closed conformational transitions. The results show that the functionally important residues mainly locate at the following regions for these two proteins: (1) the bridging point at the interface between the subdomains that control the opening and closure of the binding cleft; (2) the hinge region between different subdomains, which mediates the cooperative motions between the corresponding subdomains; and (3) the substrate binding sites. The similarity in the positions of the key residues for these two proteins may indicate a common mechanism in their conformational transitions.

  3. A multi-criteria decision analysis approach for importance identification and ranking of network components

    International Nuclear Information System (INIS)

    Almoghathawi, Yasser; Barker, Kash; Rocco, Claudio M.; Nicholson, Charles D.

    2017-01-01

    Analyzing network vulnerability is a key element of network planning in order to be prepared for any disruptive event that might impact the performance of the network. Hence, many importance measures have been proposed to identify the important components in a network with respect to vulnerability and rank them accordingly based on individual importance measure. However, in this paper, we propose a new approach to identify the most important network components based on multiple importance measures using a multi criteria decision making (MCDM) method, namely the technique for order performance by similarity to ideal solution (TOPSIS), able to take into account the preferences of decision-makers. We consider multiple edge-specific flow-based importance measures provided as the multiple criteria of a network where the alternatives are the edges. Accordingly, TOPSIS is used to rank the edges of the network based on their importance considering multiple different importance measures. The proposed approach is illustrated through different networks with different densities along with the effects of weighs. - Highlights: • We integrate several perspectives on network vulnerability to generate a component importance ranking. • We apply these measures to determine the importance of edges after disruptions. • Networks of varying size and density are explored.

  4. Corporate Environmental Responsibility in Demand Networks (summary section only)

    OpenAIRE

    Kovács, Gyöngyi

    2006-01-01

    Research on corporate responsibility has traditionally focused on the responsibilities of companies within their corporate boundaries only. Yet this view is challenged today as more and more companies face the situation in which the environmental and social performance of their suppliers, distributors, industry or other associated partners impacts on their sales performance and brand equity. Simultaneously, policy-makers have taken up the discussion on corporate responsibility from the perspe...

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

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

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

  6. Thinking in networks: artistic–architectural responses to ubiquitous information

    Directory of Open Access Journals (Sweden)

    Yvonne Spielmann

    2011-12-01

    Full Text Available The article discusses creative practices that in aesthetical-technical ways intervene into the computer networked communication systems.I am interested in artist practices that use networks in different ways to make us aware about the possibilities to rethink media-cultural environments. I use the example of the Japanese art-architectural group Double Negative Architecture to give an example of creatively thinking in networks.Yvonne Spielmann (Ph.D., Dr. habil. is presently Research Professor and Chair of New Media at The University of the West of Scotland. Her work focuses on inter-relationships between media and culture, technology, art, science and communication, and in particular on Western/European and non-Western/South-East Asian interaction. Milestones of publish research output are four authored monographs and about 90 single authored articles. Her book, “Video, the Reflexive Medium” (published by MIT Press 2008, Japanese edition by Sangen-sha Press 2011 was rewarded the 2009 Lewis Mumford Award for Outstanding Scholarship in the Ecology of Technics. Her most recent book “Hybrid Cultures” was published in German by Suhrkamp Press in 2010, English edition from MIT Press in 2012. Spielmann's work has been published in German and English and has been translated into French, Polish, Croatian, Swedish, Japanese, and Korean. She holds the 2011 Swedish Prize for Swedish–German scientific co-operation.

  7. Concept typicality responses in the semantic memory network.

    Science.gov (United States)

    Santi, Andrea; Raposo, Ana; Frade, Sofia; Marques, J Frederico

    2016-12-01

    For decades concept typicality has been recognized as critical to structuring conceptual knowledge, but only recently has typicality been applied in better understanding the processes engaged by the neurological network underlying semantic memory. This previous work has focused on one region within the network - the Anterior Temporal Lobe (ATL). The ATL responds negatively to concept typicality (i.e., the more atypical the item, the greater the activation in the ATL). To better understand the role of typicality in the entire network, we ran an fMRI study using a category verification task in which concept typicality was manipulated parametrically. We argue that typicality is relevant to both amodal feature integration centers as well as category-specific regions. Both the Inferior Frontal Gyrus (IFG) and ATL demonstrated a negative correlation with typicality, whereas inferior parietal regions showed positive effects. We interpret this in light of functional theories of these regions. Interactions between category and typicality were not observed in regions classically recognized as category-specific, thus, providing an argument against category specific regions, at least with fMRI. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Networking of TSOs in Emergency Preparedness and Response

    International Nuclear Information System (INIS)

    Weiss, F.P.; Maqua, M.; Kerner, A.; Scott-de-Martinville, E.

    2012-01-01

    On its 2011 General Conference, the IAEA announced the foundation of a forum of technical safety organizations (TSO), the TSO forum, to promote the international cooperation and networking among the TSOs worldwide and to complement the existing network of the regulators. In the light of the Fukushima accident it appears that in case of a large accident, every national crisis organization of the country immediately impacted needs the full access to its technical and human resources to face the situation and obviously the communication and collaboration with other crisis centers will suffer from such challenging conditions. Such a limiting issue can only be resolved by making the international collaboration of the technical crisis centers an official part of their work. Therefore, a new framework has to be settled to establish a worldwide network of technical emergency centers. This, certainly will require state negotiations at top level. The feedback experience from the collaboration between the technical emergency centers during the Fukushima accident could be very fruitful for the new framework. (A.C.)

  9. Response Time Test for The Application of the Data Communication Network to Nuclear Power Plant

    International Nuclear Information System (INIS)

    Shin, Y.C.; Lee, J.Y.; Park, H.Y.; Seong, S.H.; Chung, H.Y.

    2002-01-01

    This paper discusses the response time test for the application of the Data Communication Network (DCN) to Nuclear Power Plant (NPP). Conventional Instrumentation and Control (I and C) Systems using the analog technology in NPP have raised many problems regarding the lack of spare parts, maintenance burden, inaccuracy, etc.. In order to solve the problems, the Korean Next Generation Reactor (KNGR) I and C system has adopted the digital technology and new design features of using the data communication networks. It is essential to prove the response time requirements that arise from the introduction of digital I and C technology and data communication networks to nuclear power plant design. For the response time test, a high reliable data communication network structure has been developed to meet the requirements of redundancy, diversity, and segmentation. This paper presents the results of network load analysis and response time test for the KNGR DCN prototype. The test has been focused on the response time from the field components to the gateway because the response times from the gateway to the specific systems are similar to those of the existing design. It is verified that the response time requirements are met through the prototype test for KNGR I and C systems. (authors)

  10. Identification of global oil trade patterns: An empirical research based on complex network theory

    International Nuclear Information System (INIS)

    Ji, Qiang; Zhang, Hai-Ying; Fan, Ying

    2014-01-01

    Highlights: • A global oil trade core network is analyzed using complex network theory. • The global oil export core network displays a scale-free behaviour. • The current global oil trade network can be divided into three trading blocs. • The global oil trade network presents a ‘robust and yet fragile’ characteristic. - Abstract: The Global oil trade pattern becomes increasingly complex, which has become one of the most important factors affecting every country’s energy strategy and economic development. In this paper, a global oil trade core network is constructed to analyze the overall features, regional characteristics and stability of the oil trade using complex network theory. The results indicate that the global oil export core network displays a scale-free behaviour, in which the trade position of nodes presents obvious heterogeneity and the ‘hub nodes’ play a ‘bridge’ role in the formation process of the trade network. The current global oil trade network can be divided into three trading blocs, including the ‘South America-West Africa-North America’ trading bloc, the ‘Middle East–Asian–Pacific region’ trading bloc, and ‘the former Soviet Union–North Africa–Europe’ trading bloc. Geopolitics and diplomatic relations are the two main reasons for this regional oil trade structure. Moreover, the global oil trade network presents a ‘robust but yet fragile’ characteristic, and the impacts of trade interruption always tend to spread throughout the whole network even if the occurrence of export disruptions is localised

  11. Using reactor network for global identification based on residence time distribution theory

    Energy Technology Data Exchange (ETDEWEB)

    Hocine, S.; Pibouleau, L.; Azzaro-Pantel, C.; Domenech, S. [Laboratoire de Genie Chimique - UMR 5503 CNRS/ INPT ENSIACET, 31 - Toulouse (France)

    2006-07-01

    In the ventilation systems, the control of transfer contaminants is one of the principal problems during the design and control phases. The installation of a suitable ventilation system for the control of contaminant transfer is essential in industry, because it makes it possible to detect and to prevent chemical and radiological risks. Research on air distribution in ventilated rooms traditionally involves full-scale experiments, scale -model experiments and application of the computational fluid dynamics (C.F.D.) tools. Most of the time, particularly in our case of large and cluttered enclosures, the predictive approach based on C.F.D. codes can not be used. The solution retained here is the establishment of a model based on the well known residence time distribution. This model is widely used in chemical engineering to treat non-ideal flows. The proposed method is based on the experimental determination of the residence time distribution curve, generally obtained through the response of the system to tracer release. A superstructure involving the set of all the possible solutions corresponding to the physical reactor is then defined, and the model will be selected from this superstructure according to its simulated response. The superstructure is identified as a combination of elementary systems, representing ideal flow patterns, as perfect mixed flows, plug flows, continuous stirred tank reactors, etc. The selected model is derived from the comparison between the simulated response to a stimulus, and the experimental response. The structure and parameters of the model are simultaneously optimized in order to fit the experimental curve with a minimal number of elementary units, constituting a key point for future control purposes of the process. This problem is a dynamic M.I.N.L.P. (Mixed Integer Non Linear Programming) problem with bilinear equality constraints. Generally, these constraints lead to numerical difficulties for reaching an optimum solution (even a

  12. Identification of the actual state and entity availability forecasting in power engineering using neural-network technologies

    Science.gov (United States)

    Protalinsky, O. M.; Shcherbatov, I. A.; Stepanov, P. V.

    2017-11-01

    A growing number of severe accidents in RF call for the need to develop a system that could prevent emergency situations. In a number of cases accident rate is stipulated by careless inspections and neglects in developing repair programs. Across the country rates of accidents are growing because of a so-called “human factor”. In this regard, there has become urgent the problem of identification of the actual state of technological facilities in power engineering using data on engineering processes running and applying artificial intelligence methods. The present work comprises four model states of manufacturing equipment of engineering companies: defect, failure, preliminary situation, accident. Defect evaluation is carried out using both data from SCADA and ASEPCR and qualitative information (verbal assessments of experts in subject matter, photo- and video materials of surveys processed using pattern recognition methods in order to satisfy the requirements). Early identification of defects makes possible to predict the failure of manufacturing equipment using mathematical techniques of artificial neural network. In its turn, this helps to calculate predicted characteristics of reliability of engineering facilities using methods of reliability theory. Calculation of the given parameters provides the real-time estimation of remaining service life of manufacturing equipment for the whole operation period. The neural networks model allows evaluating possibility of failure of a piece of equipment consistent with types of actual defects and their previous reasons. The article presents the grounds for a choice of training and testing samples for the developed neural network, evaluates the adequacy of the neural networks model, and shows how the model can be used to forecast equipment failure. There have been carried out simulating experiments using a computer and retrospective samples of actual values for power engineering companies. The efficiency of the developed

  13. Development of visible-light responsive and mechanically enhanced "smart" UCST interpenetrating network hydrogels.

    Science.gov (United States)

    Xu, Yifei; Ghag, Onkar; Reimann, Morgan; Sitterle, Philip; Chatterjee, Prithwish; Nofen, Elizabeth; Yu, Hongyu; Jiang, Hanqing; Dai, Lenore L

    2017-12-20

    An interpenetrating polymer network (IPN), chlorophyllin-incorporated environmentally responsive hydrogel was synthesized and exhibited the following features: enhanced mechanical properties, upper critical solution temperature (UCST) swelling behavior, and promising visible-light responsiveness. Poor mechanical properties are known challenges for hydrogel-based materials. By forming an interpenetrating network between polyacrylamide (PAAm) and poly(acrylic acid) (PAAc) polymer networks, the mechanical properties of the synthesized IPN hydrogels were significantly improved compared to hydrogels made of a single network of each polymer. The formation of the interpenetrating network was confirmed by Fourier Transform Infrared Spectroscopy (FTIR), the analysis of glass transition temperature, and a unique UCST responsive swelling behavior, which is in contrast to the more prevalent lower critical solution temperature (LCST) behaviour of environmentally responsive hydrogels. The visible-light responsiveness of the synthesized hydrogel also demonstrated a positive swelling behavior, and the effect of incorporating chlorophyllin as the chromophore unit was observed to reduce the average pore size and further enhance the mechanical properties of the hydrogel. This interpenetrating network system shows potential to serve as a new route in developing "smart" hydrogels using visible-light as a simple, inexpensive, and remotely controllable stimulus.

  14. The Strategic Impact of Corporate Responsibility and Criminal Networks on Value Co-Creation

    Directory of Open Access Journals (Sweden)

    Peter Zettinig

    2011-02-01

    Full Text Available This article is motivated by the increasing concern about the ever-declining security of pharmaceutical products due to the abundance of counterfeit network actors. We argue that if networks are effective mechanisms for criminal organizations to infiltrate into any value chain, then networks should also work for responsible businesses in their quests to counter this phenomenon of value destruction, which is ultimately detrimental to the value co-creation process. Thus, this article demonstrates a nuanced understanding of the strategic impact of corporate responsibility of actors in networks on value co-creation. The current discourse on value co-creation in business networks is structured in such a way that it precludes its inherent corporate responsibility component even though they are not mutually exclusive. Moreover, research on value co-creation aimed at the proactive and responsible defence of a network substance via value co-protection has been mostly scant. We propose a model of value-optimization through value co-protection and ethical responsibility. This way of theorizing has several implications for both policy making and managerial decision making in the pharmaceutical industry and beyond.

  15. Transcriptional Profiling and Identification of Heat-Responsive Genes in Perennial Ryegrass by RNA-Sequencing

    Directory of Open Access Journals (Sweden)

    Kehua Wang

    2017-06-01

    Full Text Available Perennial ryegrass (Lolium perenne is one of the most widely used forage and turf grasses in the world due to its desirable agronomic qualities. However, as a cool-season perennial grass species, high temperature is a major factor limiting its performance in warmer and transition regions. In this study, a de novo transcriptome was generated using a cDNA library constructed from perennial ryegrass leaves subjected to short-term heat stress treatment. Then the expression profiling and identification of perennial ryegrass heat response genes by digital gene expression analyses was performed. The goal of this work was to produce expression profiles of high temperature stress responsive genes in perennial ryegrass leaves and further identify the potentially important candidate genes with altered levels of transcript, such as those genes involved in transcriptional regulation, antioxidant responses, plant hormones and signal transduction, and cellular metabolism. The de novo assembly of perennial ryegrass transcriptome in this study obtained more total and annotated unigenes compared to previously published ones. Many DEGs identified were genes that are known to respond to heat stress in plants, including HSFs, HSPs, and antioxidant related genes. In the meanwhile, we also identified four gene candidates mainly involved in C4 carbon fixation, and one TOR gene. Their exact roles in plant heat stress response need to dissect further. This study would be important by providing the gene resources for improving heat stress tolerance in both perennial ryegrass and other cool-season perennial grass plants.

  16. A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

    Science.gov (United States)

    Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

    2012-01-01

    From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

  17. Emergence of microbial networks as response to hostile environments

    Directory of Open Access Journals (Sweden)

    Dario eMadeo

    2014-08-01

    Full Text Available The majority of microorganisms live in complex communities under varying conditions. One pivotal question in evolutionary biology is the emergence of cooperative traits and their sustainment in altered environments or in the presence of free-riders. Co-occurrence patterns in the spatial distribution of biofilms can help define species' identities, and systems biology tools are revealing networks of interacting microorganisms. However, networks of inter-dependencies involving micro-organisms in the planktonic phase may be just as important, with the added complexity that they are not bounded in space. An integrated approach linking imaging, ``Omics'' and modeling has the potential to enable new hypothesis and working models. In order to understand how cooperation can emerge and be maintained without abilities like memory or recognition we use evolutionary game theory as the natural framework to model cell-cell interactions arising from evolutive decisions. We consider a finite population distributed in a spatial domain (biofilm, and divided into two interacting classes with different traits. This interaction can be weighted by distance, and produces physical connections between two elements allowing them to exchange finite amounts of energy and matter. Available strategies to each individual of one class in the population are the propensities or ``willingness'' to connect any individual of the other class. Following evolutionary game theory, we propose a mathematical model which explains the patterns of connections which emerge when individuals are able to find connection strategies that asymptotically optimize their fitness. The process explains the formation of a network for efficiently exchanging energy and matter among individuals and thus ensuring their survival in hostile environments.

  18. Process identification through modular neural networks and rule extraction (extended abstract)

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.; Blockeel, Hendrik; Denecker, Marc

    2002-01-01

    Monolithic neural networks may be trained from measured data to establish knowledge about the process. Unfortunately, this knowledge is not guaranteed to be found and – if at all – hard to extract. Modular neural networks are better suited for this purpose. Domain-ordered by topology, rule

  19. Anomaly based intrusion detection for a biometric identification system using neural networks

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

    Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

  20. [The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

    Science.gov (United States)

    Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

    2016-03-01

    combined with the artificial neural networks is proved to be a reliable and new method for the identification of the original place of Traditional Chinese Medicine.

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

    International Nuclear Information System (INIS)

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

    1993-01-01

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

  2. An evaluation of neural networks for identification of system parameters in reactor noise signals

    International Nuclear Information System (INIS)

    Miller, L.F.

    1991-01-01

    Several backpropagation neural networks for identifying fundamental mode eigenvalues were evaluated. The networks were trained and tested on analytical data and on results from other numerical methods. They were then used to predict first mode break frequencies for noise data from several sources. These predictions were, in turn, compared with analytical values and with results from alternative methods. Comparisons of results for some data sets suggest that the accuracy of predictions from neural networks are essentially equivalent to results from conventional methods while other evaluations indicate that either method may be superior. Experience gained from these numerical experiments provide insight for improving the performance of neural networks relative to other methods for identifying parameters associated with experimental data. Neural networks may also be used in support of conventional algorithms by providing starting points for nonlinear minimization algorithms

  3. Identification of the AQP members involved in abiotic stress responses from Arabidopsis.

    Science.gov (United States)

    Feng, Zhi-Juan; Xu, Sheng-Chun; Liu, Na; Zhang, Gu-Wen; Hu, Qi-Zan; Xu, Zhao-Shi; Gong, Ya-Ming

    2018-03-10

    Aquaporins (AQPs) constitute a highly diverse family of water channel proteins that play crucial biological functions in plant growth and development and stress physiology. In Arabidopsis, 35 AQPs are classified into four subfamilies (PIPs, TIPs, NIPs and SIPs). However, knowledge about the roles of different subfamily AQPs remains limited. Here, we explored the chromosomal location, gene structure and expression patterns of all AQPs in different tissues or under different abiotic stresses based on available microarray data. Tissue expression analysis showed that different AQPs had various expression patterns in tissues (root, leaf, flower and seed). Expression profiles under stress conditions revealed that most AQPs were responsive to osmotic, salt and drought stresses. Phenotypic and physiological identification showed that Tip2;2 loss-of-function mutant exhibited less sensitive to abiotic stresses (mannitol, NaCl and PEG) compared with wild-type, as evident by analysis of germination rate, root growth, survival rate, ion leakage, malondialdehyde (MDA) and proline contents. Mutant of TIP2;2 modulated the transcript levels of SOS1, SOS2, SOS3, DREB1A, DREB2A and P5CS1, under abiotic stress conditions. This study provides a basis for further functional identification of stress-related candidate AQPs in Arabidopsis. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Assessing nonchoosers' eyewitness identification accuracy from photographic showups by using confidence and response times.

    Science.gov (United States)

    Sauerland, Melanie; Sagana, Anna; Sporer, Siegfried L

    2012-10-01

    While recent research has shown that the accuracy of positive identification decisions can be assessed via confidence and decision times, gauging lineup rejections has been less successful. The current study focused on 2 different aspects which are inherent in lineup rejections. First, we hypothesized that decision times and confidence ratings should be postdictive of identification rejections if they refer to a single lineup member only. Second, we hypothesized that dividing nonchoosers according to the reasons they provided for their decisions can serve as a useful postdictor for nonchoosers' accuracy. To test these assumptions, we used (1) 1-person lineups (showups) in order to obtain confidence and response time measures referring to a single lineup member, and (2) asked nonchoosers about their reasons for making a rejection. Three hundred and eighty-four participants were asked to identify 2 different persons after watching 1 of 2 stimulus films. The results supported our hypotheses. Nonchoosers' postdecision confidence ratings were well-calibrated. Likewise, we successfully established optimum time and confidence boundaries for nonchoosers. Finally, combinations of postdictors increased the number of accurate classifications compared with individual postdictors. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  5. Response times in a two-node queueing network with feedback

    NARCIS (Netherlands)

    van der Mei, R.D.; Gijsen, B.M.M.; in 't Veld, N.; van den Berg, J.L.

    2002-01-01

    The study presented in this paper is motivated by the performance analysis of response times in distributed information systems, where transactions are handled by iterative server and database actions. We model system response times as sojourn times in a two-node open queueing network with a

  6. Response times in a two-node queueing network with feedback

    NARCIS (Netherlands)

    van der Mei, R.D.; Gijsen, B.M.M.; Gijsen, B.M.M.; in 't Veld, N.; van den Berg, Hans Leo

    The study presented in this paper is motivated by the performance analysis of response times in distributed information systems, where transactions are handled by iterative server and database actions. We model system response times as sojourn times in a two-node open queueing network with a

  7. Network fault response of wind power plants in distribution systems during reverse power flows. Part II

    NARCIS (Netherlands)

    Boemer, J.C.; Gibescu, M.; vd Meijden, M.A.M.M.; Rawn, B.G.; Kling, W.L.

    2013-01-01

    Abstract—The ability of wind power park modules to control their response to transmission network faults allows for specification of new control features directed at stabilising the power system response during and after disturbances. However, the ‘effectiveness’ of these features in situations

  8. Constructing an integrated gene similarity network for the identification of disease genes.

    Science.gov (United States)

    Tian, Zhen; Guo, Maozu; Wang, Chunyu; Xing, LinLin; Wang, Lei; Zhang, Yin

    2017-09-20

    Discovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale. We proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer's disease and predict some novel disease genes that supported by literature. RWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/ .

  9. Demand Response in Low Voltage Distribution Networks with High PV Penetration

    DEFF Research Database (Denmark)

    Nainar, Karthikeyan; Pokhrel, Basanta Raj; Pillai, Jayakrishnan Radhakrishna

    2017-01-01

    the required flexibility from the electricity market through an aggregator. The optimum demand response enables consumption of maximum renewable energy within the network constraints. Simulation studies are conducted using Matlab and DigSilent Power factory software on a Danish low-voltage distribution system......In this paper, application of demand response to accommodate maximum PV power in a low-voltage distribution network is discussed. A centralized control based on model predictive control method is proposed for the computation of optimal demand response on an hourly basis. The proposed method uses PV...

  10. Real-Time Transportation Mode Identification Using Artificial Neural Networks Enhanced with Mode Availability Layers: A Case Study in Dubai

    Directory of Open Access Journals (Sweden)

    Young-Ji Byon

    2017-09-01

    Full Text Available Traditionally, departments of transportation (DOTs have dispatched probe vehicles with dedicated vehicles and drivers for monitoring traffic conditions. Emerging assisted GPS (AGPS and accelerometer-equipped smartphones offer new sources of raw data that arise from voluntarily-traveling smartphone users provided that their modes of transportation can correctly be identified. By introducing additional raster map layers that indicate the availability of each mode, it is possible to enhance the accuracy of mode detection results. Even in its simplest form, an artificial neural network (ANN excels at pattern recognition with a relatively short processing timeframe once it is properly trained, which is suitable for real-time mode identification purposes. Dubai is one of the major cities in the Middle East and offers unique environments, such as a high density of extremely high-rise buildings that may introduce multi-path errors with GPS signals. This paper develops real-time mode identification ANNs enhanced with proposed mode availability geographic information system (GIS layers, firstly for a universal mode detection and, secondly for an auto mode detection for the particular intelligent transportation system (ITS application of traffic monitoring, and compares the results with existing approaches. It is found that ANN-based real-time mode identification, enhanced by mode availability GIS layers, significantly outperforms the existing methods.

  11. Survivors' Discursive Construction of Organizational Identification after a Downsizing

    DEFF Research Database (Denmark)

    Aggerholm, Helle Kryger; Andersen, Mona Agerholm

    of transactional contract, 3) contextual dis-identification due to radical, cultural changes, elimination of networks and poor corporate reputation, and 4) procedural dis-identification caused by lack of procedural credibility, disrespect and responsibility avoidance. The results of this study indicate...... that a strong identification with the pre-downsized organization seems to foster a strong sense of dis-identification with the post-downsized organization. The implications of these findings are discussed and recommendations for future research are provided....

  12. DNA-based identification of invasive alien species in relation to Canadian federal policy and law, and the basis of rapid-response management.

    Science.gov (United States)

    Thomas, Vernon G; Hanner, Robert H; Borisenko, Alex V

    2016-11-01

    Managing invasive alien species in Canada requires reliable taxonomic identification as the basis of rapid-response management. This can be challenging, especially when organisms are small and lack morphological diagnostic features. DNA-based techniques, such as DNA barcoding, offer a reliable, rapid, and inexpensive toolkit for taxonomic identification of individual or bulk samples, forensic remains, and even environmental DNA. Well suited for this requirement, they could be more broadly deployed and incorporated into the operating policy and practices of Canadian federal departments and should be authorized under these agencies' articles of law. These include Fisheries and Oceans Canada, Canadian Food Inspection Agency, Transport Canada, Environment Canada, Parks Canada, and Health Canada. These efforts should be harmonized with the appropriate provisions of provincial jurisdictions, for example, the Ontario Invasive Species Act. This approach necessitates that a network of accredited, certified laboratories exists, and that updated DNA reference libraries are readily accessible. Harmonizing this approach is vital among Canadian federal agencies, and between the federal and provincial levels of government. Canadian policy and law must also be harmonized with that of the USA when detecting, and responding to, invasive species in contiguous lands and waters. Creating capacity in legislation for use of DNA-based identifications brings the authority to fund, train, deploy, and certify staff, and to refine further developments in this molecular technology.

  13. MicroRNA-mediated networks underlie immune response regulation in papillary thyroid carcinoma

    Science.gov (United States)

    Huang, Chen-Tsung; Oyang, Yen-Jen; Huang, Hsuan-Cheng; Juan, Hsueh-Fen

    2014-09-01

    Papillary thyroid carcinoma (PTC) is a common endocrine malignancy with low death rate but increased incidence and recurrence in recent years. MicroRNAs (miRNAs) are small non-coding RNAs with diverse regulatory capacities in eukaryotes and have been frequently implied in human cancer. Despite current progress, however, a panoramic overview concerning miRNA regulatory networks in PTC is still lacking. Here, we analyzed the expression datasets of PTC from The Cancer Genome Atlas (TCGA) Data Portal and demonstrate for the first time that immune responses are significantly enriched and under specific regulation in the direct miRNA-target network among distinctive PTC variants to different extents. Additionally, considering the unconventional properties of miRNAs, we explore the protein-coding competing endogenous RNA (ceRNA) and the modulatory networks in PTC and unexpectedly disclose concerted regulation of immune responses from these networks. Interestingly, miRNAs from these conventional and unconventional networks share general similarities and differences but tend to be disparate as regulatory activities increase, coordinately tuning the immune responses that in part account for PTC tumor biology. Together, our systematic results uncover the intensive regulation of immune responses underlain by miRNA-mediated networks in PTC, opening up new avenues in the management of thyroid cancer.

  14. Corporate Social Responsibility and Employee Outcomes: A Moderated Mediation Model of Organizational Identification and Moral Identity.

    Science.gov (United States)

    Wang, Wei; Fu, Ying; Qiu, Huiqing; Moore, James H; Wang, Zhongming

    2017-01-01

    Corporate social responsibility (CSR) research is not new, but its importance to today's socially conscious market environment is even more evident in recent years. This study moves beyond CSR as simply the socially responsible actions and policies of organizations and focuses on the complex psychology of CSR as it relates to individuals within the organization. Given CSR can positively affect both the individuals within the organization and the organization itself, better understanding and leveraging the mechanisms and conditions of CSR that facilitate desired employee outcomes is crucial for organizational performance. However, scholars lack consensus in determining a theoretical framework for understanding how and under what conditions CSR will make an impact on employees and ultimately organizational performance. This study adds clarity by exploring the effect of perceived CSR on a more comprehensive set of employees' attitudinal and behavioral reactions (i.e., turnover intention, in-role job performance, and helping behavior) via the mediating mechanism of organizational identification and the moderating condition of moral identity. Hypotheses were derived using social identity theory. Results were based on data obtained from 340 Chinese manufacturing employee-supervisor dyads. This study found that employees' perceived CSR had an indirect relationship via organizational identification with each of the variables: (1) turnover intention, (2) in-role job performance, and (3) helping behavior. Specifically, the negative relationship between perceived CSR and turnover intention was stronger when employees had higher moral identity and the positive relationship between perceived CSR and in-role job performance and helping behavior was amplified by moral identity. Our findings show how the mediating mechanism of organizational identity and the moderating condition of moral identity work together to improve organizational effectiveness. The findings reveal several

  15. Corporate Social Responsibility and Employee Outcomes: A Moderated Mediation Model of Organizational Identification and Moral Identity

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2017-11-01

    Full Text Available Corporate social responsibility (CSR research is not new, but its importance to today’s socially conscious market environment is even more evident in recent years. This study moves beyond CSR as simply the socially responsible actions and policies of organizations and focuses on the complex psychology of CSR as it relates to individuals within the organization. Given CSR can positively affect both the individuals within the organization and the organization itself, better understanding and leveraging the mechanisms and conditions of CSR that facilitate desired employee outcomes is crucial for organizational performance. However, scholars lack consensus in determining a theoretical framework for understanding how and under what conditions CSR will make an impact on employees and ultimately organizational performance. This study adds clarity by exploring the effect of perceived CSR on a more comprehensive set of employees’ attitudinal and behavioral reactions (i.e., turnover intention, in-role job performance, and helping behavior via the mediating mechanism of organizational identification and the moderating condition of moral identity. Hypotheses were derived using social identity theory. Results were based on data obtained from 340 Chinese manufacturing employee-supervisor dyads. This study found that employees’ perceived CSR had an indirect relationship via organizational identification with each of the variables: (1 turnover intention, (2 in-role job performance, and (3 helping behavior. Specifically, the negative relationship between perceived CSR and turnover intention was stronger when employees had higher moral identity and the positive relationship between perceived CSR and in-role job performance and helping behavior was amplified by moral identity. Our findings show how the mediating mechanism of organizational identity and the moderating condition of moral identity work together to improve organizational effectiveness. The

  16. In silico identification of known osmotic stress responsive genes from Arabidopsis in soybean and Medicago

    Directory of Open Access Journals (Sweden)

    Nina M. Soares-Cavalcanti

    2012-01-01

    Full Text Available Plants experience various environmental stresses, but tolerance to these adverse conditions is a very complex phenomenon. The present research aimed to evaluate a set of genes involved in osmotic response, comparing soybean and medicago with the well-described Arabidopsis thaliana model plant. Based on 103 Arabidopsis proteins from 27 categories of osmotic stress response, comparative analyses against Genosoja and Medicago truncatula databases allowed the identification of 1,088 soybean and 1,210 Medicago sequences. The analysis showed a high number of sequences and high diversity, comprising genes from all categories in both organisms. Genes with unknown function were among the most representative, followed by transcription factors, ion transport proteins, water channel, plant defense, protein degradation, cellular structure, organization & biogenesis and senescence. An analysis of sequences with unknown function allowed the annotation of 174 soybean and 217 Medicago sequences, most of them concerning transcription factors. However, for about 30% of the sequences no function could be attributed using in silico procedures. The establishment of a gene set involved in osmotic stress responses in soybean and barrel medic will help to better understand the survival mechanisms for this type of stress condition in legumes.

  17. Identification of water-deficit responsive genes in maritime pine (Pinus pinaster Ait.) roots.

    Science.gov (United States)

    Dubos, Christian; Plomion, Christophe

    2003-01-01

    Root adaptation to soil environmental factors is very important to maritime pine, the main conifer species used for reforestation in France. The range of climates in the sites where this species is established varies from flooded in winter to drought-prone in summer. No studies have yet focused on the morphological, physiological or molecular variability of the root system to adapt its growth to such an environment. We developed a strategy to isolate drought-responsive genes in the root tissue in order to identify the molecular mechanisms that trees have evolved to cope with drought (the main problem affecting wood productivity), and to exploit this information to improve drought stress tolerance. In order to provide easy access to the root system, seedlings were raised in hydroponic solution. Polyethylene glycol was used as an osmoticum to induce water deficit. Using the cDNA-AFLP technique, we screened more than 2500 transcript derived fragments, of which 33 (1.2%) showed clear variation in presence/absence between non stressed and stressed medium. The relative abundance of these transcripts was then analysed by reverse northern. Only two out of these 33 genes showed significant opposite behaviour between both techniques. The identification and characterization of water-deficit responsive genes in roots provide the emergence of physiological understanding of the patterns of gene expression and regulation involved in the drought stress response of maritime pine.

  18. THE INTERPLANETARY NETWORK RESPONSE TO LIGO GW150914

    Energy Technology Data Exchange (ETDEWEB)

    Hurley, K. [University of California, Berkeley, Space Sciences Laboratory, 7 Gauss Way, Berkeley, CA 94720-7450 (United States); Svinkin, D. S.; Aptekar, R. L.; Golenetskii, S. V.; Frederiks, D. D. [Ioffe Physical Technical Institute, Politekhnicheskaya 26, St. Petersburg 194021 (Russian Federation); Boynton, W. [University of Arizona, Department of Planetary Sciences, Tucson, AZ 85721 (United States); Mitrofanov, I. G.; Golovin, D. V.; Kozyrev, A. S.; Litvak, M. L.; Sanin, A. B. [Space Research Institute, 84/32, Profsoyuznaya, Moscow 117997 (Russian Federation); Rau, A.; Kienlin, A. von; Zhang, X. [Max-Planck-Institut für extraterrestrische Physik, Giessenbachstrasse, Postfach 1312, Garching, D-85748 Germany (Germany); Connaughton, V.; Meegan, C. [University of Alabama in Huntsville, NSSTC, 320 Sparkman Drive, Huntsville, AL 35805 (United States); Cline, T.; Gehrels, N., E-mail: khurley@ssl.berkeley.edu [NASA Goddard Space Flight Center, Code 661, Greenbelt, MD 20771 (United States)

    2016-09-20

    We have performed a blind search for a gamma-ray transient of arbitrary duration and energy spectrum around the time of the LIGO gravitational-wave event GW150914 with the six-spacecraft interplanetary network (IPN). Four gamma-ray bursts were detected between 30 hr prior to the event and 6.1 hr after it, but none could convincingly be associated with GW150914. No other transients were detected down to limiting 15–150 keV fluences of roughly 5 ×10{sup −8}–5 × 10{sup −7} erg cm{sup −2}. We discuss the search strategies and temporal coverage of the IPN on the day of the event and compare the spatial coverage to the region where GW150914 originated. We also report the negative result of a targeted search for the Fermi -GBM event reported in conjunction with GW150914.

  19. Network Analysis Reveals a Common Host–Pathogen Interaction Pattern in Arabidopsis Immune Responses

    Directory of Open Access Journals (Sweden)

    Hong Li

    2017-05-01

    Full Text Available Many plant pathogens secrete virulence effectors into host cells to target important proteins in host cellular network. However, the dynamic interactions between effectors and host cellular network have not been fully understood. Here, an integrative network analysis was conducted by combining Arabidopsis thaliana protein–protein interaction network, known targets of Pseudomonas syringae and Hyaloperonospora arabidopsidis effectors, and gene expression profiles in the immune response. In particular, we focused on the characteristic network topology of the effector targets and differentially expressed genes (DEGs. We found that effectors tended to manipulate key network positions with higher betweenness centrality. The effector targets, especially those that are common targets of an individual effector, tended to be clustered together in the network. Moreover, the distances between the effector targets and DEGs increased over time during infection. In line with this observation, pathogen-susceptible mutants tended to have more DEGs surrounding the effector targets compared with resistant mutants. Our results suggest a common plant–pathogen interaction pattern at the cellular network level, where pathogens employ potent local impact mode to interfere with key positions in the host network, and plant organizes an in-depth defense by sequentially activating genes distal to the effector targets.

  20. Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building.

    Science.gov (United States)

    Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

    2016-07-01

    Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

  1. Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

    Directory of Open Access Journals (Sweden)

    Young-Jin Cha

    2016-07-01

    Full Text Available Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA, was characterized and modeled as a simplified lumped-mass beam model (SLMM, using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA. Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement.

  2. Field Measurement-Based System Identification and Dynamic Response Prediction of a Unique MIT Building

    Science.gov (United States)

    Cha, Young-Jin; Trocha, Peter; Büyüköztürk, Oral

    2016-01-01

    Tall buildings are ubiquitous in major cities and house the homes and workplaces of many individuals. However, relatively few studies have been carried out to study the dynamic characteristics of tall buildings based on field measurements. In this paper, the dynamic behavior of the Green Building, a unique 21-story tall structure located on the campus of the Massachusetts Institute of Technology (MIT, Cambridge, MA, USA), was characterized and modeled as a simplified lumped-mass beam model (SLMM), using data from a network of accelerometers. The accelerometer network was used to record structural responses due to ambient vibrations, blast loading, and the October 16th 2012 earthquake near Hollis Center (ME, USA). Spectral and signal coherence analysis of the collected data was used to identify natural frequencies, modes, foundation rocking behavior, and structural asymmetries. A relation between foundation rocking and structural natural frequencies was also found. Natural frequencies and structural acceleration from the field measurements were compared with those predicted by the SLMM which was updated by inverse solving based on advanced multiobjective optimization methods using the measured structural responses and found to have good agreement. PMID:27376303

  3. BLIG: A New Approach for Sensor Identification, Grouping,and Authorisation in Body Sensor Networks

    DEFF Research Database (Denmark)

    Andersen, Jacob; Bardram, Jakob Eyvind

    2007-01-01

    Using body sensor networks (BSN) in critical clinical settings like emergency units in hospitals or in accidents requires that such a network can be deployed, configured, and started in a fast and easy way, while maintaining trust in the network. In this paper we present a novel approach called...... BLIG (Blinking Led Indicated Grouping) for easy deployment of BSNs on patients in critical situations, including mechanisms for uniquely identifying and grouping sensor nodes belonging to a patient in a secure and trusted way. This approach has been designed in close cooperation with users, and easy...

  4. A comparative study of multilayer perceptron neural networks for the identification of rhubarb samples.

    Science.gov (United States)

    Zhang, Zhuoyong; Wang, Yamin; Fan, Guoqiang; Harrington, Peter de B

    2007-01-01

    Artificial neural networks have gained much attention in recent years as fast and flexible methods for quality control in traditional medicine. Near-infrared (NIR) spectroscopy has become an accepted method for the qualitative and quantitative analyses of traditional Chinese medicine since it is simple, rapid, and non-destructive. The present paper describes a method by which to discriminate official and unofficial rhubarb samples using three layer perceptron neural networks applied to NIR data. Multilayer perceptron neural networks were trained with back propagation, delta-bar-delta and quick propagation algorithms. Results obtained using these methods were all satisfactory, but the best outcomes were obtained with the delta-bar-delta algorithm.

  5. A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.

    Science.gov (United States)

    Guo, Wei-Feng; Zhang, Shao-Wu; Shi, Qian-Qian; Zhang, Cheng-Ming; Zeng, Tao; Chen, Luonan

    2018-01-19

    The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the

  6. Morally responsible decision making in networked military operations

    NARCIS (Netherlands)

    Boshuijzen-Van Burken, C.G.; Van Bezooijen, B.

    2015-01-01

    Introducing responsible innovations on the battlefield requires a rethinking of social and psychological aspects of moral decision making on the battlefield, and in particular, including how these aspects are influenced by technology. In this chapter, the social aspects of moral decision making are

  7. Parent Social Networks and Parent Responsibility: Implications for School Leadership

    Science.gov (United States)

    Curry, Katherine A.; Adams, Curt M.

    2014-01-01

    Family-school partnerships are difficult to initiate and sustain in ways that actually promote student learning, especially in high-poverty communities. This quantitative study was designed to better understand how social forces shape parent responsibility in education. Based on social cognitive theory as the conceptual framework, the…

  8. Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

    Directory of Open Access Journals (Sweden)

    Chunling DU

    2012-03-01

    Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

  9. Concept development and needs identification for intelligent network flow optimization (INFLO) : concept of operations.

    Science.gov (United States)

    2012-06-01

    The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

  10. Concept development and needs identification for intelligent network flow optimization (INFLO) : test readiness assessment.

    Science.gov (United States)

    2012-11-01

    The purpose of this project is to develop for the Intelligent Network Flow Optimization (INFLO), which is one collection (or bundle) of high-priority transformative applications identified by the United States Department of Transportation (USDOT) Mob...

  11. Considerations on command and response language features for a network of heterogeneous autonomous computers

    Science.gov (United States)

    Engelberg, N.; Shaw, C., III

    1984-01-01

    The design of a uniform command language to be used in a local area network of heterogeneous, autonomous nodes is considered. After examining the major characteristics of such a network, and after considering the profile of a scientist using the computers on the net as an investigative aid, a set of reasonable requirements for the command language are derived. Taking into account the possible inefficiencies in implementing a guest-layered network operating system and command language on a heterogeneous net, the authors examine command language naming, process/procedure invocation, parameter acquisition, help and response facilities, and other features found in single-node command languages, and conclude that some features may extend simply to the network case, others extend after some restrictions are imposed, and still others require modifications. In addition, it is noted that some requirements considered reasonable (user accounting reports, for example) demand further study before they can be efficiently implemented on a network of the sort described.

  12. The identification of zones of amplification of disruptions in network supply chains of metallurgic products

    Directory of Open Access Journals (Sweden)

    M. Kramarz

    2015-01-01

    Full Text Available An increase in the number of participants in a supply chain and network relations results in an increase in the complexity of the entire logistic and production system. Consequently, there appear additional potential sources of disruptions in material flows. The aim of the research presented in the article is to identify the zones of amplification of disruptions in network supply chains of metallurgic products.

  13. ECG Identification System Using Neural Network with Global and Local Features

    Science.gov (United States)

    Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

    2016-01-01

    This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

  14. Rebels with a cause : Group identification as a response to perceived discrimination from the mainstream

    NARCIS (Netherlands)

    Jetten, Jolanda; Branscombe, NR; Schmitt, MT; Spears, R

    Two studies involving people with body piercings tested the hypothesis that perceived discrimination increases group identification. In Study 1, group identification mediated the positive relationship between perceived discrimination and attempts to differentiate the ingroup from the mainstream. In

  15. Adaptive-impulsive synchronization in drive-response networks of continuous systems and its application

    International Nuclear Information System (INIS)

    Sun Mei; Zeng Changyan; Tao Yangwei; Tian Lixin

    2009-01-01

    Based on the comparison theorem for the stability of impulsive control system, adaptive-impulsive synchronization in drive-response networks of continuous systems with time-delay and non-time-delay is investigated. And the continuous control input, the simple updated laws and a linear impulsive controller are proposed. Moreover, two numerical examples are presented to verify the effectiveness and correctness of the theorem, using the energy resource system and Lue's system as the nodes of the networks.

  16. Boolean network identification from perturbation time series data combining dynamics abstraction and logic programming.

    Science.gov (United States)

    Ostrowski, M; Paulevé, L; Schaub, T; Siegel, A; Guziolowski, C

    2016-11-01

    Boolean networks (and more general logic models) are useful frameworks to study signal transduction across multiple pathways. Logic models can be learned from a prior knowledge network structure and multiplex phosphoproteomics data. However, most efficient and scalable training methods focus on the comparison of two time-points and assume that the system has reached an early steady state. In this paper, we generalize such a learning procedure to take into account the time series traces of phosphoproteomics data in order to discriminate Boolean networks according to their transient dynamics. To that end, we identify a necessary condition that must be satisfied by the dynamics of a Boolean network to be consistent with a discretized time series trace. Based on this condition, we use Answer Set Programming to compute an over-approximation of the set of Boolean networks which fit best with experimental data and provide the corresponding encodings. Combined with model-checking approaches, we end up with a global learning algorithm. Our approach is able to learn logic models with a true positive rate higher than 78% in two case studies of mammalian signaling networks; for a larger case study, our method provides optimal answers after 7min of computation. We quantified the gain in our method predictions precision compared to learning approaches based on static data. Finally, as an application, our method proposes erroneous time-points in the time series data with respect to the optimal learned logic models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  17. Prosumer with demand response - Distribution network impact and mitigation

    Energy Technology Data Exchange (ETDEWEB)

    Ackeby, S.; Bollen, M.; Munkhammar, J.

    2013-05-15

    This report is the result from a project funded by ELFORSK done by STRI. The project is studying the effects the introduction of so called 'prosumers' (customers with own production) and electrical vehicles will have on different types of networks. Four different cases are studied covering urban and rural areas with different types of customers. In the urban areas the power through the transformer will be the limiting factor. The major impact in the cases studied is from the introduction of production from photovoltaics at the customer-side of the meter. This will result in an introduction of surplus due to production which in one case led to an increase of the absolute power through the transformer with more than 30 %, which resulted in transformer overloading. In the rural areas the voltage drop or rise will be the limiting factor. The cases studied had already high voltage drops even in the base cases. In the case studies it was seen that the voltage drop could be slightly reduced when introducing more local production, but the production also led to that voltage rise could appear. As a result the interval of the voltage variations was increased, which in turn leads to difficulties with designing the network such that neither overvoltage nor undervoltage occurs. Introducing control algorithms had a very positive effect on reducing the net production from the photovoltaics. Using both hard and soft curtailment made it possible to remove all overcurrents or overvoltages. Using hard curtailment, where all production is turned off during overcurrent or overvoltage, leads however to a large reduction in energy from renewable energy sources. Therefore soft curtailment should as much as possible be used. The control algorithms studied for reducing the net consumption had a more limited effect and even resulted in an increase of the maximum net consumption. When trying to reduce the net consumption during an overload, the reason of the overload could only be

  18. Social Networks in Crisis Response: Trust is Vital

    Science.gov (United States)

    2016-06-01

    Meeting of the APANPIRG ATM Sub-Group (ATM/SG/2).” Hong Kong, China, August 4-8, 2014. [72] Australian Maritime Safety Authority. “Search for Malaysia ...response community as a whole. iv  2014 search for Malaysia Airlines Flight 370 (MH370)  2012 South Sudan WASH crisis  2004 Indian...Meteorological and Oceanographic MH370 Malaysia Airlines Flight 370 MOT Malaysia Ministry of Transportation xiv MSF Doctors Without Borders

  19. Biological and Theoretical Studies of Adaptive Networks: The Conditioned Response.

    Science.gov (United States)

    1992-06-30

    suggest experimental tests and provide direction for physiological studies. 14 SU~la TIPO ~IS- NIJUMS Of PAGIS 17. @1d-ftA ITY CLASSIPtCATICON...mancte suditioned inhibition of the rabbit’s nictitating membrane response, CI tasks require the active suppression of CRs in the Bull . Psychon. Soc., 20... Bull ., 84 (1977) encephalon and mesencephalon26. 690-711. Several lines of evidence suggest that the septal and 8 Evans,J.A.C. and Thornton, E.W

  20. Eyewitness Identification Accuracy and Response Latency: The Unruly 10-12-Second Rule

    Science.gov (United States)

    Weber, Nathan; Brewer, Neil; Wells, Gary L.; Semmler, Carolyn; Keast, Amber

    2004-01-01

    Data are reported from 3,213 research eyewitnesses confirming that accurate eyewitness identifications from lineups are made faster than are inaccurate identifications. However, consistent with predictions from the recognition and search literatures, the authors did not find support for the "10-12-s rule" in which lineup identifications faster…

  1. Role of architecture in the elastic response of semiflexible polymer and fiber networks

    Science.gov (United States)

    Heussinger, Claus; Frey, Erwin

    2007-01-01

    We study the elasticity of cross-linked networks of thermally fluctuating stiff polymers. As compared to their purely mechanical counterparts, it is shown that these thermal networks have a qualitatively different elastic response. By accounting for the entropic origin of the single-polymer elasticity, the networks acquire a strong susceptibility to polydispersity and structural randomness that is completely absent in athermal models. In extensive numerical studies we systematically vary the architecture of the networks and identify a wealth of phenomena that clearly show the strong dependence of the emergent macroscopic moduli on the underlying mesoscopic network structure. In particular, we highlight the importance of the polymer length, which to a large extent controls the elastic response of the network, surprisingly, even in parameter regions where it does not enter the macroscopic moduli explicitly. Understanding these subtle effects is only possible by going beyond the conventional approach that considers the response of typical polymer segments only. Instead, we propose to describe the elasticity in terms of a typical polymer filament and the spatial distribution of cross-links along its backbone. We provide theoretical scaling arguments to relate the observed macroscopic elasticity to the physical mechanisms on the microscopic and mesoscopic scales.

  2. Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

    Science.gov (United States)

    Čeko, Marta; Gracely, John L; Fitzcharles, Mary-Ann; Seminowicz, David A; Schweinhardt, Petra; Bushnell, M Catherine

    2015-08-19

    In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or "negative" [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient. We studied the

  3. Identification of Biomarkers for Defense Response to Plasmopara viticola in a Resistant Grape Variety

    Directory of Open Access Journals (Sweden)

    Giulia Chitarrini

    2017-09-01

    Full Text Available Downy mildew (Plasmopara viticola is one of the most destructive diseases of the cultivated species Vitis vinifera. The use of resistant varieties, originally derived from backcrosses of North American Vitis spp., is a promising solution to reduce disease damage in the vineyards. To shed light on the type and the timing of pathogen-triggered resistance, this work aimed at discovering biomarkers for the defense response in the resistant variety Bianca, using leaf discs after inoculation with a suspension of P. viticola. We investigated primary and secondary metabolism at 12, 24, 48, and 96 h post-inoculation (hpi. We used methods of identification and quantification for lipids (LC-MS/MS, phenols (LC-MS/MS, primary compounds (GC-MS, and semi-quantification for volatile compounds (GC-MS. We were able to identify and quantify or semi-quantify 176 metabolites, among which 53 were modulated in response to pathogen infection. The earliest changes occurred in primary metabolism at 24–48 hpi and involved lipid compounds, specifically unsaturated fatty acid and ceramide; amino acids, in particular proline; and some acids and sugars. At 48 hpi, we also found changes in volatile compounds and accumulation of benzaldehyde, a promoter of salicylic acid-mediated defense. Secondary metabolism was strongly induced only at later stages. The classes of compounds that increased at 96 hpi included phenylpropanoids, flavonols, stilbenes, and stilbenoids. Among stilbenoids we found an accumulation of ampelopsin H + vaticanol C, pallidol, ampelopsin D + quadrangularin A, Z-miyabenol C, and α-viniferin in inoculated samples. Some of these compounds are known as phytoalexins, while others are novel biomarkers for the defense response in Bianca. This work highlighted some important aspects of the host response to P. viticola in a commercial variety under controlled conditions, providing biomarkers for a better understanding of the mechanism of plant defense and a

  4. Transcriptome-wide identification of bread wheat WRKY transcription factors in response to drought stress.

    Science.gov (United States)

    Okay, Sezer; Derelli, Ebru; Unver, Turgay

    2014-10-01

    The WRKY superfamily of transcription factors was shown to be involved in biotic and abiotic stress responses in plants such as wheat (Triticum aestivum L.), one of the major crops largely cultivated and consumed all over the world. Drought is an important abiotic stress resulting in a considerable amount of loss in agronomical yield. Therefore, identification of drought responsive WRKY members in wheat has a profound significance. Here, a total of 160 TaWRKY proteins were characterized according to sequence similarity, motif varieties, and their phylogenetic relationships. The conserved sequences of the TaWRKYs were aligned and classified into three main groups and five subgroups. A novel motif in wheat, WRKYGQR, was identified. To putatively determine the drought responsive TaWRKY members, publicly available RNA-Seq data were analyzed for the first time in this study. Through in silico searches, 35 transcripts were detected having an identity to ten known TaWRKY genes. Furthermore, relative expression levels of TaWRKY16/TaWRKY16-A, TaWRKY17, TaWRKY19-C, TaWRKY24, TaWRKY59, TaWRKY61, and TaWRKY82 were measured in root and leaf tissues of drought-tolerant Sivas 111/33 and susceptible Atay 85 cultivars. All of the quantified TaWRKY transcripts were found to be up-regulated in root tissue of Sivas 111/33. Differential expression of TaWRKY16, TaWRKY24, TaWRKY59, TaWRKY61 and TaWRKY82 genes was discovered for the first time upon drought stress in wheat. These comprehensive analyses bestow a better understanding about the WRKY TFs in bread wheat under water deficit, and increased number of drought responsive WRKYs would contribute to the molecular breeding of tolerant wheat cultivars.

  5. A cascade reaction network mimicking the basic functional steps of acquired immune response

    Science.gov (United States)

    Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

    2015-01-01

    Biological systems use complex ‘information processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices. PMID:26391084

  6. A cascade reaction network mimicking the basic functional steps of adaptive immune response.

    Science.gov (United States)

    Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

    2015-10-01

    Biological systems use complex 'information-processing cores' composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

  7. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  8. Structured chaos shapes spike-response noise entropy in balanced neural networks

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

    Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  9. Genome-wide identification of soybean WRKY transcription factors in response to salt stress.

    Science.gov (United States)

    Yu, Yanchong; Wang, Nan; Hu, Ruibo; Xiang, Fengning

    2016-01-01

    Members of the large family of WRKY transcription factors are involved in a wide range of developmental and physiological processes, most particularly in the plant response to biotic and abiotic stress. Here, an analysis of the soybean genome sequence allowed the identification of the full complement of 188 soybean WRKY genes. Phylogenetic analysis revealed that soybean WRKY genes were classified into three major groups (I, II, III), with the second group further categorized into five subgroups (IIa-IIe). The soybean WRKYs from each group shared similar gene structures and motif compositions. The location of the GmWRKYs was dispersed over all 20 soybean chromosomes. The whole genome duplication appeared to have contributed significantly to the expansion of the family. Expression analysis by RNA-seq indicated that in soybean root, 66 of the genes responded rapidly and transiently to the imposition of salt stress, all but one being up-regulated. While in aerial part, 49 GmWRKYs responded, all but two being down-regulated. RT-qPCR analysis showed that in the whole soybean plant, 66 GmWRKYs exhibited distinct expression patterns in response to salt stress, of which 12 showed no significant change, 35 were decreased, while 19 were induced. The data present here provide critical clues for further functional studies of WRKY gene in soybean salt tolerance.

  10. Noninvasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet

    Directory of Open Access Journals (Sweden)

    Hans-Peter Mock

    2016-09-01

    Full Text Available Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5-99.9 % in detecting C. beticola. Metabolite analysis revealed metabolites altered by the host as part of a successful defence response: these were L-DOPA, 12-hydroxyjasmonic acid 12-O-β-D-glucoside, pantothenic acid and 5-O-feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively-produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola. The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.

  11. Identification of alterations associated with age in the clustering structure of functional brain networks.

    Science.gov (United States)

    Guzman, Grover E C; Sato, Joao R; Vidal, Maciel C; Fujita, Andre

    2018-01-01

    Initial studies using resting-state functional magnetic resonance imaging on the trajectories of the brain network from childhood to adulthood found evidence of functional integration and segregation over time. The comprehension of how healthy individuals' functional integration and segregation occur is crucial to enhance our understanding of possible deviations that may lead to brain disorders. Recent approaches have focused on the framework wherein the functional brain network is organized into spatially distributed modules that have been associated with specific cognitive functions. Here, we tested the hypothesis that the clustering structure of brain networks evolves during development. To address this hypothesis, we defined a measure of how well a brain region is clustered (network fitness index), and developed a method to evaluate its association with age. Then, we applied this method to a functional magnetic resonance imaging data set composed of 397 males under 31 years of age collected as part of the Autism Brain Imaging Data Exchange Consortium. As results, we identified two brain regions for which the clustering change over time, namely, the left middle temporal gyrus and the left putamen. Since the network fitness index is associated with both integration and segregation, our finding suggests that the identified brain region plays a role in the development of brain systems.

  12. System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

    Energy Technology Data Exchange (ETDEWEB)

    Snyder-Talkington, Brandi N. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Dymacek, Julian [Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506-6070 (United States); Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Porter, Dale W.; Wolfarth, Michael G.; Mercer, Robert R. [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Pacurari, Maricica [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States); Denvir, James [Department of Biochemistry and Microbiology, Marshall University, Huntington, WV 25755 (United States); Castranova, Vincent [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Qian, Yong, E-mail: yaq2@cdc.gov [Pathology and Physiology Research Branch, Health Effects Laboratory Division, National Institute for Occupational Safety and Health, Morgantown, WV 26505 (United States); Guo, Nancy L., E-mail: lguo@hsc.wvu.edu [Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506-9300 (United States)

    2013-10-15

    The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung

  13. System-based identification of toxicity pathways associated with multi-walled carbon nanotube-induced pathological responses

    International Nuclear Information System (INIS)

    Snyder-Talkington, Brandi N.; Dymacek, Julian; Porter, Dale W.; Wolfarth, Michael G.; Mercer, Robert R.; Pacurari, Maricica; Denvir, James; Castranova, Vincent; Qian, Yong; Guo, Nancy L.

    2013-01-01

    The fibrous shape and biopersistence of multi-walled carbon nanotubes (MWCNT) have raised concern over their potential toxicity after pulmonary exposure. As in vivo exposure to MWCNT produced a transient inflammatory and progressive fibrotic response, this study sought to identify significant biological processes associated with lung inflammation and fibrosis pathology data, based upon whole genome mRNA expression, bronchoaveolar lavage scores, and morphometric analysis from C57BL/6J mice exposed by pharyngeal aspiration to 0, 10, 20, 40, or 80 μg MWCNT at 1, 7, 28, or 56 days post-exposure. Using a novel computational model employing non-negative matrix factorization and Monte Carlo Markov Chain simulation, significant biological processes with expression similar to MWCNT-induced lung inflammation and fibrosis pathology data in mice were identified. A subset of genes in these processes was determined to be functionally related to either fibrosis or inflammation by Ingenuity Pathway Analysis and was used to determine potential significant signaling cascades. Two genes determined to be functionally related to inflammation and fibrosis, vascular endothelial growth factor A (vegfa) and C-C motif chemokine 2 (ccl2), were confirmed by in vitro studies of mRNA and protein expression in small airway epithelial cells exposed to MWCNT as concordant with in vivo expression. This study identified that the novel computational model was sufficient to determine biological processes strongly associated with the pathology of lung inflammation and fibrosis and could identify potential toxicity signaling pathways and mechanisms of MWCNT exposure which could be used for future animal studies to support human risk assessment and intervention efforts. - Highlights: • A novel computational model identified toxicity pathways matching in vivo pathology. • Systematic identification of MWCNT-induced biological processes in mouse lungs • MWCNT-induced functional networks of lung

  14. Resistance temperature sensor aging degradation identification using LCSR (Loop Current Step Response) test

    International Nuclear Information System (INIS)

    Santos, Roberto Carlos dos; Goncalves, Iraci Martine Pereira

    2013-01-01

    response time of the sensor to changes in external temperature is identified by means of the LCSR transformation. Since the response time is controlled by heat diffusion, response time could degrade either because of changes in the overall heat-transfer resistance and/or effective heat capacity of the sensor material. Response time generally degrades due to the following possible causes: changes in the properties of the filler or bonding material, material on sensor surface, and changes in contact pressure or contact area. Therefore, the LCSR test results can either give information about the time constant value and the level of RTD response-time degradation. In order to identify the time response degradation causes, LCSR laboratory tests were performed using normal and artificially degraded RTDs. This work presents the results of time response time degradation identification obtained from LCSR test. (author)

  15. Transcriptome-wide identification of Camellia sinensis WRKY transcription factors in response to temperature stress.

    Science.gov (United States)

    Wu, Zhi-Jun; Li, Xing-Hui; Liu, Zhi-Wei; Li, Hui; Wang, Yong-Xin; Zhuang, Jing

    2016-02-01

    Tea plant [Camellia sinensis (L.) O. Kuntze] is a leaf-type healthy non-alcoholic beverage crop, which has been widely introduced worldwide. Tea is rich in various secondary metabolites, which are important for human health. However, varied climate and complex geography have posed challenges for tea plant survival. The WRKY gene family in plants is a large transcription factor family that is involved in biological processes related to stress defenses, development, and metabolite synthesis. Therefore, identification and analysis of WRKY family transcription factors in tea plant have a profound significance. In the present study, 50 putative C. sinensis WRKY proteins (CsWRKYs) with complete WRKY domain were identified and divided into three Groups (Group I-III) on the basis of phylogenetic analysis results. The distribution of WRKY family transcription factors among plantae, fungi, and protozoa showed that the number of WRKY genes increased in higher plant, whereas the number of these genes did not correspond to the evolutionary relationships of different species. Structural feature and annotation analysis results showed that CsWRKY proteins contained WRKYGQK/WRKYGKK domains and C2H2/C2HC-type zinc-finger structure: D-X18-R-X1-Y-X2-C-X4-7-C-X23-H motif; CsWRKY proteins may be associated with the biological processes of abiotic and biotic stresses, tissue development, and hormone and secondary metabolite biosynthesis. Temperature stresses suggested that the candidate CsWRKY genes were involved in responses to extreme temperatures. The current study established an extensive overview of the WRKY family transcription factors in tea plant. This study also provided a global survey of CsWRKY transcription factors and a foundation of future functional identification and molecular breeding.

  16. IL17eScan: A Tool for the Identification of Peptides Inducing IL-17 Response

    Directory of Open Access Journals (Sweden)

    Sudheer Gupta

    2017-10-01

    Full Text Available IL-17 cytokines are pro-inflammatory cytokines and are crucial in host defense against various microbes. Induction of these cytokines by microbial antigens has been investigated in the case of ischemic brain injury, gingivitis, candidiasis, autoimmune myocarditis, etc. In this study, we have investigated the ability of amino acid sequence of antigens to induce IL-17 response using machine-learning approaches. A total of 338 IL-17-inducing and 984 IL-17 non-inducing peptides were retrieved from Immune Epitope Database. 80% of the data were randomly selected as training dataset and rest 20% as validation dataset. To predict the IL-17-inducing ability of peptides/protein antigens, different sequence-based machine-learning models were developed. The performance of support vector machine (SVM and random forest (RF was compared with different parameters to predict IL-17-inducing epitopes (IIEs. The dipeptide composition-based SVM-model displayed an accuracy of 82.4% with Matthews correlation coefficient = 0.62 at polynomial (t = 1 kernel on 10-fold cross-validation and outperformed RF. Amino acid residues Leu, Ser, Arg, Asn, and Phe and dipeptides LL, SL, LK, IL, LI, NL, LR, FK, SF, and LE are abundant in IIEs. The present tool helps in the identification of IIEs using machine-learning approaches. The induction of IL-17 plays an important role in several inflammatory diseases, and identification of such epitopes would be of great help to the immunologists. It is freely available at http://metagenomics.iiserb.ac.in/IL17eScan/ and http://metabiosys.iiserb.ac.in/IL17eScan/.

  17. Identification of protein sub-networks implicated in Autism Spectrum Disorders

    OpenAIRE

    Correia, C.; Diekmann, Y.; Pereira-Leal, J.B.; Vicente, A.M.; Autism Genome Project Consortium

    2011-01-01

    Autism Spectrum Disorders (ASDs) represent a group of childhood neurodevelopmental disorders characterized by three primary areas of impairment: social interaction, communication, and restricted and repetitive patterns of interest or behavior. Although autism is one of the most heritable neuropsychiatric disorders, most of the known genetic risk has been traced to rare variants. Genome-wide association studies (GWAS) have thus far met limited success in the identification of common risk varia...

  18. Project Portfolio Risk Identification and Analysis, Considering Project Risk Interactions and Using Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Foroogh Ghasemi

    2018-05-01

    Full Text Available An organization’s strategic objectives are accomplished through portfolios. However, the materialization of portfolio risks may affect a portfolio’s sustainable success and the achievement of those objectives. Moreover, project interdependencies and cause–effect relationships between risks create complexity for portfolio risk analysis. This paper presents a model using Bayesian network (BN methodology for modeling and analyzing portfolio risks. To develop this model, first, portfolio-level risks and risks caused by project interdependencies are identified. Then, based on their cause–effect relationships all portfolio risks are organized in a BN. Conditional probability distributions for this network are specified and the Bayesian networks method is used to estimate the probability of portfolio risk. This model was applied to a portfolio of a construction company located in Iran and proved effective in analyzing portfolio risk probability. Furthermore, the model provided valuable information for selecting a portfolio’s projects and making strategic decisions.

  19. Particle identification with neural networks using a rotational invariant moment representation

    Science.gov (United States)

    Sinkus, Ralph; Voss, Thomas

    1997-02-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies.

  20. Particle identification with neural networks using a rotational invariant moment representation

    International Nuclear Information System (INIS)

    Sinkus, R.

    1997-01-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. A preprocessing procedure is applied to the spatial energy distribution of the particle shower in order to account for the varying geometry of the calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The distributions of moments exhibit very different scales, thus the multidimensional input distribution for the neural network is transformed via a principal component analysis and rescaled by its respective variances to ensure input values of the order of one. This increases the sensitivity of the network and thus results in better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

  1. Identification of potential opinion leaders in child health promotion in Sweden using network analysis.

    Science.gov (United States)

    Guldbrandsson, Karin; Nordvik, Monica K; Bremberg, Sven

    2012-08-08

    Opinion leaders are often local individuals with high credibility who can influence other people. Robust effects using opinion leaders in diffusing innovations have been shown in several randomized controlled trials, for example regarding sexually transmitted infections (STI), human immunodeficiency virus (HIV) prevention, mammography rates and caesarean birth delivery rates. In a Cochrane review 2010 it was concluded that the use of opinion leaders can successfully promote evidence-based practice. Thus, using opinion leaders within the public health sector might be one means to speed up the dissemination of health promoting and disease preventing innovations. Social network analysis has been used to trace and map networks, with focus on relationships and positions, in widely spread arenas and topics. The purpose of this study was to use social network analysis in order to identify potential opinion leaders at the arena of child health promotion in Sweden. By using snowball technique a short e-mail question was spread in up to five links, starting from seven initially invited persons. This inquiry resulted in a network consisting of 153 individuals. The most often mentioned actors were researchers, public health officials and paediatricians, or a combination of these professions. Four single individuals were mentioned by five to seven other persons in the network. These individuals obviously possess qualities that make other professionals within the public health sector listen to and trust them. Social network analysis seemed to be a useful method to identify influential persons with high credibility, i.e. potential opinion leaders, at the arena of child health promotion in Sweden. If genuine opinion leaders could be identified directed measures can be carried out in order to spread new and relevant knowledge. This may facilitate for public health actors at the local, regional and national level to more rapidly progress innovations into everyday practice. However

  2. Exploiting Hidden Layer Responses of Deep Neural Networks for Language Recognition

    Science.gov (United States)

    2016-09-08

    Target Languages Arabic (ara) Egyptian , Iraqi, Levantine, Maghrebi,Modern Standard Chinese (chi) Cantonese, Mandarin, Min, Wu English (eng) British...Frame-by-frame DNN classification x1 x2 x3 xT-­1xT Figure 1: Frame-by-frame DNN Language Identification Figure 1 shows the architecture of the DNN...compare direct DNN system with proposed DNN I-vector system, we trained a single neural network to classify all 20 languages. The architecture of this

  3. Dynamical Networks Characterization of Geomagnetic Substorms and Transient Response to the Solar Wind State.

    Science.gov (United States)

    Chapman, S. C.; Dods, J.; Gjerloev, J. W.

    2017-12-01

    Observations of how the solar wind interacts with earth's magnetosphere, and its dynamical response, are increasingly becoming a data analytics challenge. Constellations of satellites observe the solar corona, the upstream solar wind and throughout earth's magnetosphere. These data are multipoint in space and extended in time, so in principle are ideal for study using dynamical networks to characterize the full time evolving spatial pattern. We focus here on analysis of data from the full set of 100+ auroral ground based magnetometer stations that have been collated by SuperMAG. Spatio-temporal patterns of correlation between the magnetometer time series can be used to form a dynamical network [1]. The properties of the network can then be captured by (time dependent) network parameters. This offers the possibility of characterizing detailed spatio-temporal pattern by a few parameters, so that many events can then be compared [2] with each other. Whilst networks are in widespread use in the data analytics of societal and commercial data, there are additional challenges in their application to physical timeseries. Determining whether two nodes (here, ground based magnetometer stations) are connected in a network (seeing the same dynamics) requires normalization w.r.t. the detailed sensitivities and dynamical responses of specific observing stations and seasonal conductivity variations and we have developed methods to achieve this dynamical normalization. The detailed properties of the network capture time dependent spatial correlation in the magnetometer responses and we will show how this can be used to infer a transient current system response to magnetospheric activity. [l] Dods et al, J. Geophys. Res 120, doi:10.1002/2015JA02 (2015). [2] Dods et al, J. Geophys. Res. 122, doi:10.1002/2016JA02 (2017).

  4. Identification of radon anomalies in soil gas using decision trees and neural networks

    International Nuclear Information System (INIS)

    Zmazek, B.; Dzeroski, S.; Torkar, D.; Vaupotic, J.; Kobal, I.

    2010-01-01

    The time series of radon ( 222 Rn) concentration in soil gas at a fault, together with the environmental parameters, have been analysed applying two machine learning techniques: (I) decision trees and (II) neural networks, with the aim at identifying radon anomalies caused by seismic events and not simply ascribed to the effect of the environmental parameters. By applying neural networks, 10 radon anomalies were observed for 12 earthquakes, while with decision trees, the anomaly was found for every earthquake, but, undesirably, some anomalies appeared also during periods without earthquakes. (authors)

  5. Particle identification with neural networks using a rotational invariant moment representation

    International Nuclear Information System (INIS)

    Sinkus, R.

    1997-01-01

    A feed-forward neural network is used to identify electromagnetic particles based upon their showering properties within a segmented calorimeter. The novel feature is the expansion of the energy distribution in terms of moments of the so-called Zernike functions which are invariant under rotation. The multidimensional input distribution for the neural network is transformed via a principle component analysis and rescaled by its respective variances to ensure input values of the order of one. This results is a better performance in identifying and separating electromagnetic from hadronic particles, especially at low energies. (orig.)

  6. Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

    Science.gov (United States)

    Antulov-Fantulin, Nino; Lančić, Alen; Šmuc, Tomislav; Štefančić, Hrvoje; Šikić, Mile

    2015-06-01

    Detection of patient zero can give new insights to epidemiologists about the nature of first transmissions into a population. In this Letter, we study the statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure. By using exact analytic calculations and Monte Carlo estimators, we demonstrate the detectability limits for the susceptible-infected-recovered model, which primarily depend on the spreading process characteristics. Finally, we demonstrate the applicability of the approach in a case of a simulated sexually transmitted infection spreading over an empirical temporal network of sexual interactions.

  7. Applying Bayesian belief networks in rapid response situations

    Energy Technology Data Exchange (ETDEWEB)

    Gibson, William L [Los Alamos National Laboratory; Deborah, Leishman, A. [Los Alamos National Laboratory; Van Eeckhout, Edward [Los Alamos National Laboratory

    2008-01-01

    The authors have developed an enhanced Bayesian analysis tool called the Integrated Knowledge Engine (IKE) for monitoring and surveillance. The enhancements are suited for Rapid Response Situations where decisions must be made based on uncertain and incomplete evidence from many diverse and heterogeneous sources. The enhancements extend the probabilistic results of the traditional Bayesian analysis by (1) better quantifying uncertainty arising from model parameter uncertainty and uncertain evidence, (2) optimizing the collection of evidence to reach conclusions more quickly, and (3) allowing the analyst to determine the influence of the remaining evidence that cannot be obtained in the time allowed. These extended features give the analyst and decision maker a better comprehension of the adequacy of the acquired evidence and hence the quality of the hurried decisions. They also describe two example systems where the above features are highlighted.

  8. A Game-Theoretic Response Strategy for Coordinator Attack in Wireless Sensor Networks

    Science.gov (United States)

    Liu, Jianhua; Yue, Guangxue; Shang, Huiliang; Li, Hongjie

    2014-01-01

    The coordinator is a specific node that controls the whole network and has a significant impact on the performance in cooperative multihop ZigBee wireless sensor networks (ZWSNs). However, the malicious node attacks coordinator nodes in an effort to waste the resources and disrupt the operation of the network. Attacking leads to a failure of one round of communication between the source nodes and destination nodes. Coordinator selection is a technique that can considerably defend against attack and reduce the data delivery delay, and increase network performance of cooperative communications. In this paper, we propose an adaptive coordinator selection algorithm using game and fuzzy logic aiming at both minimizing the average number of hops and maximizing network lifetime. The proposed game model consists of two interrelated formulations: a stochastic game for dynamic defense and a best response policy using evolutionary game formulation for coordinator selection. The stable equilibrium best policy to response defense is obtained from this game model. It is shown that the proposed scheme can improve reliability and save energy during the network lifetime with respect to security. PMID:25105171

  9. Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection.

    Science.gov (United States)

    Guthke, Reinhard; Möller, Ulrich; Hoffmann, Martin; Thies, Frank; Töpfer, Susanne

    2005-04-15

    The immune response to bacterial infection represents a complex network of dynamic gene and protein interactions. We present an optimized reverse engineering strategy aimed at a reconstruction of this kind of interaction networks. The proposed approach is based on both microarray data and available biological knowledge. The main kinetics of the immune response were identified by fuzzy clustering of gene expression profiles (time series). The number of clusters was optimized using various evaluation criteria. For each cluster a representative gene with a high fuzzy-membership was chosen in accordance with available physiological knowledge. Then hypothetical network structures were identified by seeking systems of ordinary differential equations, whose simulated kinetics could fit the gene expression profiles of the cluster-representative genes. For the construction of hypothetical network structures singular value decomposition (SVD) based methods and a newly introduced heuristic Network Generation Method here were compared. It turned out that the proposed novel method could find sparser networks and gave better fits to the experimental data. Reinhard.Guthke@hki-jena.de.

  10. Identification and characterization of starch and inulin modifying network of Aspergillus niger by functional genomics

    NARCIS (Netherlands)

    Yuan, Xiao-Lian

    2008-01-01

    Aspergillus niger produces a wide variety of carbohydrate hydrolytic enzymes which have potential applications in the baking, starch, textile, food and feed industries. The goal of this thesis is to unravel the molecular mechanisms of starch and inulin modifying network of A. niger, in order to

  11. Task and task-free fMRI reproducibility comparison for motor network identification

    NARCIS (Netherlands)

    Kristo, G.; Rutten, G.J.; Raemaekers, M.; de Gelder, B.; Rombouts, S.A.R.B.; Ramsey, N.F.

    2014-01-01

    Test-retest reliability of individual functional magnetic resonance imaging (fMRI) results is of importance in clinical practice and longitudinal experiments. While several studies have investigated reliability of task-induced motor network activation, less is known about the reliability of the

  12. Neural network approaches to tracer identification as related to PIV research

    International Nuclear Information System (INIS)

    Seeley, C.H. Jr.

    1992-12-01

    Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results

  13. Watchdog Sensor Network with Multi-Stage RF Signal Identification and Cooperative Intrusion Detection

    Science.gov (United States)

    2012-03-01

    the Nash equilibrium of the network with several misbehaving nodes is Pareto optimal for each node as well. The underlying assumption is that all...Scatternet Formation” 1st Mobihoc, Aug. 2000 pp 147–48. [24] V.B, Kirubanand, S. Palaniammal, “Performance Modeling of Client -Server with Wibree

  14. Neural network approaches to tracer identification as related to PIV research

    Energy Technology Data Exchange (ETDEWEB)

    Seeley, C.H. Jr.

    1992-12-01

    Neural networks have become very powerful tools in many fields of interest. This thesis examines the application of neural networks to another rapidly growing field flow visualization. Flow visualization research is used to experimentally determine how fluids behave and to verify computational results obtained analytically. A form of flow visualization, particle image velocimetry (PIV). determines the flow movement by tracking neutrally buoyant particles suspended in the fluid. PIV research has begun to improve rapidly with the advent of digital imagers, which can quickly digitize an image into arrays of grey levels. These grey level arrays are analyzed to determine the location of the tracer particles. Once the particles positions have been determined across multiple image frames, it is possible to track their movements, and hence, the flow of the fluid. This thesis explores the potential of several different neural networks to identify the positions of the tracer particles. Among these networks are Backpropagation, Kohonen (counter-propagation), and Cellular. Each of these algorithms were employed in their basic form, and training and testing were performed on a synthetic grey level array. Modifications were then made to them in attempts to improve the results.

  15. Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

    Directory of Open Access Journals (Sweden)

    Cristina Marino Buslje

    Full Text Available Identification of catalytic residues (CR is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI, and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls, combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

  16. Networks of high mutual information define the structural proximity of catalytic sites: implications for catalytic residue identification.

    Science.gov (United States)

    Marino Buslje, Cristina; Teppa, Elin; Di Doménico, Tomas; Delfino, José María; Nielsen, Morten

    2010-11-04

    Identification of catalytic residues (CR) is essential for the characterization of enzyme function. CR are, in general, conserved and located in the functional site of a protein in order to attain their function. However, many non-catalytic residues are highly conserved and not all CR are conserved throughout a given protein family making identification of CR a challenging task. Here, we put forward the hypothesis that CR carry a particular signature defined by networks of close proximity residues with high mutual information (MI), and that this signature can be applied to distinguish functional from other non-functional conserved residues. Using a data set of 434 Pfam families included in the catalytic site atlas (CSA) database, we tested this hypothesis and demonstrated that MI can complement amino acid conservation scores to detect CR. The Kullback-Leibler (KL) conservation measurement was shown to significantly outperform both the Shannon entropy and maximal frequency measurements. Residues in the proximity of catalytic sites were shown to be rich in shared MI. A structural proximity MI average score (termed pMI) was demonstrated to be a strong predictor for CR, thus confirming the proposed hypothesis. A structural proximity conservation average score (termed pC) was also calculated and demonstrated to carry distinct information from pMI. A catalytic likeliness score (Cls), combining the KL, pC and pMI measures, was shown to lead to significantly improved prediction accuracy. At a specificity of 0.90, the Cls method was found to have a sensitivity of 0.816. In summary, we demonstrate that networks of residues with high MI provide a distinct signature on CR and propose that such a signature should be present in other classes of functional residues where the requirement to maintain a particular function places limitations on the diversification of the structural environment along the course of evolution.

  17. Is a Responsive Default Mode Network Required for Successful Working Memory Task Performance?

    Science.gov (United States)

    Čeko, Marta; Gracely, John L.; Fitzcharles, Mary-Ann; Seminowicz, David A.; Schweinhardt, Petra

    2015-01-01

    In studies of cognitive processing using tasks with externally directed attention, regions showing increased (external-task-positive) and decreased or “negative” [default-mode network (DMN)] fMRI responses during task performance are dynamically responsive to increasing task difficulty. Responsiveness (modulation of fMRI signal by increasing load) has been linked directly to successful cognitive task performance in external-task-positive regions but not in DMN regions. To investigate whether a responsive DMN is required for successful cognitive performance, we compared healthy human subjects (n = 23) with individuals shown to have decreased DMN engagement (chronic pain patients, n = 28). Subjects performed a multilevel working-memory task (N-back) during fMRI. If a responsive DMN is required for successful performance, patients having reduced DMN responsiveness should show worsened performance; if performance is not reduced, their brains should show compensatory activation in external-task-positive regions or elsewhere. All subjects showed decreased accuracy and increased reaction times with increasing task level, with no significant group differences on either measure at any level. Patients had significantly reduced negative fMRI response (deactivation) of DMN regions (posterior cingulate/precuneus, medial prefrontal cortex). Controls showed expected modulation of DMN deactivation with increasing task difficulty. Patients showed significantly reduced modulation of DMN deactivation by task difficulty, despite their successful task performance. We found no evidence of compensatory neural recruitment in external-task-positive regions or elsewhere. Individual responsiveness of the external-task-positive ventrolateral prefrontal cortex, but not of DMN regions, correlated with task accuracy. These findings suggest that a responsive DMN may not be required for successful cognitive performance; a responsive external-task-positive network may be sufficient

  18. Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data.

    Science.gov (United States)

    Fu, Changhe; Deng, Su; Jin, Guangxu; Wang, Xinxin; Yu, Zu-Guo

    2017-09-21

    Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data. We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high. We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously

  19. Responses to olfactory signals reflect network structure of flower-visitor interactions.

    Science.gov (United States)

    Junker, Robert R; Höcherl, Nicole; Blüthgen, Nico

    2010-07-01

    1. Network analyses provide insights into the diversity and complexity of ecological interactions and have motivated conclusions about community stability and co-evolution. However, biological traits and mechanisms such as chemical signals regulating the interactions between individual species--the microstructure of a network--are poorly understood. 2. We linked the responses of receivers (flower visitors) towards signals (flower scent) to the structure of a highly diverse natural flower-insect network. For each interaction, we define link temperature--a newly developed metric--as the deviation of the observed interaction strength from neutrality, assuming that animals randomly interact with flowers. 3. Link temperature was positively correlated to the specific visitors' responses to floral scents, experimentally examined in a mobile olfactometer. Thus, communication between plants and consumers via phytochemical signals reflects a significant part of the microstructure in a complex network. Negative as well as positive responses towards floral scents contributed to these results, where individual experience was important apart from innate behaviour. 4. Our results indicate that: (1) biological mechanisms have a profound impact on the microstructure of complex networks that underlies the outcome of aggregate statistics, and (2) floral scents act as a filter, promoting the visitation of some flower visitors, but also inhibiting the visitation of others.

  20. Building Service Delivery Networks: Partnership Evolution Among Children's Behavioral Health Agencies in Response to New Funding.

    Science.gov (United States)

    Bunger, Alicia C; Doogan, Nathan J; Cao, Yiwen

    2014-12-01

    Meeting the complex needs of youth with behavioral health problems requires a coordinated network of community-based agencies. Although fiscal scarcity or retrenchment can limit coordinated services, munificence can stimulate service delivery partnerships as agencies expand programs, hire staff, and spend more time coordinating services. This study examines the 2-year evolution of referral and staff expertise sharing networks in response to substantial new funding for services within a regional network of children's mental health organizations. Quantitative network survey data were collected from directors of 22 nonprofit organizations that receive funding from a county government-based behavioral health service fund. Both referral and staff expertise sharing networks changed over time, but results of a stochastic actor-oriented model of network dynamics suggest the nature of this change varies for these networks. Agencies with higher numbers of referral and staff expertise sharing partners tend to maintain these ties and/or develop new relationships over the 2 years. Agencies tend to refer to agencies they trust, but trust was not associated with staff expertise sharing ties. However, agencies maintain or form staff expertise sharing ties with referral partners, or with organizations that provide similar services. In addition, agencies tend to reciprocate staff expertise sharing, but not referrals. Findings suggest that during periods of resource munificence and service expansion, behavioral health organizations build service delivery partnerships in complex ways that build upon prior collaborative history and coordinate services among similar types of providers. Referral partnerships can pave the way for future information sharing relationships.

  1. Microscale force response and morphology of tunable co-polymerized cytoskeleton networks

    Science.gov (United States)

    Ricketts, Shea; Yadav, Vikrant; Ross, Jennifer L.; Robertson-Anderson, Rae M.

    The cytoskeleton is largely comprised of actin and microtubules that entangle and crosslink to form complex networks and structures, giving rise to nonlinear multifunctional mechanics in cells. The relative concentrations of semiflexible actin filaments and rigid microtubules tune cytoskeleton function, allowing cells to move and divide while maintaining rigidity and resilience. To elucidate this complex tunability, we create in vitro composites of co-polymerized actin and microtubules with actin:microtubule molar ratios of 0:1-1:0. We use optical tweezers and confocal microscopy to characterize the nonlinear microscale force response and morphology of the composites. We optically drag a microsphere 30 μm through varying actin-microtubule networks at 10 μm/s and 20 μm/s, and measure the force the networks exerts to resist the strain and the force relaxation following strain. We use dual-color confocal microscopy to image distinctly-labeled filaments in the networks, and characterize the integration of actin and microtubules, network connectivity, and filament rigidity. We find that increasing the fraction of microtubules in networks non-monotonically increases elasticity and stiffness, and hinders force relaxation by suppressing network mobility and fluctuations. NSF CAREER Award (DMR-1255446), Scialog Collaborative Innovation Award funded by Research Corporation for Scientific Advancement (Grant No. 24192).

  2. Rapid identification and classification of Listeria spp. and serotype assignment of Listeria monocytogenes using fourier transform-infrared spectroscopy and artificial neural network analysis

    Science.gov (United States)

    The use of Fourier Transform-Infrared Spectroscopy (FT-IR) in conjunction with Artificial Neural Network software, NeuroDeveloper™ was examined for the rapid identification and classification of Listeria species and serotyping of Listeria monocytogenes. A spectral library was created for 245 strains...

  3. Identification of Wheat Varieties Using Matrix-assisted Laser Desorption/Ionisation Time-of-flight Mass Spectrometry and an Artificial Neural network

    DEFF Research Database (Denmark)

    Bloch, Helle Aagaard; Kesmir, Can; Petersen, Marianne Kjerstine

    1999-01-01

    A novel tool for variety identification of wheat (Triticum aestivum L,) has been developed: an artificial neural network (ANN) is used to classify the gliadin fraction analysed by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOFMS). The robustness...

  4. Communicative Dynamics and the Polyphony of Corporate Social Responsibility in the Network Society

    DEFF Research Database (Denmark)

    Castello, Itziar; Morsing, Mette; Schultz, Friederike

    2013-01-01

    This paper develops a media theoretical extension of the communicative view on corporate social responsibility by elaborating on the characteristics of network societies, arguing that new media increase the speed and connectivity, and lead to higher plurality and the potential polarization...... of reality constructions. We discuss the implications for corporate social responsibility of becoming more polyphonic and sketch the contours of “communicative legitimacy.” Finally, we present this special issue and develop some questions for future research....

  5. The grand illusion? corporate social responsibility in global garment production networks

    OpenAIRE

    Starmanns, M

    2010-01-01

    This PhD aims to generate a better understanding of corporate social responsibility (CSR) in global production networks. CSR is an umbrella term that deals with voluntary activities undertaken by companies and that indicate an ethos to act responsibly in society. This research focuses on CSR practices that aim towards improving working conditions in outsourced production factories by implementing so-called social standards, which often derive from core norms of the International Labour Organi...

  6. Network analysis of oyster transcriptome revealed a cascade of cellular responses during recovery after heat shock.

    Directory of Open Access Journals (Sweden)

    Lingling Zhang

    Full Text Available Oysters, as a major group of marine bivalves, can tolerate a wide range of natural and anthropogenic stressors including heat stress. Recent studies have shown that oysters pretreated with heat shock can result in induced heat tolerance. A systematic study of cellular recovery from heat shock may provide insights into the mechanism of acquired thermal tolerance. In this study, we performed the first network analysis of oyster transcriptome by reanalyzing microarray data from a previous study. Network analysis revealed a cascade of cellular responses during oyster recovery after heat shock and identified responsive gene modules and key genes. Our study demonstrates the power of network analysis in a non-model organism with poor gene annotations, which can lead to new discoveries that go beyond the focus on individual genes.

  7. Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

    DEFF Research Database (Denmark)

    Soares, Tiago; Faria, Pedro; Vale, Zita

    2014-01-01

    The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the wh......The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits...... the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity...

  8. Networks and learning: communities, practices and the metaphor of networks–a response

    Directory of Open Access Journals (Sweden)

    Chris Jones

    2004-12-01

    Full Text Available I am pleased to have the opportunity to react to Bruce Ingraham's response to my article ‘Networks and learning: communities, practices and the metaphor of networks' (Jones, 2004. It is rare to have a dialogue with someone who has taken the time and trouble to consider what you have written for a journal. All too often reviewing is a one-way process with the reviewer remaining anonymous. It is all the more pleasant to have a response to what you have written that gets to grips with some of the issues that the author also finds troubling. It is in that spirit that I write this reaction to Ingraham; it is an opportunity for me to develop some of the points he has identified as problematic in the original article. I want to concentrate on two main issues, firstly the network metaphor itself and secondly the usefulness of abstraction and representations of various types.

  9. Communicative dynamics and the polyphony of corporate social responsibility in the network society

    NARCIS (Netherlands)

    Castello, I.; Morsing, M.; Schultz, F.

    2013-01-01

    This paper develops a media theoretical extension of the communicative view on corporate social responsibility by elaborating on the characteristics of network societies, arguing that new media increase the speed and connectivity, and lead to higher plurality and the potential polarization of

  10. Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation

    CSIR Research Space (South Africa)

    Ngwangwa, HM

    2010-04-01

    Full Text Available -1 Journal of Terramechanics Volume 47, Issue 2, April 2010, Pages 97-111 Reconstruction of road defects and road roughness classification using vehicle responses with artificial neural networks simulation H.M. Ngwangwaa, P.S. Heynsa, , , F...

  11. Review of research on identification of factors influencing social response to technological risks

    International Nuclear Information System (INIS)

    Otway, H.J.

    1977-01-01

    Many countries are experiencing a period in which traditional values are being questioned. The social response to plans for further technological development has often taken the form of demands for a closer examination of the associated benefits and risks, and consideration of social values in public planning and decision processes. A theoretical framework for interdisciplinary risk assessment studies is presented to allow the balancing of complex technical data with measures of the corresponding social values. The cognitive limitations which affect rationality in intuitive decision-making are summarized as background for the introduction of formal decision methodologies. Methods for obtaining value measures are reviewed and an attitude-based method is developed in detail; this model allows identification of the relative importance of the technical, psychological and social factors which underlie attitudes and indicates which factors differentiate between social groups. A pilot application to nuclear power indicated that, for the subjects tested, attitudes pro and con were primarily determined by strongly differing beliefs about the benefits of nuclear power. Symbolic aspects of the nuclear controversy are reviewed, including psychological associations with nuclear weapons. It is suggested that nuclear energy is providing a forum to evaluate a wide range of social issues, perhaps playing a symbolic role in a dialogue about the shape and direction of a technologically-determined future. (author)

  12. Systematic classification and identification of noise spectra using perception-based neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A.; Kiss, S. (KFKI-Atomic Energy Research Inst., Budapest (Hungary). Applied Reactor Physics Lab.)

    1994-01-01

    A general framework for the detection of gradually developing changes in a noise generating system is presented. The procedure is based on a new learning algorithm developed for neural networks with dynamically building architecture. The method has been tested by using almost a thousand noise spectra recorded from different detector types and from different detector positions. This work is part of a larger project, aimed at developing a noise diagnostic expert system. (author).

  13. Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

    Directory of Open Access Journals (Sweden)

    Feng Li

    Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

  14. Accurate identification of RNA editing sites from primitive sequence with deep neural networks.

    Science.gov (United States)

    Ouyang, Zhangyi; Liu, Feng; Zhao, Chenghui; Ren, Chao; An, Gaole; Mei, Chuan; Bo, Xiaochen; Shu, Wenjie

    2018-04-16

    RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.

  15. Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

    Science.gov (United States)

    Schetinin, Vitaly; Jakaite, Livija; Nyah, Ndifreke; Novakovic, Dusica; Krzanowski, Wojtek

    2018-08-01

    The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

  16. Identification of some cross flow heat exchanger dynamic responses by measurement with low level binary pseudo-random input signals

    International Nuclear Information System (INIS)

    Corran, E.R.; Cummins, J.D.; Hopkinson, A.

    1964-02-01

    An experiment was performed to assess the usefulness of the binary cross-correlation method in the context of the identification problem. An auxiliary burner was excited with a discrete interval binary code and the response to the perturbation of the input heat was observed by recording the variations of the primary inlet, primary outlet and secondary outlet temperatures. The observations were analysed to yield cross-correlation functions and frequency responses were subsequently determined between primary inlet and primary outlet temperatures and also between primary inlet and secondary outlet temperatures. The analysis verified (1) that these dynamic responses of this cross flow heat exchanger may be predicted theoretically, (2) in so far as this heat exchanger is representative of the generality of plant, that the binary cross-correlation method provides adequate identification of plant dynamics for control purposes in environments where small input variations and low signal to noise ratio are obligatory. (author)

  17. Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.

    Directory of Open Access Journals (Sweden)

    Vipin Narang

    Full Text Available Human gene regulatory networks (GRN can be difficult to interpret due to a tangle of edges interconnecting thousands of genes. We constructed a general human GRN from extensive transcription factor and microRNA target data obtained from public databases. In a subnetwork of this GRN that is active during estrogen stimulation of MCF-7 breast cancer cells, we benchmarked automated algorithms for identifying core regulatory genes (transcription factors and microRNAs. Among these algorithms, we identified K-core decomposition, pagerank and betweenness centrality algorithms as the most effective for discovering core regulatory genes in the network evaluated based on previously known roles of these genes in MCF-7 biology as well as in their ability to explain the up or down expression status of up to 70% of the remaining genes. Finally, we validated the use of K-core algorithm for organizing the GRN in an easier to interpret layered hierarchy where more influential regulatory genes percolate towards the inner layers. The integrated human gene and miRNA network and software used in this study are provided as supplementary materials (S1 Data accompanying this manuscript.

  18. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    Science.gov (United States)

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  19. Prediction of energy demands using neural network with model identification by global optimization

    Energy Technology Data Exchange (ETDEWEB)

    Yokoyama, Ryohei; Wakui, Tetsuya; Satake, Ryoichi [Department of Mechanical Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531 (Japan)

    2009-02-15

    To operate energy supply plants properly from the viewpoints of stable energy supply, and energy and cost savings, it is important to predict energy demands accurately as basic conditions. Several methods of predicting energy demands have been proposed, and one of them is to use neural networks. Although local optimization methods such as gradient ones have conventionally been adopted in the back propagation procedure to identify the values of model parameters, they have the significant drawback that they can derive only local optimal solutions. In this paper, a global optimization method called ''Modal Trimming Method'' proposed for non-linear programming problems is adopted to identify the values of model parameters. In addition, the trend and periodic change are first removed from time series data on energy demand, and the converted data is used as the main input to a neural network. Furthermore, predicted values of air temperature and relative humidity are considered as additional inputs to the neural network, and their effect on the prediction of energy demand is investigated. This approach is applied to the prediction of the cooling demand in a building used for a bench mark test of a variety of prediction methods, and its validity and effectiveness are clarified. (author)

  20. Identification of unstable network modules reveals disease modules associated with the progression of Alzheimer's disease.

    Directory of Open Access Journals (Sweden)

    Masataka Kikuchi

    Full Text Available Alzheimer's disease (AD, the most common cause of dementia, is associated with aging, and it leads to neuron death. Deposits of amyloid β and aberrantly phosphorylated tau protein are known as pathological hallmarks of AD, but the underlying mechanisms have not yet been revealed. A high-throughput gene expression analysis previously showed that differentially expressed genes accompanying the progression of AD were more down-regulated than up-regulated in the later stages of AD. This suggested that the molecular networks and their constituent modules collapsed along with AD progression. In this study, by using gene expression profiles and protein interaction networks (PINs, we identified the PINs expressed in three brain regions: the entorhinal cortex (EC, hippocampus (HIP and superior frontal gyrus (SFG. Dividing the expressed PINs into modules, we examined the stability of the modules with AD progression and with normal aging. We found that in the AD modules, the constituent proteins, interactions and cellular functions were not maintained between consecutive stages through all brain regions. Interestingly, the modules were collapsed with AD progression, specifically in the EC region. By identifying the modules that were affected by AD pathology, we found the transcriptional regulation-associated modules that interact with the proteasome-associated module via UCHL5 hub protein, which is a deubiquitinating enzyme. Considering PINs as a system made of network modules, we found that the modules relevant to the transcriptional regulation are disrupted in the EC region, which affects the ubiquitin-proteasome system.

  1. Identification of efficient chelating acids responsible for Cesium, Strontium and Barium complexes solubilization in mixed wastes

    International Nuclear Information System (INIS)

    Borai, E.H.

    2007-01-01

    The present paper is focused to characterize the available multi dentate ligand species and their metal ion complexes of cesium (Cs), strontium (Sr) and barium (Ba) formed with the parent chelators, complexing agents and its fragment products in mixed waste filtrate. The developed separation programs of different ligands by different mobile phases were based on the decrease of the effective charge of the anionic species in a differentiated way hence, the retention times on the stationary phases (AS-4A and AS-12A) are changed. Ion chromatographic (IC) analysis of the metal complexes showed that the carboxylic acids that are responsible for solubilizing Cs, Sr and Ba in the waste filtrate are NTA, Citrate and PDCA, respectively. Therefore, the predominant metal complexes in the filtrate at high ph are Cs (I)-NTA, Sr (IT)-Citrate and Ba (IT)-PDCA. Identification of the metal ion complexes responsible for solubilizing Cs, Sr and Ba was applied on the fresh and aged waste filtrates, to monitor their chemical behavior, which leads to increased control of the waste treatment process. Although, concentration measurements of both fresh and aged filtrates confirmed that the degradation process has occurred mainly due to a harsh chemical environment, the concentration of Cs(I), Sr(II) and Ba(II) increased slightly in the aged filterate compared with the fresh filtrate. This is due to the decomposition and/or degradation of their metal complexes and hence leads to free metal ion species in the filtrate. These observations indicate that the organic content of mixed waste filtrate is dynamic and need continuous analytical monitoring

  2. Statistical identification of gene association by CID in application of constructing ER regulatory network

    Directory of Open Access Journals (Sweden)

    Lien Huang-Chun

    2009-03-01

    Full Text Available Abstract Background A variety of high-throughput techniques are now available for constructing comprehensive gene regulatory networks in systems biology. In this study, we report a new statistical approach for facilitating in silico inference of regulatory network structure. The new measure of association, coefficient of intrinsic dependence (CID, is model-free and can be applied to both continuous and categorical distributions. When given two variables X and Y, CID answers whether Y is dependent on X by examining the conditional distribution of Y given X. In this paper, we apply CID to analyze the regulatory relationships between transcription factors (TFs (X and their downstream genes (Y based on clinical data. More specifically, we use estrogen receptor α (ERα as the variable X, and the analyses are based on 48 clinical breast cancer gene expression arrays (48A. Results The analytical utility of CID was evaluated in comparison with four commonly used statistical methods, Galton-Pearson's correlation coefficient (GPCC, Student's t-test (STT, coefficient of determination (CoD, and mutual information (MI. When being compared to GPCC, CoD, and MI, CID reveals its preferential ability to discover the regulatory association where distribution of the mRNA expression levels on X and Y does not fit linear models. On the other hand, when CID is used to measure the association of a continuous variable (Y against a discrete variable (X, it shows similar performance as compared to STT, and appears to outperform CoD and MI. In addition, this study established a two-layer transcriptional regulatory network to exemplify the usage of CID, in combination with GPCC, in deciphering gene networks based on gene expression profiles from patient arrays. Conclusion CID is shown to provide useful information for identifying associations between genes and transcription factors of interest in patient arrays. When coupled with the relationships detected by GPCC, the

  3. Transcriptional regulatory network triggered by oxidative signals configures the early response mechanisms of japonica rice to chilling stress

    KAUST Repository

    Yun, Kil-Young; Park, Myoung Ryoul; Mohanty, Bijayalaxmi; Herath, Venura; Xu, Fuyu; Mauleon, Ramil; Wijaya, Edward; Bajic, Vladimir B.; Bruskiewich, Richard; de los Reyes, Benildo G

    2010-01-01

    -plant level analyses established a holistic view of chilling stress response mechanism of japonica rice. Early response regulatory network triggered by oxidative signals is critical for prolonged survival under sub-optimal temperature. Integration of stress

  4. An improved wavelet-Galerkin method for dynamic response reconstruction and parameter identification of shear-type frames

    Science.gov (United States)

    Bu, Haifeng; Wang, Dansheng; Zhou, Pin; Zhu, Hongping

    2018-04-01

    An improved wavelet-Galerkin (IWG) method based on the Daubechies wavelet is proposed for reconstructing the dynamic responses of shear structures. The proposed method flexibly manages wavelet resolution level according to excitation, thereby avoiding the weakness of the wavelet-Galerkin multiresolution analysis (WGMA) method in terms of resolution and the requirement of external excitation. IWG is implemented by this work in certain case studies, involving single- and n-degree-of-freedom frame structures subjected to a determined discrete excitation. Results demonstrate that IWG performs better than WGMA in terms of accuracy and computation efficiency. Furthermore, a new method for parameter identification based on IWG and an optimization algorithm are also developed for shear frame structures, and a simultaneous identification of structural parameters and excitation is implemented. Numerical results demonstrate that the proposed identification method is effective for shear frame structures.

  5. Identification of exploration strategies for electric power distribution network using simulated annealing; Identificao de estrategias de exploracao de redes de distribuicao de energia electrica utilizando simulated annealing

    Energy Technology Data Exchange (ETDEWEB)

    Pereira, Jorge; Saraiva, J. Tome; Leao, Maria Teresa Ponce de [Instituto de Engenharia de Sistemas e Computadores (INESC), Porto (Portugal). E-mail: jpereira@inescn.pt; jsaraiva@inescn.pt; mleao@inescn.pt

    1999-07-01

    This paper presents a model for identification of optimum strategies for electric power distribution networks, considering the aim of minimizing the active power losses. This objective can be attained by modifying the transformer connections or modification of the condenser groups on duty. By the other side, specifications of voltage ranges for each bar and current intensity limits for the branches are admitted, in order to obtain a more realistic the used model. The paper describes the the simulated annealing in order to surpass the mentioned difficulties. The application of the method to the problem resolution allows the identification solutions based on exact models. The application is illustrated with the results obtained by using a IEEE test network and a network based on real distribution with 645 bars.

  6. Identification of electrons in the ZEUS hadron-electron separator with neural networks

    International Nuclear Information System (INIS)

    Carstens, J.O.

    1994-10-01

    An electron finder for the ZEUS experiment was constructed, which is specialized to electrons in the momentum range 0.5 to 3.0 GeV/c. For the first time this electron finder connects the informations of the calorimeter with those of the hadron-electron separator (HES). For this purpose the electron finder was equipped with a neural network. The electron finder reached on a data set of photoproduction events with conversion electrons an efficiency and discriminance of E=(62.9±2.2±0.6)% and D=(91.4±0.8±1.1)%. From these two quantities it can be calculated that the electron finder the ratio electrons to background increases by the factoe E/(1-D)=7.4 (Signal amplification). For the comparison: A neural net, to which only calorimeter informations have been made available, reached at the same efficiency a signal amplification of 2.4. A simple cut in the variable HES-signal reaches a signal amplification of 6.3. Hints were given, how training data sets with electrons and hadrons of higher energies can be obtained. With such data sets the working range of the electron finder can be without problems extended to higher momenta. As preparation for the construction of the electron finder an introduction to the foundations of the mathematics and the application of neural networks was given. By means of examples different methods for the convergence improvement have been tested. Numerous representations mediate illustrative imaginations on the mathematical process of the training of neural networks

  7. Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification

    Directory of Open Access Journals (Sweden)

    Weikai Li

    2017-08-01

    Full Text Available Functional brain network (FBN has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L1-norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD from normal controls (NC based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.

  8. Gene regulatory networks in lactation: identification of global principles using bioinformatics

    Directory of Open Access Journals (Sweden)

    Pollard Katherine S

    2007-11-01

    Full Text Available Abstract Background The molecular events underlying mammary development during pregnancy, lactation, and involution are incompletely understood. Results Mammary gland microarray data, cellular localization data, protein-protein interactions, and literature-mined genes were integrated and analyzed using statistics, principal component analysis, gene ontology analysis, pathway analysis, and network analysis to identify global biological principles that govern molecular events during pregnancy, lactation, and involution. Conclusion Several key principles were derived: (1 nearly a third of the transcriptome fluctuates to build, run, and disassemble the lactation apparatus; (2 genes encoding the secretory machinery are transcribed prior to lactation; (3 the diversity of the endogenous portion of the milk proteome is derived from fewer than 100 transcripts; (4 while some genes are differentially transcribed near the onset of lactation, the lactation switch is primarily post-transcriptionally mediated; (5 the secretion of materials during lactation occurs not by up-regulation of novel genomic functions, but by widespread transcriptional suppression of functions such as protein degradation and cell-environment communication; (6 the involution switch is primarily transcriptionally mediated; and (7 during early involution, the transcriptional state is partially reverted to the pre-lactation state. A new hypothesis for secretory diminution is suggested – milk production gradually declines because the secretory machinery is not transcriptionally replenished. A comprehensive network of protein interactions during lactation is assembled and new regulatory gene targets are identified. Less than one fifth of the transcriptionally regulated nodes in this lactation network have been previously explored in the context of lactation. Implications for future research in mammary and cancer biology are discussed.

  9. Remodeling Pearson's Correlation for Functional Brain Network Estimation and Autism Spectrum Disorder Identification.

    Science.gov (United States)

    Li, Weikai; Wang, Zhengxia; Zhang, Limei; Qiao, Lishan; Shen, Dinggang

    2017-01-01

    Functional brain network (FBN) has been becoming an increasingly important way to model the statistical dependence among neural time courses of brain, and provides effective imaging biomarkers for diagnosis of some neurological or psychological disorders. Currently, Pearson's Correlation (PC) is the simplest and most widely-used method in constructing FBNs. Despite its advantages in statistical meaning and calculated performance, the PC tends to result in a FBN with dense connections. Therefore, in practice, the PC-based FBN needs to be sparsified by removing weak (potential noisy) connections. However, such a scheme depends on a hard-threshold without enough flexibility. Different from this traditional strategy, in this paper, we propose a new approach for estimating FBNs by remodeling PC as an optimization problem, which provides a way to incorporate biological/physical priors into the FBNs. In particular, we introduce an L 1 -norm regularizer into the optimization model for obtaining a sparse solution. Compared with the hard-threshold scheme, the proposed framework gives an elegant mathematical formulation for sparsifying PC-based networks. More importantly, it provides a platform to encode other biological/physical priors into the PC-based FBNs. To further illustrate the flexibility of the proposed method, we extend the model to a weighted counterpart for learning both sparse and scale-free networks, and then conduct experiments to identify autism spectrum disorders (ASD) from normal controls (NC) based on the constructed FBNs. Consequently, we achieved an 81.52% classification accuracy which outperforms the baseline and state-of-the-art methods.

  10. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

    Directory of Open Access Journals (Sweden)

    Ibnkahla Mohamed

    2003-01-01

    Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

  11. Identification of pp→K±π-+ K0(K0) events using artificial neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.; Polivka, G.; Vlachos, S.; Wendler, H.

    1995-01-01

    An artificial neural network classifier has been developed in order to separate on-line events containing a decaying neutral kaon. The proposed system performs better than classical selection methods in real-time applications. The biases due to the on-line selection are smaller than few per cent and are known. The system is robust against calibration variations and missing information. A hardware implementation of the algorithm allows an event selection in less than 40 μs and provides a considerable on-line rate reduction in tagged neutral kaon experiments. This classifier has been developed within the framework of the CPLEAR experiment. (orig.)

  12. Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks

    Directory of Open Access Journals (Sweden)

    Elke Roth-Mandutz

    2014-09-01

    Full Text Available The increasing deployment of small cells aimed at off-loading data traffic from macrocells in heterogeneous networks has resulted in a drastic increase in energy consumption in cellular networks. Energy consumption can be optimized in a selforganized way by adapting the number of active cells in response to the current traffic demand. In this paper we concentrate on the complex problem of how to identify small cells to be reactivated in situations where multiple cells are concurrently inactive. Solely based on the received signal strength, we present cell-specific patterns for the generation of unique cell fingerprints. The cell fingerprints of the deactivated cells are matched with measurements from a high data rate demanding mobile device to identify the most appropriate candidate. Our scheme results in a matching success rate of up to 100% to identify the best cell depending on the number of cells to be activated.

  13. How prepared are we? : The organizational network responses in two infectious disease outbreak scenarios in the Netherlands

    NARCIS (Netherlands)

    Kenis, P.N.; Raab, J.; Kraaij – Dirkzwager, Marleen; Timen, A.

    2017-01-01

    The paper will report results of a research project on the organizational network response to prevent or contain an outbreak of an infectious disease in the Netherlands. The paper is one of the first to present an attempt to conduct an ex ante evaluation of a response network in a likely future

  14. An identification method of orbit responses rooting in vibration analysis of rotor during touchdowns of active magnetic bearings

    Science.gov (United States)

    Liu, Tao; Lyu, Mindong; Wang, Zixi; Yan, Shaoze

    2018-02-01

    Identification of orbit responses can make the active protection operation more easily realize for active magnetic bearings (AMB) in case of touchdowns. This paper presents an identification method of the orbit responses rooting on signal processing of rotor displacements during touchdowns. The recognition method consists of two major steps. Firstly, the combined rub and bouncing is distinguished from the other orbit responses by the mathematical expectation of axis displacements of the rotor. Because when the combined rub and bouncing occurs, the rotor of AMB will not be always close to the touchdown bearings (TDB). Secondly, we recognize the pendulum vibration and the full rub by the Fourier spectrum of displacement in horizontal direction, as the frequency characteristics of the two responses are different. The principle of the whole identification algorithm is illustrated by two sets of signal generated by a dynamic model of the specific rotor-TDB system. The universality of the method is validated by other four sets of signal. Besides, the adaptability of noise is also tested by adding white noises with different strengths, and the result is promising. As the mathematical expectation and Discrete Fourier transform are major calculations of the algorithm, the calculation quantity of the algorithm is low, so it is fast, easily realized and embedded in the AMB controller, which has an important engineering value for the protection of AMBs during touchdowns.

  15. Real-Time Gas Identification by Analyzing the Transient Response of Capillary-Attached Conductive Gas Sensor

    Directory of Open Access Journals (Sweden)

    Behzad Bahraminejad

    2010-05-01

    Full Text Available In this study, the ability of the Capillary-attached conductive gas sensor (CGS in real-time gas identification was investigated. The structure of the prototype fabricated CGS is presented. Portions were selected from the beginning of the CGS transient response including the first 11 samples to the first 100 samples. Different feature extraction and classification methods were applied on the selected portions. Validation of methods was evaluated to study the ability of an early portion of the CGS transient response in target gas (TG identification. Experimental results proved that applying extracted features from an early part of the CGS transient response along with a classifier can distinguish short-chain alcohols from each other perfectly. Decreasing time of exposition in the interaction between target gas and sensing element improved the reliability of the sensor. Classification rate was also improved and time of identification was decreased. Moreover, the results indicated the optimum interval of the early transient response of the CGS for selecting portions to achieve the best classification rates.

  16. Network analysis of differential expression for the identification of disease-causing genes.

    Directory of Open Access Journals (Sweden)

    Daniela Nitsch

    Full Text Available Genetic studies (in particular linkage and association studies identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved. We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.

  17. Segmentation of histological images and fibrosis identification with a convolutional neural network.

    Science.gov (United States)

    Fu, Xiaohang; Liu, Tong; Xiong, Zhaohan; Smaill, Bruce H; Stiles, Martin K; Zhao, Jichao

    2018-05-16

    Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises 11 successive convolutional - rectified linear unit - batch normalization layers. It outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300,000) trainable parameters than the state-of-the-art, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment. Copyright © 2018. Published by Elsevier Ltd.

  18. Crack identification for reinforced concrete using PZT based smart rebar active sensing diagnostic network

    Science.gov (United States)

    Song, N. N.; Wu, F.

    2016-04-01

    An active sensing diagnostic system using PZT based smart rebar for SHM of RC structure has been currently under investigation. Previous test results showed that the system could detect the de-bond of concrete from reinforcement, and the diagnostic signals were increased exponentially with the de-bonding size. Previous study also showed that the smart rebar could function well like regular reinforcement to undertake tension stresses. In this study, a smart rebar network has been used to detect the crack damage of concrete based on guided waves. Experimental test has been carried out for the study. In the test, concrete beams with 2 reinforcements have been built. 8 sets of PZT elements were mounted onto the reinforcement bars in an optimized way to form an active sensing diagnostic system. A 90 kHz 5-cycle Hanning-windowed tone burst was used as input. Multiple cracks have been generated on the concrete structures. Through the guided bulk waves propagating in the structures from actuators and sensors mounted from different bars, crack damage could be detected clearly. Cases for both single and multiple cracks were tested. Different crack depths from the surface and different crack numbers have been studied. Test result shows that the amplitude of sensor output signals is deceased linearly with a propagating crack, and is decreased exponentially with increased crack numbers. From the study, the active sensing diagnostic system using PZT based smart rebar network shows a promising way to provide concrete crack damage information through the "talk" among sensors.

  19. Identification and characterization of sigma, a novel component of the Staphylococcus aureus stress and virulence responses.

    Directory of Open Access Journals (Sweden)

    Lindsey N Shaw

    Full Text Available S. aureus is a highly successful pathogen that is speculated to be the most common cause of human disease. The progression of disease in S. aureus is subject to multi-factorial regulation, in response to the environments encountered during growth. This adaptive nature is thought to be central to pathogenesis, and is the result of multiple regulatory mechanisms employed in gene regulation. In this work we describe the existence of a novel S. aureus regulator, an as yet uncharacterized ECF-sigma factor (sigma(S, that appears to be an important component of the stress and pathogenic responses of this organism. Using biochemical approaches we have shown that sigma(S is able to associates with core-RNAP, and initiate transcription from its own coding region. Using a mutant strain we determined that sigma(S is important for S. aureus survival during starvation, extended exposure to elevated growth temperatures, and Triton X-100 induced lysis. Coculture studies reveal that a sigma(S mutant is significantly outcompeted by its parental strain, which is only exacerbated during prolonged growth (7 days, or in the presence of stressor compounds. Interestingly, transcriptional analysis determined that under standard conditions, S. aureus SH1000 does not initiate expression of sigS. Assays performed hourly for 72 h revealed expression in typically background ranges. Analysis of a potential anti-sigma factor, encoded downstream of sigS, revealed it to have no obvious role in the upregulation of sigS expression. Using a murine model of septic arthritis, sigS-mutant infected animals lost significantly less weight, developed septic arthritis at significantly lower levels, and had increased survival rates. Studies of mounted immune responses reveal that sigS-mutant infected animals had significantly lower levels of IL-6, indicating only a weak immunological response. Finally, strains of S. aureus lacking sigS were far less able to undergo systemic dissemination

  20. Filling the gap between disaster preparedness and response networks of urban emergency management: Following the 2013 Seoul Floods.

    Science.gov (United States)

    Song, Minsun; Jung, Kyujin

    2015-01-01

    To examine the gap between disaster preparedness and response networks following the 2013 Seoul Floods in which the rapid transmission of disaster information and resources was impeded by severe changes of interorganizational collaboration networks. This research uses the 2013 Seoul Emergency Management Survey data that were collected before and after the floods, and total 94 organizations involving in coping with the floods were analyzed in bootstrap independent-sample t-test and social network analysis through UCINET 6 and STATA 12. The findings show that despite the primary network form that is more hierarchical, horizontal collaboration has been relatively invigorated in actual response. Also, interorganizational collaboration networks for response operations seem to be more flexible grounded on improvisation to coping with unexpected victims and damages. Local organizations under urban emergency management are recommended to tightly build a strong commitment for joint response operations through full-size exercises at the metropolitan level before a catastrophic event. Also, interorganizational emergency management networks need to be restructured by reflecting the actual response networks to reduce collaboration risk during a disaster. This research presents a critical insight into inverse thinking of the view designing urban emergency management networks and provides original evidences for filling the gap between previously coordinated networks for disaster preparedness and practical response operations after a disaster.

  1. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users.

    Science.gov (United States)

    Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl

    2016-09-01

    Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.

  2. Exploring the Neural Basis of Avatar Identification in Pathological Internet Gamers and of Self-Reflection in Pathological Social Network Users

    Science.gov (United States)

    Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl

    2016-01-01

    Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one’s avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one’s avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603

  3. Transcriptome-wide Identification and Expression Analysis of Brachypodium distachyon Transposons in Response to Viral Infection

    Directory of Open Access Journals (Sweden)

    Tuğba Gürkök

    2017-10-01

    Full Text Available Transposable elements (TEs are the most abundant group of genomic elements in plants that can be found in genic or intergenic regions of their host genomes. Several stimuli such as biotic or abiotic stress have roles in either activating their transcription or transposition. Here the effect of the Panicum mosaic virus (PMV and its satellite virus (SPMV infection on the transposon transcription of the Brachypodium distachyon model plant was investigated. To evaluate the transcription activity of TEs, transcriptomic data of mock and virus inoculated plants were compared. Our results indicate that major components of TEs are retroelements in all RNA-seq libraries. The number of transcribed TEs detected in mock inoculated plants is higher than virus inoculated plants. In comparison with mock inoculated plants 13% of the TEs showed at least two folds alteration upon PMV infection and 21% upon PMV+SPMV infection. Rather than inoculation with PMV alone inoculation with PMV+SPMV together also increased various TE encoding transcripts expressions. MuDR-N78C_OS encoding transcript was strongly up-regulated against both PMV and PMV+SPMV infection. The synergism generated by PMV and SPMV together enhanced TE transcripts expressions than PMV alone. It was observed that viral infection induced the transcriptional activity of several transposons. The results suggest that increased expressions of TEs might have a role in response to biotic stress in B. distachyon. Identification of TEs which are taking part in stress can serve useful information for functional genomics and designing novel breeding strategies in developing stress resistance crops.

  4. Hsf and Hsp gene families in Populus: genome-wide identification, organization and correlated expression during development and in stress responses.

    Science.gov (United States)

    Zhang, Jin; Liu, Bobin; Li, Jianbo; Zhang, Li; Wang, Yan; Zheng, Huanquan; Lu, Mengzhu; Chen, Jun

    2015-03-14

    Heat shock proteins (Hsps) are molecular chaperones that are involved in many normal cellular processes and stress responses, and heat shock factors (Hsfs) are the transcriptional activators of Hsps. Hsfs and Hsps are widely coordinated in various biological processes. Although the roles of Hsfs and Hsps in stress responses have been well characterized in Arabidopsis, their roles in perennial woody species undergoing various environmental stresses remain unclear. Here, a comprehensive identification and analysis of Hsf and Hsp families in poplars is presented. In Populus trichocarpa, we identified 42 paralogous pairs, 66.7% resulting from a whole genome duplication. The gene structure and motif composition are relatively conserved in each subfamily. Microarray and quantitative real-time RT-PCR analyses showed that most of the Populus Hsf and Hsp genes are differentially expressed upon exposure to various stresses. A coexpression network between Populus Hsf and Hsp genes was generated based on their expression. Coordinated relationships were validated by transient overexpression and subsequent qPCR analyses. The comprehensive analysis indicates that different sets of PtHsps are downstream of particular PtHsfs and provides a basis for functional studies aimed at revealing the roles of these families in poplar development and stress responses.

  5. Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes.

    Directory of Open Access Journals (Sweden)

    Josefin Skogsberg

    2008-03-01

    Full Text Available Despite the well-documented effects of plasma lipid lowering regimes halting atherosclerosis lesion development and reducing morbidity and mortality of coronary artery disease and stroke, the transcriptional response in the atherosclerotic lesion mediating these beneficial effects has not yet been carefully investigated. We performed transcriptional profiling at 10-week intervals in atherosclerosis-prone mice with human-like hypercholesterolemia and a genetic switch to lower plasma lipoproteins (Ldlr(-/-Apo(100/100Mttp(flox/flox Mx1-Cre. Atherosclerotic lesions progressed slowly at first, then expanded rapidly, and plateaued after advanced lesions formed. Analysis of lesion expression profiles indicated that accumulation of lipid-poor macrophages reached a point that led to the rapid expansion phase with accelerated foam-cell formation and inflammation, an interpretation supported by lesion histology. Genetic lowering of plasma cholesterol (e.g., lipoproteins at this point all together prevented the formation of advanced plaques and parallel transcriptional profiling of the atherosclerotic arterial wall identified 37 cholesterol-responsive genes mediating this effect. Validation by siRNA-inhibition in macrophages incubated with acetylated-LDL revealed a network of eight cholesterol-responsive atherosclerosis genes regulating cholesterol-ester accumulation. Taken together, we have identified a network of atherosclerosis genes that in response to plasma cholesterol-lowering prevents the formation of advanced plaques. This network should be of interest for the development of novel atherosclerosis therapies.

  6. Repeated exposure to media violence is associated with diminished response in an inhibitory frontolimbic network.

    Directory of Open Access Journals (Sweden)

    Christopher R Kelly

    Full Text Available BACKGROUND: Media depictions of violence, although often claimed to induce viewer aggression, have not been shown to affect the cortical networks that regulate behavior. METHODOLOGY/PRINCIPAL FINDINGS: Using functional magnetic resonance imaging (fMRI, we found that repeated exposure to violent media, but not to other equally arousing media, led to both diminished response in right lateral orbitofrontal cortex (right ltOFC and a decrease in right ltOFC-amygdala interaction. Reduced function in this network has been previously associated with decreased control over a variety of behaviors, including reactive aggression. Indeed, we found reduced right ltOFC responses to be characteristic of those subjects that reported greater tendencies toward reactive aggression. Furthermore, the violence-induced reduction in right ltOFC response coincided with increased throughput to behavior planning regions. CONCLUSIONS: These novel findings establish that even short-term exposure to violent media can result in diminished responsiveness of a network associated with behaviors such as reactive aggression.

  7. Plasticity of the MAPK signaling network in response to mechanical stress.

    Directory of Open Access Journals (Sweden)

    Andrea M Pereira

    Full Text Available Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity.

  8. Identification of the BRD1 interaction network and its impact on mental disorder risk

    DEFF Research Database (Denmark)

    Fryland, Tue; Christensen, Jane H; Pallesen, Jonatan

    2016-01-01

    and regulates expression of numerous genes, many of which are involved with brain development and susceptibility to mental disorders. Our findings indicate that BRD1 acts as a regulatory hub in a comprehensive schizophrenia risk network which plays a role in many brain regions throughout life, implicating e......Background: The bromodomain containing 1 (BRD1) gene has been implicated with transcriptional regulation, brain development, and susceptibility to schizophrenia and bipolar disorder. To advance the understanding of BRD1 and its role in mental disorders, we characterized the protein and chromatin...... functional molecular data were integrated with human genomic and transcriptomic data using available GWAS, exome-sequencing datasets as well as spatiotemporal transcriptomic datasets from the human brain. Results: We present several novel protein interactions of BRD1, including isoform-specific interactions...

  9. Identification of determinants for globalization of SMEs using multi-layer perceptron neural networks

    International Nuclear Information System (INIS)

    Draz, U.; Jahanzaib, M.; Asghar, G.

    2016-01-01

    SMEs (Small and Medium Sized Enterprises) sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network) has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that firm registered with OEM (Original Equipment Manufacturer) and size of firm are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies. (author)

  10. Networked Learning as a Process of Identification in the Intersection of collaborative Knowledge Building

    DEFF Research Database (Denmark)

    Østergaard, Rina; Sorensen, Elsebeth Korsgaard

    2011-01-01

    goals. In relation to objectives of competencies, attention is given to creative, innovative and action-oriented types. This paper addresses the role of OER in design of innovative, networked learning processes in diverse educational contexts of higher education, continuing education and in relation......Within professional education a recent shift has taken place. Professional education has moved from specialized education and update of professional knowledge, over competence-based education, to, recently, education with goals such as creativity, innovation, entrepreneur- and entrepreneurship....... OECDs Centre for Educational Research and Innovation (CERI) reveals this tendency. The core idea here is that education, in a very goal-directed way, supports initiatives, which – in turn – results in added-value to society. As such, the educational shift may be interpreted as related to societal change...

  11. Identification of long lived charginos in the CMS pixel tracker with a Deep Neural Network

    CERN Document Server

    Bury, Florian Joel J

    2017-01-01

    In many models of physics beyond the Standard Model (BSM), Dark Matter (DM) particles are part of some multiplet and could be produced from the decay of other states in the multiplet. An example of this is the production of SUSY neutralinos from chargino decays. The mass split between the two states could be very small, such that the DM partner could become long-living and decay far from the interaction region. In this report is investigated a scenario where the decay occurs before the strip tracker resulting on a short track hard to distinguish from the background and pile-up. The analysis used here focused on the energy deposit in the pixel tracker by using a deep neural network.

  12. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Directory of Open Access Journals (Sweden)

    Yong-Yeol Ahn

    Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  13. Identification of nonlinear dynamics in power plant components using neural networks

    International Nuclear Information System (INIS)

    Parlos, A.G.; Fernandez, B.; Tsai, W.K.

    1990-01-01

    Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

  14. Physiological modules for generating discrete and rhythmic movements: action identification by a dynamic recurrent neural network.

    Science.gov (United States)

    Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana M; Dan, Bernard; McIntyre, Joseph; Cheron, Guy

    2014-01-01

    In this study we employed a dynamic recurrent neural network (DRNN) in a novel fashion to reveal characteristics of control modules underlying the generation of muscle activations when drawing figures with the outstretched arm. We asked healthy human subjects to perform four different figure-eight movements in each of two workspaces (frontal plane and sagittal plane). We then trained a DRNN to predict the movement of the wrist from information in the EMG signals from seven different muscles. We trained different instances of the same network on a single movement direction, on all four movement directions in a single movement plane, or on all eight possible movement patterns and looked at the ability of the DRNN to generalize and predict movements for trials that were not included in the training set. Within a single movement plane, a DRNN trained on one movement direction was not able to predict movements of the hand for trials in the other three directions, but a DRNN trained simultaneously on all four movement directions could generalize across movement directions within the same plane. Similarly, the DRNN was able to reproduce the kinematics of the hand for both movement planes, but only if it was trained on examples performed in each one. As we will discuss, these results indicate that there are important dynamical constraints on the mapping of EMG to hand movement that depend on both the time sequence of the movement and on the anatomical constraints of the musculoskeletal system. In a second step, we injected EMG signals constructed from different synergies derived by the PCA in order to identify the mechanical significance of each of these components. From these results, one can surmise that discrete-rhythmic movements may be constructed from three different fundamental modules, one regulating the co-activation of all muscles over the time span of the movement and two others elliciting patterns of reciprocal activation operating in orthogonal directions.

  15. Nearest patch matching for color image segmentation supporting neural network classification in pulmonary tuberculosis identification

    Science.gov (United States)

    Rulaningtyas, Riries; Suksmono, Andriyan B.; Mengko, Tati L. R.; Saptawati, Putri

    2016-03-01

    Pulmonary tuberculosis is a deadly infectious disease which occurs in many countries in Asia and Africa. In Indonesia, many people with tuberculosis disease are examined in the community health center. Examination of pulmonary tuberculosis is done through sputum smear with Ziehl - Neelsen staining using conventional light microscope. The results of Ziehl - Neelsen staining will give effect to the appearance of tuberculosis (TB) bacteria in red color and sputum background in blue color. The first examination is to detect the presence of TB bacteria from its color, then from the morphology of the TB bacteria itself. The results of Ziehl - Neelsen staining in sputum smear give the complex color images, so that the clinicians have difficulty when doing slide examination manually because it is time consuming and needs highly training to detect the presence of TB bacteria accurately. The clinicians have heavy workload to examine many sputum smear slides from the patients. To assist the clinicians when reading the sputum smear slide, this research built computer aided diagnose with color image segmentation, feature extraction, and classification method. This research used K-means clustering with patch technique to segment digital sputum smear images which separated the TB bacteria images from the background images. This segmentation method gave the good accuracy 97.68%. Then, feature extraction based on geometrical shape of TB bacteria was applied to this research. The last step, this research used neural network with back propagation method to classify TB bacteria and non TB bacteria images in sputum slides. The classification result of neural network back propagation are learning time (42.69±0.02) second, the number of epoch 5000, error rate of learning 15%, learning accuracy (98.58±0.01)%, and test accuracy (96.54±0.02)%.

  16. DE-IDENTIFICATION TECHNIQUE FOR IOT WIRELESS SENSOR NETWORK PRIVACY PROTECTION

    Directory of Open Access Journals (Sweden)

    Yennun Huang

    2017-02-01

    Full Text Available As the IoT ecosystem becoming more and more mature, hardware and software vendors are trying create new value by connecting all kinds of devices together via IoT. IoT devices are usually equipped with sensors to collect data, and the data collected are transmitted over the air via different kinds of wireless connection. To extract the value of the data collected, the data owner may choose to seek for third-party help on data analysis, or even of the data to the public for more insight. In this scenario it is important to protect the released data from privacy leakage. Here we propose that differential privacy, as a de-identification technique, can be a useful approach to add privacy protection to the data released, as well as to prevent the collected from intercepted and decoded during over-the-air transmission. A way to increase the accuracy of the count queries performed on the edge cases in a synthetic database is also presented in this research.

  17. Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

    Science.gov (United States)

    Prilianti, K. R.; Hariyanto, S.; Natali, F. D. D.; Indriatmoko, Adhiwibawa, M. A. S.; Limantara, L.; Brotosudarmo, T. H. P.

    2016-04-01

    The development of rapid and automatic pigment characterization method become an important issue due to the fact that there are only less than 1% of plant pigments in the earth have been explored. In this research, a mathematical model based on artificial intelligence approach was developed to simplify and accelerate pigment characterization process from HPLC (high-performance liquid chromatography) procedure. HPLC is a widely used technique to separate and identify pigments in a mixture. Input of the model is chromatographic data from HPLC device and output of the model is a list of pigments which is the spectrum pattern is discovered in it. This model provides two dimensional (retention time and wavelength) fingerprints for pigment characterization which is proven to be more accurate than one dimensional fingerprint (fixed wavelength). Moreover, by mimicking interconnection of the neuron in the nervous systems of the human brain, the model have learning ability that could be replacing expert judgement on evaluating spectrum pattern. In the preprocessing step, principal component analysis (PCA) was used to reduce the huge dimension of the chromatographic data. The aim of this step is to simplify the model and accelerate the identification process. Six photosynthetic pigments i.e. zeaxantin, pheophytin a, α-carotene, β-carotene, lycopene and lutein could be well identified by the model with accuracy up to 85.33% and processing time less than 1 second.

  18. Factors influencing identification of and response to intimate partner violence: a survey of physicians and nurses

    Directory of Open Access Journals (Sweden)

    Wathen C Nadine

    2007-01-01

    Full Text Available Abstract Background Intimate partner violence against women (IPV has been identified as a serious public health problem. Although the health care system is an important site for identification and intervention, there have been challenges in determining how health care professionals can best address this issue in practice. We surveyed nurses and physicians in 2004 regarding their attitudes and behaviours with respect to IPV, including whether they routinely inquire about IPV, as well as potentially relevant barriers, facilitators, experiential, and practice-related factors. Methods A modified Dillman Tailored Design approach was used to survey 1000 nurses and 1000 physicians by mail in Ontario, Canada. Respondents were randomly selected from professional directories and represented practice areas pre-identified from the literature as those most likely to care for women at the point of initial IPV disclosure: family practice, obstetrics and gynecology, emergency care, maternal/newborn care, and public health. The survey instrument had a case-based scenario followed by 43 questions asking about behaviours and resources specific to woman abuse. Results In total, 931 questionnaires were returned; 597 by nurses (59.7% response rate and 328 by physicians (32.8% response rate. Overall, 32% of nurses and 42% of physicians reported routinely initiating the topic of IPV in practice. Principal components analysis identified eight constructs related to whether routine inquiry was conducted: preparedness, self-confidence, professional supports, abuse inquiry, practitioner consequences of asking, comfort following disclosure, practitioner lack of control, and practice pressures. Each construct was analyzed according to a number of related issues, including clinician training and experience with woman abuse, area of practice, and type of health care provider. Preparedness emerged as a key construct related to whether respondents routinely initiated the topic of

  19. A review of research on the identification of factors influencing the social response to technological risks

    International Nuclear Information System (INIS)

    Otway, H.J.

    1977-01-01

    Many countries are experiencing a period in which traditional values are being questioned; plans for technological development are being met by a variety of individual and group demands for a closer examination of the associated benefits and risks and a consideration of social values and concerns in the regulatory process. This has created conditions of conflict with some groups sponsoring proposals intended to fulfill perceived social needs while other groups, with different perceptions of society's needs, work actively in opposition. Only in recent years, as the rate of technological innovation has increased, has significant attention been given to the rapid rate of social and cultural change and the associated risks which have emerged. The degree to which an individual responds to these changes is related to his perception of their importance in his life; likewise, the repsonse to risk situations, on the individual and societal level, is based upon how the risks are perceived. The social response to the introduction of nuclear power provides an interesting case in point: here several studies have indicated that nuclear power plants might be considered ''safe''; however, nuclear power is nevertheless being opposed in many countries by those who perceive it as an unacceptable source of risk. This paper reviews research oriented toward understanding the response to technological risks through identification of the specific technical, social and psychological factors which influence their perception. Several such factors have been identified which relate to the risk object, the individual at risk, and the risk situation in which the individual encounters this object. Nuclear power was found to be characterised by more of the factors tending to increase risk perception than was any other technology. It is hypothesized that, in part due to its high ''risk visibility'' and an unconscious psychological coupling with the death imagery of nuclear weapons, nuclear energy

  20. Identification and Characterization of Sterol Acyltransferases Responsible for Steryl Ester Biosynthesis in Tomato

    Directory of Open Access Journals (Sweden)

    Juan A. Lara

    2018-05-01

    Full Text Available Steryl esters (SEs serve as a storage pool of sterols that helps to maintain proper levels of free sterols (FSs in cell membranes throughout plant growth and development, and participates in the recycling of FSs and fatty acids released from cell membranes in aging tissues. SEs are synthesized by sterol acyltransferases, a family of enzymes that catalyze the transfer of fatty acil groups to the hydroxyl group at C-3 position of the sterol backbone. Sterol acyltransferases are categorized into acyl-CoA:sterol acyltransferases (ASAT and phospholipid:sterol acyltransferases (PSAT depending on whether the fatty acyl donor substrate is a long-chain acyl-CoA or a phospolipid. Until now, only Arabidopsis ASAT and PSAT enzymes (AtASAT1 and AtPSAT1 have been cloned and characterized in plants. Here we report the identification, cloning, and functional characterization of the tomato (Solanum lycopersicum cv. Micro-Tom orthologs. SlPSAT1 and SlASAT1 were able to restore SE to wild type levels in the Arabidopsis psat1-2 and asat1-1 knock-out mutants, respectively. Expression of SlPSAT1 in the psat1-2 background also prevented the toxicity caused by an external supply of mevalonate and the early senescence phenotype observed in detached leaves of this mutant, whereas expression of SlASAT1 in the asat1-1 mutant revealed a clear substrate preference of the tomato enzyme for the sterol precursors cycloartenol and 24-methylene cycloartanol. Subcellular localization studies using fluorescently tagged SlPSAT1 and SlASAT1 proteins revealed that SlPSAT1 localize in cytoplasmic lipid droplets (LDs while, in contrast to the endoplasmic reticulum (ER localization of AtASAT1, SlASAT1 resides in the plasma membrane (PM. The possibility that PM-localized SlASAT1 may act catalytically in trans on their sterol substrates, which are presumably embedded in the ER membrane, is discussed. The widespread expression of SlPSAT1 and SlASAT1 genes in different tomato organs together

  1. Identification and Transcription Profiling of NDUFS8 in Aedes taeniorhynchus (Diptera: Culicidae): Developmental Regulation and Environmental Response

    Science.gov (United States)

    2014-12-18

    Identification and transcription profiling of NDUFS8 in Aedes taeniorhynchus (Diptera: Culicidae): developmental regulation and environmental response...7205 Email lmzhao@ufl.edu Abstract: The cDNA of a NADH dehydrogenase-ubiquinone Fe-S protein 8 subunit (NDUFS8) gene from Aedes (Ochlerotatus...information useful for developing dsRNA pesticide for mosquito control. Keywords: Aedes taeniorhynchus, AetNDUFS8, mRNA expression, development

  2. Prefrontal mediation of the reading network predicts intervention response in dyslexia.

    Science.gov (United States)

    Aboud, Katherine S; Barquero, Laura A; Cutting, Laurie E

    2018-04-01

    A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

  3. Incorporating price-responsive customers in day-ahead scheduling of smart distribution networks

    International Nuclear Information System (INIS)

    Mazidi, Mohammadreza; Monsef, Hassan; Siano, Pierluigi

    2016-01-01

    Highlights: • Proposing a model for incorporating price-responsive customers in day-ahead scheduling of smart distribution networks; this model provides a win–win situation. • Introducing a risk management model based on a bi-level information-gap decision theory and recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. • Utilizing mixed-integer linear programing formulation that is efficiently solved by commercial optimization software. - Abstract: Demand response and real-time pricing of electricity are key factors in a smart grid as they can increase economic efficiency and technical performances of power grids. This paper focuses on incorporating price-responsive customers in day-ahead scheduling of smart distribution networks under a dynamic pricing environment. A novel method is proposed and formulated as a tractable mixed integer linear programming optimization problem whose objective is to find hourly sale prices offered to customers, transactions (purchase/sale) with the wholesale market, commitment of distribution generation units, dispatch of battery energy storage systems and planning of interruptible loads in a way that the profit of the distribution network operator is maximized while customers’ benefit is guaranteed. To hedge distribution network operator against financial risk arising from uncertainty of wholesale market prices, a risk management model based on a bi-level information-gap decision theory is proposed. The proposed bi-level problem is solved by recasting it into its equivalent single-level robust optimization problem using Karush–Kuhn–Tucker optimality conditions. Performance of the proposed model is verified by applying it to a modified version of the IEEE 33-bus distribution test network. Numerical results demonstrate the effectiveness and efficiency of the proposed method.

  4. COMPARISON OF FREQUENCY RESPONSE AND NEURAL NETWORK TECHNIQUES FOR SYSTEM IDENTIFICATION OF AN ACTIVELY CONTROLLED STRUCTURE

    Directory of Open Access Journals (Sweden)

    DANIEL GÓMEZ PIZANO

    2011-01-01

    Full Text Available La identificación de sistemas es un método que puede ser utilizado para obtener las propiedades dinámicas de un sistema estructural integrado por sensores, actuadores yun algoritmo de control, sometido a diferentes tipos de excitación. Estas propiedades dinámicas son utilizadas en varios propósitos, tales como: Actualización de modelos, Monitoreo de salud estructural y Sistemas de control. En este artículo se presenta la identificación de una estructura con un sistema de control activo colocado en la parte superior por medio de la relación entre las señales de entrada (movimiento en la base y fuerza de control y la señal de salida (respuesta de la estructura. Para esto se utiliza la respuesta en frecuencia con funciones de transferencia y se compara con las relaciones no lineales obtenidas mediante Redes Neuronales Artificiales (RNA de una entrada-una salida (SISO y de múltiples entradas-una salida (MISO. Finalmente, se demuestra que la identificación del sistema estructural MISO con RNA presenta una mejor aproximación al sistema real que las obtenidas con la matriz de transferencia conformada a partir de funciones de transferencia.

  5. Identification of a gene module associated with BMD through the integration of network analysis and genome-wide association data.

    Science.gov (United States)

    Farber, Charles R

    2010-11-01

    Bone mineral density (BMD) is influenced by a complex network of gene interactions; therefore, elucidating the relationships between genes and how those genes, in turn, influence BMD is critical for developing a comprehensive understanding of osteoporosis. To investigate the role of transcriptional networks in the regulation of BMD, we performed a weighted gene coexpression network analysis (WGCNA) using microarray expression data on monocytes from young individuals with low or high BMD. WGCNA groups genes into modules based on patterns of gene coexpression. and our analysis identified 11 gene modules. We observed that the overall expression of one module (referred to as module 9) was significantly higher in the low-BMD group (p = .03). Module 9 was highly enriched for genes belonging to the immune system-related gene ontology (GO) category "response to virus" (p = 7.6 × 10(-11)). Using publically available genome-wide association study data, we independently validated the importance of module 9 by demonstrating that highly connected module 9 hubs were more likely, relative to less highly connected genes, to be genetically associated with BMD. This study highlights the advantages of systems-level analyses to uncover coexpression modules associated with bone mass and suggests that particular monocyte expression patterns may mediate differences in BMD. © 2010 American Society for Bone and Mineral Research.

  6. Responses to a self-presented suicide attempt in social media: a social network analysis.

    Science.gov (United States)

    Fu, King-Wa; Cheng, Qijin; Wong, Paul W C; Yip, Paul S F

    2013-01-01

    The self-presentation of suicidal acts in social media has become a public health concern. This article centers on a Chinese microblogger who posted a wrist-cutting picture that was widely circulated in Chinese social media in 2011. This exploratory study examines written reactions of a group of Chinese microbloggers exposed to the post containing a self-harming message and photo. In addition, we investigate the pattern of information diffusion via a social network. We systematically collected and analyzed 5,971 generated microblogs and the network of information diffusion. We found that a significant portion of written responses (36.6%) could help vulnerable netizens by providing peer-support and calls for help. These responses were reposted and diffused via an online social network with markedly more clusters of users--and at a faster pace-- than a set of randomly generated networks. We conclude that social media can be a double-edged sword: While it may contagiously affect others by spreading suicidal thoughts and acts, it may also play a positive role by assisting people at risk for suicide, providing rescue or support. More research is needed to learn how suicidally vulnerable people interact with online suicide information, and how we can effectively intervene.

  7. Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Federico Nuñez-Piña

    2018-01-01

    Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

  8. 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.

  9. Multi-stimulus-responsive shape-memory polymer nanocomposite network cross-linked by cellulose nanocrystals.

    Science.gov (United States)

    Liu, Ye; Li, Ying; Yang, Guang; Zheng, Xiaotong; Zhou, Shaobing

    2015-02-25

    In this study, we developed a thermoresponsive and water-responsive shape-memory polymer nanocomposite network by chemically cross-linking cellulose nanocrystals (CNCs) with polycaprolactone (PCL) and polyethylene glycol (PEG). The nanocomposite network was fully characterized, including the microstructure, cross-link density, water contact angle, water uptake, crystallinity, thermal properties, and static and dynamic mechanical properties. We found that the PEG[60]-PCL[40]-CNC[10] nanocomposite exhibited excellent thermo-induced and water-induced shape-memory effects in water at 37 °C (close to body temperature), and the introduction of CNC clearly improved the mechanical properties of the mixture of both PEG and PCL polymers with low molecular weights. In addition, Alamar blue assays based on osteoblasts indicated that the nanocomposites possessed good cytocompatibility. Therefore, this thermoresponsive and water-responsive shape-memory nanocomposite could be potentially developed into a new smart biomaterial.

  10. Photo-responsive liquid crystalline epoxy networks with exchangeable disulfide bonds

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yuzhan [Washington State Univ., Pullman, WA (United States); Zhang, Yuehong [Washington State Univ., Pullman, WA (United States); Rios, Orlando [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Keum, Jong K. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Kessler, Michael R. [Washington State Univ., Pullman, WA (United States); North Dakota State Univ., Fargo, ND (United States)

    2017-07-27

    The increasing demand for intelligent materials has driven the development of polymers with a variety of functionalities. However, combining multiple functionalities within one polymer is still challenging because of the difficulties encountered in coordinating different functional building blocks during fabrication. In this work, we demonstrate the fabrication of a multifunctional liquid crystalline epoxy network (LCEN) using the combination of thermotropic liquid crystals, photo-responsive azobenzene molecules, and exchangeable disulfide bonds. In addition to shape memory behavior enabled by the reversible liquid crystalline phase transition and photo-induced bending behavior resulting from the photo-responsive azobenzene molecules, the introduction of dynamic disulfide bonds into the LCEN resulted in a structurally dynamic network, allowing the reshaping, repairing, and recycling of the material.

  11. ABC optimized RBF network for classification of EEG signal for epileptic seizure identification

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar Satapathy

    2017-03-01

    Full Text Available The brain signals usually generate certain electrical signals that can be recorded and analyzed for detection in several brain disorder diseases. These small signals are expressly called as Electroencephalogram (EEG signals. This research work analyzes the epileptic disorder in human brain through EEG signal analysis by integrating the best attributes of Artificial Bee Colony (ABC and radial basis function networks (RBFNNs. We have used Discrete Wavelet Transform (DWT technique for extraction of potential features from the signal. In our study, for classification of these signals, in this paper, the RBFNNs have been trained by a modified version of ABC algorithm. In the modified ABC, the onlooker bees are selected based on binary tournament unlike roulette wheel selection of ABC. Additionally, kernels such as Gaussian, Multi-quadric, and Inverse-multi-quadric are used for measuring the effectiveness of the method in numerous mixtures of healthy segments, seizure-free segments, and seizure segments. Our experimental outcomes confirm that RBFNN with inverse-multi-quadric kernel trained with modified ABC is significantly better than RBFNNs with other kernels trained by ABC and modified ABC.

  12. Rapid detection and identification of pedestrian impacts using a distributed sensor network

    Science.gov (United States)

    Kim, Andrew C.; Chang, Fu-Kuo

    2005-05-01

    Pedestrian fatalities from automobile accidents often occur as a result of head injuries suffered from impacts with an automobile front end. Active pedestrian protection systems with proper pedestrian recognition algorithms can protect pedestrians from such head trauma. An investigation was conducted to assess the feasibility of using a network of piezoelectric sensors mounted on the front bumper beam of an automobile to discriminate between impacts with "pedestrian" and "non-pedestrian" objects. This information would be used to activate a safety device (e.g., external airbag or pop-up hood) to provide protection for the vulnerable pedestrian. An analytical foundation for the object-bumper impact problem will be presented, as well as the classical beam impact theory. The mechanical waves that propagate in the structure from an external impact contain a wealth of information about the specifics of a particular impact -- object mass, size, impact speed, etc. -- but most notably the object stiffness, which identifies the impacted object. Using the frequency content of the sensor signals, it can be shown that impacts with a "pedestrian" object of varying size, weight, and speed can be easily differentiated from impacts with other "non-pedestrian" objects. Simulation results will illustrate this phenomenon, and experimental tests will verify the results. A comprehensive series of impact tests were performed for validation, using both a stationary front bumper with a drop-pendulum impactor and a moving car with stationary impact objects. Results from both tests will be presented.

  13. Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    P. V. V. Kishore

    2018-01-01

    Full Text Available Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN. In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording and online (live performances, YouTube data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

  14. RNAi-Based Identification of Gene-Specific Nuclear Cofactor Networks Regulating Interleukin-1 Target Genes

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    Johanna Meier-Soelch

    2018-04-01

    Full Text Available The potent proinflammatory cytokine interleukin (IL-1 triggers gene expression through the NF-κB signaling pathway. Here, we investigated the cofactor requirements of strongly regulated IL-1 target genes whose expression is impaired in p65 NF-κB-deficient murine embryonic fibroblasts. By two independent small-hairpin (shRNA screens, we examined 170 genes annotated to encode nuclear cofactors for their role in Cxcl2 mRNA expression and identified 22 factors that modulated basal or IL-1-inducible Cxcl2 levels. The functions of 16 of these factors were validated for Cxcl2 and further analyzed for their role in regulation of 10 additional IL-1 target genes by RT-qPCR. These data reveal that each inducible gene has its own (quantitative requirement of cofactors to maintain basal levels and to respond to IL-1. Twelve factors (Epc1, H2afz, Kdm2b, Kdm6a, Mbd3, Mta2, Phf21a, Ruvbl1, Sin3b, Suv420h1, Taf1, and Ube3a have not been previously implicated in inflammatory cytokine functions. Bioinformatics analysis indicates that they are components of complex nuclear protein networks that regulate chromatin functions and gene transcription. Collectively, these data suggest that downstream from the essential NF-κB signal each cytokine-inducible target gene has further subtle requirements for individual sets of nuclear cofactors that shape its transcriptional activation profile.

  15. Network pharmacology-based identification of key pharmacological pathways of Yin-Huang-Qing-Fei capsule acting on chronic bronchitis.

    Science.gov (United States)

    Yu, Guohua; Zhang, Yanqiong; Ren, Weiqiong; Dong, Ling; Li, Junfang; Geng, Ya; Zhang, Yi; Li, Defeng; Xu, Haiyu; Yang, Hongjun

    2017-01-01

    For decades in China, the Yin-Huang-Qing-Fei capsule (YHQFC) has been widely used in the treatment of chronic bronchitis, with good curative effects. Owing to the complexity of traditional Chinese herbal formulas, the pharmacological mechanism of YHQFC remains unclear. To address this problem, a network pharmacology-based strategy was proposed in this study. At first, the putative target profile of YHQFC was predicted using MedChem Studio, based on structural and functional similarities of all available YHQFC components to the known drugs obtained from the DrugBank database. Then, an interaction network was constructed using links between putative YHQFC targets and known therapeutic targets of chronic bronchitis. Following the calculation of four topological features (degree, betweenness, closeness, and coreness) of each node in the network, 475 major putative targets of YHQFC and their topological importance were identified. In addition, a pathway enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes pathway database indicated that the major putative targets of YHQFC are significantly associated with various pathways involved in anti-inflammation processes, immune responses, and pathological changes caused by asthma. More interestingly, eight major putative targets of YHQFC (interleukin [IL]-3, IL-4, IL-5, IL-10, IL-13, FCER1G, CCL11, and EPX) were demonstrated to be associated with the inflammatory process that occurs during the progression of asthma. Finally, a molecular docking simulation was performed and the results exhibited that 17 pairs of chemical components and candidate YHQFC targets involved in asthma pathway had strong binding efficiencies. In conclusion, this network pharmacology-based investigation revealed that YHQFC may attenuate the inflammatory reaction of chronic bronchitis by regulating its candidate targets, which may be implicated in the major pathological processes of the asthma pathway.

  16. Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli.

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    Elisa M Tartaglia

    2015-02-01

    Full Text Available Persistent activity and match effects are widely regarded as neuronal correlates of short-term storage and manipulation of information, with the first serving active maintenance and the latter supporting the comparison between memory contents and incoming sensory information. The mechanistic and functional relationship between these two basic neurophysiological signatures of working memory remains elusive. We propose that match signals are generated as a result of transient changes in local network excitability brought about by persistent activity. Neurons more active will be more excitable, and thus more responsive to external inputs. Accordingly, network responses are jointly determined by the incoming stimulus and the ongoing pattern of persistent activity. Using a spiking model network, we show that this mechanism is able to reproduce most of the experimental phenomenology of match effects as exposed by single-cell recordings during delayed-response tasks. The model provides a unified, parsimonious mechanistic account of the main neuronal correlates of working memory, makes several experimentally testable predictions, and demonstrates a new functional role for persistent activity.

  17. Acoustic stimulation can induce a selective neural network response mediated by piezoelectric nanoparticles

    Science.gov (United States)

    Rojas, Camilo; Tedesco, Mariateresa; Massobrio, Paolo; Marino, Attilio; Ciofani, Gianni; Martinoia, Sergio; Raiteri, Roberto

    2018-06-01

    Objective. We aim to develop a novel non-invasive or minimally invasive method for neural stimulation to be applied in the study and treatment of brain (dys)functions and neurological disorders. Approach. We investigate the electrophysiological response of in vitro neuronal networks when subjected to low-intensity pulsed acoustic stimulation, mediated by piezoelectric nanoparticles adsorbed on the neuronal membrane. Main results. We show that the presence of piezoelectric barium titanate nanoparticles induces, in a reproducible way, an increase in network activity when excited by stationary ultrasound waves in the MHz regime. Such a response can be fully recovered when switching the ultrasound pulse off, depending on the generated pressure field amplitude, whilst it is insensitive to the duration of the ultrasound pulse in the range 0.5 s–1.5 s. We demonstrate that the presence of piezoelectric nanoparticles is necessary, and when applying the same acoustic stimulation to neuronal cultures without nanoparticles or with non-piezoelectric nanoparticles with the same size distribution, no network response is observed. Significance. We believe that our results open up an extremely interesting approach when coupled with suitable functionalization strategies of the nanoparticles in order to address specific neurons and/or brain areas and applied in vivo, thus enabling remote, non-invasive, and highly selective modulation of the activity of neuronal subpopulations of the central nervous system of mammalians.

  18. Biomarker Identification for Prostate Cancer and Lymph Node Metastasis from Microarray Data and Protein Interaction Network Using Gene Prioritization Method

    Directory of Open Access Journals (Sweden)

    Carlos Roberto Arias

    2012-01-01

    Full Text Available Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF. The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

  19. Recurrent-neural-network-based identification of a cascade hydraulic actuator for closed-loop automotive power transmission control

    International Nuclear Information System (INIS)

    You, Seung Han; Hahn, Jin Oh

    2012-01-01

    By virtue of its ease of operation compared with its conventional manual counterpart, automatic transmissions are commonly used as automotive power transmission control system in today's passenger cars. In accordance with this trend, research efforts on closed-loop automatic transmission controls have been extensively carried out to improve ride quality and fuel economy. State-of-the-art power transmission control algorithms may have limitations in performance because they rely on the steady-state characteristics of the hydraulic actuator rather than fully exploit its dynamic characteristics. Since the ultimate viability of closed-loop power transmission control is dominated by precise pressure control at the level of hydraulic actuator, closed-loop control can potentially attain superior efficacy in case the hydraulic actuator can be easily incorporated into model-based observer/controller design. In this paper, we propose to use a recurrent neural network (RNN) to establish a nonlinear empirical model of a cascade hydraulic actuator in a passenger car automatic transmission, which has potential to be easily incorporated in designing observers and controllers. Experimental analysis is performed to grasp key system characteristics, based on which a nonlinear system identification procedure is carried out. Extensive experimental validation of the established model suggests that it has superb one-step-ahead prediction capability over appropriate frequency range, making it an attractive approach for model-based observer/controller design