WorldWideScience

Sample records for network classifiers constructed

  1. Constructing and Classifying Email Networks from Raw Forensic Images

    Science.gov (United States)

    2016-09-01

    AUC value will be closer to 1. Figure 2.6 compares 3 different ROC curves. Figure 2.6. ROC curves compared. The dashed black curve at the top has the...main difference was the computation time. For example, on a 1275-node jazz musician network, the fast algorithm ran to completion in about one...königsberg bridges,” Scientific American , vol. 189, no. 1, pp. 66–70, 1953. [8] N. Biggs, E. K. Lloyd, and R. J. Wilson, Graph Theory, 1736-1936. Great

  2. Automatic construction of a recurrent neural network based classifier for vehicle passage detection

    Science.gov (United States)

    Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

    2017-03-01

    Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

  3. Design of Robust Neural Network Classifiers

    DEFF Research Database (Denmark)

    Larsen, Jan; Andersen, Lars Nonboe; Hintz-Madsen, Mads

    1998-01-01

    This paper addresses a new framework for designing robust neural network classifiers. The network is optimized using the maximum a posteriori technique, i.e., the cost function is the sum of the log-likelihood and a regularization term (prior). In order to perform robust classification, we present...... a modified likelihood function which incorporates the potential risk of outliers in the data. This leads to the introduction of a new parameter, the outlier probability. Designing the neural classifier involves optimization of network weights as well as outlier probability and regularization parameters. We...... suggest to adapt the outlier probability and regularisation parameters by minimizing the error on a validation set, and a simple gradient descent scheme is derived. In addition, the framework allows for constructing a simple outlier detector. Experiments with artificial data demonstrate the potential...

  4. Arabic Handwriting Recognition Using Neural Network Classifier

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

  5. Optical Neural Network Classifier Architectures

    National Research Council Canada - National Science Library

    Getbehead, Mark

    1998-01-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air...

  6. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Neural Network Classifiers for Local Wind Prediction.

    Science.gov (United States)

    Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

    2004-05-01

    This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

  8. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  9. Revealing effective classifiers through network comparison

    Science.gov (United States)

    Gallos, Lazaros K.; Fefferman, Nina H.

    2014-11-01

    The ability to compare complex systems can provide new insight into the fundamental nature of the processes captured, in ways that are otherwise inaccessible to observation. Here, we introduce the n-tangle method to directly compare two networks for structural similarity, based on the distribution of edge density in network subgraphs. We demonstrate that this method can efficiently introduce comparative analysis into network science and opens the road for many new applications. For example, we show how the construction of a “phylogenetic tree” across animal taxa according to their social structure can reveal commonalities in the behavioral ecology of the populations, or how students create similar networks according to the University size. Our method can be expanded to study many additional properties, such as network classification, changes during time evolution, convergence of growth models, and detection of structural changes during damage.

  10. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  11. Neural network classifier of attacks in IP telephony

    Science.gov (United States)

    Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

    2014-05-01

    Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

  12. Classifying emotion in Twitter using Bayesian network

    Science.gov (United States)

    Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

    2018-03-01

    Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

  13. Feature selection for Bayesian network classifiers using the MDL-FS score

    NARCIS (Netherlands)

    Drugan, Madalina M.; Wiering, Marco A.

    When constructing a Bayesian network classifier from data, the more or less redundant features included in a dataset may bias the classifier and as a consequence may result in a relatively poor classification accuracy. In this paper, we study the problem of selecting appropriate subsets of features

  14. A convolutional neural network neutrino event classifier

    International Nuclear Information System (INIS)

    Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

    2016-01-01

    Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

  15. Using Neural Networks to Classify Digitized Images of Galaxies

    Science.gov (United States)

    Goderya, S. N.; McGuire, P. C.

    2000-12-01

    Automated classification of Galaxies into Hubble types is of paramount importance to study the large scale structure of the Universe, particularly as survey projects like the Sloan Digital Sky Survey complete their data acquisition of one million galaxies. At present it is not possible to find robust and efficient artificial intelligence based galaxy classifiers. In this study we will summarize progress made in the development of automated galaxy classifiers using neural networks as machine learning tools. We explore the Bayesian linear algorithm, the higher order probabilistic network, the multilayer perceptron neural network and Support Vector Machine Classifier. The performance of any machine classifier is dependant on the quality of the parameters that characterize the different groups of galaxies. Our effort is to develop geometric and invariant moment based parameters as input to the machine classifiers instead of the raw pixel data. Such an approach reduces the dimensionality of the classifier considerably, and removes the effects of scaling and rotation, and makes it easier to solve for the unknown parameters in the galaxy classifier. To judge the quality of training and classification we develop the concept of Mathews coefficients for the galaxy classification community. Mathews coefficients are single numbers that quantify classifier performance even with unequal prior probabilities of the classes.

  16. Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers

    NARCIS (Netherlands)

    Bolt, J.H.; van der Gaag, L.C.

    Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological structure, which are tailored to classifying data instances into multiple dimensions. Like more traditional classifiers, multi-dimensional classifiers are typically learned from data and may include

  17. Ecological network analysis: network construction

    NARCIS (Netherlands)

    Fath, B.D.; Scharler, U.M.; Ulanowicz, R.E.; Hannon, B.

    2007-01-01

    Ecological network analysis (ENA) is a systems-oriented methodology to analyze within system interactions used to identify holistic properties that are otherwise not evident from the direct observations. Like any analysis technique, the accuracy of the results is as good as the data available, but

  18. Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

    Science.gov (United States)

    Geng, S.; Ren, G.; Ogihara, M.

    2017-05-01

    Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

  19. Classifying Radio Galaxies with the Convolutional Neural Network

    International Nuclear Information System (INIS)

    Aniyan, A. K.; Thorat, K.

    2017-01-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  20. Classifying Radio Galaxies with the Convolutional Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Aniyan, A. K.; Thorat, K. [Department of Physics and Electronics, Rhodes University, Grahamstown (South Africa)

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff–Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ∼200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  1. Classifying Radio Galaxies with the Convolutional Neural Network

    Science.gov (United States)

    Aniyan, A. K.; Thorat, K.

    2017-06-01

    We present the application of a deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks (CNN). In this study, we have taken the case of the Fanaroff-Riley (FR) class of radio galaxies as well as radio galaxies with bent-tailed morphology. We have used archival data from the Very Large Array (VLA)—Faint Images of the Radio Sky at Twenty Centimeters survey and existing visually classified samples available in the literature to train a neural network for morphological classification of these categories of radio sources. Our training sample size for each of these categories is ˜200 sources, which has been augmented by rotated versions of the same. Our study shows that CNNs can classify images of the FRI and FRII and bent-tailed radio galaxies with high accuracy (maximum precision at 95%) using well-defined samples and a “fusion classifier,” which combines the results of binary classifications, while allowing for a mechanism to find sources with unusual morphologies. The individual precision is highest for bent-tailed radio galaxies at 95% and is 91% and 75% for the FRI and FRII classes, respectively, whereas the recall is highest for FRI and FRIIs at 91% each, while the bent-tailed class has a recall of 79%. These results show that our results are comparable to that of manual classification, while being much faster. Finally, we discuss the computational and data-related challenges associated with the morphological classification of radio galaxies with CNNs.

  2. High voltage power network construction

    CERN Document Server

    Harker, Keith

    2018-01-01

    This book examines the key requirements, considerations, complexities and constraints relevant to the task of high voltage power network construction, from design, finance, contracts and project management to installation and commissioning, with the aim of providing an overview of the holistic end to end construction task in a single volume.

  3. Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

    Science.gov (United States)

    Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

    2018-06-13

    Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

  4. Generating prior probabilities for classifiers of brain tumours using belief networks

    Directory of Open Access Journals (Sweden)

    Arvanitis Theodoros N

    2007-09-01

    Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

  5. Classifying images using restricted Boltzmann machines and convolutional neural networks

    Science.gov (United States)

    Zhao, Zhijun; Xu, Tongde; Dai, Chenyu

    2017-07-01

    To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

  6. Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

    Science.gov (United States)

    Wang, Leilei; Cheng, Jinyong

    2018-03-01

    Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

  7. Classifying magnetic resonance image modalities with convolutional neural networks

    Science.gov (United States)

    Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis

    2018-02-01

    Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.

  8. Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search

    NARCIS (Netherlands)

    Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.

    2001-01-01

    In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.

  9. Sequential Construction of Costly Networks

    Energy Technology Data Exchange (ETDEWEB)

    Gutfraind, Alexander [Los Alamos National Laboratory

    2011-01-01

    Natural disasters or attacks often disrupt infrastructure networks requiring a costly recovery. This motivates an optimization problem where the objecitve is to construct the nodes of a graph G(V;E), and the cost of each node is dependent on the number of its neighbors previously constructed, or more generally, any properties of the previously-completed subgraph. In this optimization problem the objective is to find a permutation of the nodes which results in the least construction cost. We prove that in the case where the cost of nodes is a convex function in the number of neighbors, the optimal construction sequence is to start at a single node and move outwards. We also introduce algorithms and heuristics for solving various instances of the problem. Those methods can be applied to help reduce the cost of recovering from disasters as well as to plan the deployment of new network infrastructure.

  10. Machine learning classifier using abnormal brain network topological metrics in major depressive disorder.

    Science.gov (United States)

    Guo, Hao; Cao, Xiaohua; Liu, Zhifen; Li, Haifang; Chen, Junjie; Zhang, Kerang

    2012-12-05

    Resting state functional brain networks have been widely studied in brain disease research. However, it is currently unclear whether abnormal resting state functional brain network metrics can be used with machine learning for the classification of brain diseases. Resting state functional brain networks were constructed for 28 healthy controls and 38 major depressive disorder patients by thresholding partial correlation matrices of 90 regions. Three nodal metrics were calculated using graph theory-based approaches. Nonparametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in six different algorithms. We used statistical significance as the threshold for selecting features and measured the accuracies of six classifiers with different number of features. A sensitivity analysis method was used to evaluate the importance of different features. The result indicated that some of the regions exhibited significantly abnormal nodal centralities, including the limbic system, basal ganglia, medial temporal, and prefrontal regions. Support vector machine with radial basis kernel function algorithm and neural network algorithm exhibited the highest average accuracy (79.27 and 78.22%, respectively) with 28 features (Pdisorder is associated with abnormal functional brain network topological metrics and statistically significant nodal metrics can be successfully used for feature selection in classification algorithms.

  11. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  12. Functional Interaction Network Construction and Analysis for Disease Discovery.

    Science.gov (United States)

    Wu, Guanming; Haw, Robin

    2017-01-01

    Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

  13. Strategies for Transporting Data Between Classified and Unclassified Networks

    Science.gov (United States)

    2016-03-01

    datagram protocol (UDP) must be used. The UDP is typically used when speed is a higher priority than data integrity, such as in music or video streaming ...and the exit point of data are separate and can be tightly controlled. This does effectively prevent the comingling of data and is used in industry to...perform functions such as streaming video and audio from secure to insecure networks (ref. 1). A second disadvantage lies in the fact that the

  14. Classifying medical relations in clinical text via convolutional neural networks.

    Science.gov (United States)

    He, Bin; Guan, Yi; Dai, Rui

    2018-05-16

    Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.

  15. A deep convolutional neural network model to classify heartbeats.

    Science.gov (United States)

    Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

    2017-10-01

    The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

    Science.gov (United States)

    Ornostay, Anna; Cowie, Andrew M; Hindle, Matthew; Baker, Christopher J O; Martyniuk, Christopher J

    2013-12-01

    The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17β-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species. © 2013.

  17. Construction of the NIFS campus information network, NIFS-LAN

    Energy Technology Data Exchange (ETDEWEB)

    Tsuda, Kenzo; Yamamoto, Takashi; Kato, Takeo; Nakamura, Osamu; Watanabe, Kunihiko; Watanabe, Reiko; Tsugawa, Kazuko; Kamimura, Tetsuo

    2000-10-01

    The advanced NIFS campus information network, NIFS-LAN, was designed and constructed as an informational infrastructure in 1996, 1997 and 1998 fiscal year. NIFS-LAN was composed of three autonomous clusters classified from research purpose; Research Information cluster, Large Helical Device Experiment cluster and Large-Scale Computer Simulation Research cluster. Many ATM(Asychronous Transfer Mode) switching systems and switching equipments were used for NIFS-LAN. Here, the outline of NIFS-LAN is described. (author)

  18. A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

    Directory of Open Access Journals (Sweden)

    M. Njah

    2017-06-01

    Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

  19. CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Smita K Magdum

    2017-10-01

    Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

  20. A Critical Evaluation of Network and Pathway-Based Classifiers for Outcome Prediction in Breast Cancer

    NARCIS (Netherlands)

    C. Staiger (Christine); S. Cadot; R Kooter; M. Dittrich (Marcus); T. Müller (Tobias); G.W. Klau (Gunnar); L.F.A. Wessels (Lodewyk)

    2012-01-01

    htmlabstractRecently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically

  1. FERAL : Network-based classifier with application to breast cancer outcome prediction

    NARCIS (Netherlands)

    Allahyar, A.; De Ridder, J.

    2015-01-01

    Motivation: Breast cancer outcome prediction based on gene expression profiles is an important strategy for personalize patient care. To improve performance and consistency of discovered markers of the initial molecular classifiers, network-based outcome prediction methods (NOPs) have been proposed.

  2. Feature extraction using convolutional neural network for classifying breast density in mammographic images

    Science.gov (United States)

    Thomaz, Ricardo L.; Carneiro, Pedro C.; Patrocinio, Ana C.

    2017-03-01

    Breast cancer is the leading cause of death for women in most countries. The high levels of mortality relate mostly to late diagnosis and to the direct proportionally relationship between breast density and breast cancer development. Therefore, the correct assessment of breast density is important to provide better screening for higher risk patients. However, in modern digital mammography the discrimination among breast densities is highly complex due to increased contrast and visual information for all densities. Thus, a computational system for classifying breast density might be a useful tool for aiding medical staff. Several machine-learning algorithms are already capable of classifying small number of classes with good accuracy. However, machinelearning algorithms main constraint relates to the set of features extracted and used for classification. Although well-known feature extraction techniques might provide a good set of features, it is a complex task to select an initial set during design of a classifier. Thus, we propose feature extraction using a Convolutional Neural Network (CNN) for classifying breast density by a usual machine-learning classifier. We used 307 mammographic images downsampled to 260x200 pixels to train a CNN and extract features from a deep layer. After training, the activation of 8 neurons from a deep fully connected layer are extracted and used as features. Then, these features are feedforward to a single hidden layer neural network that is cross-validated using 10-folds to classify among four classes of breast density. The global accuracy of this method is 98.4%, presenting only 1.6% of misclassification. However, the small set of samples and memory constraints required the reuse of data in both CNN and MLP-NN, therefore overfitting might have influenced the results even though we cross-validated the network. Thus, although we presented a promising method for extracting features and classifying breast density, a greater database is

  3. Neural network construction via back-propagation

    International Nuclear Information System (INIS)

    Burwick, T.T.

    1994-06-01

    A method is presented that combines back-propagation with multi-layer neural network construction. Back-propagation is used not only to adjust the weights but also the signal functions. Going from one network to an equivalent one that has additional linear units, the non-linearity of these units and thus their effective presence is then introduced via back-propagation (weight-splitting). The back-propagated error causes the network to include new units in order to minimize the error function. We also show how this formalism allows to escape local minima

  4. Constructing level-2 phylogenetic networks from triplets

    OpenAIRE

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

    2009-01-01

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

  5. Neural-network classifiers for automatic real-world aerial image recognition

    Science.gov (United States)

    Greenberg, Shlomo; Guterman, Hugo

    1996-08-01

    We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

  6. Infrared dim moving target tracking via sparsity-based discriminative classifier and convolutional network

    Science.gov (United States)

    Qian, Kun; Zhou, Huixin; Wang, Bingjian; Song, Shangzhen; Zhao, Dong

    2017-11-01

    Infrared dim and small target tracking is a great challenging task. The main challenge for target tracking is to account for appearance change of an object, which submerges in the cluttered background. An efficient appearance model that exploits both the global template and local representation over infrared image sequences is constructed for dim moving target tracking. A Sparsity-based Discriminative Classifier (SDC) and a Convolutional Network-based Generative Model (CNGM) are combined with a prior model. In the SDC model, a sparse representation-based algorithm is adopted to calculate the confidence value that assigns more weights to target templates than negative background templates. In the CNGM model, simple cell feature maps are obtained by calculating the convolution between target templates and fixed filters, which are extracted from the target region at the first frame. These maps measure similarities between each filter and local intensity patterns across the target template, therefore encoding its local structural information. Then, all the maps form a representation, preserving the inner geometric layout of a candidate template. Furthermore, the fixed target template set is processed via an efficient prior model. The same operation is applied to candidate templates in the CNGM model. The online update scheme not only accounts for appearance variations but also alleviates the migration problem. At last, collaborative confidence values of particles are utilized to generate particles' importance weights. Experiments on various infrared sequences have validated the tracking capability of the presented algorithm. Experimental results show that this algorithm runs in real-time and provides a higher accuracy than state of the art algorithms.

  7. Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

    Science.gov (United States)

    Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

    2015-10-01

    Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

  8. Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.

    Science.gov (United States)

    Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin

    2017-01-01

    Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.

  9. Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

    Directory of Open Access Journals (Sweden)

    Demi Soetraprawata

    2013-06-01

    Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.

  10. ELHnet: a convolutional neural network for classifying cochlear endolymphatic hydrops imaged with optical coherence tomography.

    Science.gov (United States)

    Liu, George S; Zhu, Michael H; Kim, Jinkyung; Raphael, Patrick; Applegate, Brian E; Oghalai, John S

    2017-10-01

    Detection of endolymphatic hydrops is important for diagnosing Meniere's disease, and can be performed non-invasively using optical coherence tomography (OCT) in animal models as well as potentially in the clinic. Here, we developed ELHnet, a convolutional neural network to classify endolymphatic hydrops in a mouse model using learned features from OCT images of mice cochleae. We trained ELHnet on 2159 training and validation images from 17 mice, using only the image pixels and observer-determined labels of endolymphatic hydrops as the inputs. We tested ELHnet on 37 images from 37 mice that were previously not used, and found that the neural network correctly classified 34 of the 37 mice. This demonstrates an improvement in performance from previous work on computer-aided classification of endolymphatic hydrops. To the best of our knowledge, this is the first deep CNN designed for endolymphatic hydrops classification.

  11. Automating the construction of scene classifiers for content-based video retrieval

    NARCIS (Netherlands)

    Khan, L.; Israël, Menno; Petrushin, V.A.; van den Broek, Egon; van der Putten, Peter

    2004-01-01

    This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a

  12. Construction of Pancreatic Cancer Classifier Based on SVM Optimized by Improved FOA

    Science.gov (United States)

    Ma, Xiaoqi

    2015-01-01

    A novel method is proposed to establish the pancreatic cancer classifier. Firstly, the concept of quantum and fruit fly optimal algorithm (FOA) are introduced, respectively. Then FOA is improved by quantum coding and quantum operation, and a new smell concentration determination function is defined. Finally, the improved FOA is used to optimize the parameters of support vector machine (SVM) and the classifier is established by optimized SVM. In order to verify the effectiveness of the proposed method, SVM and other classification methods have been chosen as the comparing methods. The experimental results show that the proposed method can improve the classifier performance and cost less time. PMID:26543867

  13. Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

    Science.gov (United States)

    Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

    2018-05-01

    Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

  14. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Bach Phi Duong

    2018-04-01

    Full Text Available The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs. The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  15. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

    Science.gov (United States)

    Duong, Bach Phi; Kim, Jong-Myon

    2018-04-07

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

  16. Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

    Science.gov (United States)

    Kim, Jong-Myon

    2018-01-01

    The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

  17. Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

    Directory of Open Access Journals (Sweden)

    El-Sayed M. El-Alfy

    2015-01-01

    Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

  18. A Neural Network Classifier Model for Forecasting Safety Behavior at Workplaces

    Directory of Open Access Journals (Sweden)

    Fakhradin Ghasemi

    2017-07-01

    Full Text Available The construction industry is notorious for having an unacceptable rate of fatal accidents. Unsafe behavior has been recognized as the main cause of most accidents occurring at workplaces, particularly construction sites. Having a predictive model of safety behavior can be helpful in preventing construction accidents. The aim of the present study was to build a predictive model of unsafe behavior using the Artificial Neural Network approach. A brief literature review was conducted on factors affecting safe behavior at workplaces and nine factors were selected to be included in the study. Data were gathered using a validated questionnaire from several construction sites. Multilayer perceptron approach was utilized for constructing the desired neural network. Several models with various architectures were tested to find the best one. Sensitivity analysis was conducted to find the most influential factors. The model with one hidden layer containing fourteen hidden neurons demonstrated the best performance (Sum of Squared Errors=6.73. The error rate of the model was approximately 21 percent. The results of sensitivity analysis showed that safety attitude, safety knowledge, supportive environment, and management commitment had the highest effects on safety behavior, while the effects from resource allocation and perceived work pressure were identified to be lower than those of others. The complex nature of human behavior at workplaces and the presence of many influential factors make it difficult to achieve a model with perfect performance.

  19. Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

    Science.gov (United States)

    Luo, Yuan; Cheng, Yu; Uzuner, Özlem; Szolovits, Peter; Starren, Justin

    2018-01-01

    We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  20. Performance Analysis and Optimization for Cognitive Radio Networks with Classified Secondary Users and Impatient Packets

    Directory of Open Access Journals (Sweden)

    Yuan Zhao

    2017-01-01

    Full Text Available A cognitive radio network with classified Secondary Users (SUs is considered. There are two types of SU packets, namely, SU1 packets and SU2 packets, in the system. The SU1 packets have higher priority than the SU2 packets. Considering the diversity of the SU packets and the real-time need of the interrupted SU packets, a novel spectrum allocation strategy with classified SUs and impatient packets is proposed. Based on the number of PU packets, SU1 packets, and SU2 packets in the system, by modeling the queue dynamics of the networks users as a three-dimensional discrete-time Markov chain, the transition probability matrix of the Markov chain is given. Then with the steady-state analysis, some important performance measures of the SU2 packets are derived to show the system performance with numerical results. Specially, in order to optimize the system actions of the SU2 packets, the individually optimal strategy and the socially optimal strategy for the SU2 packets are demonstrated. Finally, a pricing mechanism is provided to oblige the SU2 packets to follow the socially optimal strategy.

  1. Use of artificial neural networks and geographic objects for classifying remote sensing imagery

    Directory of Open Access Journals (Sweden)

    Pedro Resende Silva

    2014-06-01

    Full Text Available The aim of this study was to develop a methodology for mapping land use and land cover in the northern region of Minas Gerais state, where, in addition to agricultural land, the landscape is dominated by native cerrado, deciduous forests, and extensive areas of vereda. Using forest inventory data, as well as RapidEye, Landsat TM and MODIS imagery, three specific objectives were defined: 1 to test use of image segmentation techniques for an object-based classification encompassing spectral, spatial and temporal information, 2 to test use of high spatial resolution RapidEye imagery combined with Landsat TM time series imagery for capturing the effects of seasonality, and 3 to classify data using Artificial Neural Networks. Using MODIS time series and forest inventory data, time signatures were extracted from the dominant vegetation formations, enabling selection of the best periods of the year to be represented in the classification process. Objects created with the segmentation of RapidEye images, along with the Landsat TM time series images, were classified by ten different Multilayer Perceptron network architectures. Results showed that the methodology in question meets both the purposes of this study and the characteristics of the local plant life. With excellent accuracy values for native classes, the study showed the importance of a well-structured database for classification and the importance of suitable image segmentation to meet specific purposes.

  2. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  3. Tabular data base construction and analysis from thematic classified Landsat imagery of Portland, Oregon

    Science.gov (United States)

    Bryant, N. A.; George, A. J., Jr.; Hegdahl, R.

    1977-01-01

    A systematic verification of Landsat data classifications of the Portland, Oregon metropolitan area has been undertaken on the basis of census tract data. The degree of systematic misclassification due to the Bayesian classifier used to process the Landsat data was noted for the various suburban, industrialized and central business districts of the metropolitan area. The Landsat determinations of residential land use were employed to estimate the number of automobile trips generated in the region and to model air pollution hazards.

  4. Unexpected properties of bandwidth choice when smoothing discrete data for constructing a functional data classifier

    KAUST Repository

    Carroll, Raymond J.

    2013-12-01

    The data functions that are studied in the course of functional data analysis are assembled from discrete data, and the level of smoothing that is used is generally that which is appropriate for accurate approximation of the conceptually smooth functions that were not actually observed. Existing literature shows that this approach is effective, and even optimal, when using functional data methods for prediction or hypothesis testing. However, in the present paper we show that this approach is not effective in classification problems. There a useful rule of thumb is that undersmoothing is often desirable, but there are several surprising qualifications to that approach. First, the effect of smoothing the training data can be more significant than that of smoothing the new data set to be classified; second, undersmoothing is not always the right approach, and in fact in some cases using a relatively large bandwidth can be more effective; and third, these perverse results are the consequence of very unusual properties of error rates, expressed as functions of smoothing parameters. For example, the orders of magnitude of optimal smoothing parameter choices depend on the signs and sizes of terms in an expansion of error rate, and those signs and sizes can vary dramatically from one setting to another, even for the same classifier.

  5. Unexpected properties of bandwidth choice when smoothing discrete data for constructing a functional data classifier

    KAUST Repository

    Carroll, Raymond J.; Delaigle, Aurore; Hall, Peter

    2013-01-01

    The data functions that are studied in the course of functional data analysis are assembled from discrete data, and the level of smoothing that is used is generally that which is appropriate for accurate approximation of the conceptually smooth functions that were not actually observed. Existing literature shows that this approach is effective, and even optimal, when using functional data methods for prediction or hypothesis testing. However, in the present paper we show that this approach is not effective in classification problems. There a useful rule of thumb is that undersmoothing is often desirable, but there are several surprising qualifications to that approach. First, the effect of smoothing the training data can be more significant than that of smoothing the new data set to be classified; second, undersmoothing is not always the right approach, and in fact in some cases using a relatively large bandwidth can be more effective; and third, these perverse results are the consequence of very unusual properties of error rates, expressed as functions of smoothing parameters. For example, the orders of magnitude of optimal smoothing parameter choices depend on the signs and sizes of terms in an expansion of error rate, and those signs and sizes can vary dramatically from one setting to another, even for the same classifier.

  6. Constructing Better Classifier Ensemble Based on Weighted Accuracy and Diversity Measure

    Directory of Open Access Journals (Sweden)

    Xiaodong Zeng

    2014-01-01

    Full Text Available A weighted accuracy and diversity (WAD method is presented, a novel measure used to evaluate the quality of the classifier ensemble, assisting in the ensemble selection task. The proposed measure is motivated by a commonly accepted hypothesis; that is, a robust classifier ensemble should not only be accurate but also different from every other member. In fact, accuracy and diversity are mutual restraint factors; that is, an ensemble with high accuracy may have low diversity, and an overly diverse ensemble may negatively affect accuracy. This study proposes a method to find the balance between accuracy and diversity that enhances the predictive ability of an ensemble for unknown data. The quality assessment for an ensemble is performed such that the final score is achieved by computing the harmonic mean of accuracy and diversity, where two weight parameters are used to balance them. The measure is compared to two representative measures, Kappa-Error and GenDiv, and two threshold measures that consider only accuracy or diversity, with two heuristic search algorithms, genetic algorithm, and forward hill-climbing algorithm, in ensemble selection tasks performed on 15 UCI benchmark datasets. The empirical results demonstrate that the WAD measure is superior to others in most cases.

  7. From gesture to sign language: conventionalization of classifier constructions by adult hearing learners of British Sign Language.

    Science.gov (United States)

    Marshall, Chloë R; Morgan, Gary

    2015-01-01

    There has long been interest in why languages are shaped the way they are, and in the relationship between sign language and gesture. In sign languages, entity classifiers are handshapes that encode how objects move, how they are located relative to one another, and how multiple objects of the same type are distributed in space. Previous studies have shown that hearing adults who are asked to use only manual gestures to describe how objects move in space will use gestures that bear some similarities to classifiers. We investigated how accurately hearing adults, who had been learning British Sign Language (BSL) for 1-3 years, produce and comprehend classifiers in (static) locative and distributive constructions. In a production task, learners of BSL knew that they could use their hands to represent objects, but they had difficulty choosing the same, conventionalized, handshapes as native signers. They were, however, highly accurate at encoding location and orientation information. Learners therefore show the same pattern found in sign-naïve gesturers. In contrast, handshape, orientation, and location were comprehended with equal (high) accuracy, and testing a group of sign-naïve adults showed that they too were able to understand classifiers with higher than chance accuracy. We conclude that adult learners of BSL bring their visuo-spatial knowledge and gestural abilities to the tasks of understanding and producing constructions that contain entity classifiers. We speculate that investigating the time course of adult sign language acquisition might shed light on how gesture became (and, indeed, becomes) conventionalized during the genesis of sign languages. Copyright © 2014 Cognitive Science Society, Inc.

  8. Discrimination of panti p → tanti t events by a neural network classifier

    International Nuclear Information System (INIS)

    Cherubini, A.; Odorico, R.

    1992-01-01

    Neural network and conventional statistical techniques are compared in the problem of discriminating panti p→tanti t events, with top quarks decaying into anything, from the associated hadronic background at the energy of the Fermilab collider. The NN we develop for this sake is an improved version of Kohonen's learning vector quantization scheme. Performance of the NN as a tanti t event classifier is found to be less satisfactory than that achievable by statistical methods. We conclude that the probable reasons for that are: i) The NN approach presents advantages only when dealing with event distributions in the feature space which substantially differ from Gaussians; ii) NN's require much larger training sets of events than statistical discrimination in order to give comparable results. (orig.)

  9. Distributed Classification of Localization Attacks in Sensor Networks Using Exchange-Based Feature Extraction and Classifier

    Directory of Open Access Journals (Sweden)

    Su-Zhe Wang

    2016-01-01

    Full Text Available Secure localization under different forms of attack has become an essential task in wireless sensor networks. Despite the significant research efforts in detecting the malicious nodes, the problem of localization attack type recognition has not yet been well addressed. Motivated by this concern, we propose a novel exchange-based attack classification algorithm. This is achieved by a distributed expectation maximization extractor integrated with the PECPR-MKSVM classifier. First, the mixed distribution features based on the probabilistic modeling are extracted using a distributed expectation maximization algorithm. After feature extraction, by introducing the theory from support vector machine, an extensive contractive Peaceman-Rachford splitting method is derived to build the distributed classifier that diffuses the iteration calculation among neighbor sensors. To verify the efficiency of the distributed recognition scheme, four groups of experiments were carried out under various conditions. The average success rate of the proposed classification algorithm obtained in the presented experiments for external attacks is excellent and has achieved about 93.9% in some cases. These testing results demonstrate that the proposed algorithm can produce much greater recognition rate, and it can be also more robust and efficient even in the presence of excessive malicious scenario.

  10. Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles

    Science.gov (United States)

    Ha, Jin Gwan; Moon, Hyeonjoon; Kwak, Jin Tae; Hassan, Syed Ibrahim; Dang, Minh; Lee, O. New; Park, Han Yong

    2017-10-01

    Recently, unmanned aerial vehicles (UAVs) have gained much attention. In particular, there is a growing interest in utilizing UAVs for agricultural applications such as crop monitoring and management. We propose a computerized system that is capable of detecting Fusarium wilt of radish with high accuracy. The system adopts computer vision and machine learning techniques, including deep learning, to process the images captured by UAVs at low altitudes and to identify the infected radish. The whole radish field is first segmented into three distinctive regions (radish, bare ground, and mulching film) via a softmax classifier and K-means clustering. Then, the identified radish regions are further classified into healthy radish and Fusarium wilt of radish using a deep convolutional neural network (CNN). In identifying radish, bare ground, and mulching film from a radish field, we achieved an accuracy of ≥97.4%. In detecting Fusarium wilt of radish, the CNN obtained an accuracy of 93.3%. It also outperformed the standard machine learning algorithm, obtaining 82.9% accuracy. Therefore, UAVs equipped with computational techniques are promising tools for improving the quality and efficiency of agriculture today.

  11. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

    Science.gov (United States)

    Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

    2018-05-10

    The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

  12. The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

    Directory of Open Access Journals (Sweden)

    Chih-Feng Chao

    2015-01-01

    Full Text Available The setting of parameters in the support vector machines (SVMs is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM. This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI, machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM. The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

  13. Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

    Directory of Open Access Journals (Sweden)

    Shanshan Han

    2018-04-01

    Full Text Available Over the past decade, big data, including Global Positioning System (GPS data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

  14. WAVELET ANALYSIS AND NEURAL NETWORK CLASSIFIERS TO DETECT MID-SAGITTAL SECTIONS FOR NUCHAL TRANSLUCENCY MEASUREMENT

    Directory of Open Access Journals (Sweden)

    Giuseppa Sciortino

    2016-04-01

    Full Text Available We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.

  15. An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier.

    Science.gov (United States)

    He, Jian; Bai, Shuang; Wang, Xiaoyi

    2017-06-16

    Falls are one of the main health risks among the elderly. A fall detection system based on inertial sensors can automatically detect fall event and alert a caregiver for immediate assistance, so as to reduce injuries causing by falls. Nevertheless, most inertial sensor-based fall detection technologies have focused on the accuracy of detection while neglecting quantization noise caused by inertial sensor. In this paper, an activity model based on tri-axial acceleration and gyroscope is proposed, and the difference between activities of daily living (ADLs) and falls is analyzed. Meanwhile, a Kalman filter is proposed to preprocess the raw data so as to reduce noise. A sliding window and Bayes network classifier are introduced to develop a wearable fall detection system, which is composed of a wearable motion sensor and a smart phone. The experiment shows that the proposed system distinguishes simulated falls from ADLs with a high accuracy of 95.67%, while sensitivity and specificity are 99.0% and 95.0%, respectively. Furthermore, the smart phone can issue an alarm to caregivers so as to provide timely and accurate help for the elderly, as soon as the system detects a fall.

  16. Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.

    Science.gov (United States)

    Kumudha, P; Venkatesan, R

    Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.

  17. Automatic Assessing of Tremor Severity Using Nonlinear Dynamics, Artificial Neural Networks and Neuro-Fuzzy Classifier

    Directory of Open Access Journals (Sweden)

    GEMAN, O.

    2014-02-01

    Full Text Available Neurological diseases like Alzheimer, epilepsy, Parkinson's disease, multiple sclerosis and other dementias influence the lives of patients, their families and society. Parkinson's disease (PD is a neurodegenerative disease that occurs due to loss of dopamine, a neurotransmitter and slow destruction of neurons. Brain area affected by progressive destruction of neurons is responsible for controlling movements, and patients with PD reveal rigid and uncontrollable gestures, postural instability, small handwriting and tremor. Commercial activity-promoting gaming systems such as the Nintendo Wii and Xbox Kinect can be used as tools for tremor, gait or other biomedical signals acquisitions. They also can aid for rehabilitation in clinical settings. This paper emphasizes the use of intelligent optical sensors or accelerometers in biomedical signal acquisition, and of the specific nonlinear dynamics parameters or fuzzy logic in Parkinson's disease tremor analysis. Nowadays, there is no screening test for early detection of PD. So, we investigated a method to predict PD, based on the image processing of the handwriting belonging to a candidate of PD. For classification and discrimination between healthy people and PD people we used Artificial Neural Networks (Radial Basis Function - RBF and Multilayer Perceptron - MLP and an Adaptive Neuro-Fuzzy Classifier (ANFC. In general, the results may be expressed as a prognostic (risk degree to contact PD.

  18. Construction of the main building network environment in IHEP

    International Nuclear Information System (INIS)

    Wang Yanming

    2004-01-01

    Based on the new network structure at IHEP, we re-constructed the network in Main Building at IHEP in order to realize the goal of steadily supporting the network application. We chose high performance and administrable switches and configured them properly. This improved that the network security, management ability. So the network at Main Building became more safe, steady, reliable and high-efficient. (authors)

  19. A new algorithm to construct phylogenetic networks from trees.

    Science.gov (United States)

    Wang, J

    2014-03-06

    Developing appropriate methods for constructing phylogenetic networks from tree sets is an important problem, and much research is currently being undertaken in this area. BIMLR is an algorithm that constructs phylogenetic networks from tree sets. The algorithm can construct a much simpler network than other available methods. Here, we introduce an improved version of the BIMLR algorithm, QuickCass. QuickCass changes the selection strategy of the labels of leaves below the reticulate nodes, i.e., the nodes with an indegree of at least 2 in BIMLR. We show that QuickCass can construct simpler phylogenetic networks than BIMLR. Furthermore, we show that QuickCass is a polynomial-time algorithm when the output network that is constructed by QuickCass is binary.

  20. Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

    DEFF Research Database (Denmark)

    Gajhede, Nicolai; Beck, Oliver; Purwins, Hendrik

    2016-01-01

    After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples...

  1. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

    Directory of Open Access Journals (Sweden)

    Evangelos Stromatias

    2017-06-01

    Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  2. An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

    Science.gov (United States)

    Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

    2017-01-01

    This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

  3. Construct Validation of Wenger's Support Network Typology.

    Science.gov (United States)

    Szabo, Agnes; Stephens, Christine; Allen, Joanne; Alpass, Fiona

    2016-10-07

    The study aimed to validate Wenger's empirically derived support network typology of responses to the Practitioner Assessment of Network Type (PANT) in an older New Zealander population. The configuration of network types was tested across ethnic groups and in the total sample. Data (N = 872, Mage = 67 years, SDage = 1.56 years) from the 2006 wave of the New Zealand Health, Work and Retirement study were analyzed using latent profile analysis. In addition, demographic differences among the emerging profiles were tested. Competing models were evaluated based on a range of fit criteria, which supported a five-profile solution. The "locally integrated," "community-focused," "local self-contained," "private-restricted," and "friend- and family-dependent" network types were identified as latent profiles underlying the data. There were no differences between Māori and non-Māori in final profile configurations. However, Māori were more likely to report integrated network types. Findings confirm the validity of Wenger's network types. However, the level to which participants endorse accessibility of family, frequency of interactions, and community engagement can be influenced by sample and contextual characteristics. Future research using the PANT items should empirically verify and derive the social support network types, rather than use a predefined scoring system. © The Author 2016. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  4. On library information resources construction under network environment

    International Nuclear Information System (INIS)

    Guo Huifang; Wang Jingjing

    2014-01-01

    Information resources construction is the primary task and critical measures for libraries. In the 2lst century, the knowledge economy era, with the continuous development of computer network technology, information resources have become an important part of libraries which have been a significant indicator of its capacity construction. The development of socialized Information, digitalization and internalization has put forward new requirements for library information resources construction. This paper describes the impact of network environment on construction of library information resources and proposes the measures of library information resources. (authors)

  5. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  6. Label-Driven Learning Framework: Towards More Accurate Bayesian Network Classifiers through Discrimination of High-Confidence Labels

    Directory of Open Access Journals (Sweden)

    Yi Sun

    2017-12-01

    Full Text Available Bayesian network classifiers (BNCs have demonstrated competitive classification accuracy in a variety of real-world applications. However, it is error-prone for BNCs to discriminate among high-confidence labels. To address this issue, we propose the label-driven learning framework, which incorporates instance-based learning and ensemble learning. For each testing instance, high-confidence labels are first selected by a generalist classifier, e.g., the tree-augmented naive Bayes (TAN classifier. Then, by focusing on these labels, conditional mutual information is redefined to more precisely measure mutual dependence between attributes, thus leading to a refined generalist with a more reasonable network structure. To enable finer discrimination, an expert classifier is tailored for each high-confidence label. Finally, the predictions of the refined generalist and the experts are aggregated. We extend TAN to LTAN (Label-driven TAN by applying the proposed framework. Extensive experimental results demonstrate that LTAN delivers superior classification accuracy to not only several state-of-the-art single-structure BNCs but also some established ensemble BNCs at the expense of reasonable computation overhead.

  7. Fault Diagnosis for Distribution Networks Using Enhanced Support Vector Machine Classifier with Classical Multidimensional Scaling

    Directory of Open Access Journals (Sweden)

    Ming-Yuan Cho

    2017-09-01

    Full Text Available In this paper, a new fault diagnosis techniques based on time domain reflectometry (TDR method with pseudo-random binary sequence (PRBS stimulus and support vector machine (SVM classifier has been investigated to recognize the different types of fault in the radial distribution feeders. This novel technique has considered the amplitude of reflected signals and the peaks of cross-correlation (CCR between the reflected and incident wave for generating fault current dataset for SVM. Furthermore, this multi-layer enhanced SVM classifier is combined with classical multidimensional scaling (CMDS feature extraction algorithm and kernel parameter optimization to increase training speed and improve overall classification accuracy. The proposed technique has been tested on a radial distribution feeder to identify ten different types of fault considering 12 input features generated by using Simulink software and MATLAB Toolbox. The success rate of SVM classifier is over 95% which demonstrates the effectiveness and the high accuracy of proposed method.

  8. Moving image compression and generalization capability of constructive neural networks

    Science.gov (United States)

    Ma, Liying; Khorasani, Khashayar

    2001-03-01

    To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks NNs have been developed. Particularly, several constructive feed-forward neural networks FNNs have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference 2000, we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network OHL-FNN for compressing digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/ or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images, video sequences. The first, or other specified frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a football game.

  9. Seeded Bayesian Networks: Constructing genetic networks from microarray data

    Directory of Open Access Journals (Sweden)

    Quackenbush John

    2008-07-01

    Full Text Available Abstract Background DNA microarrays and other genomics-inspired technologies provide large datasets that often include hidden patterns of correlation between genes reflecting the complex processes that underlie cellular metabolism and physiology. The challenge in analyzing large-scale expression data has been to extract biologically meaningful inferences regarding these processes – often represented as networks – in an environment where the datasets are often imperfect and biological noise can obscure the actual signal. Although many techniques have been developed in an attempt to address these issues, to date their ability to extract meaningful and predictive network relationships has been limited. Here we describe a method that draws on prior information about gene-gene interactions to infer biologically relevant pathways from microarray data. Our approach consists of using preliminary networks derived from the literature and/or protein-protein interaction data as seeds for a Bayesian network analysis of microarray results. Results Through a bootstrap analysis of gene expression data derived from a number of leukemia studies, we demonstrate that seeded Bayesian Networks have the ability to identify high-confidence gene-gene interactions which can then be validated by comparison to other sources of pathway data. Conclusion The use of network seeds greatly improves the ability of Bayesian Network analysis to learn gene interaction networks from gene expression data. We demonstrate that the use of seeds derived from the biomedical literature or high-throughput protein-protein interaction data, or the combination, provides improvement over a standard Bayesian Network analysis, allowing networks involving dynamic processes to be deduced from the static snapshots of biological systems that represent the most common source of microarray data. Software implementing these methods has been included in the widely used TM4 microarray analysis package.

  10. Using Unsupervised Learning to Improve the Naive Bayes Classifier for Wireless Sensor Networks

    NARCIS (Netherlands)

    Zwartjes, G.J.; Havinga, Paul J.M.; Smit, Gerardus Johannes Maria; Hurink, Johann L.

    2012-01-01

    Online processing is essential for many sensor network applications. Sensor nodes can sample far more data than what can practically be transmitted using state of the art sensor network radios. Online processing, however, is complicated due to limited resources of individual nodes. The naive Bayes

  11. Tailoring silver nanoparticle construction using dendrimer templated silica networks

    International Nuclear Information System (INIS)

    Liu Xiaojun; Kakkar, Ashok

    2008-01-01

    We have examined the role of the internal environment of dendrimer templated silica networks in tailoring the construction of silver nanoparticle assemblies. Silica networks from which 3,5-dihydroxybenzyl alcohol based dendrimer templates have been completely removed, slowly wet with an aqueous solution of silver acetate. The latter then reacts with internal silica silanol groups, leading to chemisorption of silver ions, followed by the growth of silver oxide nanoparticles. Silica network constructed using generation 4 dendrimer contains residual dendrimer template, and mixes with aqueous silver acetate solution easily. Upon chemisorption, silver ions get photolytically reduced to silver metal under a stabilizing dendrimer environment, leading to the formation of silver metal nanoparticles

  12. Classifying Sensors Depending on their IDs to Reduce Power Consumption in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ayman Mohammd Brisha

    2010-05-01

    Full Text Available Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. Cluster-based is an effective architecture for data-gathering in wireless sensor networks. Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, in order to distribute the energy consumption among nodes in each cluster and extend the network lifetime. Clustering sensors are divided into groups, so that sensors will communicate information only to cluster heads and then the cluster heads will communicate the aggregated information to the processing center, and this may save energy. In this paper we show Two Relay Sensor Algorithm (TRSA, which divide wireless Sensor Network (WSN into unequaled clusters, showing that it can effectively save power for maximizing the life time of the network. Simulation results show that the proposed unequal clustering mechanism (TRSA balances the energy consumption among all sensor nodes and achieves an obvious improvement on the network lifetime.

  13. How to construct the statistic network? An association network of herbaceous

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2012-06-01

    Full Text Available In present study I defined a new type of network, the statistic network. The statistic network is a weighted and non-deterministic network. In the statistic network, a connection value, i.e., connection weight, represents connection strength and connection likelihood between two nodes and its absolute value falls in the interval (0,1]. The connection value is expressed as a statistical measure such as correlation coefficient, association coefficient, or Jaccard coefficient, etc. In addition, all connections of the statistic network can be statistically tested for their validity. A connection is true if the connection value is statistically significant. If all connection values of a node are not statistically significant, it is an isolated node. An isolated node has not any connection to other nodes in the statistic network. Positive and negative connection values denote distinct connectiontypes (positive or negative association or interaction. In the statistic network, two nodes with the greater connection value will show more similar trend in the change of their states. At any time we can obtain a sample network of the statistic network. A sample network is a non-weighted and deterministic network. Thestatistic network, in particular the plant association network that constructed from field sampling, is mostly an information network. Most of the interspecific relationships in plant community are competition and cooperation. Therefore in comparison to animal networks, the methodology of statistic network is moresuitable to construct plant association networks. Some conclusions were drawn from this study: (1 in the plant association network, most connections are weak and positive interactions. The association network constructed from Spearman rank correlation has most connections and isolated taxa are fewer. From net linear correlation,linear correlation, to Spearman rank correlation, the practical number of connections and connectance in the

  14. Graphic Symbol Recognition using Graph Based Signature and Bayesian Network Classifier

    OpenAIRE

    Luqman, Muhammad Muzzamil; Brouard, Thierry; Ramel, Jean-Yves

    2010-01-01

    We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational g...

  15. A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

    Science.gov (United States)

    Pang, Shuchao; Yu, Zhezhou; Orgun, Mehmet A

    2017-03-01

    Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust

  16. Carbon classified?

    DEFF Research Database (Denmark)

    Lippert, Ingmar

    2012-01-01

    . Using an actor- network theory (ANT) framework, the aim is to investigate the actors who bring together the elements needed to classify their carbon emission sources and unpack the heterogeneous relations drawn on. Based on an ethnographic study of corporate agents of ecological modernisation over...... a period of 13 months, this paper provides an exploration of three cases of enacting classification. Drawing on ANT, we problematise the silencing of a range of possible modalities of consumption facts and point to the ontological ethics involved in such performances. In a context of global warming...

  17. Model of hierarchical self-organizing neural networks for detecting and classifying diabetic retinopathy

    Directory of Open Access Journals (Sweden)

    Hossein Ghayoumi Zadeh

    2018-04-01

    Conclusion: These days, the cases of diabetes with hypertension are constantly increasing, and one of the main adverse effects of this disease is related to eyes. In this respect, the diagnosis of retinopathy, which is the same as identification of exudates, microanurysm and bleeding, is of particular importance. The results show that the proposed model is able to detect lesions in diabetic retinopathy images and classify them with an acceptable accuracy. In addition, the results suggest that this method has an acceptable performance compared to other methods.

  18. Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

    Science.gov (United States)

    Menezes, Mozart B C; Kim, Seokjin; Huang, Rongbing

    2017-01-01

    Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

  19. Network Intrusion Detection System (NIDS in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

    Directory of Open Access Journals (Sweden)

    Hafza A. Mahmood

    2018-04-01

    Full Text Available Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB Classifier to handle DoS attacks which is a data mining (DM model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB, proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

  20. A Machine Learning Approach for Identifying and Classifying Faults in Wireless Sensor Networks

    NARCIS (Netherlands)

    Warriach, Ehsan Ullah; Aiello, Marco; Tei, Kenji

    2012-01-01

    Wireless Sensor Network (WSN) deployment experiences show that collected data is prone to be faulty. Faults are due to internal and external influences, such as calibration, low battery, environmental interference and sensor aging. However, only few solutions exist to deal with faulty sensory data

  1. On the complexity of neural network classifiers: a comparison between shallow and deep architectures.

    Science.gov (United States)

    Bianchini, Monica; Scarselli, Franco

    2014-08-01

    Recently, researchers in the artificial neural network field have focused their attention on connectionist models composed by several hidden layers. In fact, experimental results and heuristic considerations suggest that deep architectures are more suitable than shallow ones for modern applications, facing very complex problems, e.g., vision and human language understanding. However, the actual theoretical results supporting such a claim are still few and incomplete. In this paper, we propose a new approach to study how the depth of feedforward neural networks impacts on their ability in implementing high complexity functions. First, a new measure based on topological concepts is introduced, aimed at evaluating the complexity of the function implemented by a neural network, used for classification purposes. Then, deep and shallow neural architectures with common sigmoidal activation functions are compared, by deriving upper and lower bounds on their complexity, and studying how the complexity depends on the number of hidden units and the used activation function. The obtained results seem to support the idea that deep networks actually implements functions of higher complexity, so that they are able, with the same number of resources, to address more difficult problems.

  2. Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database.

    Science.gov (United States)

    Choi, Joon Yul; Yoo, Tae Keun; Seo, Jeong Gi; Kwak, Jiyong; Um, Terry Taewoong; Rim, Tyler Hyungtaek

    2017-01-01

    Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.

  3. Investigation of the Development of 7th Grade Students’ Skills to Define, Construct and Classify Polygons with Cabri Geometry

    Directory of Open Access Journals (Sweden)

    Ahmet Yanık

    2013-03-01

    Full Text Available The aim of the study is to investigate the development of 7th Grade students’ skills to define, construct and classify polygons in geometry course with Cabri Geometry II Plus software geometry, an example of dynamic geometry software. The study used qualitative and quantitative research methods in accordance with the research objectives and focus, so it was designed as a mixed method research. The participants of the study were 21 7th Grade students, 11 girls and 10 boys, who were attending a secondary school in Eskişehir city center during 2012-2013 school year. As a source of qualitative data, four students in this class were selected for the interview. The data were collected with “Polygon Identification and Classification Scale”, one group pre-test and post-test in order to determine the level of development and significance level of the gender variable, and Cabri Geometry worksheets developed by the researchers. The quantitative data were analyzed with SPSS Statistics 20. Also, t-test and Wilcoxon test were used in data analysis. The data obtained from the interviews were analyzed through descriptive analysis. The qualitative data showed that the mean of correct answers given by the students to the questions in the Polygon Identification and Classification Scale was higher in the post-test than the pre-test. The ttest results for the pre-test and post-test mean scores and the results of the paired samples test showed a significant difference in favor of the post-test. There was no significant difference based on the gender variable. On the other hand, the data obtained from the interviews were coded under five different themes. The activities about the concept of formation showed that incorrect formations caused incorrect generalizations about the shapes. The study found that, as a result of the teaching practice in the study, hierarchical relations among polygons were expressed correctly. Finally, after the practice, the

  4. An Analysis of Construction Accident Factors Based on Bayesian Network

    OpenAIRE

    Yunsheng Zhao; Jinyong Pei

    2013-01-01

    In this study, we have an analysis of construction accident factors based on bayesian network. Firstly, accidents cases are analyzed to build Fault Tree method, which is available to find all the factors causing the accidents, then qualitatively and quantitatively analyzes the factors with Bayesian network method, finally determines the safety management program to guide the safety operations. The results of this study show that bad condition of geological environment has the largest posterio...

  5. Crystal surface analysis using matrix textural features classified by a Probabilistic Neural Network

    International Nuclear Information System (INIS)

    Sawyer, C.R.; Quach, V.T.; Nason, D.; van den Berg, L.

    1991-01-01

    A system is under development in which surface quality of a growing bulk mercuric iodide crystal is monitored by video camera at regular intervals for early detection of growth irregularities. Mercuric iodide single crystals are employed in radiation detectors. A microcomputer system is used for image capture and processing. The digitized image is divided into multiple overlappings subimage and features are extracted from each subimage based on statistical measures of the gray tone distribution, according to the method of Haralick [1]. Twenty parameters are derived from each subimage and presented to a Probabilistic Neural Network (PNN) [2] for classification. This number of parameters was found to be optimal for the system. The PNN is a hierarchical, feed-forward network that can be rapidly reconfigured as additional training data become available. Training data is gathered by reviewing digital images of many crystals during their growth cycle and compiling two sets of images, those with and without irregularities. 6 refs., 4 figs

  6. Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers

    Directory of Open Access Journals (Sweden)

    Ryan Henderson

    2017-09-01

    Full Text Available Picasso is a free open-source (Eclipse Public License web application written in Python for rendering standard visualizations useful for analyzing convolutional neural networks. Picasso ships with occlusion maps and saliency maps, two visualizations which help reveal issues that evaluation metrics like loss and accuracy might hide: for example, learning a proxy classification task. Picasso works with the Tensorflow deep learning framework, and Keras (when the model can be loaded into the Tensorflow backend. Picasso can be used with minimal configuration by deep learning researchers and engineers alike across various neural network architectures. Adding new visualizations is simple: the user can specify their visualization code and HTML template separately from the application code.

  7. Noticing climate change in electricity network design and construction

    International Nuclear Information System (INIS)

    Syri, S.; Martikeinen, A.; Lehtonen, M.

    2007-01-01

    The climate change is widely known to cause remarkable effects to electricity network systems on the whole. Some of the changes are good but the most of the changes cause disadvantages to electricity network. Consequence of climate change, blackouts can be long-standing which affect remarkable society and economic life. Most of electricity networks are coming to a renovation phase and the solutions, that are being made nowadays, affect still after decades. Taking account of climate change, now when networks are being developed and planned, it is possible to avoid possible large repair operation and increase reliability of distribution in the future. The aim of this project is to clarify how climate change should be noticed in planning and construction processes. According to the results of this project electricity network companies can be prepared for climate change by developing planning processes and network cost effectively. Also construction processes are being developed but emphasis is on planning process. The results and developed knowledge of VTT research project 'Impacts of climate change on electricity network business' are exploited in this project. In addition, impacts of climate change on cables and transformers are analyzed in collaboration with TKK in the project. (orig.)

  8. Project plan for PACS networking construction and cabling

    International Nuclear Information System (INIS)

    Luo Min; Wang Xiaolin; Luo Song; Lei Wenyong; Wang Xuejian; Wen Hongyue; Wu Hongxing

    2002-01-01

    Objective: To meet the networking requirement of the heave data flow, load balance, and potential networking storm during expanding the application of PACS. Methods: Intel Net Structure 480T Giga Switch was used as main switch and connected to each building by optical channel at 1 Giga speed to archive 100 MB/s to each port. At the same time, the in dependence of the original networking construction was physically kept. The layer 3 and 4 switchers was used as load balance to reduce the heavy load of the networking, and all the cabling for PACS used the super CAT5 along with the Intel Net Structure 1520 to prepare for the potential networking storm. Results: An advanced intranet was set up to fully meet the high standard requirement of PACS. The good foundation for upgrading the whole networking system to 1 Giga application was built for realized share and transmission of image, information, and patient data within the hospital. The base was established for the standardized management of the hospital. Conclusion: Good planning is the 1 st step in setting up PACS and the equipment are the platform to run PACS and all kinds of HIS. The networking construction is the foundation of e-hospitals

  9. Construction of ontology augmented networks for protein complex prediction.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  10. Constructing ordinal partition transition networks from multivariate time series.

    Science.gov (United States)

    Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

    2017-08-10

    A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

  11. Robust Template Decomposition without Weight Restriction for Cellular Neural Networks Implementing Arbitrary Boolean Functions Using Support Vector Classifiers

    Directory of Open Access Journals (Sweden)

    Yih-Lon Lin

    2013-01-01

    Full Text Available If the given Boolean function is linearly separable, a robust uncoupled cellular neural network can be designed as a maximal margin classifier. On the other hand, if the given Boolean function is linearly separable but has a small geometric margin or it is not linearly separable, a popular approach is to find a sequence of robust uncoupled cellular neural networks implementing the given Boolean function. In the past research works using this approach, the control template parameters and thresholds are restricted to assume only a given finite set of integers, and this is certainly unnecessary for the template design. In this study, we try to remove this restriction. Minterm- and maxterm-based decomposition algorithms utilizing the soft margin and maximal margin support vector classifiers are proposed to design a sequence of robust templates implementing an arbitrary Boolean function. Several illustrative examples are simulated to demonstrate the efficiency of the proposed method by comparing our results with those produced by other decomposition methods with restricted weights.

  12. Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

    Science.gov (United States)

    Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

    2014-09-07

    Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Refining a Heuristic for Constructing Bayesian Networks from Structured Arguments

    NARCIS (Netherlands)

    Wieten, G.M.; Bex, F.J.; van der Gaag, L.C.; Prakken, H.; Renooij, S.

    2018-01-01

    Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured arguments. This heuristic helps domain experts who are accustomed to argumentation to transform their reasoning into a BN and subsequently weigh their case evidence in a probabilistic manner. While the

  14. A Constructive Neural-Network Approach to Modeling Psychological Development

    Science.gov (United States)

    Shultz, Thomas R.

    2012-01-01

    This article reviews a particular computational modeling approach to the study of psychological development--that of constructive neural networks. This approach is applied to a variety of developmental domains and issues, including Piagetian tasks, shift learning, language acquisition, number comparison, habituation of visual attention, concept…

  15. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  16. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, de H.C.W.; Zwaan, L.; Timmermans, D.R.M.; Groenewegen, P.P.; Wagner, C.

    2009-01-01

    BACKGROUND: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis: is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  17. Analysis of unintended events in hospitals : inter-rater reliability of constructing causal trees and classifying root causes

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Groenewegen, P.P.; Timmermans, D.

    2009-01-01

    Background. Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  18. Analysis of unintended events in hospitals: inter-rater reliability of constructing causal trees and classifying root causes.

    NARCIS (Netherlands)

    Smits, M.; Janssen, J.; Vet, R. de; Zwaan, L.; Timmermans, D.; Groenewegen, P.; Wagner, C.

    2009-01-01

    Background: Root cause analysis is a method to examine causes of unintended events. PRISMA (Prevention and Recovery Information System for Monitoring and Analysis) is a root cause analysis tool. With PRISMA, events are described in causal trees and root causes are subsequently classified with the

  19. Muon Neutrino Disappearance in NOvA with a Deep Convolutional Neural Network Classifier

    Energy Technology Data Exchange (ETDEWEB)

    Rocco, Dominick Rosario [Minnesota U.

    2016-03-01

    The NuMI Off-axis Neutrino Appearance Experiment (NOvA) is designed to study neutrino oscillation in the NuMI (Neutrinos at the Main Injector) beam. NOvA observes neutrino oscillation using two detectors separated by a baseline of 810 km; a 14 kt Far Detector in Ash River, MN and a functionally identical 0.3 kt Near Detector at Fermilab. The experiment aims to provide new measurements of Δm2 and θ23 and has potential to determine the neutrino mass hierarchy as well as observe CP violation in the neutrino sector. Essential to these analyses is the classification of neutrino interaction events in NOvA detectors. Raw detector output from NOvA is interpretable as a pair of images which provide orthogonal views of particle interactions. A recent advance in the field of computer vision is the advent of convolutional neural networks, which have delivered top results in the latest image recognition contests. This work presents an approach novel to particle physics analysis in which a convolutional neural network is used for classification of particle interactions. The approach has been demonstrated to improve the signal efficiency and purity of the event selection, and thus physics sensitivity. Early NOvA data has been analyzed (2.74×1020 POT, 14 kt equivalent) to provide new best- fit measurements of sin2(θ23) = 0.43 (with a statistically-degenerate compliment near 0.60) and |Δm2 | = 2.48 × 10-3 eV2.

  20. Classifying and profiling Social Networking Site users: a latent segmentation approach.

    Science.gov (United States)

    Alarcón-del-Amo, María-del-Carmen; Lorenzo-Romero, Carlota; Gómez-Borja, Miguel-Ángel

    2011-09-01

    Social Networking Sites (SNSs) have showed an exponential growth in the last years. The first step for an efficient use of SNSs stems from an understanding of the individuals' behaviors within these sites. In this research, we have obtained a typology of SNS users through a latent segmentation approach, based on the frequency by which users perform different activities within the SNSs, sociodemographic variables, experience in SNSs, and dimensions related to their interaction patterns. Four different segments have been obtained. The "introvert" and "novel" users are the more occasional. They utilize SNSs mainly to communicate with friends, although "introverts" are more passive users. The "versatile" user performs different activities, although occasionally. Finally, the "expert-communicator" performs a greater variety of activities with a higher frequency. They tend to perform some marketing-related activities such as commenting on ads or gathering information about products and brands as well as commenting ads. The companies can take advantage of these segmentation schemes in different ways: first, by tracking and monitoring information interchange between users regarding their products and brands. Second, they should match the SNS users' profiles with their market targets to use SNSs as marketing tools. Finally, for most business, the expert users could be interesting opinion leaders and potential brand influencers.

  1. Construction of Injectable Double-Network Hydrogels for Cell Delivery.

    Science.gov (United States)

    Yan, Yan; Li, Mengnan; Yang, Di; Wang, Qian; Liang, Fuxin; Qu, Xiaozhong; Qiu, Dong; Yang, Zhenzhong

    2017-07-10

    Herein we present a unique method of using dynamic cross-links, which are dynamic covalent bonding and ionic interaction, for the construction of injectable double-network (DN) hydrogels, with the objective of cell delivery for cartilage repair. Glycol chitosan and dibenzaldhyde capped poly(ethylene oxide) formed the first network, while calcium alginate formed the second one, and in the resultant DN hydrogel, either of the networks could be selectively removed. The moduli of the DN hydrogel were significantly improved compared to that of the parent single-network hydrogels and were tunable by changing the chemical components. In situ 3D cell encapsulation could be easily performed by mixing cell suspension to the polymer solutions and transferred through a syringe needle before sol-gel transition. Cell proliferation and mediated differentiation of mouse chondrogenic cells were achieved in the DN hydrogel extracellular matrix.

  2. Properties of healthcare teaming networks as a function of network construction algorithms.

    Directory of Open Access Journals (Sweden)

    Martin S Zand

    Full Text Available Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year, making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast

  3. Monitoring and classifying animal behavior using ZigBee-based mobile ad hoc wireless sensor networks and artificial neural networks

    DEFF Research Database (Denmark)

    S. Nadimi, Esmaeil; Nyholm Jørgensen, Rasmus; Blanes-Vidal, Victoria

    2012-01-01

    Animal welfare is an issue of great importance in modern food production systems. Because animal behavior provides reliable information about animal health and welfare, recent research has aimed at designing monitoring systems capable of measuring behavioral parameters and transforming them...... into their corresponding behavioral modes. However, network unreliability and high-energy consumption have limited the applicability of those systems. In this study, a 2.4-GHz ZigBee-based mobile ad hoc wireless sensor network (MANET) that is able to overcome those problems is presented. The designed MANET showed high...... communication reliability, low energy consumption and low packet loss rate (14.8%) due to the deployment of modern communication protocols (e.g. multi-hop communication and handshaking protocol). The measured behavioral parameters were transformed into the corresponding behavioral modes using a multilayer...

  4. Constructing Battery-Aware Virtual Backbones in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Yuanyuan

    2007-01-01

    Full Text Available A critical issue in battery-powered sensor networks is to construct energy efficient virtual backbones for network routing. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper we first provide a mathematical battery model suitable for implementation in sensor networks. We then introduce the concept of battery-aware connected dominating set (BACDS and show that in general the minimum BACDS (MBACDS can achieve longer lifetime than the previous backbone structures. Then we show that finding a MBACDS is NP-hard and give a distributed approximation algorithm to construct the BACDS. The resulting BACDS constructed by our algorithm is at most opt size, where is the maximum node degree and opt is the size of an optimal BACDS. Simulation results show that the BACDS can save a significant amount of energy and achieve up to longer network lifetime than previous schemes.

  5. Constructing Battery-Aware Virtual Backbones in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chi Ma

    2007-05-01

    Full Text Available A critical issue in battery-powered sensor networks is to construct energy efficient virtual backbones for network routing. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper we first provide a mathematical battery model suitable for implementation in sensor networks. We then introduce the concept of battery-aware connected dominating set (BACDS and show that in general the minimum BACDS (MBACDS can achieve longer lifetime than the previous backbone structures. Then we show that finding a MBACDS is NP-hard and give a distributed approximation algorithm to construct the BACDS. The resulting BACDS constructed by our algorithm is at most (8+Δopt size, where Δ is the maximum node degree and opt is the size of an optimal BACDS. Simulation results show that the BACDS can save a significant amount of energy and achieve up to 30% longer network lifetime than previous schemes.

  6. Constructing financial network based on PMFG and threshold method

    Science.gov (United States)

    Nie, Chun-Xiao; Song, Fu-Tie

    2018-04-01

    Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

  7. Scalable Brain Network Construction on White Matter Fibers.

    Science.gov (United States)

    Chung, Moo K; Adluru, Nagesh; Dalton, Kim M; Alexander, Andrew L; Davidson, Richard J

    2011-02-12

    DTI offers a unique opportunity to characterize the structural connectivity of the human brain non-invasively by tracing white matter fiber tracts. Whole brain tractography studies routinely generate up to half million tracts per brain, which serves as edges in an extremely large 3D graph with up to half million edges. Currently there is no agreed-upon method for constructing the brain structural network graphs out of large number of white matter tracts. In this paper, we present a scalable iterative framework called the ε-neighbor method for building a network graph and apply it to testing abnormal connectivity in autism.

  8. Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

    Science.gov (United States)

    McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

    2018-04-01

    Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

  9. Sensitivity analysis of human brain structural network construction

    Directory of Open Access Journals (Sweden)

    Kuang Wei

    2017-12-01

    Full Text Available Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP, we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes. Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey

  10. Construction of functional linkage gene networks by data integration.

    Science.gov (United States)

    Linghu, Bolan; Franzosa, Eric A; Xia, Yu

    2013-01-01

    Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.

  11. 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

  12. Constructing a research network: accounting knowledge in production

    OpenAIRE

    Joannides , Vassili; Berland , Nicolas

    2013-01-01

    Avec Nicolas Berland; International audience; Purpose - This paper contributes to the sociology-of-science type of accounting literature, addressing how accounting knowledge is established, advanced and extended. Design/methodology/approach - The research question is answered through the example of research into linkages between accounting and religion. Adopting an Actor-Network Theory (ANT) approach, we follow the actors involved in the construction of accounting as an academic discipline th...

  13. Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

    Directory of Open Access Journals (Sweden)

    Mozart B C Menezes

    Full Text Available Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.

  14. Bayesian-network-based safety risk analysis in construction projects

    International Nuclear Information System (INIS)

    Zhang, Limao; Wu, Xianguo; Skibniewski, Miroslaw J.; Zhong, Jingbing; Lu, Yujie

    2014-01-01

    This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical

  15. Constructing general partial differential equations using polynomial and neural networks.

    Science.gov (United States)

    Zjavka, Ladislav; Pedrycz, Witold

    2016-01-01

    Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier.

    Science.gov (United States)

    Raghu, S; Sriraam, N; Kumar, G Pradeep

    2017-02-01

    Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.

  17. Construction of Individual Morphological Brain Networks with Multiple Morphometric Features

    Directory of Open Access Journals (Sweden)

    Chunlan Yang

    2017-04-01

    Full Text Available In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28 each scanned twice, and reproducibility was evaluated through test–retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20–40%, and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78 indicates

  18. Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks.

    Science.gov (United States)

    Murakami, Masaya; Kominami, Daichi; Leibnitz, Kenji; Murata, Masayuki

    2018-04-08

    Virtualization of wireless sensor networks (WSN) is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT) networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN) topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions) and nodes (i.e., neurons). We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR) model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

  19. Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks

    Directory of Open Access Journals (Sweden)

    Masaya Murakami

    2018-04-01

    Full Text Available Virtualization of wireless sensor networks (WSN is widely considered as a foundational block of edge/fog computing, which is a key technology that can help realize next-generation Internet of things (IoT networks. In such scenarios, multiple IoT devices and service modules will be virtually deployed and interconnected over the Internet. Moreover, application services are expected to be more sophisticated and complex, thereby increasing the number of modifications required for the construction of network topologies. Therefore, it is imperative to establish a method for constructing a virtualized WSN (VWSN topology that achieves low latency on information transmission and high resilience against network failures, while keeping the topological construction cost low. In this study, we draw inspiration from inter-modular connectivity in human brain networks, which achieves high performance when dealing with large-scale networks composed of a large number of modules (i.e., regions and nodes (i.e., neurons. We propose a method for assigning inter-modular links based on a connectivity model observed in the cerebral cortex of the brain, known as the exponential distance rule (EDR model. We then choose endpoint nodes of these links by controlling inter-modular assortativity, which characterizes the topological connectivity of brain networks. We test our proposed methods using simulation experiments. The results show that the proposed method based on the EDR model can construct a VWSN topology with an optimal combination of communication efficiency, robustness, and construction cost. Regarding the selection of endpoint nodes for the inter-modular links, the results also show that high assortativity enhances the robustness and communication efficiency because of the existence of inter-modular links of two high-degree nodes.

  20. Construction of programmable interconnected 3D microfluidic networks

    International Nuclear Information System (INIS)

    Hunziker, Patrick R; Wolf, Marc P; Wang, Xueya; Zhang, Bei; Marsch, Stephan; Salieb-Beugelaar, Georgette B

    2015-01-01

    Microfluidic systems represent a key-enabling platform for novel diagnostic tools for use at the point-of-care in clinical contexts as well as for evolving single cell diagnostics. The design of 3D microfluidic systems is an active field of development, but construction of true interconnected 3D microfluidic networks is still a challenge, in particular when the goal is rapid prototyping, accurate design and flexibility. We report a novel approach for the construction of programmable 3D microfluidic systems consisting of modular 3D template casting of interconnected threads to allow user-programmable flow paths and examine its structural characteristics and its modular function. To overcome problems with thread template casting reported in the literature, low-surface-energy polymer threads were used, that allow solvent-free production. Connected circular channels with excellent roundness and low diameter variability were created. Variable channel termination allowed programming a flow path on-the-fly, thus rendering the resulting 3D microfluidic systems highly customizable even after production. Thus, construction of programmable/reprogrammable fully 3D microfluidic systems by template casting of a network of interconnecting threads is feasible, leads to high-quality and highly reproducible, complex 3D geometries. (paper)

  1. SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment.

    Science.gov (United States)

    Shi, Mingguang; He, Jianmin

    2016-04-01

    Adjuvant chemotherapy (CTX) should be individualized to provide potential survival benefit and avoid potential harm to cancer patients. Our goal was to establish a computational approach for making personalized estimates of the survival benefit from adjuvant CTX. We developed Sub-Network based Random Forest classifier for predicting Chemotherapy Benefit (SNRFCB) based gene expression datasets of lung cancer. The SNRFCB approach was then validated in independent test cohorts for identifying chemotherapy responder cohorts and chemotherapy non-responder cohorts. SNRFCB involved the pre-selection of gene sub-network signatures based on the mutations and on protein-protein interaction data as well as the application of the random forest algorithm to gene expression datasets. Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer patients in the chemotherapy responder group (P = 0.008), but it was not beneficial to patients in the chemotherapy non-responder group (P = 0.657). Adjuvant CTX was significantly associated with the prolonged overall survival of lung cancer squamous cell carcinoma (SQCC) subtype patients in the chemotherapy responder cohorts (P = 0.024), but it was not beneficial to patients in the chemotherapy non-responder cohorts (P = 0.383). SNRFCB improved prediction performance as compared to the machine learning method, support vector machine (SVM). To test the general applicability of the predictive model, we further applied the SNRFCB approach to human breast cancer datasets and also observed superior performance. SNRFCB could provide recurrent probability for individual patients and identify which patients may benefit from adjuvant CTX in clinical trials.

  2. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers.

    Science.gov (United States)

    Lee, Yu-Hao; Hsieh, Ya-Ju; Shiah, Yung-Jong; Lin, Yu-Huei; Chen, Chiao-Yun; Tyan, Yu-Chang; GengQiu, JiaCheng; Hsu, Chung-Yao; Chen, Sharon Chia-Ju

    2017-04-01

    To quantitate the meditation experience is a subjective and complex issue because it is confounded by many factors such as emotional state, method of meditation, and personal physical condition. In this study, we propose a strategy with a cross-sectional analysis to evaluate the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant normalizing scaling are manipulated as the evaluating variables for the detection of accuracy. Thereafter, by modulating the sliding window (the period of the analyzed data) and shifting interval of the window (the time interval to shift the analyzed data), the effect of immediate analysis for the 2 methods is compared. This analysis system is performed on 3 meditation groups, categorizing their meditation experiences in 10-year intervals from novice to junior and to senior. After an exhausted calculation and cross-validation across all variables, the high accuracy rate >98% is achievable under the criterion of 0.5-minute sliding window and 2 seconds shifting interval for both methods. In a word, the minimum analyzable data length is 0.5 minute and the minimum recognizable temporal resolution is 2 seconds in the decision of meditative classification. Our proposed classifier of the meditation experience promotes a rapid evaluation system to distinguish meditation experience and a beneficial utilization of artificial techniques for the big-data analysis.

  3. Using SAR images to delineate ocean oil slicks with a texture-classifying neural network algorithm (TCNNA)

    Energy Technology Data Exchange (ETDEWEB)

    Garcia-Pineda, O.; MacDonald, I.R. [Florida State Univ., Tallahassee, FL (United States). Dept. of Oceanography; Zimmer, B. [Texas A and M Univ., Corpus Christi, TX (United States). Dept. of Mathematics and Statistics; Howard, M. [Texas A and M Univ., College Station, TX (United States). Dept. of Oceanography; Pichel, W. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Center for Satellite Applications and Research, National Environmental Satellite, Data and Information Service; Li, X. [National Oceanic and Atmospheric Administration, Camp Springs, MD (United States). Systems Group, National Environmental Satellite, Data and Information

    2009-10-15

    Synthetic aperture radar (SAR) is used to detect surfactant layers produced by floating oil on the ocean surface. This study presented details of a texture-classifying neural network algorithm (TCNNA) designed to process SAR data from a wide selection of beam modes. Patterns from SAR imagery were extracted in a semi-supervised procedure using a combination of edge-detection filters; texture descriptors; collection information; and environmental data. Various natural oil seeps in the Gulf of Mexico were used as case studies. An analysis of the case studies demonstrated that the TCNNA was able to extract targets and rapidly interpret images collected under a range of environmental conditions. Results presented by the TCNNA were used to evaluate the effects of different environmental conditions on the expressions of oil slicks detected by the data. Optimal incidence angle ranges and wind speed ranges for surfactant film detection were also presented. Results obtained by the TCNNA can be stored and manipulated in geographic information system (GIS) data layers. 26 refs., 1 tab., 7 figs.

  4. Causality in Psychiatry: A Hybrid Symptom Network Construct Model

    Directory of Open Access Journals (Sweden)

    Gerald eYoung

    2015-11-01

    Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

  5. Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

    Science.gov (United States)

    Guo, Hao; Qin, Mengna; Chen, Junjie; Xu, Yong; Xiang, Jie

    2017-01-01

    High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.

  6. Construction of coffee transcriptome networks based on gene annotation semantics

    Directory of Open Access Journals (Sweden)

    Castillo Luis F.

    2012-12-01

    Full Text Available Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.

  7. A Novel HMM Distributed Classifier for the Detection of Gait Phases by Means of a Wearable Inertial Sensor Network

    Directory of Open Access Journals (Sweden)

    Juri Taborri

    2014-09-01

    Full Text Available In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98 for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95 when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

  8. [Regional ecological planning and ecological network construction: a case study of "Ji Triangle" Region].

    Science.gov (United States)

    Li, Bo; Han, Zeng-Lin; Tong, Lian-Jun

    2009-05-01

    By the methods of in situ investigation and regional ecological planning, the present ecological environment, ecosystem vulnerability, and ecological environment sensitivity in "Ji Triangle" Region were analyzed, and the ecological network of the study area was constructed. According to the ecological resources abundance degree, ecological recovery, farmland windbreak system, environmental carrying capacity, forestry foundation, and ecosystem integrity, the study area was classified into three regional ecological function ecosystems, i. e., east low hill ecosystem, middle plain ecosystem, and west plain wetland ecosystem. On the basis of marking regional ecological nodes, the regional ecological corridor (Haerbin-Dalian regional axis, Changchun-Jilin, Changchun-Songyuan, Jilin-Songyuan, Jilin-Siping, and Songyuan-Siping transportation corridor) and regional ecological network (one ring, three links, and three belts) were constructed. Taking the requests of regional ecological security into consideration, the ecological environment security system of "Ji Triangle" Region, including regional ecological conservation district, regional ecological restored district, and regional ecological management district, was built.

  9. Classifying Microorganisms

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2006-01-01

    This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

  10. Hash function construction using weighted complex dynamical networks

    International Nuclear Information System (INIS)

    Song Yu-Rong; Jiang Guo-Ping

    2013-01-01

    A novel scheme to construct a hash function based on a weighted complex dynamical network (WCDN) generated from an original message is proposed in this paper. First, the original message is divided into blocks. Then, each block is divided into components, and the nodes and weighted edges are well defined from these components and their relations. Namely, the WCDN closely related to the original message is established. Furthermore, the node dynamics of the WCDN are chosen as a chaotic map. After chaotic iterations, quantization and exclusive-or operations, the fixed-length hash value is obtained. This scheme has the property that any tiny change in message can be diffused rapidly through the WCDN, leading to very different hash values. Analysis and simulation show that the scheme possesses good statistical properties, excellent confusion and diffusion, strong collision resistance and high efficiency. (general)

  11. Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

    Science.gov (United States)

    Mahmud, Faisal; Samiul, Hasan

    2010-01-01

    It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations.

  12. Naïve Bayesian Classifier for Selecting Good/Bad Projects during the Early Stage of International Construction Bidding Decisions

    Directory of Open Access Journals (Sweden)

    Woosik Jang

    2015-01-01

    Full Text Available Since the 1970s, revenues generated by Korean contractors in international construction have increased rapidly, exceeding USD 70 billion per year in recent years. However, Korean contractors face significant risks from market uncertainty and sensitivity to economic volatility and technical difficulties. As the volatility of these risks threatens project profitability, approximately 15% of bad projects were found to account for 74% of losses from the same international construction sector. Anticipating bad projects via preemptive risk management can better prevent losses so that contractors can enhance the efficiency of bidding decisions during the early stages of a project cycle. In line with these objectives, this paper examines the effect of such factors on the degree of project profitability. The Naïve Bayesian classifier is applied to identify a good project screening tool, which increases practical applicability using binomial variables with limited information that is obtainable in the early stages. The proposed model produced superior classification results that adequately reflect contractor views of risk. It is anticipated that when users apply the proposed model based on their own knowledge and expertise, overall firm profit rates will increase as a result of early abandonment of bad projects as well as the prioritization of good projects before final bidding decisions are made.

  13. New neural network classifier of fall-risk based on the Mahalanobis distance and kinematic parameters assessed by a wearable device

    International Nuclear Information System (INIS)

    Giansanti, Daniele; Macellari, Velio; Maccioni, Giovanni

    2008-01-01

    Fall prevention lacks easy, quantitative and wearable methods for the classification of fall-risk (FR). Efforts must be thus devoted to the choice of an ad hoc classifier both to reduce the size of the sample used to train the classifier and to improve performances. A new methodology that uses a neural network (NN) and a wearable device are hereby proposed for this purpose. The NN uses kinematic parameters assessed by a wearable device with accelerometers and rate gyroscopes during a posturography protocol. The training of the NN was based on the Mahalanobis distance and was carried out on two groups of 30 elderly subjects with varying fall-risk Tinetti scores. The validation was done on two groups of 100 subjects with different fall-risk Tinetti scores and showed that, both in terms of specificity and sensitivity, the NN performed better than other classifiers (naive Bayes, Bayes net, multilayer perceptron, support vector machines, statistical classifiers). In particular, (i) the proposed NN methodology improved the specificity and sensitivity by a mean of 3% when compared to the statistical classifier based on the Mahalanobis distance (SCMD) described in Giansanti (2006 Physiol. Meas. 27 1081–90); (ii) the assessed specificity was 97%, the assessed sensitivity was 98% and the area under receiver operator characteristics was 0.965. (note)

  14. Recurrent Neural Network Model for Constructive Peptide Design.

    Science.gov (United States)

    Müller, Alex T; Hiss, Jan A; Schneider, Gisbert

    2018-02-26

    We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

  15. 76 FR 63811 - Structural Reforms To Improve the Security of Classified Networks and the Responsible Sharing and...

    Science.gov (United States)

    2011-10-13

    ... implementation of policies and minimum standards regarding information security, personnel security, and systems security; address both internal and external security threats and vulnerabilities; and provide policies and... policies and minimum standards will address all agencies that operate or access classified computer...

  16. Research on network information security model and system construction

    OpenAIRE

    Wang Haijun

    2016-01-01

    It briefly describes the impact of large data era on China’s network policy, but also brings more opportunities and challenges to the network information security. This paper reviews for the internationally accepted basic model and characteristics of network information security, and analyses the characteristics of network information security and their relationship. On the basis of the NIST security model, this paper describes three security control schemes in safety management model and the...

  17. A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

    Directory of Open Access Journals (Sweden)

    Marko Scholze

    2010-03-01

    Full Text Available Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN and Spectral Angle Mapper (SAM classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878 the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795. Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ~1% for ANN and ~6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting.

  18. [Strategic thinking of the construction of national schistosomiasis laboratory network in China].

    Science.gov (United States)

    Qin, Zhi-Qiang; Xu, Jing; Feng, Ting; Zhu, Hong-Qing; Li, Shi-Zhu; Xiao, Ning; Zhou, Xiao-Nong

    2013-08-01

    A schistosomiasis laboratory network and its quality assurance system have been built and will be more and more perfect in China. This paper introduces the present situation of schistosomiasis diagnosis in China and expounds the basic ideas and the progress in the construction of schistosomiasis network platform. Furthermore, the face of schistosomiasis diagnosis network platform construction and operation of the challenge and the future work will be put forward in the latter part of this paper.

  19. Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer

    DEFF Research Database (Denmark)

    Fjeldbo, Christina S; Julin, Cathinka H; Lando, Malin

    2016-01-01

    platforms. The prognostic value was independent of existing clinical markers, regardless of clinical endpoints. CONCLUSIONS: A robust DCE-MRI-associated gene classifier has been constructed that may be used to achieve an early indication of patients' risk of hypoxia-related chemoradiotherapy failure.......PURPOSE: A 31-gene expression signature reflected in dynamic contrast enhanced (DCE)-MR images and correlated with hypoxia-related aggressiveness in cervical cancer was identified in previous work. We here aimed to construct a dichotomous classifier with key signature genes and a predefined...... as an indicator of hypoxia. RESULTS: Classifier candidates were constructed by integrative analysis of ABrix and gene expression profiles in the training cohort and evaluated by a leave-one-out cross-validation approach. On the basis of their ability to separate patients correctly according to hypoxia status, a 6...

  20. Constructing Precisely Computing Networks with Biophysical Spiking Neurons.

    Science.gov (United States)

    Schwemmer, Michael A; Fairhall, Adrienne L; Denéve, Sophie; Shea-Brown, Eric T

    2015-07-15

    While spike timing has been shown to carry detailed stimulus information at the sensory periphery, its possible role in network computation is less clear. Most models of computation by neural networks are based on population firing rates. In equivalent spiking implementations, firing is assumed to be random such that averaging across populations of neurons recovers the rate-based approach. Recently, however, Denéve and colleagues have suggested that the spiking behavior of neurons may be fundamental to how neuronal networks compute, with precise spike timing determined by each neuron's contribution to producing the desired output (Boerlin and Denéve, 2011; Boerlin et al., 2013). By postulating that each neuron fires to reduce the error in the network's output, it was demonstrated that linear computations can be performed by networks of integrate-and-fire neurons that communicate through instantaneous synapses. This left open, however, the possibility that realistic networks, with conductance-based neurons with subthreshold nonlinearity and the slower timescales of biophysical synapses, may not fit into this framework. Here, we show how the spike-based approach can be extended to biophysically plausible networks. We then show that our network reproduces a number of key features of cortical networks including irregular and Poisson-like spike times and a tight balance between excitation and inhibition. Lastly, we discuss how the behavior of our model scales with network size or with the number of neurons "recorded" from a larger computing network. These results significantly increase the biological plausibility of the spike-based approach to network computation. We derive a network of neurons with standard spike-generating currents and synapses with realistic timescales that computes based upon the principle that the precise timing of each spike is important for the computation. We then show that our network reproduces a number of key features of cortical networks

  1. Construction of a novel multi-gene assay (42-gene classifier) for prediction of late recurrence in ER-positive breast cancer patients.

    Science.gov (United States)

    Tsunashima, Ryo; Naoi, Yasuto; Shimazu, Kenzo; Kagara, Naofumi; Shimoda, Masashi; Tanei, Tomonori; Miyake, Tomohiro; Kim, Seung Jin; Noguchi, Shinzaburo

    2018-05-04

    Prediction models for late (> 5 years) recurrence in ER-positive breast cancer need to be developed for the accurate selection of patients for extended hormonal therapy. We attempted to develop such a prediction model focusing on the differences in gene expression between breast cancers with early and late recurrence. For the training set, 779 ER-positive breast cancers treated with tamoxifen alone for 5 years were selected from the databases (GSE6532, GSE12093, GSE17705, and GSE26971). For the validation set, 221 ER-positive breast cancers treated with adjuvant hormonal therapy for 5 years with or without chemotherapy at our hospital were included. Gene expression was assayed by DNA microarray analysis (Affymetrix U133 plus 2.0). With the 42 genes differentially expressed in early and late recurrence breast cancers in the training set, a prediction model (42GC) for late recurrence was constructed. The patients classified by 42GC into the late recurrence-like group showed a significantly (P = 0.006) higher late recurrence rate as expected but a significantly (P = 1.62 × E-13) lower rate for early recurrence than non-late recurrence-like group. These observations were confirmed for the validation set, i.e., P = 0.020 for late recurrence and P = 5.70 × E-5 for early recurrence. We developed a unique prediction model (42GC) for late recurrence by focusing on the biological differences between breast cancers with early and late recurrence. Interestingly, patients in the late recurrence-like group by 42GC were at low risk for early recurrence.

  2. Construction of road network vulnerability evaluation index based on general travel cost

    Science.gov (United States)

    Leng, Jun-qiang; Zhai, Jing; Li, Qian-wen; Zhao, Lin

    2018-03-01

    With the development of China's economy and the continuous improvement of her urban road network, the vulnerability of the urban road network has attracted increasing attention. Based on general travel cost, this work constructs the vulnerability evaluation index for the urban road network, and evaluates the vulnerability of the urban road network from the perspective of user generalised travel cost. Firstly, the generalised travel cost model is constructed based on vehicle cost, travel time, and traveller comfort. Then, the network efficiency index is selected as an evaluation index of vulnerability: the network efficiency index is composed of the traffic volume and the generalised travel cost, which are obtained from the equilibrium state of the network. In addition, the research analyses the influence of traffic capacity decrease, road section attribute value, and location of road section, on vulnerability. Finally, the vulnerability index is used to analyse the local area network of Harbin and verify its applicability.

  3. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by {sup 123}I-FP-CIT brain SPECT

    Energy Technology Data Exchange (ETDEWEB)

    Palumbo, Barbara [University of Perugia, Nuclear Medicine Section, Department of Surgical, Radiological and Odontostomatological Sciences, Ospedale S. Maria della Misericordia, Perugia (Italy); Fravolini, Mario Luca [University of Perugia, Department of Electronic and Information Engineering, Perugia (Italy); Nuvoli, Susanna; Spanu, Angela; Madeddu, Giuseppe [University of Sassari, Department of Nuclear Medicine, Sassari (Italy); Paulus, Kai Stephan [University of Sassari, Department of Neurology, Sassari (Italy); Schillaci, Orazio [University Tor Vergata, Department of Biopathology and Diagnostic Imaging, Rome (Italy); IRCSS Neuromed, Pozzilli (Italy)

    2010-11-15

    To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using {sup 123}I-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). {sup 123}I-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H and Y) score of {<=}2 (early PD), and 63 with PD with a H and Y score of {>=}2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an ''average performance PNN'' was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9{+-}8.1% (mean{+-}SD), in patients with advanced PD 78.9{+-}8.1%, and in ET patients 96.6{+-}2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5{+-}3.4%. This means that patients with putamen values >5.99 were classified as having ET, while patients with putamen values <5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8{+-}5.3%), and if the value was <6.97 patients were classified as having advanced PD (probability 88.1%{+-}8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities. (orig.)

  4. Comparison of two neural network classifiers in the differential diagnosis of essential tremor and Parkinson's disease by 123I-FP-CIT brain SPECT

    International Nuclear Information System (INIS)

    Palumbo, Barbara; Fravolini, Mario Luca; Nuvoli, Susanna; Spanu, Angela; Madeddu, Giuseppe; Paulus, Kai Stephan; Schillaci, Orazio

    2010-01-01

    To contribute to the differentiation of Parkinson's disease (PD) and essential tremor (ET), we compared two different artificial neural network classifiers using 123 I-FP-CIT SPECT data, a probabilistic neural network (PNN) and a classification tree (ClT). 123 I-FP-CIT brain SPECT with semiquantitative analysis was performed in 216 patients: 89 with ET, 64 with PD with a Hoehn and Yahr (H and Y) score of ≤2 (early PD), and 63 with PD with a H and Y score of ≥2.5 (advanced PD). For each of the 1,000 experiments carried out, 108 patients were randomly selected as the PNN training set, while the remaining 108 validated the trained PNN, and the percentage of the validation data correctly classified in the three groups of patients was computed. The expected performance of an ''average performance PNN'' was evaluated. In analogy, for ClT 1,000 classification trees with similar structures were generated. For PNN, the probability of correct classification in patients with early PD was 81.9±8.1% (mean±SD), in patients with advanced PD 78.9±8.1%, and in ET patients 96.6±2.6%. For ClT, the first decision rule gave a mean value for the putamen of 5.99, which resulted in a probability of correct classification of 93.5±3.4%. This means that patients with putamen values >5.99 were classified as having ET, while patients with putamen values <5.99 were classified as having PD. Furthermore, if the caudate nucleus value was higher than 6.97 patients were classified as having early PD (probability 69.8±5.3%), and if the value was <6.97 patients were classified as having advanced PD (probability 88.1%±8.8%). These results confirm that PNN achieved valid classification results. Furthermore, ClT provided reliable cut-off values able to differentiate ET and PD of different severities. (orig.)

  5. Constructive Lower Bounds on Model Complexity of Shallow Perceptron Networks

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra

    2018-01-01

    Roč. 29, č. 7 (2018), s. 305-315 ISSN 0941-0643 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : shallow and deep networks * model complexity and sparsity * signum perceptron networks * finite mappings * variational norms * Hadamard matrices Subject RIV: IN - Informatics, Computer Science Impact factor: 2.505, year: 2016

  6. Dynamic Innovation strategies and stable networks in the construction industry

    DEFF Research Database (Denmark)

    Andersen, Poul Houman; Cook, Nicole; Marceau, Jane

    2004-01-01

    This paper investigate the role of interorganisational networking for technology diffusion in an industry characterized by very limited R&D activity. Udgivelsesdato: APR......This paper investigate the role of interorganisational networking for technology diffusion in an industry characterized by very limited R&D activity. Udgivelsesdato: APR...

  7. Construct mine environment monitoring system based on wireless mesh network

    Science.gov (United States)

    Chen, Xin; Ge, Gengyu; Liu, Yinmei; Cheng, Aimin; Wu, Jun; Fu, Jun

    2018-04-01

    The system uses wireless Mesh network as a network transmission medium, and strive to establish an effective and reliable underground environment monitoring system. The system combines wireless network technology and embedded technology to monitor the internal data collected in the mine and send it to the processing center for analysis and environmental assessment. The system can be divided into two parts: the main control network module and the data acquisition terminal, and the SPI bus technology is used for mutual communication between them. Multi-channel acquisition and control interface design Data acquisition and control terminal in the analog signal acquisition module, digital signal acquisition module, and digital signal output module. The main control network module running Linux operating system, in which the transplant SPI driver, USB card driver and AODV routing protocol. As a result, the internal data collection and reporting of the mine are realized.

  8. Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

    Directory of Open Access Journals (Sweden)

    Hesham El-Sayed

    2018-05-01

    Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

  9. Exploring the field of public construction clients by a graphical network analysis

    OpenAIRE

    Eisma, P.R.; Volker, L.

    2014-01-01

    Because public construction clients form the majority of construction clients and procure over 40% of the construction output in most countries, they are important actors in the construction industry. Yet, the field of research on clients is still underdeveloped. In order to identify the research gaps in this field, a graphical network analysis of existing literature is performed. The analysis is based on a query executed in the scientific database Scopus resulting in around 3,300 publication...

  10. Constructing Social Networks From Secondary Storage With Bulk Analysis Tools

    Science.gov (United States)

    2016-06-01

    soccer team. However, for that drive if the k-value was reduced by one members of the drive owner’s family started to emerge . The same can be said for...including suggestions for reducing this burden to Washington headquarters Services , Directorate for Information Operations and Reports, 1215 Jefferson...Network of Online Application Usernames 53 Figure 5.6 Demonstration of a Network of Soccer Teammates . . . . . . 55 Figure 5.7 Demonstration of a

  11. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

    International Nuclear Information System (INIS)

    Sahiner, B.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Adler, D.D.; Goodsitt, M.M.; Wei, D.

    1996-01-01

    The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI's containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms

  12. Construction and repair of highly ordered 2D covalent networks by chemical equilibrium regulation.

    Science.gov (United States)

    Guan, Cui-Zhong; Wang, Dong; Wan, Li-Jun

    2012-03-21

    The construction of well-ordered 2D covalent networks via the dehydration of di-borate aromatic molecules was successfully realized through introducing a small amount of water into a closed reaction system to regulate the chemical equilibrium.

  13. 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

  14. Quantum Google algorithm. Construction and application to complex networks

    Science.gov (United States)

    Paparo, G. D.; Müller, M.; Comellas, F.; Martin-Delgado, M. A.

    2014-07-01

    We review the main findings on the ranking capabilities of the recently proposed Quantum PageRank algorithm (G.D. Paparo et al., Sci. Rep. 2, 444 (2012) and G.D. Paparo et al., Sci. Rep. 3, 2773 (2013)) applied to large complex networks. The algorithm has been shown to identify unambiguously the underlying topology of the network and to be capable of clearly highlighting the structure of secondary hubs of networks. Furthermore, it can resolve the degeneracy in importance of the low-lying part of the list of rankings. Examples of applications include real-world instances from the WWW, which typically display a scale-free network structure and models of hierarchical networks. The quantum algorithm has been shown to display an increased stability with respect to a variation of the damping parameter, present in the Google algorithm, and a more clearly pronounced power-law behaviour in the distribution of importance among the nodes, as compared to the classical algorithm.

  15. A new method to construct co-author networks

    Science.gov (United States)

    Liu, Jie; Li, Yunpeng; Ruan, Zichan; Fu, Guangyuan; Chen, Xiaowu; Sadiq, Rehan; Deng, Yong

    2015-02-01

    In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.

  16. Constructing Social Networks from Unstructured Group Dialog in Virtual Worlds

    Science.gov (United States)

    Shah, Fahad; Sukthankar, Gita

    Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. In this paper, we present techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world. We present an algorithm for addressing this problem, Shallow Semantic Temporal Overlap (SSTO), that combines temporal and language information to create directional links between participants, and a second approach that relies on temporal overlap alone to create undirected links between participants. Relying on temporal overlap is noisy, resulting in a low precision and networks with many extraneous links. In this paper, we demonstrate that we can ameliorate this problem by using network modularity optimization to perform community detection in the noisy networks and severing cross-community links. Although using the content of the communications still results in the best performance, community detection is effective as a noise reduction technique for eliminating the extra links created by temporal overlap alone.

  17. Digging into construction: social networks and their potential impact on knowledge transfer.

    Science.gov (United States)

    Carlan, N A; Kramer, D M; Bigelow, P; Wells, R; Garritano, E; Vi, P

    2012-01-01

    A six-year study is exploring the most effective ways to disseminate ideas to reduce musculoskeletal disorders (MSDs) in the construction sector. The sector was targeted because MSDs account for 35% of all lost time injuries. This paper reports on the organization of the construction sector, and maps potential pathways of communication, including social networks, to set the stage for future dissemination. The managers, health and safety specialists, union health and safety representatives, and 28 workers from small, medium and large construction companies participated. Over a three-year period, data were collected from 47 qualitative interviews. Questions were guided by the PARIHS (Promoting Action on Research Implementation in Health Services) knowledge-transfer conceptual framework and adapted for the construction sector. The construction sector is a complex and dynamic sector, with non-linear reporting relationships, and divided and diluted responsibilities. Four networks were identified that can potentially facilitate the dissemination of new knowledge: worksite-project networks; union networks; apprenticeship program networks; and networks established by the Construction Safety Association/Infrastructure Health and Safety Association. Flexible and multi-directional lines of communication must be used in this complex environment. This has implications for the future choice of knowledge transfer strategies.

  18. Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

    Science.gov (United States)

    Mirzaei, Sajad; Wu, Yufeng

    2016-01-01

    Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

  19. Fingerprint prediction using classifier ensembles

    CSIR Research Space (South Africa)

    Molale, P

    2011-11-01

    Full Text Available ); logistic discrimination (LgD), k-nearest neighbour (k-NN), artificial neural network (ANN), association rules (AR) decision tree (DT), naive Bayes classifier (NBC) and the support vector machine (SVM). The performance of several multiple classifier systems...

  20. Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment

    Science.gov (United States)

    Lee, Woojin; Kim, Juil; Kang, JangMook

    2010-01-01

    In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric—the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment. PMID:22163678

  1. Automated Construction of Node Software Using Attributes in a Ubiquitous Sensor Network Environment

    Directory of Open Access Journals (Sweden)

    JangMook Kang

    2010-09-01

    Full Text Available In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric—the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment.

  2. Influence of the time scale on the construction of financial networks.

    Science.gov (United States)

    Emmert-Streib, Frank; Dehmer, Matthias

    2010-09-30

    In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspond to none vanishing correlation coefficients. That means only if a correlation coefficient is statistically significant different from zero, we include an edge in the network. This construction procedure results in unweighted, undirected networks. By separating the time series of stock prices in non-overlapping intervals, we obtain one network per interval. The length of these intervals corresponds to the time scale of the data, whose influence on the construction of the networks will be studied in this paper. Numerical analysis of four different measures in dependence on the time scale for the construction of networks allows us to gain insights about the intrinsic time scale of the stock market with respect to a meaningful graph-theoretical analysis.

  3. Automated construction of node software using attributes in a ubiquitous sensor network environment.

    Science.gov (United States)

    Lee, Woojin; Kim, Juil; Kang, JangMook

    2010-01-01

    In sensor networks, nodes must often operate in a demanding environment facing restrictions such as restricted computing resources, unreliable wireless communication and power shortages. Such factors make the development of ubiquitous sensor network (USN) applications challenging. To help developers construct a large amount of node software for sensor network applications easily and rapidly, this paper proposes an approach to the automated construction of node software for USN applications using attributes. In the proposed technique, application construction proceeds by first developing a model for the sensor network and then designing node software by setting the values of the predefined attributes. After that, the sensor network model and the design of node software are verified. The final source codes of the node software are automatically generated from the sensor network model. We illustrate the efficiency of the proposed technique by using a gas/light monitoring application through a case study of a Gas and Light Monitoring System based on the Nano-Qplus operating system. We evaluate the technique using a quantitative metric-the memory size of execution code for node software. Using the proposed approach, developers are able to easily construct sensor network applications and rapidly generate a large number of node softwares at a time in a ubiquitous sensor network environment.

  4. Influence of the Time Scale on the Construction of Financial Networks

    OpenAIRE

    Emmert-Streib, Frank; Dehmer, Matthias

    2010-01-01

    BACKGROUND: In this paper we investigate the definition and formation of financial networks. Specifically, we study the influence of the time scale on their construction. METHODOLOGY/PRINCIPAL FINDINGS: For our analysis we use correlation-based networks obtained from the daily closing prices of stock market data. More precisely, we use the stocks that currently comprise the Dow Jones Industrial Average (DJIA) and estimate financial networks where nodes correspond to stocks and edges correspon...

  5. Construction and discussion of the science and technology information network of SWIP

    International Nuclear Information System (INIS)

    Wang Li; Zhang Yiming

    2010-01-01

    The digital library needs to be developed with the advancement of digitalisation and network, and the construction of digital information resource is more indispensable. This paper introduces the exploration and the work performed by the Research Office of Science and Technology Information of the Southwestern Institute of Physics with respect to preliminary construction of science and technology (S and T) information network resources and the magnitude alteration of S and T information service platform subsequently and the building of a dynamical network information service mode of its own characteristics. (authors)

  6. The construction of an amino acid network for understanding protein structure and function.

    Science.gov (United States)

    Yan, Wenying; Zhou, Jianhong; Sun, Maomin; Chen, Jiajia; Hu, Guang; Shen, Bairong

    2014-06-01

    Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

  7. A large number of stepping motor network construction by PLC

    Science.gov (United States)

    Mei, Lin; Zhang, Kai; Hongqiang, Guo

    2017-11-01

    In the flexible automatic line, the equipment is complex, the control mode is flexible, how to realize the large number of step and servo motor information interaction, the orderly control become a difficult control. Based on the existing flexible production line, this paper makes a comparative study of its network strategy. After research, an Ethernet + PROFIBUSE communication configuration based on PROFINET IO and profibus was proposed, which can effectively improve the data interaction efficiency of the equipment and stable data interaction information.

  8. Query construction, entropy, and generalization in neural-network models

    Science.gov (United States)

    Sollich, Peter

    1994-05-01

    We study query construction algorithms, which aim at improving the generalization ability of systems that learn from examples by choosing optimal, nonredundant training sets. We set up a general probabilistic framework for deriving such algorithms from the requirement of optimizing a suitable objective function; specifically, we consider the objective functions entropy (or information gain) and generalization error. For two learning scenarios, the high-low game and the linear perceptron, we evaluate the generalization performance obtained by applying the corresponding query construction algorithms and compare it to training on random examples. We find qualitative differences between the two scenarios due to the different structure of the underlying rules (nonlinear and ``noninvertible'' versus linear); in particular, for the linear perceptron, random examples lead to the same generalization ability as a sequence of queries in the limit of an infinite number of examples. We also investigate learning algorithms which are ill matched to the learning environment and find that, in this case, minimum entropy queries can in fact yield a lower generalization ability than random examples. Finally, we study the efficiency of single queries and its dependence on the learning history, i.e., on whether the previous training examples were generated randomly or by querying, and the difference between globally and locally optimal query construction.

  9. Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

    Directory of Open Access Journals (Sweden)

    A. Moosavian

    2013-01-01

    Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

  10. Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.

    Science.gov (United States)

    Kim, Seong Gon; Theera-Ampornpunt, Nawanol; Fang, Chih-Hao; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

    2016-08-01

    Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications. Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was

  11. Hardware and software constructs for a vibration analysis network

    International Nuclear Information System (INIS)

    Cook, S.A.; Crowe, R.D.; Toffer, H.

    1985-01-01

    Vibration level monitoring and analysis has been initiated at N Reactor, the dual purpose reactor operated at Hanford, Washington by UNC Nuclear Industries (UNC) for the Department of Energy (DOE). The machinery to be monitored was located in several buildings scattered over the plant site, necessitating an approach using satellite stations to collect, monitor and temporarily store data. The satellite stations are, in turn, linked to a centralized processing computer for further analysis. The advantages of a networked data analysis system are discussed in this paper along with the hardware and software required to implement such a system

  12. Efficient Construction of Mesostate Networks from Molecular Dynamics Trajectories.

    Science.gov (United States)

    Vitalis, Andreas; Caflisch, Amedeo

    2012-03-13

    The coarse-graining of data from molecular simulations yields conformational space networks that may be used for predicting the system's long time scale behavior, to discover structural pathways connecting free energy basins in the system, or simply to represent accessible phase space regions of interest and their connectivities in a two-dimensional plot. In this contribution, we present a tree-based algorithm to partition conformations of biomolecules into sets of similar microstates, i.e., to coarse-grain trajectory data into mesostates. On account of utilizing an architecture similar to that of established tree-based algorithms, the proposed scheme operates in near-linear time with data set size. We derive expressions needed for the fast evaluation of mesostate properties and distances when employing typical choices for measures of similarity between microstates. Using both a pedagogically useful and a real-word application, the algorithm is shown to be robust with respect to tree height, which in addition to mesostate threshold size is the main adjustable parameter. It is demonstrated that the derived mesostate networks can preserve information regarding the free energy basins and barriers by which the system is characterized.

  13. Connection with seismic networks and construction of real time earthquake monitoring system

    International Nuclear Information System (INIS)

    Chi, Heon Cheol; Lee, H. I.; Shin, I. C.; Lim, I. S.; Park, J. H.; Lee, B. K.; Whee, K. H.; Cho, C. S.

    2000-12-01

    It is natural to use the nuclear power plant seismic network which have been operated by KEPRI(Korea Electric Power Research Institute) and local seismic network by KIGAM(Korea Institute of Geology, Mining and Material). The real time earthquake monitoring system is composed with monitoring module and data base module. Data base module plays role of seismic data storage and classification and the other, monitoring module represents the status of acceleration in the nuclear power plant area. This research placed the target on the first, networking the KIN's seismic monitoring system with KIGAM and KEPRI seismic network and the second, construction the KIN's Independent earthquake monitoring system

  14. Route Network Construction with Location-Direction-Enabled Photographs

    Science.gov (United States)

    Fujita, Hideyuki; Sagara, Shota; Ohmori, Tadashi; Shintani, Takahiko

    2018-05-01

    We propose a method for constructing a geometric graph for generating routes that summarize a geographical area and also have visual continuity by using a set of location-direction-enabled photographs. A location- direction-enabled photograph is a photograph that has information about the location (position of the camera at the time of shooting) and the direction (direction of the camera at the time of shooting). Each nodes of the graph corresponds to a location-direction-enabled photograph. The location of each node is the location of the corresponding photograph, and a route on the graph corresponds to a route in the geographic area and a sequence of photographs. The proposed graph is constructed to represent characteristic spots and paths linking the spots, and it is assumed to be a kind of a spatial summarization of the area with the photographs. Therefore, we call the routes on the graph as spatial summary route. Each route on the proposed graph also has a visual continuity, which means that we can understand the spatial relationship among the continuous photographs on the route such as moving forward, backward, turning right, etc. In this study, when the changes in the shooting position and shooting direction satisfied a given threshold, the route was defined to have visual continuity. By presenting the photographs in order along the generated route, information can be presented sequentially, while maintaining visual continuity to a great extent.

  15. Construction of binary status information system using PC network

    International Nuclear Information System (INIS)

    Kurnianto, K.; Azriani, A.; Teddy, S.

    1998-01-01

    Binary status information system is a part of establishing reactor parameter with Pc that function as MPR-30 Process Computer. Binary Alarm system, consist of interface hardware and input binary module terminal, prepare the information that be displayed in text message and graphical form. Monitor software give facilities that binary status of RSG-GAS components can be monitored using computer network (LAN). This program consist of two part : reside in server computer and reside in user computer. Program in server acquire data from interface and than store it in data base (Access file). Than, user computer read this file and display it in Dynamic Process and Instrumentation Diagram. The number of user computer can be more then one because data base was designed for multi-user operation

  16. A Lindenmayer system-based approach for the design of nutrient delivery networks in tissue constructs

    Energy Technology Data Exchange (ETDEWEB)

    Yasar, Ozlem; Starly, Binil [School of Industrial Engineering, University of Oklahoma, Norman, OK 73019 (United States); Lan, S-F [University of Oklahoma Bioengineering Center, University of Oklahoma, Norman, OK 73019 (United States)

    2009-12-15

    Large thick tissue constructs have reported limited success primarily due to the inability of cells to survive deep within the scaffold. Without access to adequate nutrients, cells placed deep within the tissue construct will die out, leading to non-uniform tissue regeneration. Currently, there is a necessity to design nutrient conduit networks within the tissue construct to enable cells to survive in the matrix. However, the design of complex networks within a tissue construct is challenging. In this paper, we present the Lindenmayer system, an elegant fractal-based language algorithm framework, to generate conduit networks in two- and three-dimensional architecture with several degrees of complexity. The conduit network maintains a parent-child relationship between each branch of the network. Several L-system parameters have been studied-branching angle, branch length, ratio of parent to child branch diameter, etc-to simulate several architectures under a given L-system notation. We have also presented a layered manufacturing-based UV-photopolymerization process using the Texas Instruments DLP(TM) system to fabricate the branched structures. This preliminary work showcases the applicability of L-system-based construct designs to drive scaffold fabrication systems.

  17. A Lindenmayer system-based approach for the design of nutrient delivery networks in tissue constructs

    International Nuclear Information System (INIS)

    Yasar, Ozlem; Starly, Binil; Lan, S-F

    2009-01-01

    Large thick tissue constructs have reported limited success primarily due to the inability of cells to survive deep within the scaffold. Without access to adequate nutrients, cells placed deep within the tissue construct will die out, leading to non-uniform tissue regeneration. Currently, there is a necessity to design nutrient conduit networks within the tissue construct to enable cells to survive in the matrix. However, the design of complex networks within a tissue construct is challenging. In this paper, we present the Lindenmayer system, an elegant fractal-based language algorithm framework, to generate conduit networks in two- and three-dimensional architecture with several degrees of complexity. The conduit network maintains a parent-child relationship between each branch of the network. Several L-system parameters have been studied-branching angle, branch length, ratio of parent to child branch diameter, etc-to simulate several architectures under a given L-system notation. We have also presented a layered manufacturing-based UV-photopolymerization process using the Texas Instruments DLP(TM) system to fabricate the branched structures. This preliminary work showcases the applicability of L-system-based construct designs to drive scaffold fabrication systems.

  18. BIMLR: a method for constructing rooted phylogenetic networks from rooted phylogenetic trees.

    Science.gov (United States)

    Wang, Juan; Guo, Maozu; Xing, Linlin; Che, Kai; Liu, Xiaoyan; Wang, Chunyu

    2013-09-15

    Rooted phylogenetic trees constructed from different datasets (e.g. from different genes) are often conflicting with one another, i.e. they cannot be integrated into a single phylogenetic tree. Phylogenetic networks have become an important tool in molecular evolution, and rooted phylogenetic networks are able to represent conflicting rooted phylogenetic trees. Hence, the development of appropriate methods to compute rooted phylogenetic networks from rooted phylogenetic trees has attracted considerable research interest of late. The CASS algorithm proposed by van Iersel et al. is able to construct much simpler networks than other available methods, but it is extremely slow, and the networks it constructs are dependent on the order of the input data. Here, we introduce an improved CASS algorithm, BIMLR. We show that BIMLR is faster than CASS and less dependent on the input data order. Moreover, BIMLR is able to construct much simpler networks than almost all other methods. BIMLR is available at http://nclab.hit.edu.cn/wangjuan/BIMLR/. © 2013 Elsevier B.V. All rights reserved.

  19. Composite Classifiers for Automatic Target Recognition

    National Research Council Canada - National Science Library

    Wang, Lin-Cheng

    1998-01-01

    ...) using forward-looking infrared (FLIR) imagery. Two existing classifiers, one based on learning vector quantization and the other on modular neural networks, are used as the building blocks for our composite classifiers...

  20. Multilevel regularized regression for simultaneous taxa selection and network construction with metagenomic count data.

    Science.gov (United States)

    Liu, Zhenqiu; Sun, Fengzhu; Braun, Jonathan; McGovern, Dermot P B; Piantadosi, Steven

    2015-04-01

    Identifying disease associated taxa and constructing networks for bacteria interactions are two important tasks usually studied separately. In reality, differentiation of disease associated taxa and correlation among taxa may affect each other. One genus can be differentiated because it is highly correlated with another highly differentiated one. In addition, network structures may vary under different clinical conditions. Permutation tests are commonly used to detect differences between networks in distinct phenotypes, and they are time-consuming. In this manuscript, we propose a multilevel regularized regression method to simultaneously identify taxa and construct networks. We also extend the framework to allow construction of a common network and differentiated network together. An efficient algorithm with dual formulation is developed to deal with the large-scale n ≪ m problem with a large number of taxa (m) and a small number of samples (n) efficiently. The proposed method is regularized with a general Lp (p ∈ [0, 2]) penalty and models the effects of taxa abundance differentiation and correlation jointly. We demonstrate that it can identify both true and biologically significant genera and network structures. Software MLRR in MATLAB is available at http://biostatistics.csmc.edu/mlrr/. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  1. Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program

    Science.gov (United States)

    Benaouda, D.; Wadge, G.; Whitmarsh, R. B.; Rothwell, R. G.; MacLeod, C.

    1999-02-01

    In boreholes with partial or no core recovery, interpretations of lithology in the remainder of the hole are routinely attempted using data from downhole geophysical sensors. We present a practical neural net-based technique that greatly enhances lithological interpretation in holes with partial core recovery by using downhole data to train classifiers to give a global classification scheme for those parts of the borehole for which no core was retrieved. We describe the system and its underlying methods of data exploration, selection and classification, and present a typical example of the system in use. Although the technique is equally applicable to oil industry boreholes, we apply it here to an Ocean Drilling Program (ODP) borehole (Hole 792E, Izu-Bonin forearc, a mixture of volcaniclastic sandstones, conglomerates and claystones). The quantitative benefits of quality-control measures and different subsampling strategies are shown. Direct comparisons between a number of discriminant analysis methods and the use of neural networks with back-propagation of error are presented. The neural networks perform better than the discriminant analysis techniques both in terms of performance rates with test data sets (2-3 per cent better) and in qualitative correlation with non-depth-matched core. We illustrate with the Hole 792E data how vital it is to have a system that permits the number and membership of training classes to be changed as analysis proceeds. The initial classification for Hole 792E evolved from a five-class to a three-class and then to a four-class scheme with resultant classification performance rates for the back-propagation neural network method of 83, 84 and 93 per cent respectively.

  2. Construction of Neural Networks for Realization of Localized Deep Learning

    Directory of Open Access Journals (Sweden)

    Charles K. Chui

    2018-05-01

    Full Text Available The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component is also designed to deal with outliers. The main theoretical result in this paper is the order O(m-2s/(2s+d of approximation of the regression function with regularity s, in terms of the number m of sample points, where the (unknown manifold dimension d replaces the dimension D of the sampling (Euclidean space for shallow nets.

  3. 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.

  4. COGITA network has constructed a glossary of diagnostic reasoning terms.

    Science.gov (United States)

    Barais, Marie; Hauswaldt, Johannes; Dinant, Geert-Jan; van de Wiel, Margje; Stolper, C F Erik; Van Royen, Paul

    2017-12-01

    The role of gut feelings in diagnostic reasoning is recognized by most GPs throughout Europe, and probably throughout the world. Studies on this topic have emerged from different countries but there is the risk that authors will use different terms for similar concepts. The European Expert Group on Cognitive and Interactive Processes in Diagnosis and Management in General Practice, COGITA for short, was founded in 2008 to conduct cross-border research in the area of non-analytical diagnostic reasoning. Academic GPs, PhD students, psychologists, linguists and students meet once a year to share their experiences, exchange results and initiate new studies on the topic. A milestone in their research is this publication of a short glossary of diagnostic reasoning terms relating to the gut feelings research topic. It was constructed by the COGITA group members following a literature review, which aimed to define salient terms used in their publications. They described the terms, cross-reviewed the wording and reached consensus within the group. Two sections were created: (1) a diagnostic reasoning section that describes concepts such as analytical and non-analytical reasoning, clinical mind lines, and intuition, and (2) a research methods section describing concepts such as linguistic validity and saturation. The glossary, including relevant literature, has been published on the website http://www.gutfeelingsingeneralpractice.eu . In the future, the glossary will be modified if necessary and completed by members of the COGITA group. [Box: see text].

  5. [Construction and optimization of ecological network for nature reserves in Fujian Province, China].

    Science.gov (United States)

    Gu, Fan; Huang, Yi Xiong; Chen, Chuan Ming; Cheng, Dong Liang; Guo, Jia Lei

    2017-03-18

    The nature reserve is very important to biodiversity maintenance. However, due to the urbanization, the nature reserve has been fragmented with reduction in area, leading to the loss of species diversity. Establishing ecological network can effectively connect the fragmented habitats and plays an important role in species conversation. In this paper, based on deciding habitat patches and the landscape cost surface in ArcGIS, a minimum cumulative resistance model was used to simulate the potential ecological network of Fujian provincial nature reserves. The connectivity and importance of network were analyzed and evaluated based on comparison of connectivity indices (including the integral index of connectivity and probability of connectivity) and gravity model both before and after the potential ecological network construction. The optimum ecological network optimization measures were proposed. The result demonstrated that woodlands, grasslands and wetlands together made up the important part of the nature reserve ecological network. The habitats with large area had a higher degree of importance in the network. After constructing the network, the connectivity level was significantly improved. Although interaction strength between different patches va-ried greatly, the corridors between patches with large interaction were very important. The research could provide scientific reference and basis for nature protection and planning in Fujian Province.

  6. A computational geometry approach to pore network construction for granular packings

    Science.gov (United States)

    van der Linden, Joost H.; Sufian, Adnan; Narsilio, Guillermo A.; Russell, Adrian R.; Tordesillas, Antoinette

    2018-03-01

    Pore network construction provides the ability to characterize and study the pore space of inhomogeneous and geometrically complex granular media in a range of scientific and engineering applications. Various approaches to the construction have been proposed, however subtle implementational details are frequently omitted, open access to source code is limited, and few studies compare multiple algorithms in the context of a specific application. This study presents, in detail, a new pore network construction algorithm, and provides a comprehensive comparison with two other, well-established Delaunay triangulation-based pore network construction methods. Source code is provided to encourage further development. The proposed algorithm avoids the expensive non-linear optimization procedure in existing Delaunay approaches, and is robust in the presence of polydispersity. Algorithms are compared in terms of structural, geometrical and advanced connectivity parameters, focusing on the application of fluid flow characteristics. Sensitivity of the various networks to permeability is assessed through network (Stokes) simulations and finite-element (Navier-Stokes) simulations. Results highlight strong dependencies of pore volume, pore connectivity, throat geometry and fluid conductance on the degree of tetrahedra merging and the specific characteristics of the throats targeted by the merging algorithm. The paper concludes with practical recommendations on the applicability of the three investigated algorithms.

  7. Ecological risk assessment of hydropower dam construction based on ecological network analysis

    OpenAIRE

    Chen, Shaoqing; Fath, Brian D.; Chen, Bin

    2010-01-01

    Dam construction is regarded as one of the major factors contributing to significant modifications of the river ecosystems, and the ecological risk (ER) assessment of dam construction has received growing attention in recent years. In the present study, we explored the potential ecological risk caused by dam project based on the general principles of the ecological risk assessment. Ecological network analysis was proposed as the usable analytic method for the implement of ecological risk asse...

  8. Systematic construction and control of stereo nerve vision network in intelligent manufacturing

    Science.gov (United States)

    Liu, Hua; Wang, Helong; Guo, Chunjie; Ding, Quanxin; Zhou, Liwei

    2017-10-01

    A system method of constructing stereo vision by using neural network is proposed, and the operation and control mechanism in actual operation are proposed. This method makes effective use of the neural network in learning and memory function, by after training with samples. Moreover, the neural network can learn the nonlinear relationship in the stereoscopic vision system and the internal and external orientation elements. These considerations are Worthy of attention, which includes limited constraints, the scientific of critical group, the operating speed and the operability in technical aspects. The results support our theoretical forecast.

  9. Genonets server-a web server for the construction, analysis and visualization of genotype networks.

    Science.gov (United States)

    Khalid, Fahad; Aguilar-Rodríguez, José; Wagner, Andreas; Payne, Joshua L

    2016-07-08

    A genotype network is a graph in which vertices represent genotypes that have the same phenotype. Edges connect vertices if their corresponding genotypes differ in a single small mutation. Genotype networks are used to study the organization of genotype spaces. They have shed light on the relationship between robustness and evolvability in biological systems as different as RNA macromolecules and transcriptional regulatory circuits. Despite the importance of genotype networks, no tool exists for their automatic construction, analysis and visualization. Here we fill this gap by presenting the Genonets Server, a tool that provides the following features: (i) the construction of genotype networks for categorical and univariate phenotypes from DNA, RNA, amino acid or binary sequences; (ii) analyses of genotype network topology and how it relates to robustness and evolvability, as well as analyses of genotype network topography and how it relates to the navigability of a genotype network via mutation and natural selection; (iii) multiple interactive visualizations that facilitate exploratory research and education. The Genonets Server is freely available at http://ieu-genonets.uzh.ch. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Characterizing system dynamics with a weighted and directed network constructed from time series data

    International Nuclear Information System (INIS)

    Sun, Xiaoran; Small, Michael; Zhao, Yi; Xue, Xiaoping

    2014-01-01

    In this work, we propose a novel method to transform a time series into a weighted and directed network. For a given time series, we first generate a set of segments via a sliding window, and then use a doubly symbolic scheme to characterize every windowed segment by combining absolute amplitude information with an ordinal pattern characterization. Based on this construction, a network can be directly constructed from the given time series: segments corresponding to different symbol-pairs are mapped to network nodes and the temporal succession between nodes is represented by directed links. With this conversion, dynamics underlying the time series has been encoded into the network structure. We illustrate the potential of our networks with a well-studied dynamical model as a benchmark example. Results show that network measures for characterizing global properties can detect the dynamical transitions in the underlying system. Moreover, we employ a random walk algorithm to sample loops in our networks, and find that time series with different dynamics exhibits distinct cycle structure. That is, the relative prevalence of loops with different lengths can be used to identify the underlying dynamics

  11. Characterizing system dynamics with a weighted and directed network constructed from time series data

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Xiaoran, E-mail: sxr0806@gmail.com [Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055 (China); School of Mathematics and Statistics, The University of Western Australia, Crawley WA 6009 (Australia); Small, Michael, E-mail: michael.small@uwa.edu.au [School of Mathematics and Statistics, The University of Western Australia, Crawley WA 6009 (Australia); Zhao, Yi [Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055 (China); Xue, Xiaoping [Department of Mathematics, Harbin Institute of Technology, Harbin 150025 (China)

    2014-06-15

    In this work, we propose a novel method to transform a time series into a weighted and directed network. For a given time series, we first generate a set of segments via a sliding window, and then use a doubly symbolic scheme to characterize every windowed segment by combining absolute amplitude information with an ordinal pattern characterization. Based on this construction, a network can be directly constructed from the given time series: segments corresponding to different symbol-pairs are mapped to network nodes and the temporal succession between nodes is represented by directed links. With this conversion, dynamics underlying the time series has been encoded into the network structure. We illustrate the potential of our networks with a well-studied dynamical model as a benchmark example. Results show that network measures for characterizing global properties can detect the dynamical transitions in the underlying system. Moreover, we employ a random walk algorithm to sample loops in our networks, and find that time series with different dynamics exhibits distinct cycle structure. That is, the relative prevalence of loops with different lengths can be used to identify the underlying dynamics.

  12. A computational framework for the automated construction of glycosylation reaction networks.

    Science.gov (United States)

    Liu, Gang; Neelamegham, Sriram

    2014-01-01

    Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All

  13. A computational framework for the automated construction of glycosylation reaction networks.

    Directory of Open Access Journals (Sweden)

    Gang Liu

    Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

  14. 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/ .

  15. The Discursive Construction of Teachers’ Desirable Identity on a Social Networking Site

    Directory of Open Access Journals (Sweden)

    Radzuwan Ab Rashid

    2016-09-01

    Full Text Available This study is situated in the broader identity-construction literature. Bringing discourse community theory to examine teachers’ postings on Facebook Timelines, we explored how teachers discursively construct socially desirable identities to fit into the Timeline community. Data were gathered from the Status updates and Comments on 29 Timelines belonged to Malaysian English language teachers who were purposively chosen as they often posted and commented on teaching-related issues on their Timelines. The analysis shows that the commonest form of identity construction on the teachers’ Timelines was as an engager which had been carefully constructed to portray positive self-image. This paper concludes that when participating on a public networking site, the teachers were being strategic as not to construct identities which could tarnish their professional image.

  16. A greedy construction heuristic for the liner service network design problem

    DEFF Research Database (Denmark)

    Brouer, Berit Dangaard

    is challenging due to the size of a global liner shipping operation and due to the hub-and-spoke network design, where a high percentage of the total cargo is transshipped. We present the first construction heuristic for large scale instances of the LSN-DP. The heuristic is able to find a solution for a real...

  17. Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis

    Directory of Open Access Journals (Sweden)

    Sandra Hellberg

    2016-09-01

    Full Text Available Multiple sclerosis (MS is a chronic inflammatory disease of the CNS and has a varying disease course as well as variable response to treatment. Biomarkers may therefore aid personalized treatment. We tested whether in vitro activation of MS patient-derived CD4+ T cells could reveal potential biomarkers. The dynamic gene expression response to activation was dysregulated in patient-derived CD4+ T cells. By integrating our findings with genome-wide association studies, we constructed a highly connected MS gene module, disclosing cell activation and chemotaxis as central components. Changes in several module genes were associated with differences in protein levels, which were measurable in cerebrospinal fluid and were used to classify patients from control individuals. In addition, these measurements could predict disease activity after 2 years and distinguish low and high responders to treatment in two additional, independent cohorts. While further validation is needed in larger cohorts prior to clinical implementation, we have uncovered a set of potentially promising biomarkers.

  18. Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

    Science.gov (United States)

    Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

    2016-10-01

    Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

  19. Construction of Pipelined Strategic Connected Dominating Set for Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Ceronmani Sharmila

    2016-06-01

    Full Text Available Efficient routing between nodes is the most important challenge in a Mobile Ad Hoc Network (MANET. A Connected Dominating Set (CDS acts as a virtual backbone for routing in a MANET. Hence, the construction of CDS based on the need and its application plays a vital role in the applications of MANET. The PipeLined Strategic CDS (PLS-CDS is constructed based on strategy, dynamic diameter and transmission range. The strategy used for selecting the starting node is, any source node in the network, which has its entire destination within a virtual pipelined coverage, instead of the node with maximum connectivity. The other nodes are then selected based on density and velocity. The proposed CDS also utilizes the energy of the nodes in the network in an optimized manner. Simulation results showed that the proposed algorithm is better in terms of size of the CDS and average hop per path length.

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

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  1. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

    Science.gov (United States)

    CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

    2016-01-01

    Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

  2. Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

    Science.gov (United States)

    Du, Yuanwei; Guo, Yubin

    2015-01-01

    The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

  3. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

    Science.gov (United States)

    Khan, Abhinandan; Mandal, Sudip; Pal, Rajat Kumar; Saha, Goutam

    2016-01-01

    We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.

  4. Connection with seismic networks and construction of real time earthquake monitoring system

    Energy Technology Data Exchange (ETDEWEB)

    Chi, Heon Cheol; Lee, H. I.; Shin, I. C.; Lim, I. S.; Park, J. H.; Lee, B. K.; Whee, K. H.; Cho, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    2000-12-15

    It is natural to use the nuclear power plant seismic network which have been operated by KEPRI(Korea Electric Power Research Institute) and local seismic network by KIGAM(Korea Institute of Geology, Mining and Material). The real time earthquake monitoring system is composed with monitoring module and data base module. Data base module plays role of seismic data storage and classification and the other, monitoring module represents the status of acceleration in the nuclear power plant area. This research placed the target on the first, networking the KIN's seismic monitoring system with KIGAM and KEPRI seismic network and the second, construction the KIN's Independent earthquake monitoring system.

  5. An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rosana Lachowski

    2015-01-01

    Full Text Available Monitoring and data collection are the two main functions in wireless sensor networks (WSNs. Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.

  6. Constructing networks from a dynamical system perspective for multivariate nonlinear time series.

    Science.gov (United States)

    Nakamura, Tomomichi; Tanizawa, Toshihiro; Small, Michael

    2016-03-01

    We describe a method for constructing networks for multivariate nonlinear time series. We approach the interaction between the various scalar time series from a deterministic dynamical system perspective and provide a generic and algorithmic test for whether the interaction between two measured time series is statistically significant. The method can be applied even when the data exhibit no obvious qualitative similarity: a situation in which the naive method utilizing the cross correlation function directly cannot correctly identify connectivity. To establish the connectivity between nodes we apply the previously proposed small-shuffle surrogate (SSS) method, which can investigate whether there are correlation structures in short-term variabilities (irregular fluctuations) between two data sets from the viewpoint of deterministic dynamical systems. The procedure to construct networks based on this idea is composed of three steps: (i) each time series is considered as a basic node of a network, (ii) the SSS method is applied to verify the connectivity between each pair of time series taken from the whole multivariate time series, and (iii) the pair of nodes is connected with an undirected edge when the null hypothesis cannot be rejected. The network constructed by the proposed method indicates the intrinsic (essential) connectivity of the elements included in the system or the underlying (assumed) system. The method is demonstrated for numerical data sets generated by known systems and applied to several experimental time series.

  7. Constructing Ecological Networks Based on Habitat Quality Assessment: A Case Study of Changzhou, China

    Science.gov (United States)

    Gao, Yu; Ma, Lei; Liu, Jiaxun; Zhuang, Zhuzhou; Huang, Qiuhao; Li, Manchun

    2017-01-01

    Fragmentation and reduced continuity of habitat patches threaten the environment and biodiversity. Recently, ecological networks are increasingly attracting the attention of researchers as they provide fundamental frameworks for environmental protection. This study suggests a set of procedures to construct an ecological network. First, we proposed a method to construct a landscape resistance surface based on the assessment of habitat quality. Second, to analyze the effect of the resistance surface on corridor simulations, we used three methods to construct resistance surfaces: (1) the method proposed in this paper, (2) the entropy coefficient method, and (3) the expert scoring method. Then, we integrated habitat patches and resistance surfaces to identify potential corridors using graph theory. These procedures were tested in Changzhou, China. Comparing the outputs of using different resistance surfaces demonstrated that: (1) different landscape resistance surfaces contribute to how corridors are identified, but only slightly affect the assessment of the importance of habitat patches and potential corridors; (2) the resistance surface, which is constructed based on habitat quality, is more applicable to corridor simulations; and (3) the assessment of the importance of habitat patches is fundamental for ecological network optimization in the conservation of critical habitat patches and corridors. PMID:28393879

  8. Dynamic Construction Scheme for Virtualization Security Service in Software-Defined Networks.

    Science.gov (United States)

    Lin, Zhaowen; Tao, Dan; Wang, Zhenji

    2017-04-21

    For a Software Defined Network (SDN), security is an important factor affecting its large-scale deployment. The existing security solutions for SDN mainly focus on the controller itself, which has to handle all the security protection tasks by using the programmability of the network. This will undoubtedly involve a heavy burden for the controller. More devastatingly, once the controller itself is attacked, the entire network will be paralyzed. Motivated by this, this paper proposes a novel security protection architecture for SDN. We design a security service orchestration center in the control plane of SDN, and this center physically decouples from the SDN controller and constructs SDN security services. We adopt virtualization technology to construct a security meta-function library, and propose a dynamic security service composition construction algorithm based on web service composition technology. The rule-combining method is used to combine security meta-functions to construct security services which meet the requirements of users. Moreover, the RETE algorithm is introduced to improve the efficiency of the rule-combining method. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to achieve the effective security protection with a small burden of the SDN controller.

  9. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier

    Science.gov (United States)

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.

  10. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier.

    Science.gov (United States)

    Rasti, Reza; Mehridehnavi, Alireza; Rabbani, Hossein; Hajizadeh, Fedra

    2018-03-01

    The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task. (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  11. Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP

    Directory of Open Access Journals (Sweden)

    Kihara Daisuke

    2010-05-01

    Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP

  12. Time for a real shift to relations: appraisal of Social Network Analysis applications in the UK construction industry

    Directory of Open Access Journals (Sweden)

    Ximing Ruan

    2013-03-01

    Full Text Available The Social Network Analysis (SNA has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in construction industry from three leading construction management journals.  From the review, the research proposed some advance in the application of SNA in the construction industry. 

  13. Time for a real shift to relations: appraisal of Social Network Analysis applications in the UK construction industry

    Directory of Open Access Journals (Sweden)

    Ximing Ruan

    2013-03-01

    Full Text Available The Social Network Analysis (SNA has been adopted in the UK construction management research and generated meaningful insights in analysing project management organisations from network perspectives. As an effective tool, social network analysis has been used to analyse information and knowledge flow between construction project teams which is considered as foundation for collaborative working and subsequently improving overall performance. Social network analysis is based on an assumption of the importance of relationships among interacting units. The social network perspective encompasses theories, models and applications that are expressed in terms of relational concepts or processes. Many believe, moreover, that the success or failure of organisations often depends on the patterning of their internal structure. This paper reviewed existing literatures on SNA applications in construction industry from three leading construction management journals.  From the review, the research proposed some advance in the application of SNA in the construction industry.

  14. A Time-constrained Network Voronoi Construction and Accessibility Analysis in Location-based Service Technology

    Science.gov (United States)

    Yu, W.; Ai, T.

    2014-11-01

    Accessibility analysis usually requires special models of spatial location analysis based on some geometric constructions, such as Voronoi diagram (abbreviated to VD). There are many achievements in classic Voronoi model research, however suffering from the following limitations for location-based services (LBS) applications. (1) It is difficult to objectively reflect the actual service areas of facilities by using traditional planar VDs, because human activities in LBS are usually constrained only to the network portion of the planar space. (2) Although some researchers have adopted network distance to construct VDs, their approaches are used in a static environment, where unrealistic measures of shortest path distance based on assumptions about constant travel speeds through the network were often used. (3) Due to the computational complexity of the shortest-path distance calculating, previous researches tend to be very time consuming, especially for large datasets and if multiple runs are required. To solve the above problems, a novel algorithm is developed in this paper. We apply network-based quadrat system and 1-D sequential expansion to find the corresponding subnetwork for each focus. The idea is inspired by the natural phenomenon that water flow extends along certain linear channels until meets others or arrives at the end of route. In order to accommodate the changes in traffic conditions, the length of network-quadrat is set upon the traffic condition of the corresponding street. The method has the advantage over Dijkstra's algorithm in that the time cost is avoided, and replaced with a linear time operation.

  15. Deciphering deterioration mechanisms of complex diseases based on the construction of dynamic networks and systems analysis

    Science.gov (United States)

    Li, Yuanyuan; Jin, Suoqin; Lei, Lei; Pan, Zishu; Zou, Xiufen

    2015-03-01

    The early diagnosis and investigation of the pathogenic mechanisms of complex diseases are the most challenging problems in the fields of biology and medicine. Network-based systems biology is an important technique for the study of complex diseases. The present study constructed dynamic protein-protein interaction (PPI) networks to identify dynamical network biomarkers (DNBs) and analyze the underlying mechanisms of complex diseases from a systems level. We developed a model-based framework for the construction of a series of time-sequenced networks by integrating high-throughput gene expression data into PPI data. By combining the dynamic networks and molecular modules, we identified significant DNBs for four complex diseases, including influenza caused by either H3N2 or H1N1, acute lung injury and type 2 diabetes mellitus, which can serve as warning signals for disease deterioration. Function and pathway analyses revealed that the identified DNBs were significantly enriched during key events in early disease development. Correlation and information flow analyses revealed that DNBs effectively discriminated between different disease processes and that dysfunctional regulation and disproportional information flow may contribute to the increased disease severity. This study provides a general paradigm for revealing the deterioration mechanisms of complex diseases and offers new insights into their early diagnoses.

  16. EEG-based motor network biomarkers for identifying target patients with stroke for upper limb rehabilitation and its construct validity.

    Directory of Open Access Journals (Sweden)

    Chun-Chuan Chen

    Full Text Available Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation. In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

  17. Cost effectiveness of DH-network construction. Final report; Kaukolaempoeverkon rakentamisen kehittaeminen; Loppuraportti

    Energy Technology Data Exchange (ETDEWEB)

    Kivistoe, V.M. [Ekono Energy Ltd, Espoo (Finland)

    1993-12-31

    Construction cost of DH networks were analyzed in the study. Basing on the analysis, those areas of construction activities were selected, where cost effectiveness could be improved. According to the study, the civil works` cost form about half of the total construction cost on small diameter networks and 30..40 % of the cost of larger sizes. The impact of the design on the construction cost should be emphasized. According to the study it is possible to find significant reduction in the construction cost by increased use of twin pipe where the both carrier pipes are included in the same insulation casing. In small network sizes the reduction of cost achievable by the above is about 30..35 % and in larger sizes about 10 % (DN 125..DN 200) when comparing with a design by individual pipes. The use of twin pipe also causes savings in thermal loss. In sizes DN 65 and up, the saving in heat loss is about 90..95 FIM/m which represents about half of the total savings when compared with design by individual pipes. The possibility of prestressing the twin pipe element at factory in order to shorten the installation time at site was also studied and test pipes was also done. By factory prestressing it would be possible to avoid preheating of pipes at construction site. The trench could be backfilled immediately after pipe laying, welding and inspections. Theoretically and based on test pipes done the prestressing of twin pipe element looks very promising. When factory prestressing would be used, the open time of the trench is reduced significantly and for instance the disturbance to traffic is smaller. As well the use of labour and machinery at site could be more effective

  18. Analysis of Informationization Construction of Business Financial Management under the Network Economy

    Science.gov (United States)

    Dong, Yahui; Zhang, Pengwei; Li, Wei

    To strengthen the informationization construction of the financial management has great significance to the achievement of business management informationization, and under the network economic environment, it is an important task of the financial management that how to conduct informationization construction of traditional financial management to provide true, reliable and complete financial information system for the business managers. This paper thoroughly researches the problem of financial information orientation management (FIOM) by taking the method of combining theory with practice. This paper puts forward the thinking method of financial information management, makes the new contents of E-finance. At last, this paper rebuilds the system of finance internal control from four aspects such as control of organization and management, system development control and safety control of network system.

  19. A KST framework for correlation network construction from time series signals

    Science.gov (United States)

    Qi, Jin-Peng; Gu, Quan; Zhu, Ying; Zhang, Ping

    2018-04-01

    A KST (Kolmogorov-Smirnov test and T statistic) method is used for construction of a correlation network based on the fluctuation of each time series within the multivariate time signals. In this method, each time series is divided equally into multiple segments, and the maximal data fluctuation in each segment is calculated by a KST change detection procedure. Connections between each time series are derived from the data fluctuation matrix, and are used for construction of the fluctuation correlation network (FCN). The method was tested with synthetic simulations and the result was compared with those from using KS or T only for detection of data fluctuation. The novelty of this study is that the correlation analyses was based on the data fluctuation in each segment of each time series rather than on the original time signals, which would be more meaningful for many real world applications and for analysis of large-scale time signals where prior knowledge is uncertain.

  20. Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition

    OpenAIRE

    Li, Xiangang; Wu, Xihong

    2014-01-01

    Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed an...

  1. The construction of gender through discourse on the social network Badoo: exploring virtual interaction

    OpenAIRE

    Martínez Lirola, María

    2013-01-01

    Nowadays many meetings and conversations take place through social networks. Badoo.com is one of the best known, with more than 102 million users in 2010. This article concentrates on communication through the chat in Badoo between 150 men and the author. The study analyses the main linguistic characteristics in the conversations (orthography, use of capital letters, emoticons and strategies of courtesy and discourtesy) in order to observe how gender is constructed in interaction. The analysi...

  2. Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks.

    Science.gov (United States)

    Oh, S June; Joung, Je-Gun; Chang, Jeong-Ho; Zhang, Byoung-Tak

    2006-06-06

    To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway structures using meta-level information rather than sequence

  3. Construction of phylogenetic trees by kernel-based comparative analysis of metabolic networks

    Directory of Open Access Journals (Sweden)

    Chang Jeong-Ho

    2006-06-01

    Full Text Available Abstract Background To infer the tree of life requires knowledge of the common characteristics of each species descended from a common ancestor as the measuring criteria and a method to calculate the distance between the resulting values of each measure. Conventional phylogenetic analysis based on genomic sequences provides information about the genetic relationships between different organisms. In contrast, comparative analysis of metabolic pathways in different organisms can yield insights into their functional relationships under different physiological conditions. However, evaluating the similarities or differences between metabolic networks is a computationally challenging problem, and systematic methods of doing this are desirable. Here we introduce a graph-kernel method for computing the similarity between metabolic networks in polynomial time, and use it to profile metabolic pathways and to construct phylogenetic trees. Results To compare the structures of metabolic networks in organisms, we adopted the exponential graph kernel, which is a kernel-based approach with a labeled graph that includes a label matrix and an adjacency matrix. To construct the phylogenetic trees, we used an unweighted pair-group method with arithmetic mean, i.e., a hierarchical clustering algorithm. We applied the kernel-based network profiling method in a comparative analysis of nine carbohydrate metabolic networks from 81 biological species encompassing Archaea, Eukaryota, and Eubacteria. The resulting phylogenetic hierarchies generally support the tripartite scheme of three domains rather than the two domains of prokaryotes and eukaryotes. Conclusion By combining the kernel machines with metabolic information, the method infers the context of biosphere development that covers physiological events required for adaptation by genetic reconstruction. The results show that one may obtain a global view of the tree of life by comparing the metabolic pathway

  4. Efficient Geo-Computational Algorithms for Constructing Space-Time Prisms in Road Networks

    Directory of Open Access Journals (Sweden)

    Hui-Ping Chen

    2016-11-01

    Full Text Available The Space-time prism (STP is a key concept in time geography for analyzing human activity-travel behavior under various Space-time constraints. Most existing time-geographic studies use a straightforward algorithm to construct STPs in road networks by using two one-to-all shortest path searches. However, this straightforward algorithm can introduce considerable computational overhead, given the fact that accessible links in a STP are generally a small portion of the whole network. To address this issue, an efficient geo-computational algorithm, called NTP-A*, is proposed. The proposed NTP-A* algorithm employs the A* and branch-and-bound techniques to discard inaccessible links during two shortest path searches, and thereby improves the STP construction performance. Comprehensive computational experiments are carried out to demonstrate the computational advantage of the proposed algorithm. Several implementation techniques, including the label-correcting technique and the hybrid link-node labeling technique, are discussed and analyzed. Experimental results show that the proposed NTP-A* algorithm can significantly improve STP construction performance in large-scale road networks by a factor of 100, compared with existing algorithms.

  5. Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

    Directory of Open Access Journals (Sweden)

    Lo Kenneth

    2012-08-01

    Full Text Available Abstract Background Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge. Results We formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models. Conclusions We demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.

  6. Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

    Science.gov (United States)

    Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

    2017-09-01

    The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

  7. Paving the Way Towards Reactive Planar Spanner Construction in Wireless Networks

    Science.gov (United States)

    Frey, Hannes; Rührup, Stefan

    A spanner is a subgraph of a given graph that supports the original graph's shortest path lengths up to a constant factor. Planar spanners and their distributed construction are of particular interest for geographic routing, which is an efficient localized routing scheme for wireless ad hoc and sensor networks. Planarity of the network graph is a key criterion for guaranteed delivery, while the spanner property supports efficiency in terms of path length. We consider the problem of reactive local spanner construction, where a node's local topology is determined on demand. Known message-efficient reactive planarization algorithms do not preserve the spanner property, while reactive spanner constructions with a low message overhead have not been described so far. We introduce the concept of direct planarization which may be an enabler of efficient reactive spanner construction. Given an edge, nodes check for all incident intersecting edges a certain geometric criterion and withdraw the edge if this criterion is not satisfied. We use this concept to derive a generic reactive topology control mechanism and consider two geometric criteria. Simulation results show that direct planarization increases the performance of localized geographic routing by providing shorter paths than existing reactive approaches.

  8. Construction of Network Management Information System of Agricultural Products Supply Chain Based on 3PLs

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    The necessity to construct the network management information system of 3PLs agricultural supply chain is analyzed,showing that 3PLs can improve the overall competitive advantage of agricultural supply chain.3PLs changes the homogeneity management into specialized management of logistics service and achieves the alliance of the subjects at different nodes of agricultural products supply chain.Network management information system structure of agricultural products supply chain based on 3PLs is constructed,including the four layers (the network communication layer,the hardware and software environment layer,the database layer,and the application layer) and 7 function modules (centralized control,transportation process management,material and vehicle scheduling,customer relationship,storage management,customer inquiry,and financial management).Framework for the network management information system of agricultural products supply chain based on 3PLs is put forward.The management of 3PLs mainly includes purchasing management,supplier relationship management,planning management,customer relationship management,storage management and distribution management.Thus,a management system of internal and external integrated agricultural enterprises is obtained.The network management information system of agricultural products supply chain based on 3PLs has realized the effective sharing of enterprise information of agricultural products supply chain at different nodes,establishing a long-term partnership revolving around the 3PLs core enterprise,as well as a supply chain with stable relationship based on the supply chain network system,so as to improve the circulation efficiency of agricultural products,and to explore the sales market for agricultural products.

  9. Forecasting Construction Cost Index based on visibility graph: A network approach

    Science.gov (United States)

    Zhang, Rong; Ashuri, Baabak; Shyr, Yu; Deng, Yong

    2018-03-01

    Engineering News-Record (ENR), a professional magazine in the field of global construction engineering, publishes Construction Cost Index (CCI) every month. Cost estimators and contractors assess projects, arrange budgets and prepare bids by forecasting CCI. However, fluctuations and uncertainties of CCI cause irrational estimations now and then. This paper aims at achieving more accurate predictions of CCI based on a network approach in which time series is firstly converted into a visibility graph and future values are forecasted relied on link prediction. According to the experimental results, the proposed method shows satisfactory performance since the error measures are acceptable. Compared with other methods, the proposed method is easier to implement and is able to forecast CCI with less errors. It is convinced that the proposed method is efficient to provide considerably accurate CCI predictions, which will make contributions to the construction engineering by assisting individuals and organizations in reducing costs and making project schedules.

  10. Discussion on the nuclear information resources co-constructing and sharing under network information

    International Nuclear Information System (INIS)

    Wu Yang

    2010-01-01

    During the tenth five-year plan, along with the digitization of information, and the development of information transmission network, the co-construction and sharing of China's nuclear industry information is facing a new development opportunities and challenges. This paper is based on the analysis of the nuclear library status and characteristics, combined of the development process of nuclear information resources over the past 20 years. For the characteristic of information sharing and services in the net environment, the problem in the current co-construction and sharing of nuclear information, and the needs of the future nuclear research and development of nuclear production, this paper forecast the work trends of nuclear information, and gives some countermeasure to strength the development of the co-construction and sharing of nuclear information. (author)

  11. A systems biology-based classifier for hepatocellular carcinoma diagnosis.

    Directory of Open Access Journals (Sweden)

    Yanqiong Zhang

    Full Text Available AIM: The diagnosis of hepatocellular carcinoma (HCC in the early stage is crucial to the application of curative treatments which are the only hope for increasing the life expectancy of patients. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with HCC progression. However, those marker sets shared few genes in common and were poorly validated using independent data. Therefore, we developed a systems biology based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC diagnosis. METHODS AND RESULTS: In the Oncomine platform, genes differentially expressed in HCC tissues relative to their corresponding normal tissues were filtered by a corrected Q value cut-off and Concept filters. The identified genes that are common to different microarray datasets were chosen as the candidate markers. Then, their networks were analyzed by GeneGO Meta-Core software and the hub genes were chosen. After that, an HCC diagnostic classifier was constructed by Partial Least Squares modeling based on the microarray gene expression data of the hub genes. Validations of diagnostic performance showed that this classifier had high predictive accuracy (85.88∼92.71% and area under ROC curve (approximating 1.0, and that the network topological features integrated into this classifier contribute greatly to improving the predictive performance. Furthermore, it has been demonstrated that this modeling strategy is not only applicable to HCC, but also to other cancers. CONCLUSION: Our analysis suggests that the systems biology-based classifier that combines the differential gene expression and topological features of human protein interaction network may enhance the diagnostic performance of HCC classifier.

  12. Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

    Science.gov (United States)

    Zhong, Suyu; He, Yong; Gong, Gaolang

    2015-05-01

    Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley

  13. Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.

    Science.gov (United States)

    Ballouz, S; Verleyen, W; Gillis, J

    2015-07-01

    RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ∼0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain 'gold-standard' co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology. jgillis@cshl.edu or sballouz@cshl.edu Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Hybrid classifiers methods of data, knowledge, and classifier combination

    CERN Document Server

    Wozniak, Michal

    2014-01-01

    This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

  15. Construction and evaluation of yeast expression networks by database-guided predictions

    Directory of Open Access Journals (Sweden)

    Katharina Papsdorf

    2016-05-01

    Full Text Available DNA-Microarrays are powerful tools to obtain expression data on the genome-wide scale. We performed microarray experiments to elucidate the transcriptional networks, which are up- or down-regulated in response to the expression of toxic polyglutamine proteins in yeast. Such experiments initially generate hit lists containing differentially expressed genes. To look into transcriptional responses, we constructed networks from these genes. We therefore developed an algorithm, which is capable of dealing with very small numbers of microarrays by clustering the hits based on co-regulatory relationships obtained from the SPELL database. Here, we evaluate this algorithm according to several criteria and further develop its statistical capabilities. Initially, we define how the number of SPELL-derived co-regulated genes and the number of input hits influences the quality of the networks. We then show the ability of our networks to accurately predict further differentially expressed genes. Including these predicted genes into the networks improves the network quality and allows quantifying the predictive strength of the networks based on a newly implemented scoring method. We find that this approach is useful for our own experimental data sets and also for many other data sets which we tested from the SPELL microarray database. Furthermore, the clusters obtained by the described algorithm greatly improve the assignment to biological processes and transcription factors for the individual clusters. Thus, the described clustering approach, which will be available through the ClusterEx web interface, and the evaluation parameters derived from it represent valuable tools for the fast and informative analysis of yeast microarray data.

  16. Brain without anatomy: construction and comparison of fully network-driven structural MRI connectomes.

    Directory of Open Access Journals (Sweden)

    Olga Tymofiyeva

    Full Text Available MRI connectomics methods treat the brain as a network and provide new information about its organization, efficiency, and mechanisms of disruption. The most commonly used method of defining network nodes is to register the brain to a standardized anatomical atlas based on the Brodmann areas. This approach is limited by inter-subject variability and can be especially problematic in the context of brain maturation or neuroplasticity (cerebral reorganization after brain damage. In this study, we combined different image processing and network theory methods and created a novel approach that enables atlas-free construction and connection-wise comparison of diffusion MRI-based brain networks. We illustrated the proposed approach in three age groups: neonates, 6-month-old infants, and adults. First, we explored a data-driven method of determining the optimal number of equal-area nodes based on the assumption that all cortical areas of the brain are connected and, thus, no part of the brain is structurally isolated. Second, to enable a connection-wise comparison, alignment to a "reference brain" was performed in the network domain within each group using a matrix alignment algorithm with simulated annealing. The correlation coefficients after pair-wise network alignment ranged from 0.6102 to 0.6673. To test the method's reproducibility, one subject from the 6-month-old group and one from the adult group were scanned twice, resulting in correlation coefficients of 0.7443 and 0.7037, respectively. While being less than 1 due to parcellation and noise, statistically, these values were significantly higher than inter-subject values. Rotation of the parcellation largely explained the variability. Through the abstraction from anatomy, the developed framework allows for a fully network-driven analysis of structural MRI connectomes and can be applied to subjects at any stage of development and with substantial differences in cortical anatomy.

  17. Bringing abundance into environmental politics: Constructing a Zionist network of water abundance, immigration, and colonization.

    Science.gov (United States)

    Alatout, Samer

    2009-06-01

    For more than five decades, resource scarcity has been the lead story in debates over environmental politics. More importantly, and whenever environmental politics implies conflict, resource scarcity is constructed as the culprit. Abundance of resources, if at all visited in the literature, holds less importance. Resource abundance is seen, at best, as the other side of scarcity--maybe the successful conclusion of multiple interventions that may turn scarcity into abundance. This paper reinstates abundance as a politico-environmental category in its own right. Rather than relegating abundance to a second-order environmental actor that matters only on occasion, this paper foregrounds it as a crucial element in modern environmental politics. On the substantive level, and using insights from science and technology studies, especially a slightly modified actor-network framework, I describe the emergence and consolidation of a Zionist network of abundance, immigration, and colonization in Palestine between 1918 and 1948. The essential argument here is that water abundance was constructed as fact, and became a political rallying point around which a techno-political network emerged that included a great number of elements. To name just a few, the following were enrolled in the service of such a network: geologists, geophysicists, Zionist settlement experts, Zionist organizations, political and technical categories of all sorts, Palestinians as the negated others, Palestinian revolts in search of political rights, the British Mandate authorities, the hydrological system of Palestine, and the absorptive capacity of Palestine, among others. The point was to successfully articulate these disparate elements into a network that seeks opening Palestine for Jewish immigration, redefining Palestinian geography and history through Judeo-Christian Biblical narratives, and, in the process, de-legitimizing political Palestinian presence in historic Palestine.

  18. Construction and analysis of circular RNA molecular regulatory networks in liver cancer.

    Science.gov (United States)

    Ren, Shuangchun; Xin, Zhuoyuan; Xu, Yinyan; Xu, Jianting; Wang, Guoqing

    2017-01-01

    Liver cancer is the sixth most prevalent cancer, and the third most frequent cause of cancer-related deaths. Circular RNAs (circRNAs), a kind of special endogenous ncRNAs, have been coming back to the forefront of cancer genomics research. In this study, we used a systems biology approach to construct and analyze the circRNA molecular regulatory networks in the context of liver cancer. We detected a total of 127 differentially expressed circRNAs and 3,235 differentially expressed mRNAs. We selected the top-5 upregulated circRNAs to construct a circRNA-miRNA-mRNA network. We enriched the pathways and gene ontology items and determined their participation in cancer-related pathways such as p53 signaling pathway and pathways involved in angiogenesis and cell cycle. Quantitative real-time PCR was performed to verify the top-five circRNAs. ROC analysis showed circZFR, circFUT8, circIPO11 could significantly distinguish the cancer samples, with an AUC of 0.7069, 0.7575, and 0.7103, respectively. Our results suggest the circRNA-miRNA-mRNA network may help us further understand the molecular mechanisms of tumor progression in liver cancer, and reveal novel biomarkers and therapeutic targets.

  19. The construction of a public key infrastructure for healthcare information networks in Japan.

    Science.gov (United States)

    Sakamoto, N

    2001-01-01

    The digital signature is a key technology in the forthcoming Internet society for electronic healthcare as well as for electronic commerce. Efficient exchanges of authorized information with a digital signature in healthcare information networks require a construction of a public key infrastructure (PKI). In order to introduce a PKI to healthcare information networks in Japan, we proposed a development of a user authentication system based on a PKI for user management, user authentication and privilege management of healthcare information systems. In this paper, we describe the design of the user authentication system and its implementation. The user authentication system provides a certification authority service and a privilege management service while it is comprised of a user authentication client and user authentication serves. It is designed on a basis of an X.509 PKI and is implemented with using OpenSSL and OpenLDAP. It was incorporated into the financial information management system for the national university hospitals and has been successfully working for about one year. The hospitals plan to use it as a user authentication method for their whole healthcare information systems. One implementation of the system is free to the national university hospitals with permission of the Japanese Ministry of Education, Culture, Sports, Science and Technology. Another implementation is open to the other healthcare institutes by support of the Medical Information System Development Center (MEDIS-DC). We are moving forward to a nation-wide construction of a PKI for healthcare information networks based on it.

  20. Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking

    International Nuclear Information System (INIS)

    Yildiz, Nihat; Akkoyun, Serkan

    2013-01-01

    Highlights: ► Detector responses in neutron–gamma discrimination were estimated by neural networks. ► Novel consistent neural network empirical physical formulas (EPFs) were constructed for detector responses. ► The EPFs are of explicit mathematical functional form. ► The EPFs can be used to derive various physical functions relevant to neutron–gamma discrimination in gamma ray tracking. -- Abstract: Gamma ray tracking is an efficient detection technique in studying exotic nuclei which lies far from beta stability line. To achieve very powerful and extraordinary resolution ability, new detectors based on gamma ray tracking are currently being developed. To reach this achievement, the neutron–gamma discrimination in these detectors is also an important task. In this paper, by suitable layered feedforward neural networks (LFNNs), we have constructed novel and consistent empirical physical formulas (EPFs) for some highly nonlinear detector counts measured in neutron–gamma discrimination. The detector counts data used in the discrimination was actually borrowed from our previous paper. The counts used here had been originally measured versus the following parameters: energy deposited in the first interaction points, difference in the incoming direction of initial gamma rays, and finally figure of merit values of the clusters determined by tracking. The LFNN–EPFs are of explicit mathematical functional form. Therefore, by various suitable operations of mathematical analysis, these LFNN–EPFs can be used to derivate further physical functions which might be potentially relevant to neutron–gamma discrimination performance of gamma ray tracking.

  1. Natural and Unnatural Oil Layers on the Surface of the Gulf of Mexico Detected and Quantified in Synthetic Aperture RADAR Images with Texture Classifying Neural Network Algorithms

    Science.gov (United States)

    MacDonald, I. R.; Garcia-Pineda, O. G.; Morey, S. L.; Huffer, F.

    2011-12-01

    Effervescent hydrocarbons rise naturally from hydrocarbon seeps in the Gulf of Mexico and reach the ocean surface. This oil forms thin (~0.1 μm) layers that enhance specular reflectivity and have been widely used to quantify the abundance and distribution of natural seeps using synthetic aperture radar (SAR). An analogous process occurred at a vastly greater scale for oil and gas discharged from BP's Macondo well blowout. SAR data allow direct comparison of the areas of the ocean surface covered by oil from natural sources and the discharge. We used a texture classifying neural network algorithm to quantify the areas of naturally occurring oil-covered water in 176 SAR image collections from the Gulf of Mexico obtained between May 1997 and November 2007, prior to the blowout. Separately we also analyzed 36 SAR images collections obtained between 26 April and 30 July, 2010 while the discharged oil was visible in the Gulf of Mexico. For the naturally occurring oil, we removed pollution events and transient oceanographic effects by including only the reflectance anomalies that that recurred in the same locality over multiple images. We measured the area of oil layers in a grid of 10x10 km cells covering the entire Gulf of Mexico. Floating oil layers were observed in only a fraction of the total Gulf area amounting to 1.22x10^5 km^2. In a bootstrap sample of 2000 replications, the combined average area of these layers was 7.80x10^2 km^2 (sd 86.03). For a regional comparison, we divided the Gulf of Mexico into four quadrates along 90° W longitude, and 25° N latitude. The NE quadrate, where the BP discharge occurred, received on average 7.0% of the total natural seepage in the Gulf of Mexico (5.24 x10^2 km^2, sd 21.99); the NW quadrate received on average 68.0% of this total (5.30 x10^2 km^2, sd 69.67). The BP blowout occurred in the NE quadrate of the Gulf of Mexico; discharged oil that reached the surface drifted over a large area north of 25° N. Performing a

  2. Constructing a Bayesian network model for improving safety behavior of employees at workplaces.

    Science.gov (United States)

    Mohammadfam, Iraj; Ghasemi, Fakhradin; Kalatpour, Omid; Moghimbeigi, Abbas

    2017-01-01

    Unsafe behavior increases the risk of accident at workplaces and needs to be managed properly. The aim of the present study was to provide a model for managing and improving safety behavior of employees using the Bayesian networks approach. The study was conducted in several power plant construction projects in Iran. The data were collected using a questionnaire composed of nine factors, including management commitment, supporting environment, safety management system, employees' participation, safety knowledge, safety attitude, motivation, resource allocation, and work pressure. In order for measuring the score of each factor assigned by a responder, a measurement model was constructed for each of them. The Bayesian network was constructed using experts' opinions and Dempster-Shafer theory. Using belief updating, the best intervention strategies for improving safety behavior also were selected. The result of the present study demonstrated that the majority of employees do not tend to consider safety rules, regulation, procedures and norms in their behavior at the workplace. Safety attitude, safety knowledge, and supporting environment were the best predictor of safety behavior. Moreover, it was determined that instantaneous improvement of supporting environment and employee participation is the best strategy to reach a high proportion of safety behavior at the workplace. The lack of a comprehensive model that can be used for explaining safety behavior was one of the most problematic issues of the study. Furthermore, it can be concluded that belief updating is a unique feature of Bayesian networks that is very useful in comparing various intervention strategies and selecting the best one form them. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. A Hierarchical Approach to Persistent Scatterer Network Construction and Deformation Time Series Estimation

    Directory of Open Access Journals (Sweden)

    Rui Zhang

    2014-12-01

    Full Text Available This paper presents a hierarchical approach to network construction and time series estimation in persistent scatterer interferometry (PSI for deformation analysis using the time series of high-resolution satellite SAR images. To balance between computational efficiency and solution accuracy, a dividing and conquering algorithm (i.e., two levels of PS networking and solution is proposed for extracting deformation rates of a study area. The algorithm has been tested using 40 high-resolution TerraSAR-X images collected between 2009 and 2010 over Tianjin in China for subsidence analysis, and validated by using the ground-based leveling measurements. The experimental results indicate that the hierarchical approach can remarkably reduce computing time and memory requirements, and the subsidence measurements derived from the hierarchical solution are in good agreement with the leveling data.

  4. Mastering the political Process of Building Innovation Networks - A Case from the Danish Construction Industry

    DEFF Research Database (Denmark)

    Stissing Jensen, Jens; Koch, Christian; Thomassen, Mikkel

    2008-01-01

    Drawing on network of innovation and organizational politics perspectives this paper analyzes the role of an innovation broker organization in developing and supporting an inter-organizational innovation process in the Danish construction industry. The aim is to implement an ICT-based product...... configuration tool to support the production, sale, and installation of balconies. It is suggested that the innovation broker was successful in stabilizing the innovation process by supplying minimal structures which provided a template which facilitated a combination of individual flexibility and overall...... the network. The innovation thus grew strong enough to replace existing practices and identities and to embed new ones into new organizational structures and a new business-concept...

  5. CORRECTION OF FAULTY LINES IN MUSCLE MODEL, TO BE USED IN 3D BUILDING NETWORK CONSTRUCTION

    Directory of Open Access Journals (Sweden)

    I. R. Karas

    2012-07-01

    Full Text Available This paper describes the usage of MUSCLE (Multidirectional Scanning for Line Extraction Model for automatic generation of 3D networks in CityGML format (from raster floor plans. MUSCLE (Multidirectional Scanning for Line Extraction Model is a conversion method which was developed to vectorize the straight lines through the raster images including floor plans, maps for GIS, architectural drawings, and machine plans. The model allows user to define specific criteria which are crucial for acquiring the vectorization process. Unlike traditional vectorization process, this model generates straight lines based on a line thinning algorithm, without performing line following-chain coding and vector reduction stages. In this method the nearly vertical lines were obtained by scanning the images horizontally, while the nearly horizontal lines were obtained by scanning the images vertically. In a case where two or more consecutive lines are nearly horizontal or nearly vertical, raster data become unmanageable and the process generates wrongly vectorized lines. In this situation, to obtain the precise lines, the image with the wrongly vectorized lines is diagonally scanned. By using MUSCLE model, the network models are topologically structured in CityGML format. After the generation process, it is possible to perform 3D network analysis based on these models. Then, by using the software that was designed based on the generated models, a geodatabase of the models could be established. This paper presents the correction application in MUSCLE and explains 3D network construction in detail.

  6. Use of limited data to construct Bayesian networks for probabilistic risk assessment.

    Energy Technology Data Exchange (ETDEWEB)

    Groth, Katrina M.; Swiler, Laura Painton

    2013-03-01

    Probabilistic Risk Assessment (PRA) is a fundamental part of safety/quality assurance for nuclear power and nuclear weapons. Traditional PRA very effectively models complex hardware system risks using binary probabilistic models. However, traditional PRA models are not flexible enough to accommodate non-binary soft-causal factors, such as digital instrumentation&control, passive components, aging, common cause failure, and human errors. Bayesian Networks offer the opportunity to incorporate these risks into the PRA framework. This report describes the results of an early career LDRD project titled %E2%80%9CUse of Limited Data to Construct Bayesian Networks for Probabilistic Risk Assessment%E2%80%9D. The goal of the work was to establish the capability to develop Bayesian Networks from sparse data, and to demonstrate this capability by producing a data-informed Bayesian Network for use in Human Reliability Analysis (HRA) as part of nuclear power plant Probabilistic Risk Assessment (PRA). This report summarizes the research goal and major products of the research.

  7. Problem-Solving Skills among Precollege Students in Clinical Immunology and Microbiology: Classifying Strategies with a Rubric and Artificial Neural Network Technology.

    Science.gov (United States)

    Kanowith-Klein, Susan; Stave, Mel; Stevens, Ron; Casillas, Adrian M.

    2001-01-01

    Investigates methods for classifying problem solving strategies of high school students who studied infectious and non-infectious diseases by using a software system that can generate a picture of students' strategies in solving problems. (Contains 24 references.) (Author/YDS)

  8. Research on Risk Manage of Power Construction Project Based on Bayesian Network

    Science.gov (United States)

    Jia, Zhengyuan; Fan, Zhou; Li, Yong

    With China's changing economic structure and increasingly fierce competition in the market, the uncertainty and risk factors in the projects of electric power construction are increasingly complex, the projects will face huge risks or even fail if we don't consider or ignore these risk factors. Therefore, risk management in the projects of electric power construction plays an important role. The paper emphatically elaborated the influence of cost risk in electric power projects through study overall risk management and the behavior of individual in risk management, and introduced the Bayesian network to the project risk management. The paper obtained the order of key factors according to both scene analysis and causal analysis for effective risk management.

  9. Construction of diabatic energy surfaces for LiFH with artificial neural networks

    Science.gov (United States)

    Guan, Yafu; Fu, Bina; Zhang, Dong H.

    2017-12-01

    A new set of diabatic potential energy surfaces (PESs) for LiFH is constructed with artificial neural networks (NNs). The adiabatic PESs of the ground state and the first excited state are directly fitted with NNs. Meanwhile, the adiabatic-to-diabatic transformation (ADT) angles (mixing angles) are obtained by simultaneously fitting energy difference and interstate coupling gradients. No prior assumptions of the functional form of ADT angles are used before fitting, and the ab initio data including energy difference and interstate coupling gradients are well reproduced. Converged dynamical results show remarkable differences between adiabatic and diabatic PESs, which suggests the significance of non-adiabatic processes.

  10. Case Library Construction Technology of Energy Loss in Distribution Networks Considering Regional Differentiation Theory

    Directory of Open Access Journals (Sweden)

    Ze Yuan

    2017-11-01

    Full Text Available The grid structures, load levels, and running states of distribution networks in different supply regions are known as the influencing factors of energy loss. In this paper, the case library of energy loss is constructed to differentiate the crucial factors of energy loss in the different supply regions. First of all, the characteristic state values are selected as the representation of the cases based on the analysis of energy loss under various voltage classes and in different types of regions. Then, the methods of Grey Relational Analysis and the K-Nearest Neighbor are utilized to implement the critical technologies of case library construction, including case representation, processing, analysis, and retrieval. Moreover, the analysis software of the case library is designed based on the case library construction technology. Some case studies show that there are many differences and similarities concerning the factors that influence the energy loss in different types of regions. In addition, the most relevant sample case can be retrieved from the case library. Compared with the traditional techniques, constructing a case library provides a new way to find out the characteristics of energy loss in different supply regions and constitutes differentiated loss-reducing programs.

  11. Autonomous construction agents: An investigative framework for large sensor network self-management

    Energy Technology Data Exchange (ETDEWEB)

    Koch, Joshua Bruce [Iowa State Univ., Ames, IA (United States)

    2008-01-01

    Recent technological advances have made it cost effective to utilize massive, heterogeneous sensor networks. To gain appreciable value from these informational systems, there must be a control scheme that coordinates information flow to produce meaningful results. This paper will focus on tools developed to manage the coordination of autonomous construction agents using stigmergy, in which a set of basic low-level rules are implemented through various environmental cues. Using VE-Suite, an open-source virtual engineering software package, an interactive environment is created to explore various informational configurations for the construction problem. A simple test case is developed within the framework, and construction times are analyzed for possible functional relationships pertaining to performance of a particular set of parameters and a given control process. Initial experiments for the test case show sensor saturation occurs relatively quickly with 5-7 sensors, and construction time is generally independent of sensor range except for small numbers of sensors. Further experiments using this framework are needed to define other aspects of sensor performance. These trends can then be used to help decide what kinds of sensing capabilities are required to simultaneously achieve the most cost-effective solution and provide the required value of information when applied to the development of real world sensor applications.

  12. Visual Representations of Microcosm in Textbooks of Chemistry: Constructing a Systemic Network for Their Main Conceptual Framework

    Science.gov (United States)

    Papageorgiou, George; Amariotakis, Vasilios; Spiliotopoulou, Vasiliki

    2017-01-01

    The main objective of this work is to analyse the visual representations (VRs) of the microcosm depicted in nine Greek secondary chemistry school textbooks of the last three decades in order to construct a systemic network for their main conceptual framework and to evaluate the contribution of each one of the resulting categories to the network.…

  13. Design and Construction of a High-speed Network Connecting All the Protein Crystallography Beamlines at the Photon Factory

    International Nuclear Information System (INIS)

    Matsugaki, Naohiro; Yamada, Yusuke; Igarashi, Noriyuki; Wakatsuki, Soichi

    2007-01-01

    A private network, physically separated from the facility network, was designed and constructed which covered all the four protein crystallography beamlines at the Photon Factory (PF) and Structural Biology Research Center (SBRC). Connecting all the beamlines in the same network allows for simple authentication and a common working environment for a user who uses multiple beamlines. Giga-bit Ethernet wire-speed was achieved for the communication among the beamlines and SBRC buildings

  14. A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

    Science.gov (United States)

    Choi, Yoonha; Coram, Marc; Peng, Jie; Tang, Hua

    2017-07-01

    Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions method of multipliers to find the penalized maximum likelihood estimates. The proposed method is evaluated and compared with GGMs using both simulated and real RNA-seq data. The proposed method shows improved performance in detecting edges that represent covarying pairs of genes, particularly for edges connecting low-abundant genes and edges around regulatory hubs.

  15. Construction of an integrated gene regulatory network link to stress-related immune system in cattle.

    Science.gov (United States)

    Behdani, Elham; Bakhtiarizadeh, Mohammad Reza

    2017-10-01

    The immune system is an important biological system that is negatively impacted by stress. This study constructed an integrated regulatory network to enhance our understanding of the regulatory gene network used in the stress-related immune system. Module inference was used to construct modules of co-expressed genes with bovine leukocyte RNA-Seq data. Transcription factors (TFs) were then assigned to these modules using Lemon-Tree algorithms. In addition, the TFs assigned to each module were confirmed using the promoter analysis and protein-protein interactions data. Therefore, our integrated method identified three TFs which include one TF that is previously known to be involved in immune response (MYBL2) and two TFs (E2F8 and FOXS1) that had not been recognized previously and were identified for the first time in this study as novel regulatory candidates in immune response. This study provides valuable insights on the regulatory programs of genes involved in the stress-related immune system.

  16. Linear programming model to construct phylogenetic network for 16S rRNA sequences of photosynthetic organisms and influenza viruses.

    Science.gov (United States)

    Mathur, Rinku; Adlakha, Neeru

    2014-06-01

    Phylogenetic trees give the information about the vertical relationships of ancestors and descendants but phylogenetic networks are used to visualize the horizontal relationships among the different organisms. In order to predict reticulate events there is a need to construct phylogenetic networks. Here, a Linear Programming (LP) model has been developed for the construction of phylogenetic network. The model is validated by using data sets of chloroplast of 16S rRNA sequences of photosynthetic organisms and Influenza A/H5N1 viruses. Results obtained are in agreement with those obtained by earlier researchers.

  17. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  18. 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

  19. Context-Aware Community Construction in Proximity-Based Mobile Networks

    Directory of Open Access Journals (Sweden)

    Na Yu

    2015-01-01

    Full Text Available Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

  20. cooccurNet: an R package for co-occurrence network construction and analysis.

    Science.gov (United States)

    Zou, Yuanqiang; Wu, Zhiqiang; Deng, Lizong; Wu, Aiping; Wu, Fan; Li, Kenli; Jiang, Taijiao; Peng, Yousong

    2017-06-15

    Previously, we developed a computational model to identify genomic co-occurrence networks that was applied to capture the coevolution patterns within genomes of influenza viruses. To facilitate easy public use of this model, an R package 'cooccurNet' is presented here. 'cooccurNet' includes functionalities of construction and analysis of residues (e.g. nucleotides, amino acids and SNPs) co-occurrence network. In addition, a new method for measuring residues coevolution, defined as residue co-occurrence score (RCOS), is proposed and implemented in 'cooccurNet' based on the co-occurrence network. 'cooccurNet' is publicly available on CRAN repositories under the GPL-3 Open Source License ( http://cran.r-project.org/package=cooccurNet ). taijiao@ibms.pumc.edu.cn or pys2013@hnu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  1. Construction and analysis of a genome-scale metabolic network for Bacillus licheniformis WX-02.

    Science.gov (United States)

    Guo, Jing; Zhang, Hong; Wang, Cheng; Chang, Ji-Wei; Chen, Ling-Ling

    2016-05-01

    We constructed the genome-scale metabolic network of Bacillus licheniformis (B. licheniformis) WX-02 by combining genomic annotation, high-throughput phenotype microarray (PM) experiments and literature-based metabolic information. The accuracy of the metabolic network was assessed by an OmniLog PM experiment. The final metabolic model iWX1009 contains 1009 genes, 1141 metabolites and 1762 reactions, and the predicted metabolic phenotypes showed an agreement rate of 76.8% with experimental PM data. In addition, key metabolic features such as growth yield, utilization of different substrates and essential genes were identified by flux balance analysis. A total of 195 essential genes were predicted from LB medium, among which 149 were verified with the experimental essential gene set of B. subtilis 168. With the removal of 5 reactions from the network, pathways for poly-γ-glutamic acid (γ-PGA) synthesis were optimized and the γ-PGA yield reached 83.8 mmol/h. Furthermore, the important metabolites and pathways related to γ-PGA synthesis and bacterium growth were comprehensively analyzed. The present study provides valuable clues for exploring the metabolisms and metabolic regulation of γ-PGA synthesis in B. licheniformis WX-02. Copyright © 2016 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  2. Online identity: constructing interpersonal trust and openness through participating in hospitality social networks

    Directory of Open Access Journals (Sweden)

    Alexander Ronzhyn

    2013-06-01

    Full Text Available The present article describes the results of research on online identity construction during the participation in the hospitality social networks. Specifically the user references are analysed to understand patterns that form the image of a member. CouchSurfing service (couchsurfing.org allows users to leave short texts where the experience of hosting/being hosted by a CS member is described, is an evaluation of the CS members of each other’s personal traits, skills and common experience. Therefore references can become a good instrument for portraying a CouchSurfing member and understanding his or her particular traits. References form an important part of a user’s virtual identity in the network. Using a sample of references of Spanish CouchSurfing users, the research established main characteristics of the references, which are the openness, readiness to share ideas and experiences and trustworthiness. These concepts illustrate the typical traits associated with a user of the network and also shed light on the activities common during offl ine CS meetings

  3. A dynamic Bayesian network based approach to safety decision support in tunnel construction

    International Nuclear Information System (INIS)

    Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

    2015-01-01

    This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

  4. Construction cost estimation of spherical storage tanks: artificial neural networks and hybrid regression—GA algorithms

    Science.gov (United States)

    Arabzadeh, Vida; Niaki, S. T. A.; Arabzadeh, Vahid

    2017-10-01

    One of the most important processes in the early stages of construction projects is to estimate the cost involved. This process involves a wide range of uncertainties, which make it a challenging task. Because of unknown issues, using the experience of the experts or looking for similar cases are the conventional methods to deal with cost estimation. The current study presents data-driven methods for cost estimation based on the application of artificial neural network (ANN) and regression models. The learning algorithms of the ANN are the Levenberg-Marquardt and the Bayesian regulated. Moreover, regression models are hybridized with a genetic algorithm to obtain better estimates of the coefficients. The methods are applied in a real case, where the input parameters of the models are assigned based on the key issues involved in a spherical tank construction. The results reveal that while a high correlation between the estimated cost and the real cost exists; both ANNs could perform better than the hybridized regression models. In addition, the ANN with the Levenberg-Marquardt learning algorithm (LMNN) obtains a better estimation than the ANN with the Bayesian-regulated learning algorithm (BRNN). The correlation between real data and estimated values is over 90%, while the mean square error is achieved around 0.4. The proposed LMNN model can be effective to reduce uncertainty and complexity in the early stages of the construction project.

  5. A template for constructing Bayesian networks in forensic biology cases when considering activity level propositions.

    Science.gov (United States)

    Taylor, Duncan; Biedermann, Alex; Hicks, Tacha; Champod, Christophe

    2018-03-01

    The hierarchy of propositions has been accepted amongst the forensic science community for some time. It is also accepted that the higher up the hierarchy the propositions are, against which the scientist are competent to evaluate their results, the more directly useful the testimony will be to the court. Because each case represents a unique set of circumstances and findings, it is difficult to come up with a standard structure for evaluation. One common tool that assists in this task is Bayesian networks (BNs). There is much diversity in the way that BN can be constructed. In this work, we develop a template for BN construction that allows sufficient flexibility to address most cases, but enough commonality and structure that the flow of information in the BN is readily recognised at a glance. We provide seven steps that can be used to construct BNs within this structure and demonstrate how they can be applied, using a case example. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

  6. Construction and application of a protein and genetic interaction network (yeast interactome).

    Science.gov (United States)

    Stuart, Gregory R; Copeland, William C; Strand, Micheline K

    2009-04-01

    Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.

  7. Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

    Directory of Open Access Journals (Sweden)

    Xiu Jin

    2018-03-01

    Full Text Available Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN. After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

  8. The development of the collagen fibre network in tissue-engineered cartilage constructs in vivo. Engineered cartilage reorganises fibre network

    Directory of Open Access Journals (Sweden)

    H Paetzold

    2012-04-01

    Full Text Available For long term durability of tissue-engineered cartilage implanted in vivo, the development of the collagen fibre network orientation is essential as well as the distribution of collagen, since expanded chondrocytes are known to synthesise collagen type I. Typically, these properties differ strongly between native and tissue-engineered cartilage. Nonetheless, the clinical results of a pilot study with implanted tissue-engineered cartilage in pigs were surprisingly good. The purpose of this study was therefore to analyse if the structure and composition of the artificial cartilage tissue changes in the first 52 weeks after implantation. Thus, collagen network orientation and collagen type distribution in tissue-engineered cartilage-carrier-constructs implanted in the knee joints of Göttinger minipigs for 2, 26 or 52 weeks have been further investigated by processing digitised microscopy images of histological sections. The comparison to native cartilage demonstrated that fibre orientation over the cartilage depth has a clear tendency towards native cartilage with increasing time of implantation. After 2 weeks, the collagen fibres of the superficial zone were oriented parallel to the articular surface with little anisotropy present in the middle and deep zones. Overall, fibre orientation and collagen distribution within the implants were less homogenous than in native cartilage tissue. Despite a relatively low number of specimens, the consistent observation of a continuous approximation to native tissue is very promising and suggests that it may not be necessary to engineer the perfect tissue for implantation but rather to provide an intermediate solution to help the body to heal itself.

  9. The construction of digital 3D arterial vascular network of uterine leiomyomas and its clinical significance

    International Nuclear Information System (INIS)

    Chen Chunlin; Xu Yujing; Liu Ping

    2012-01-01

    Objective: To discuss the method of constructing digital 3D arterial vascular network of uterine leiomyomas based on the CTA data, by which to lay the fundamental work for the observation of the origin and distribution of hysteromyoma blood supply. Methods: A total of 64 cases of uterine leiomyomas were enrolled in this study. Dual-source CT angiography was performed in all the patients, and the CTA original images were obtained. By using Mimics 10.01 software the digital 3D arterial vascular network of uterine was reconstructed. The reconstructed models were analyzed. Results: (1) The constructing process of arterial vascular network was successfully accomplished in all 64 patients. The pelvic main arteries, the uterine arteries and tumor-feeding arteries as well as the blood distribution type were clearly demonstrated on the reconstructed images. (2) The origins of hysteromyoma blood supply included uterine artery (81.25%), uterine artery and unilateral ovarian artery (10.94%), uterine artery and bilateral ovarian artery (4.69%) and ovarian artery (3.12%). (3) Distribution pattern of blood supply of uterine leiomyomas could be divided into 4 types: (1) Type Ⅰ. The unilateral arterial blood supply dominant type (unilateral uterine artery with or without ipsilateral ovarian arterial, providing more than 1/2 blood supply of hysteromyoma), which accounted for 35.94% of all patients (23/26); (2) Type Ⅱ. The bilateral arterial blood supply balanced type (bilateral uterine artery with or without ipsilateral ovarian artery, providing about 1/2 blood supply of hysteromyoma), which accounted for 53.13% of all patients (34/64); (3) Type Ⅲ. The unilateral uterine artery was the main blood supply of uterine leiomyomas, which accounted for 7.81% of all patients (5/64); (4) Type Ⅳ. The ovarian artery was the main blood supply of uterine leiomyomas, which accounted for 3.13% of all patients (3/64). Conclusion: Based on CTA data and with the help of reconstruction

  10. Analysis and minimization of overtraining effect in rule-based classifiers for computer-aided diagnosis

    International Nuclear Information System (INIS)

    Li Qiang; Doi Kunio

    2006-01-01

    Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists detect various lesions in medical images. In CAD schemes, classifiers play a key role in achieving a high lesion detection rate and a low false-positive rate. Although many popular classifiers such as linear discriminant analysis and artificial neural networks have been employed in CAD schemes for reduction of false positives, a rule-based classifier has probably been the simplest and most frequently used one since the early days of development of various CAD schemes. However, with existing rule-based classifiers, there are major disadvantages that significantly reduce their practicality and credibility. The disadvantages include manual design, poor reproducibility, poor evaluation methods such as resubstitution, and a large overtraining effect. An automated rule-based classifier with a minimized overtraining effect can overcome or significantly reduce the extent of the above-mentioned disadvantages. In this study, we developed an 'optimal' method for the selection of cutoff thresholds and a fully automated rule-based classifier. Experimental results performed with Monte Carlo simulation and a real lung nodule CT data set demonstrated that the automated threshold selection method can completely eliminate overtraining effect in the procedure of cutoff threshold selection, and thus can minimize overall overtraining effect in the constructed rule-based classifier. We believe that this threshold selection method is very useful in the construction of automated rule-based classifiers with minimized overtraining effect

  11. Concept mapping and network analysis: an analytic approach to measure ties among constructs.

    Science.gov (United States)

    Goldman, Alyssa W; Kane, Mary

    2014-12-01

    Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

  12. Construction of a magnetostrictive hysteresis operator using a tripod-like primitive hopfield neural network

    Science.gov (United States)

    Adly, A. A.; Abd-El-Hafiz, S. K.

    2018-05-01

    It is well known that accurate modeling of magnetostrictive hysteresis is crucial to different industrial applications. Although several magnetostrictive models have been developed in the past, the accuracy-efficiency balance has always been crucial. Recently, the possibility of constructing a primitive vector hysteresis operator using a tri-node Hopfield Neural Network (HNN) was demonstrated. Based upon the fact that mechanical stress along a certain direction results in dimensional deformation, this paper introduces a novel extension to the aforementioned recently developed approach. More specifically, a stress-driven evolution of a tri-node HNN hysteresis operator pair is proposed, thus yielding a tripod-like HNN pair having different input offset values. Model identification, sample simulation results and comparison with experimental measurements are given in the paper.

  13. Construction of an interatomic potential for zinc oxide surfaces by high-dimensional neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Artrith, Nongnuch; Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum, D-44780 Bochum (Germany)

    2011-07-01

    Zinc oxide (ZnO) is a technologically important material with many applications, e.g. in heterogeneous catalysis. For theoretical studies of the structural properties of ZnO surfaces, defects, and crystal structures it is necessary to simulate large systems over long time-scales with sufficient accuracy. Often, the required system size is not accessible by computationally rather demanding density-functional theory (DFT) calculations. Recently, artificial Neural Networks (NN) trained to first principles data have shown to provide accurate potential-energy surfaces (PESs) for condensed systems. We present the construction and analysis of a NN PES for ZnO. The structural and energetic properties of bulk ZnO and ZnO surfaces are investigated using this potential and compared to DFT calculations.

  14. The parametric modified limited penetrable visibility graph for constructing complex networks from time series

    Science.gov (United States)

    Li, Xiuming; Sun, Mei; Gao, Cuixia; Han, Dun; Wang, Minggang

    2018-02-01

    This paper presents the parametric modified limited penetrable visibility graph (PMLPVG) algorithm for constructing complex networks from time series. We modify the penetrable visibility criterion of limited penetrable visibility graph (LPVG) in order to improve the rationality of the original penetrable visibility and preserve the dynamic characteristics of the time series. The addition of view angle provides a new approach to characterize the dynamic structure of the time series that is invisible in the previous algorithm. The reliability of the PMLPVG algorithm is verified by applying it to three types of artificial data as well as the actual data of natural gas prices in different regions. The empirical results indicate that PMLPVG algorithm can distinguish the different time series from each other. Meanwhile, the analysis results of natural gas prices data using PMLPVG are consistent with the detrended fluctuation analysis (DFA). The results imply that the PMLPVG algorithm may be a reasonable and significant tool for identifying various time series in different fields.

  15. Construction and performance research on variable-length codes for multirate OCDMA multimedia networks

    Science.gov (United States)

    Li, Chuan-qi; Yang, Meng-jie; Luo, De-jun; Lu, Ye; Kong, Yi-pu; Zhang, Dong-chuang

    2014-09-01

    A new kind of variable-length codes with good correlation properties for the multirate asynchronous optical code division multiple access (OCDMA) multimedia networks is proposed, called non-repetition interval (NRI) codes. The NRI codes can be constructed by structuring the interval-sets with no repetition, and the code length depends on the number of users and the code weight. According to the structural characteristics of NRI codes, the formula of bit error rate (BER) is derived. Compared with other variable-length codes, the NRI codes have lower BER. A multirate OCDMA multimedia simulation system is designed and built, the longer codes are assigned to the users who need slow speed, while the shorter codes are assigned to the users who need high speed. It can be obtained by analyzing the eye diagram that the user with slower speed has lower BER, and the conclusion is the same as the actual demand in multimedia data transport.

  16. Systematic construction of kinetic models from genome-scale metabolic networks.

    Directory of Open Access Journals (Sweden)

    Natalie J Stanford

    Full Text Available The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments.

  17. Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

    Science.gov (United States)

    Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

    2013-01-01

    The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

  18. Feasibility of Construction of the Continuously Operating Geodetic GPS Network of Sinaloa, Mexico

    Science.gov (United States)

    Vazquez, G. E.; Jacobo, C.

    2011-12-01

    This research is based on the study and analysis of feasibility for the construction of the geodetic network for GPS continuous operation for Sinaloa, hereafter called (RGOCSIN). A GPS network of continuous operation is defined as that materialized structure physically through permanent monuments where measurements to the systems of Global Positioning (GPS) is performed continuously throughout a region. The GPS measurements in this network are measurements of accuracy according to international standards to define its coordinates, thus constituting the basic structure of geodetic referencing for a country. In this context is that in the near future the RGOCSIN constitutes a system state only accurate and reliable georeferencing in real-time (continuous and permanent operation) and will be used for different purposes; i.e., in addition to being fundamental basis for any lifting topographic or geodetic survey, and other areas such as: (1) Different construction processes (control and monitoring of engineering works); (2) Studies of deformation of the Earth's crust (before and after a seismic event); (3) GPS meteorology (weather forecasting); (4) Demarcation projects (natural and political); (5) Establishment of bases to generate mapping (necessary for the economic and social development of the state); (6) Precision agriculture (optimization of economic resources to the various crops); (7) Geographic information systems (Organization and planning activities associated with the design and construction of public services); (8) Urban growth (possible settlements in the appropriate form and taking care of the environmental aspect), among others. However there are criteria and regulations according to the INEGI (Instituto Nacional de Estadística y Geografía, http://www.inegi.org.mx/) that must be met; even for this stage of feasibility of construction that sees this project as a first phase. The fundamental criterion to be taken into account according to INEGI is a

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

    Science.gov (United States)

    Sung, Woong Je

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

  20. Promotion of cooperation in the form C0C1D classified by 'degree grads' in a scale-free network

    International Nuclear Information System (INIS)

    Zhao, Li; Ye, Xiang-Jun; Huang, Zi-Gang; Sun, Jin-Tu; Yang, Lei; Wang, Ying-Hai; Do, Younghae

    2010-01-01

    In this paper, we revisit the issue of the public goods game (PGG) on a heterogeneous graph. By introducing a new effective topology parameter, 'degree grads' ψ, we clearly classify the agents into three kinds, namely, C 0 , C 1 , and D. The mechanism for the heterogeneous topology promoting cooperation is discussed in detail from the perspective of C 0 C 1 D, which reflects the fact that the unreasoning imitation behaviour of C 1 agents, who are 'cheated' by the well-paid C 0 agents inhabiting special positions, stabilizes the formation of the cooperation community. The analytical and simulation results for certain parameters are found to coincide well with each other. The C 0 C 1 D case provides a picture of the actual behaviours in real society and thus is potentially of interest

  1. Biocomputional construction of a gene network under acid stress in Synechocystis sp. PCC 6803.

    Science.gov (United States)

    Li, Yi; Rao, Nini; Yang, Feng; Zhang, Ying; Yang, Yang; Liu, Han-ming; Guo, Fengbiao; Huang, Jian

    2014-01-01

    Acid stress is one of the most serious threats that cyanobacteria have to face, and it has an impact at all levels from genome to phenotype. However, very little is known about the detailed response mechanism to acid stress in this species. We present here a general analysis of the gene regulatory network of Synechocystis sp. PCC 6803 in response to acid stress using comparative genome analysis and biocomputational prediction. In this study, we collected 85 genes and used them as an initial template to predict new genes through co-regulation, protein-protein interactions and the phylogenetic profile, and 179 new genes were obtained to form a complete template. In addition, we found that 11 enriched pathways such as glycolysis are closely related to the acid stress response. Finally, we constructed a regulatory network for the intricate relationship of these genes and summarize the key steps in response to acid stress. This is the first time a bioinformatic approach has been taken systematically to gene interactions in cyanobacteria and the elaboration of their cell metabolism and regulatory pathways under acid stress, which is more efficient than a traditional experimental study. The results also provide theoretical support for similar research into environmental stresses in cyanobacteria and possible industrial applications. Copyright © 2014 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

  2. Construction of a global pain systems network highlights phospholipid signaling as a regulator of heat nociception.

    Directory of Open Access Journals (Sweden)

    G Gregory Neely

    Full Text Available The ability to perceive noxious stimuli is critical for an animal's survival in the face of environmental danger, and thus pain perception is likely to be under stringent evolutionary pressure. Using a neuronal-specific RNAi knock-down strategy in adult Drosophila, we recently completed a genome-wide functional annotation of heat nociception that allowed us to identify α2δ3 as a novel pain gene. Here we report construction of an evolutionary-conserved, system-level, global molecular pain network map. Our systems map is markedly enriched for multiple genes associated with human pain and predicts a plethora of novel candidate pain pathways. One central node of this pain network is phospholipid signaling, which has been implicated before in pain processing. To further investigate the role of phospholipid signaling in mammalian heat pain perception, we analysed the phenotype of PIP5Kα and PI3Kγ mutant mice. Intriguingly, both of these mice exhibit pronounced hypersensitivity to noxious heat and capsaicin-induced pain, which directly mapped through PI3Kγ kinase-dead knock-in mice to PI3Kγ lipid kinase activity. Using single primary sensory neuron recording, PI3Kγ function was mechanistically linked to a negative regulation of TRPV1 channel transduction. Our data provide a systems map for heat nociception and reinforces the extraordinary conservation of molecular mechanisms of nociception across different species.

  3. Construction of a Global Pain Systems Network Highlights Phospholipid Signaling as a Regulator of Heat Nociception

    Science.gov (United States)

    Mair, Norbert; Racz, Ildiko; Milinkeviciute, Giedre; Meixner, Arabella; Nayanala, Swetha; Griffin, Robert S.; Belfer, Inna; Dai, Feng; Smith, Shad; Diatchenko, Luda; Marengo, Stefano; Haubner, Bernhard J.; Novatchkova, Maria; Gibson, Dustin; Maixner, William; Pospisilik, J. Andrew; Hirsch, Emilio; Whishaw, Ian Q.; Zimmer, Andreas; Gupta, Vaijayanti; Sasaki, Junko; Kanaho, Yasunori; Sasaki, Takehiko; Kress, Michaela; Woolf, Clifford J.; Penninger, Josef M.

    2012-01-01

    The ability to perceive noxious stimuli is critical for an animal's survival in the face of environmental danger, and thus pain perception is likely to be under stringent evolutionary pressure. Using a neuronal-specific RNAi knock-down strategy in adult Drosophila, we recently completed a genome-wide functional annotation of heat nociception that allowed us to identify α2δ3 as a novel pain gene. Here we report construction of an evolutionary-conserved, system-level, global molecular pain network map. Our systems map is markedly enriched for multiple genes associated with human pain and predicts a plethora of novel candidate pain pathways. One central node of this pain network is phospholipid signaling, which has been implicated before in pain processing. To further investigate the role of phospholipid signaling in mammalian heat pain perception, we analysed the phenotype of PIP5Kα and PI3Kγ mutant mice. Intriguingly, both of these mice exhibit pronounced hypersensitivity to noxious heat and capsaicin-induced pain, which directly mapped through PI3Kγ kinase-dead knock-in mice to PI3Kγ lipid kinase activity. Using single primary sensory neuron recording, PI3Kγ function was mechanistically linked to a negative regulation of TRPV1 channel transduction. Our data provide a systems map for heat nociception and reinforces the extraordinary conservation of molecular mechanisms of nociception across different species. PMID:23236288

  4. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Science.gov (United States)

    Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

    2014-01-01

    The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  5. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  6. Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

    Science.gov (United States)

    Jose, K V Jovan; Artrith, Nongnuch; Behler, Jörg

    2012-05-21

    An accurate determination of the potential energy is the crucial step in computer simulations of chemical processes, but using electronic structure methods on-the-fly in molecular dynamics (MD) is computationally too demanding for many systems. Constructing more efficient interatomic potentials becomes intricate with increasing dimensionality of the potential-energy surface (PES), and for numerous systems the accuracy that can be achieved is still not satisfying and far from the reliability of first-principles calculations. Feed-forward neural networks (NNs) have a very flexible functional form, and in recent years they have been shown to be an accurate tool to construct efficient PESs. High-dimensional NN potentials based on environment-dependent atomic energy contributions have been presented for a number of materials. Still, these potentials may be improved by a more detailed structural description, e.g., in form of atom pairs, which directly reflect the atomic interactions and take the chemical environment into account. We present an implementation of an NN method based on atom pairs, and its accuracy and performance are compared to the atom-based NN approach using two very different systems, the methanol molecule and metallic copper. We find that both types of NN potentials provide an excellent description of both PESs, with the pair-based method yielding a slightly higher accuracy making it a competitive alternative for addressing complex systems in MD simulations.

  7. Construction of an miRNA-Regulated Pathway Network Reveals Candidate Biomarkers for Postmenopausal Osteoporosis

    Directory of Open Access Journals (Sweden)

    Min Shao

    2017-01-01

    Full Text Available We aimed to identify risk pathways for postmenopausal osteoporosis (PMOP via establishing an microRNAs- (miRNA- regulated pathway network (MRPN. Firstly, we identified differential pathways through calculating gene- and pathway-level statistics based on the accumulated normal samples using the individual pathway aberrance score (iPAS. Significant pathways based on differentially expressed genes (DEGs using DAVID were extracted, followed by identifying the common pathways between iPAS and DAVID methods. Next, miRNAs prediction was implemented via calculating TargetScore values with precomputed input (log fold change (FC, TargetScan context score (TSCS, and probabilities of conserved targeting (PCT. An MRPN construction was constructed using the common genes in the common pathways and the predicted miRNAs. Using false discovery rate (FDR < 0.05, 279 differential pathways were identified. Using the criteria of FDR < 0.05 and log⁡FC≥2, 39 DEGs were retrieved, and these DEGs were enriched in 64 significant pathways identified by DAVID. Overall, 27 pathways were the common ones between two methods. Importantly, MAPK signaling pathway and PI3K-Akt signaling pathway were the first and second significantly enriched ones, respectively. These 27 common pathways separated PMOP from controls with the accuracy of 0.912. MAPK signaling pathway and PI3K/Akt signaling pathway might play crucial roles in PMOP.

  8. Bayesian state space models for dynamic genetic network construction across multiple tissues.

    Science.gov (United States)

    Liang, Yulan; Kelemen, Arpad

    2016-08-01

    Construction of gene-gene interaction networks and potential pathways is a challenging and important problem in genomic research for complex diseases while estimating the dynamic changes of the temporal correlations and non-stationarity are the keys in this process. In this paper, we develop dynamic state space models with hierarchical Bayesian settings to tackle this challenge for inferring the dynamic profiles and genetic networks associated with disease treatments. We treat both the stochastic transition matrix and the observation matrix time-variant and include temporal correlation structures in the covariance matrix estimations in the multivariate Bayesian state space models. The unevenly spaced short time courses with unseen time points are treated as hidden state variables. Hierarchical Bayesian approaches with various prior and hyper-prior models with Monte Carlo Markov Chain and Gibbs sampling algorithms are used to estimate the model parameters and the hidden state variables. We apply the proposed Hierarchical Bayesian state space models to multiple tissues (liver, skeletal muscle, and kidney) Affymetrix time course data sets following corticosteroid (CS) drug administration. Both simulation and real data analysis results show that the genomic changes over time and gene-gene interaction in response to CS treatment can be well captured by the proposed models. The proposed dynamic Hierarchical Bayesian state space modeling approaches could be expanded and applied to other large scale genomic data, such as next generation sequence (NGS) combined with real time and time varying electronic health record (EHR) for more comprehensive and robust systematic and network based analysis in order to transform big biomedical data into predictions and diagnostics for precision medicine and personalized healthcare with better decision making and patient outcomes.

  9. The development of natural-draught cooling towers of prestressed wire-rope network construction of aerodynamic design

    International Nuclear Information System (INIS)

    Braun, R.; Jasch, E.

    1975-01-01

    Natural-draught cooling towers carried to a height of up to 200 m will be required for the dissipation of the residual heat from the thermal processes of large-capacity power stations to be erected in future. The structural problems involved in such large-size towers can be overcome by using prestressed wire-rope network construction. A structural concept is discussed which proposes to use a cooling tower shell constructed of a prestressed, planked wire-rope network of circular hyperbolic form carried by a spacer ring attached to the central mast. Comments are given on the ensuing problems of aerodynamics, stress-strength assessment, and erection. (orig.) [de

  10. Hybrid Neuro-Fuzzy Classifier Based On Nefclass Model

    Directory of Open Access Journals (Sweden)

    Bogdan Gliwa

    2011-01-01

    Full Text Available The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods. Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

  11. Classifying features in CT imagery: accuracy for some single- and multiple-species classifiers

    Science.gov (United States)

    Daniel L. Schmoldt; Jing He; A. Lynn Abbott

    1998-01-01

    Our current approach to automatically label features in CT images of hardwood logs classifies each pixel of an image individually. These feature classifiers use a back-propagation artificial neural network (ANN) and feature vectors that include a small, local neighborhood of pixels and the distance of the target pixel to the center of the log. Initially, this type of...

  12. Content-rich biological network constructed by mining PubMed abstracts

    Directory of Open Access Journals (Sweden)

    Sharp Burt M

    2004-10-01

    Full Text Available Abstract Background The integration of the rapidly expanding corpus of information about the genome, transcriptome, and proteome, engendered by powerful technological advances, such as microarrays, and the availability of genomic sequence from multiple species, challenges the grasp and comprehension of the scientific community. Despite the existence of text-mining methods that identify biological relationships based on the textual co-occurrence of gene/protein terms or similarities in abstract texts, knowledge of the underlying molecular connections on a large scale, which is prerequisite to understanding novel biological processes, lags far behind the accumulation of data. While computationally efficient, the co-occurrence-based approaches fail to characterize (e.g., inhibition or stimulation, directionality biological interactions. Programs with natural language processing (NLP capability have been created to address these limitations, however, they are in general not readily accessible to the public. Results We present a NLP-based text-mining approach, Chilibot, which constructs content-rich relationship networks among biological concepts, genes, proteins, or drugs. Amongst its features, suggestions for new hypotheses can be generated. Lastly, we provide evidence that the connectivity of molecular networks extracted from the biological literature follows the power-law distribution, indicating scale-free topologies consistent with the results of previous experimental analyses. Conclusions Chilibot distills scientific relationships from knowledge available throughout a wide range of biological domains and presents these in a content-rich graphical format, thus integrating general biomedical knowledge with the specialized knowledge and interests of the user. Chilibot http://www.chilibot.net can be accessed free of charge to academic users.

  13. The influence of management and construction methods in the repair costs of Spain’s low-volume road network

    Directory of Open Access Journals (Sweden)

    Eutiquio Gallego

    2016-06-01

    Full Text Available This paper describes the entire process of the implementation of the Spanish low volume road network, including the design criteria, the construction techniques and the management policies during all the periods. The current situation of low volume roads in Spain was analyzed with respect to the legal framework and their actual condition. In addition, the budget required for the repair of 41 low volume roads throughout Spain was calculated in order to statistically analyze the influence of the pavement materials and the period of construction. The main conclusions were that low volume roads constructed during the 1970´s are currently those in the best state of repair and those requiring the lower repair costs, even lower than those constructed after 1980´s. In addition, low volume roads constructed with higher quality materials and using standardized techniques required five times lower repair costs than those made of lower quality materials.

  14. Classifying Returns as Extreme

    DEFF Research Database (Denmark)

    Christiansen, Charlotte

    2014-01-01

    I consider extreme returns for the stock and bond markets of 14 EU countries using two classification schemes: One, the univariate classification scheme from the previous literature that classifies extreme returns for each market separately, and two, a novel multivariate classification scheme tha...

  15. LCC: Light Curves Classifier

    Science.gov (United States)

    Vo, Martin

    2017-08-01

    Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio). Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.

  16. Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: application to geophysical prospecting.

    Science.gov (United States)

    Valdés, Julio J; Barton, Alan J

    2007-05-01

    A method for the construction of virtual reality spaces for visual data mining using multi-objective optimization with genetic algorithms on nonlinear discriminant (NDA) neural networks is presented. Two neural network layers (the output and the last hidden) are used for the construction of simultaneous solutions for: (i) a supervised classification of data patterns and (ii) an unsupervised similarity structure preservation between the original data matrix and its image in the new space. A set of spaces are constructed from selected solutions along the Pareto front. This strategy represents a conceptual improvement over spaces computed by single-objective optimization. In addition, genetic programming (in particular gene expression programming) is used for finding analytic representations of the complex mappings generating the spaces (a composition of NDA and orthogonal principal components). The presented approach is domain independent and is illustrated via application to the geophysical prospecting of caves.

  17. A Pattern Construction Scheme for Neural Network-Based Cognitive Communication

    Directory of Open Access Journals (Sweden)

    Ozgur Orcay

    2011-01-01

    Full Text Available Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR, are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS is an adaptive and perceptual communication method based on a Cognitive Radio (CR approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE. The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN in the receiver site.

  18. Fast method of constructing image correlations to build a free network based on image multivocabulary trees

    Science.gov (United States)

    Zhan, Zongqian; Wang, Xin; Wei, Minglu

    2015-05-01

    In image-based three-dimensional (3-D) reconstruction, one topic of growing importance is how to quickly obtain a 3-D model from a large number of images. The retrieval of the correct and relevant images for the model poses a considerable technological challenge. The "image vocabulary tree" has been proposed as a method to search for similar images. However, a significant drawback of this approach is identified in its low time efficiency and barely satisfactory classification result. The method proposed is inspired by, and improves upon, some recent methods. Specifically, vocabulary quality is considered and multivocabulary trees are designed to improve the classification result. A marked improvement was, indeed, observed in our evaluation of the proposed method. To improve time efficiency, graphics processing unit (GPU) computer unified device architecture parallel computation is applied in the multivocabulary trees. The results of the experiments showed that the GPU was three to four times more efficient than the enumeration matching and CPU methods when the number of images is large. This paper presents a reliable reference method for the rapid construction of a free network to be used for the computing of 3-D information.

  19. Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

    Science.gov (United States)

    Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

    2017-12-01

    State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with

  20. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  1. Deconvolution When Classifying Noisy Data Involving Transformations.

    Science.gov (United States)

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-09-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  2. Deconvolution When Classifying Noisy Data Involving Transformations

    KAUST Repository

    Carroll, Raymond; Delaigle, Aurore; Hall, Peter

    2012-01-01

    In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We introduce a fully data-driven procedure based on cross-validation, and use several classifiers to illustrate numerical properties of our approach. Theoretical arguments are given in support of our claims. Our procedure is applied to data generated by light detection and ranging (Lidar) technology, where we improve on earlier approaches to classifying aerosols. This article has supplementary materials online.

  3. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

    Energy Technology Data Exchange (ETDEWEB)

    Carta, Jose A. [Department of Mechanical Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Velazquez, Sergio [Department of Electronics and Automatics Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira s/n, 35017 Las Palmas de Gran Canaria, Canary Islands (Spain); Matias, J.M. [Department of Statistics, University of Vigo, Lagoas Marcosende, 36200 Vigo (Spain)

    2011-02-15

    Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station. (author)

  4. Use of Bayesian networks classifiers for long-term mean wind turbine energy output estimation at a potential wind energy conversion site

    International Nuclear Information System (INIS)

    Carta, Jose A.; Velazquez, Sergio; Matias, J.M.

    2011-01-01

    Due to the interannual variability of wind speed a feasibility analysis for the installation of a Wind Energy Conversion System at a particular site requires estimation of the long-term mean wind turbine energy output. A method is proposed in this paper which, based on probabilistic Bayesian networks (BNs), enables estimation of the long-term mean wind speed histogram for a site where few measurements of the wind resource are available. For this purpose, the proposed method allows the use of multiple reference stations with a long history of wind speed and wind direction measurements. That is to say, the model that is proposed in this paper is able to involve and make use of regional information about the wind resource. With the estimated long-term wind speed histogram and the power curve of a wind turbine it is possible to use the method of bins to determine the long-term mean energy output for that wind turbine. The intelligent system employed, the knowledgebase of which is a joint probability function of all the model variables, uses efficient calculation techniques for conditional probabilities to perform the reasoning. This enables automatic model learning and inference to be performed efficiently based on the available evidence. The proposed model is applied in this paper to wind speeds and wind directions recorded at four weather stations located in the Canary Islands (Spain). Ten years of mean hourly wind speed and direction data are available for these stations. One of the conclusions reached is that the BN with three reference stations gave fewer errors between the real and estimated long-term mean wind turbine energy output than when using two measure-correlate-predict algorithms which were evaluated and which use a linear regression between the candidate station and one reference station.

  5. Exploring the field of public construction clients by a graphical network analysis

    NARCIS (Netherlands)

    Eisma, P.R.; Volker, L.

    2014-01-01

    Because public construction clients form the majority of construction clients and procure over 40% of the construction output in most countries, they are important actors in the construction industry. Yet, the field of research on clients is still underdeveloped. In order to identify the research

  6. Intelligent Garbage Classifier

    Directory of Open Access Journals (Sweden)

    Ignacio Rodríguez Novelle

    2008-12-01

    Full Text Available IGC (Intelligent Garbage Classifier is a system for visual classification and separation of solid waste products. Currently, an important part of the separation effort is based on manual work, from household separation to industrial waste management. Taking advantage of the technologies currently available, a system has been built that can analyze images from a camera and control a robot arm and conveyor belt to automatically separate different kinds of waste.

  7. Classifying Linear Canonical Relations

    OpenAIRE

    Lorand, Jonathan

    2015-01-01

    In this Master's thesis, we consider the problem of classifying, up to conjugation by linear symplectomorphisms, linear canonical relations (lagrangian correspondences) from a finite-dimensional symplectic vector space to itself. We give an elementary introduction to the theory of linear canonical relations and present partial results toward the classification problem. This exposition should be accessible to undergraduate students with a basic familiarity with linear algebra.

  8. The network construction of CSELF for earthquake monitoring and its preliminary observation

    Science.gov (United States)

    Tang, J.; Zhao, G.; Chen, X.; Bing, H.; Wang, L.; Zhan, Y.; Xiao, Q.; Dong, Z.

    2017-12-01

    The Electromagnetic (EM) anomaly in short-term earthquake precursory is most sensitive physical phenomena. Scientists believe that EM monitoring for earthquake is one of the most promising means of forecasting. However, existing ground-base EM observation confronted with increasing impact cultural noises, and the lack of a frequency range of higher than 1Hz observations. Control source of extremely low frequency (CSELF) EM is a kind of good prospective new approach. It not only has many advantages with high S/N ratio, large coverage area, probing depth ect., thereby facilitating the identification and capture anomaly signal, and it also can be used to study the electromagnetic field variation and to study the crustal medium changes of the electric structure.The first CSELF EM network for earthquake precursory monitoring with 30 observatories in China has been constructed. The observatories distribute in Beijing surrounding area and in the southern part of North-South Seismic Zone. GMS-07 system made by Metronix is equipped at each station. The observation mixed CSELF and nature source, that is, if during the control source is off transmitted, the nature source EM signal will be recorded. In genernal, there are 3 5 frequencies signals in the 0.1-300Hz frequency band will be transmit in every morning and evening in a fixed time (length 2 hours). Besides time, natural field to extend the frequency band (0.001 1000 Hz) will be observed by using 3 sample frequencies, 4096Hz sampling rate for HF, 256Hz for MF and 16Hz for LF. The low frequency band records continuously all-day and the high and medium frequency band use a slices record, the data records by cycling acquisition in every 10 minutes with length of about 4 to 8 seconds and 64 to 128 seconds , respectively. All the data is automatically processed by server installed in the observatory. The EDI file including EM field spectrums and MT responses and time series files will be sent the data center by internet

  9. Exemplar-based optical neural net classifier for color pattern recognition

    Science.gov (United States)

    Yu, Francis T. S.; Uang, Chii-Maw; Yang, Xiangyang

    1992-10-01

    We present a color exemplar-based neural network that can be used as an optimum image classifier or an associative memory. Color decomposition and composition technique is used for constructing the polychromatic interconnection weight matrix (IWM). The Hamming net algorithm is modified to relax the dynamic range requirement of the spatial light modulator and to reduce the number of iteration cycles in the winner-take-all layer. Computer simulation results demonstrated the feasibility of this approach

  10. A systems biology approach to construct the gene regulatory network of systemic inflammation via microarray and databases mining

    Directory of Open Access Journals (Sweden)

    Lan Chung-Yu

    2008-09-01

    Full Text Available Abstract Background Inflammation is a hallmark of many human diseases. Elucidating the mechanisms underlying systemic inflammation has long been an important topic in basic and clinical research. When primary pathogenetic events remains unclear due to its immense complexity, construction and analysis of the gene regulatory network of inflammation at times becomes the best way to understand the detrimental effects of disease. However, it is difficult to recognize and evaluate relevant biological processes from the huge quantities of experimental data. It is hence appealing to find an algorithm which can generate a gene regulatory network of systemic inflammation from high-throughput genomic studies of human diseases. Such network will be essential for us to extract valuable information from the complex and chaotic network under diseased conditions. Results In this study, we construct a gene regulatory network of inflammation using data extracted from the Ensembl and JASPAR databases. We also integrate and apply a number of systematic algorithms like cross correlation threshold, maximum likelihood estimation method and Akaike Information Criterion (AIC on time-lapsed microarray data to refine the genome-wide transcriptional regulatory network in response to bacterial endotoxins in the context of dynamic activated genes, which are regulated by transcription factors (TFs such as NF-κB. This systematic approach is used to investigate the stochastic interaction represented by the dynamic leukocyte gene expression profiles of human subject exposed to an inflammatory stimulus (bacterial endotoxin. Based on the kinetic parameters of the dynamic gene regulatory network, we identify important properties (such as susceptibility to infection of the immune system, which may be useful for translational research. Finally, robustness of the inflammatory gene network is also inferred by analyzing the hubs and "weak ties" structures of the gene network

  11. Construction of natural radiation exposure study network - overview and current status

    International Nuclear Information System (INIS)

    Tokonami, Shinji

    2010-01-01

    A new project entitled 'Construction of natural radiation exposure study network' was adopted in the Program of Promotion of International Joint Research under the Special Coordination Funds for Promoting Science and Technology operated by the Ministry of Education, Culture, Sports, Science and Technology of Japan. Eight institutions were involved in this project and the project will continue until March, 2012. The aims of the project are to assess the dose for natural radiation exposures using state-of- the-art measurement techniques in four Asian countries (China, India, Korea and Thailand) and their outcomes will be distributed worldwide. Throughout the project, conventional measurement techniques will be improved and be optimized. More scientific data and results will be obtained as well. In particular, the following advanced technologies for inhalation exposures will be introduced: (1) Discriminative measurements of radon ( 222 Rn) and thoron ( 220 Rn) gases, (2) Evaluation of thoron decay products concentration, (3) Simple but effective particle size distribution measurements. In China, we are conducting a case-control study of radon and lung cancer in Gansu, China. This Gansu area was investigated in the past for the case-control study. New data are being accumulated. In India, we focused on Orissa in order to carry out radiation measurements in this project. In parallel, Kerala is currently involved as the comparative study area. In Korea, we are now measuring radon and thoron in radon/thoron prone areas. These results will give us new information for further understanding of exposure due to radon and thoron. In Thailand, we are carrying out comprehensively radiation measurements in NORM industries. Not only these surveys but also quality assurance of radon measurements are being addressed in Japan. We have managed an international intercomparison exercise of passive radon detectors at NIRS. This study presents an overview of the project and current status

  12. A construct-network approach to bridging diagnostic and physiological domains: application to assessment of externalizing psychopathology.

    Science.gov (United States)

    Patrick, Christopher J; Venables, Noah C; Yancey, James R; Hicks, Brian M; Nelson, Lindsay D; Kramer, Mark D

    2013-08-01

    A crucial challenge in efforts to link psychological disorders to neural systems, with the aim of developing biologically informed conceptions of such disorders, is the problem of method variance (Campbell & Fiske, 1959). Since even measures of the same construct in differing domains correlate only moderately, it is unsurprising that large sample studies of diagnostic biomarkers yield only modest associations. To address this challenge, a construct-network approach is proposed in which psychometric operationalizations of key neurobehavioral constructs serve as anchors for identifying neural indicators of psychopathology-relevant dispositions, and as vehicles for bridging between domains of clinical problems and neurophysiology. An empirical illustration is provided for the construct of inhibition-disinhibition, which is of central relevance to problems entailing deficient impulse control. Findings demonstrate that: (1) a well-designed psychometric index of trait disinhibition effectively predicts externalizing problems of multiple types, (2) this psychometric measure of disinhibition shows reliable brain response correlates, and (3) psychometric and brain-response indicators can be combined to form a joint psychoneurometric factor that predicts effectively across clinical and physiological domains. As a methodology for bridging between clinical problems and neural systems, the construct-network approach provides a concrete means by which existing conceptions of psychological disorders can accommodate and be reshaped by neurobiological insights. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  13. Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    2015-07-01

    Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

  14. Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using 123I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).

    Science.gov (United States)

    Cascianelli, S; Tranfaglia, C; Fravolini, M L; Bianconi, F; Minestrini, M; Nuvoli, S; Tambasco, N; Dottorini, M E; Palumbo, B

    2017-01-01

    The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. 123 I-FP-CIT brain SPET is able to contribute to the differential diagnosis. Semiquantitative analysis of radiopharmaceutical uptake in basal ganglia (caudate nuclei and putamina) is very useful to support the diagnostic process. An artificial neural network classifier using 123 I-FP-CIT brain SPET data, a classification tree (CIT), was applied. CIT is an automatic classifier composed of a set of logical rules, organized as a decision tree to produce an optimised threshold based classification of data to provide discriminative cut-off values. We applied a CIT to 123 I-FP-CIT brain SPET semiquantitave data, to obtain cut-off values of radiopharmaceutical uptake ratios in caudate nuclei and putamina with the aim to diagnose PD versus other conditions. We retrospectively investigated 187 patients undergoing 123 I-FP-CIT brain SPET (Millenium VG, G.E.M.S.) with semiquantitative analysis performed with Basal Ganglia (BasGan) V2 software according to EANM guidelines; among them 113 resulted affected by PD (PD group) and 74 (N group) by other non parkinsonian conditions, such as Essential Tremor and drug-induced PD. PD group included 113 subjects (60M and 53F of age: 60-81yrs) having Hoehn and Yahr score (HY): 0.5-1.5; Unified Parkinson Disease Rating Scale (UPDRS) score: 6-38; N group included 74 subjects (36M and 38 F range of age 60-80 yrs). All subjects were clinically followed for at least 6-18 months to confirm the diagnosis. To examinate data obtained by using CIT, for each of the 1,000 experiments carried out, 10% of patients were randomly selected as the CIT training set, while the remaining 90% validated the trained CIT, and the percentage of the validation data correctly classified in the two groups of patients was computed. The expected performance of an "average

  15. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha

    2013-11-25

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  16. Classifiers based on optimal decision rules

    KAUST Repository

    Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata

    2013-01-01

    Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification-exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).

  17. Method of construction of rational corporate network using the simulation model

    Directory of Open Access Journals (Sweden)

    V.N. Pakhomovа

    2013-06-01

    Full Text Available Purpose. Search for new options of the transition from Ethernet technology. Methodology. Physical structuring of the Fast Ethernet network based on hubs and logical structuring of Fast Ethernet network using commutators. Organization of VLAN based on ports grouping and in accordance with the standard IEEE 802 .1Q. Findings. The options for improving of the Ethernet network are proposed. According to the Fast Ethernet and VLAN technologies on the simulation models in packages NetCraker and Cisco Packet Traker respectively. Origiality. The technique of designing of local area network using the VLAN technology is proposed. Practical value.Each of the options of "Dniprozaliznychproekt" network improving has its advantages. Transition from the Ethernet to Fast Ethernet technology is simple and economical, it requires only one commutator, when the VLAN organization requires at least two. VLAN technology, however, has the following advantages: reducing the load on the network, isolation of the broadcast traffic, change of the logical network structure without changing its physical structure, improving the network security. The transition from Ethernet to the VLAN technology allows you to separate the physical topology from the logical one, and the format of the ÌEEE 802.1Q standard frames allows you to simplify the process of virtual networks implementation to enterprises.

  18. Classifying network attack scenarios using an ontology

    CSIR Research Space (South Africa)

    Van Heerden, RP

    2012-03-01

    Full Text Available ) or to the target?s reputation. The Residue sub-phase refers to damage or artefacts of the attack that occur after the attack goal has been achieved, and occurs because the attacker loses control of some systems. For example after the launch of a DDOS..., A. (1995). Hacking theft of $10 million from citibank revealed. Retrieved 10/10, 2011, from http://articles.latimes.com/1995-08-19/business/fi-36656_1_citibank-system Hurley, E. (2004). SCO site succumbs to DDoS attack. Retrieved 10/10, 2011, from...

  19. A Simple Neural Network Contextual Classifier

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Tidemann, J.

    1997-01-01

    I. Kanellopoulos, G.G. Wilkinson, F. Roli and J. Austin (Eds.)Proceedings of European Union Environment and Climate Programme Concerted Action COMPARES (COnnectionist Methods in Pre-processing and Analysis of REmote Sensing data)....

  20. Stack filter classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory

    2009-01-01

    Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.

  1. Dual Cross-Linked Biofunctional and Self-Healing Networks to Generate User-Defined Modular Gradient Hydrogel Constructs.

    Science.gov (United States)

    Wei, Zhao; Lewis, Daniel M; Xu, Yu; Gerecht, Sharon

    2017-08-01

    Gradient hydrogels have been developed to mimic the spatiotemporal differences of multiple gradient cues in tissues. Current approaches used to generate such hydrogels are restricted to a single gradient shape and distribution. Here, a hydrogel is designed that includes two chemical cross-linking networks, biofunctional, and self-healing networks, enabling the customizable formation of modular gradient hydrogel construct with various gradient distributions and flexible shapes. The biofunctional networks are formed via Michael addition between the acrylates of oxidized acrylated hyaluronic acid (OAHA) and the dithiol of matrix metalloproteinase (MMP)-sensitive cross-linker and RGD peptides. The self-healing networks are formed via dynamic Schiff base reaction between N-carboxyethyl chitosan (CEC) and OAHA, which drives the modular gradient units to self-heal into an integral modular gradient hydrogel. The CEC-OAHA-MMP hydrogel exhibits excellent flowability at 37 °C under shear stress, enabling its injection to generate gradient distributions and shapes. Furthermore, encapsulated sarcoma cells respond to the gradient cues of RGD peptides and MMP-sensitive cross-linkers in the hydrogel. With these superior properties, the dual cross-linked CEC-OAHA-MMP hydrogel holds significant potential for generating customizable gradient hydrogel constructs, to study and guide cellular responses to their microenvironment such as in tumor mimicking, tissue engineering, and stem cell differentiation and morphogenesis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. A Real-Time Construction Safety Monitoring System for Hazardous Gas Integrating Wireless Sensor Network and Building Information Modeling Technologies.

    Science.gov (United States)

    Cheung, Weng-Fong; Lin, Tzu-Hsuan; Lin, Yu-Cheng

    2018-02-02

    In recent years, many studies have focused on the application of advanced technology as a way to improve management of construction safety management. A Wireless Sensor Network (WSN), one of the key technologies in Internet of Things (IoT) development, enables objects and devices to sense and communicate environmental conditions; Building Information Modeling (BIM), a revolutionary technology in construction, integrates database and geometry into a digital model which provides a visualized way in all construction lifecycle management. This paper integrates BIM and WSN into a unique system which enables the construction site to visually monitor the safety status via a spatial, colored interface and remove any hazardous gas automatically. Many wireless sensor nodes were placed on an underground construction site and to collect hazardous gas level and environmental condition (temperature and humidity) data, and in any region where an abnormal status is detected, the BIM model will alert the region and an alarm and ventilator on site will start automatically for warning and removing the hazard. The proposed system can greatly enhance the efficiency in construction safety management and provide an important reference information in rescue tasks. Finally, a case study demonstrates the applicability of the proposed system and the practical benefits, limitations, conclusions, and suggestions are summarized for further applications.

  3. General and Local: Averaged k-Dependence Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Limin Wang

    2015-06-01

    Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

  4. A constructive logic for services and information flow in computer networks

    NARCIS (Netherlands)

    Borghuis, V.A.J.; Feijs, L.M.G.

    2000-01-01

    In this paper we introduce a typed -calculus in which computer networks can be formalized and directed at situations where the services available on the network are stationary, while the information can flow freely. For this calculus, an analogue of the ‘propositions-as-types ’interpretation of

  5. NetRaVE: constructing dependency networks using sparse linear regression

    DEFF Research Database (Denmark)

    Phatak, A.; Kiiveri, H.; Clemmensen, Line Katrine Harder

    2010-01-01

    NetRaVE is a small suite of R functions for generating dependency networks using sparse regression methods. Such networks provide an alternative to interpreting 'top n lists' of genes arising out of an analysis of microarray data, and they provide a means of organizing and visualizing the resulting...

  6. A reverse engineering approach to optimize experiments for the construction of biological regulatory networks.

    Science.gov (United States)

    Zhang, Xiaomeng; Shao, Bin; Wu, Yangle; Qi, Ouyang

    2013-01-01

    One of the major objectives in systems biology is to understand the relation between the topological structures and the dynamics of biological regulatory networks. In this context, various mathematical tools have been developed to deduct structures of regulatory networks from microarray expression data. In general, from a single data set, one cannot deduct the whole network structure; additional expression data are usually needed. Thus how to design a microarray expression experiment in order to get the most information is a practical problem in systems biology. Here we propose three methods, namely, maximum distance method, trajectory entropy method, and sampling method, to derive the optimal initial conditions for experiments. The performance of these methods is tested and evaluated in three well-known regulatory networks (budding yeast cell cycle, fission yeast cell cycle, and E. coli. SOS network). Based on the evaluation, we propose an efficient strategy for the design of microarray expression experiments.

  7. The role of “network of cities” in construction of global urban culture

    OpenAIRE

    Baycan-Levent, Tüzin; Kundak, Seda; Gülümser, Aliye Ahu

    2004-01-01

    The globalization process has led to an increased interaction between cities and to a new urban system/network in which they need to be competitive and complementary at the same time. “Network of cities”, such as World Cities, Eurocities or Sister Cities are among the well known examples of interaction and cooperation of the cities at the regional and global level. The cities of different regions and countries tend to share their experiences and their cultures within these networks in order t...

  8. Unification and mechanistic detail as drivers of model construction: models of networks in economics and sociology.

    Science.gov (United States)

    Kuorikoski, Jaakko; Marchionni, Caterina

    2014-12-01

    We examine the diversity of strategies of modelling networks in (micro) economics and (analytical) sociology. Field-specific conceptions of what explaining (with) networks amounts to or systematic preference for certain kinds of explanatory factors are not sufficient to account for differences in modelling methodologies. We argue that network models in both sociology and economics are abstract models of network mechanisms and that differences in their modelling strategies derive to a large extent from field-specific conceptions of the way in which a good model should be a general one. Whereas the economics models aim at unification, the sociological models aim at a set of mechanism schemas that are extrapolatable to the extent that the underlying psychological mechanisms are general. These conceptions of generality induce specific biases in mechanistic explanation and are related to different views of when knowledge from different fields should be seen as relevant.

  9. Self-constructed tree-shape high thermal conductivity nanosilver networks in epoxy.

    Science.gov (United States)

    Pashayi, Kamyar; Fard, Hafez Raeisi; Lai, Fengyuan; Iruvanti, Sushumna; Plawsky, Joel; Borca-Tasciuc, Theodorian

    2014-04-21

    We report the formation of high aspect ratio nanoscale tree-shape silver networks in epoxy, at low temperatures (thermal conductivity (κ) of the nanocomposite compared to the polymer matrix. The networks form through a three-step process comprising of self-assembly by diffusion limited aggregation of polyvinylpyrrolidone (PVP) coated nanoparticles, removal of PVP coating from the surface, and sintering of silver nanoparticles in high aspect ratio networked structures. Controlling self-assembly and sintering by carefully designed multistep temperature and time processing leads to κ of our silver nanocomposites that are up to 300% of the present state of the art polymer nanocomposites at similar volume fractions. Our investigation of the κ enhancements enabled by tree-shaped network nanocomposites provides a basis for the development of new polymer nanocomposites for thermal transport and storage applications.

  10. Network design and operational modelling for construction green supply chain management

    Directory of Open Access Journals (Sweden)

    Pengfei Zhou Dong Chen

    2013-01-01

    Full Text Available Based on studying organizational structure of Construction Green Supply Chain Management (CGSCM, a mathematical programming model of CGSCM was proposed. The model aimed to maximize the aggregate profits of normalized construction logistics, the reverse logistics and the environmental performance. Numerical experiments show that the proposed approach can improve the aggregate profit effectively. In addition, return ratio, subsidies from governmental organizations, and environmental performance were analyzed for CGSCM performance. Herein, the proper return, subsidy and control strategy could optimize construction green supply chain.

  11. Mining for constructions in texts using N-gram and network analysis

    DEFF Research Database (Denmark)

    Shibuya, Yoshikata; Jensen, Kim Ebensgaard

    2015-01-01

    N-gram analysis to Lewis Carroll's novel Alice's Adventures in Wonderland and Mark Twain's novelThe Adventures of Huckleberry Finn and extrapolate a number of likely constructional phenomena from recurring N-gram patterns in the two texts. In addition to simple N-gram analysis, the following....... The main premise is that, if constructions are functional units, then configurations of words that tend to recur together in discourse are likely to have some sort of function that speakers utilize in discourse. Writers of fiction, for instance, may use constructions in characterizations, mind-styles, text...

  12. Research on Construction of Road Network Database Based on Video Retrieval Technology

    Directory of Open Access Journals (Sweden)

    Wang Fengling

    2017-01-01

    Full Text Available Based on the characteristics of the video database and the basic structure of the video database and several typical video data models, the segmentation-based multi-level data model is used to describe the landscape information video database, the network database model and the road network management database system. Landscape information management system detailed design and implementation of a detailed preparation.

  13. Synchronization and symmetry-breaking bifurcations in constructive networks of coupled chaotic oscillators

    International Nuclear Information System (INIS)

    Jiang Yu; Lozada-Cassou, M.; Vinet, A.

    2003-01-01

    The spatiotemporal dynamics of networks based on a ring of coupled oscillators with regular shortcuts beyond the nearest-neighbor couplings is studied by using master stability equations and numerical simulations. The generic criterion for dynamic synchronization has been extended to arbitrary network topologies with zero row-sum. The symmetry-breaking oscillation patterns that resulted from the Hopf bifurcation from synchronous states are analyzed by the symmetry group theory

  14. Constructing a generalized network design model to study air distribution in ventilation networks in subway with a single-track tunnel

    Science.gov (United States)

    Lugin, IV

    2018-03-01

    In focus are the features of construction of the generalized design model for the network method to study air distribution in ventilation system in subway with the single-track tunnel. The generalizations, assumptions and simplifications included in the model are specified. The air distribution is calculated with regard to the influence of topology and air resistances of the ventilation network sections. The author studies two variants of the subway line: half-open and closed with dead end on the both sides. It is found that the total air exchange at a subway station depends on the station location within the line. The operating mode of fans remains unaltered in this case. The article shows that elimination of air leakage in the station ventilation room allows an increase in the air flow rate by 7–8% at the same energy consumption by fans. The influence of the stop of a train in the tunnel on the air distribution is illustrated.

  15. A GIS analysis of suitability for construction aggregate recycling sites using regional transportation network and population density features

    Science.gov (United States)

    Robinson, G.R.; Kapo, K.E.

    2004-01-01

    Aggregate is used in road and building construction to provide bulk, strength, support, and wear resistance. Reclaimed asphalt pavement (RAP) and reclaimed Portland cement concrete (RPCC) are abundant and available sources of recycled aggregate. In this paper, current aggregate production operations in Virginia, Maryland, and the District of Columbia are used to develop spatial association models for the recycled aggregate industry with regional transportation network and population density features. The cost of construction aggregate to the end user is strongly influenced by the cost of transporting processed aggregate from the production site to the construction site. More than 60% of operations recycling aggregate in the mid-Atlantic study area are located within 4.8 km (3 miles) of an interstate highway. Transportation corridors provide both sites of likely road construction where aggregate is used and an efficient means to move both materials and on-site processing equipment back and forth from various work sites to the recycling operations. Urban and developing areas provide a high market demand for aggregate and a ready source of construction debris that may be processed into recycled aggregate. Most aggregate recycling operators in the study area are sited in counties with population densities exceeding 77 people/km2 (200 people/mile 2). No aggregate recycling operations are sited in counties with less than 19 people/km2 (50 people/mile2), reflecting the lack of sufficient long-term sources of construction debris to be used as an aggregate source, as well as the lack of a sufficient market demand for aggregate in most rural areas to locate a recycling operation there or justify the required investment in the equipment to process and produce recycled aggregate. Weights of evidence analyses (WofE), measuring correlation on an area-normalized basis, and weighted logistic regression (WLR), are used to model the distribution of RAP and RPCC operations relative

  16. AN INITIATIVE FOR CONSTRUCTION OF NEW-GENERATION LUNAR GLOBAL CONTROL NETWORK USING MULTI-MISSION DATA

    Directory of Open Access Journals (Sweden)

    K. Di

    2017-07-01

    Full Text Available A lunar global control network provides geodetic datum and control points for mapping of the lunar surface. The widely used Unified Lunar Control Network 2005 (ULCN2005 was built based on a combined photogrammetric solution of Clementine images acquired in 1994 and earlier photographic data. In this research, we propose an initiative for construction of a new-generation lunar global control network using multi-mission data newly acquired in the 21st century, which have much better resolution and precision than the old data acquired in the last century. The new control network will be based on a combined photogrammetric solution of an extended global image and laser altimetry network. The five lunar laser ranging retro-reflectors, which can be identified in LROC NAC images and have cm level 3D position accuracy, will be used as absolute control points in the least squares photogrammetric adjustment. Recently, a new radio total phase ranging method has been developed and used for high-precision positioning of Chang’e-3 lander; this shall offer a new absolute control point. Systematic methods and key techniques will be developed or enhanced, including rigorous and generic geometric modeling of orbital images, multi-scale feature extraction and matching among heterogeneous multi-mission remote sensing data, optimal selection of images at areas of multiple image coverages, and large-scale adjustment computation, etc. Based on the high-resolution new datasets and developed new techniques, the new generation of global control network is expected to have much higher accuracy and point density than the ULCN2005.

  17. Highly sensitive piezo-resistive graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone composites with improved conductive network construction.

    Science.gov (United States)

    Zhao, Hang; Bai, Jinbo

    2015-05-13

    The constructions of internal conductive network are dependent on microstructures of conductive fillers, determining various electrical performances of composites. Here, we present the advanced graphite nanoplatelet-carbon nanotube hybrids/polydimethylsilicone (GCHs/PDMS) composites with high piezo-resistive performance. GCH particles were synthesized by the catalyst chemical vapor deposition approach. The synthesized GCHs can be well dispersed in the matrix through the mechanical blending process. Due to the exfoliated GNP and aligned CNTs coupling structure, the flexible composite shows an ultralow percolation threshold (0.64 vol %) and high piezo-resistive sensitivity (gauge factor ∼ 10(3) and pressure sensitivity ∼ 0.6 kPa(-1)). Slight motions of finger can be detected and distinguished accurately using the composite film as a typical wearable sensor. These results indicate that designing the internal conductive network could be a reasonable strategy to improve the piezo-resistive performance of composites.

  18. The Construction of Higher Education Entrepreneur Services Network System a Research Based on Ecological Systems Theory

    Science.gov (United States)

    Xue, Jingxin

    The article aims to completely, systematically and objectively analyze the current situation of Entrepreneurship Education in China with Ecological Systems Theory. From this perspective, the author discusses the structure, function and its basic features of higher education entrepreneur services network system, and puts forward the opinion that every entrepreneurship organization in higher education institution does not limited to only one platform. Different functional supporting platforms should be combined closed through composite functional organization to form an integrated network system, in which each unit would impels others' development.

  19. Assessment of the expected construction company’s net profit using neural network and multiple regression models

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

    This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

  20. Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs.

    Science.gov (United States)

    Forkmann, Thomas; Teismann, Tobias; Stenzel, Jana-Sophie; Glaesmer, Heide; de Beurs, Derek

    2018-01-25

    Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic techniques have trouble estimating the right number of clusters in highly correlated data. In this study, we applied a novel approach based on network analysis that can deal with correlated data to establish whether defeat and entrapment are best thought of as one or multiple constructs. Explanatory graph analysis was used to estimate the number of dimensions within the 32 items that make up the defeat and entrapment scales in two samples: an online community sample of 480 participants, and a clinical sample of 147 inpatients admitted to a psychiatric hospital after a suicidal attempt or severe suicidal crisis. Confirmatory Factor analysis (CFA) was used to test whether the proposed structure fits the data. In both samples, bootstrapped exploratory graph analysis suggested that the defeat and entrapment items belonged to different dimensions. Within the entrapment items, two separate dimensions were detected, labelled internal and external entrapment. Defeat appeared to be multifaceted only in the online sample. When comparing the CFA outcomes of the one, two, three and four factor models, the one factor model was preferred. Defeat and entrapment can be viewed as distinct, yet, highly associated constructs. Thus, although replication is needed, results are in line with theories differentiating between these two constructs.

  1. Par@Graph - a parallel toolbox for the construction and analysis of large complex climate networks

    NARCIS (Netherlands)

    Tantet, A.J.J.

    2015-01-01

    In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid

  2. Bottom-up GGM algorithm for constructing multiple layered hierarchical gene regulatory networks

    Science.gov (United States)

    Multilayered hierarchical gene regulatory networks (ML-hGRNs) are very important for understanding genetics regulation of biological pathways. However, there are currently no computational algorithms available for directly building ML-hGRNs that regulate biological pathways. A bottom-up graphic Gaus...

  3. Passive house networks : A social innovation targeting innovation in SME's in the construction sector

    NARCIS (Netherlands)

    Mlecnik, E.

    2011-01-01

    This study examines opportunities for the emergence of SME networks regarding highly energy-efficient housing, as well as the barriers they face. A theoretical innovation diffusion model is developed from the point-of-view of social and environmental entrepreneurship and sustainable consumption. The

  4. GPU implementation of Bayesian neural network construction for data-intensive applications

    International Nuclear Information System (INIS)

    Perry, Michelle; Meyer-Baese, Anke; Prosper, Harrison B

    2014-01-01

    We describe a graphical processing unit (GPU) implementation of the Hybrid Markov Chain Monte Carlo (HMC) method for training Bayesian Neural Networks (BNN). Our implementation uses NVIDIA's parallel computing architecture, CUDA. We briefly review BNNs and the HMC method and we describe our implementations and give preliminary results.

  5. Scalable Approach To Construct Free-Standing and Flexible Carbon Networks for Lithium–Sulfur Battery

    KAUST Repository

    Li, Mengliu; Wahyudi, Wandi; Kumar, Pushpendra; Wu, Feng-Yu; Yang, Xiulin; Li, Henan; Li, Lain-Jong; Ming, Jun

    2017-01-01

    for their large-scale applications, such as utilizing as interlayers in lithium-sulfur battery. The capability of holding polysulfides (i.e., suppressing the sulfur diffusion) for the networks made from CNTs, graphene, or their mixture is pronounced, among which

  6. Construction and Application of a National Data-Sharing Service Network of Material Environmental Corrosion

    Directory of Open Access Journals (Sweden)

    Xiaogang Li

    2007-12-01

    Full Text Available This article discusses the key features of a newly developed national data-sharing online network for material environmental corrosion. Written in Java language and based on Oracle database technology, the central database in the network is supported with two unique series of corrosion failure data, both of which were accumulated during a long period of time. The first category of data, provided by national environment corrosion test sites, is corrosion failure data for different materials in typical environments (atmosphere, seawater and soil. The other category is corrosion data in production environments, provided by a variety of firms. This network system enables standardized management of environmental corrosion data, an effective data sharing process, and research and development support for new products and after-sale services. Moreover this network system provides a firm base and data-service platform for the evaluation of project bids, safety, and service life. This article also discusses issues including data quality management and evaluation in the material corrosion data sharing process, access authority of different users, compensation for providers of shared historical data, and finally, the related policy and law legal processes, which are required to protect the intellectual property rights of the database.

  7. Gearbox Condition Monitoring Using Advanced Classifiers

    Directory of Open Access Journals (Sweden)

    P. Večeř

    2010-01-01

    Full Text Available New efficient and reliable methods for gearbox diagnostics are needed in automotive industry because of growing demand for production quality. This paper presents the application of two different classifiers for gearbox diagnostics – Kohonen Neural Networks and the Adaptive-Network-based Fuzzy Interface System (ANFIS. Two different practical applications are presented. In the first application, the tested gearboxes are separated into two classes according to their condition indicators. In the second example, ANFIS is applied to label the tested gearboxes with a Quality Index according to the condition indicators. In both applications, the condition indicators were computed from the vibration of the gearbox housing. 

  8. A Bayesian classifier for symbol recognition

    OpenAIRE

    Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

    2007-01-01

    URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

  9. Delineating the Construct Network of the Personnel Reaction Blank: Associations with Externalizing Tendencies and Normal Personality

    Science.gov (United States)

    Blonigen, Daniel M.; Patrick, Christopher J.; Gasperi, Marianna; Steffen, Benjamin; Ones, Deniz S.; Arvey, Richard D.; de Oliveira Baumgartl, Viviane; do Nascimento, Elizabeth

    2011-01-01

    Integrity testing has long been utilized in personnel selection to screen for tendencies toward counterproductive workplace behaviors. The construct of externalizing from the psychopathology literature represents a coherent spectrum marked by disinhibitory traits and behaviors. The present study drew on a sample of male and female undergraduates…

  10. Construction and analysis of protein-protein interaction network correlated with ankylosing spondylitis.

    Science.gov (United States)

    Kanwal, Attiya; Fazal, Sahar

    2018-01-05

    Ankylosing spondylitis, a systemic illness is a foundation of progressing joint swelling that for the most part influences the spine. However, it frequently causes aggravation in different joints far from the spine, and in addition organs, for example, the eyes, heart, lungs, and kidneys. It's an immune system ailment that may be activated by specific sorts of bacterial or viral diseases that initiate an invulnerable reaction that don't close off after the contamination is recuperated. The particular reason for ankylosing spondylitis is obscure, yet hereditary qualities assume a huge part in this condition. The rising apparatuses of network medicine offer a stage to investigate an unpredictable illness at framework level. In this study, we meant to recognize the key proteins and the biological regulator pathways including in AS and further investigating the molecular connectivity between these pathways by the topological examination of the Protein-protein communication (PPI) system. The extended network including of 93 nodes and have 199 interactions respectively scanned from STRING database and some separated small networks. 24 proteins with high BC at the threshold of 0.01 and 55 proteins with large degree at the threshold of 1 have been identified. CD4 with highest BC and Closeness centrality located in the centre of the network. The backbone network derived from high BC proteins presents a clear and visual overview which shows all important regulatory pathways for AS and the crosstalk between them. The finding of this research suggests that AS variation is orchestrated by an integrated PPI network centered on CD4 out of 93 nodes. Ankylosing spondylitis, a systemic disease is an establishment of advancing joint swelling that generally impacts the spine. Be that as it may, it as often as possible causes disturbance in various joints a long way from the spine, and what's more organs. It's a resistant framework affliction that might be actuated by particular sorts

  11. Electrifying Europe. The power of Europe in the construction of electricity networks

    International Nuclear Information System (INIS)

    Lagendijk, V.C.

    2008-01-01

    This book sets out to uncover the origins of the idea of a European electricity network. It explores historically the roots of a transnational European system, showing how engineers came to think in terms of 'Europe' already in the 1920s, and how these ideas continued to influence network-building in later decades. This thinking not only corresponded to economic and technical attributes of the system. This thesis claims that a European system was also legitimised by ideological motives. Covering the period between 1918 and 2001 the book provides a detailed analysis of ideas on, and the building of, a European electricity system. A substantial contribution is made to the much-neglected history of international collaboration in Europe. Prevailing histories of electricity infrastructures the focus is on national developments. Also, the history is placed in the broader historical context of the twentieth century, paying ample attention to the influence of both hot and cold wars, and interwar developments. By combining the specific history of this international collaboration with a more general political and economic history of the twentieth century, the author explains why a European solution emerged. The thesis primarily focuses on Western European developments and explains how this network took its specific shape through the building of different regional powerpools among national systems. In addition, the thesis presents a contribution to the emerging field of transnational history by focusing on the work and activities of international organisations, without neglecting the power and influence of nation-states. The book starts by revealing how an international community of electricity entrepreneurs and electrical engineers had existed since the turn of the century. Yet at the same time, national legislations came to limit the extent of international network development and operation. Whereas the first objections to these limitations were general, they became

  12. Detecting and classifying faults on transmission systems using a backpropagation neural network; Deteccion y clasificacion de fallas en sistemas de transmision empleando una red neuronal con retropropagacion del error

    Energy Technology Data Exchange (ETDEWEB)

    Rosas Ortiz, German

    2000-01-01

    Fault detection and diagnosis on transmission systems is an interesting area of investigation to Artificial Intelligence (AI) based systems. Neurocomputing is one of fastest growing areas of research in the fields of AI and pattern recognition. This work explores the possible suitability of pattern recognition approach of neural networks for fault detection and classification on power systems. The conventional detection techniques in modern relays are based in digital processing of signals and it need some time (around 1 cycle) to send a tripping signal, also they are likely to make incorrect decisions if the signals are noisy. It's desirable to develop a fast, accurate and robust approach that perform accurately for changing system conditions (like load variations and fault resistance). The aim of this work is to develop a novel technique based on Artificial Neural Networks (ANN), which explores the suitability of a pattern classification approach for fault detection and diagnosis. The suggested approach is based in the fact that when a fault occurs, a change in the system impedance take place and, as a consequence changes in amplitude and phase of line voltage and current signals take place. The ANN-based fault discriminator is trained to detect this changes as indicators of the instant of fault inception. This detector uses instantaneous values of these signals to make decisions. Suitability of using neural network as pattern classifiers for transmission systems fault diagnosis is described in detail a neural network design and simulation environment for real-time is presented. Results showing the performance of this approach are presented and indicate that it is fast, secure and exact enough, and it can be used in high speed fault detection and classification schemes. [Spanish] El diagnostico y la deteccion de fallas en sistemas de transmision es una area de interes en investigacion para sistemas basados en Inteligencia Artificial (IA). El calculo neuronal

  13. Construction and analysis of lncRNA-lncRNA synergistic networks to reveal clinically relevant lncRNAs in cancer.

    Science.gov (United States)

    Li, Yongsheng; Chen, Juan; Zhang, Jinwen; Wang, Zishan; Shao, Tingting; Jiang, Chunjie; Xu, Juan; Li, Xia

    2015-09-22

    Long non-coding RNAs (lncRNAs) play key roles in diverse biological processes. Moreover, the development and progression of cancer often involves the combined actions of several lncRNAs. Here we propose a multi-step method for constructing lncRNA-lncRNA functional synergistic networks (LFSNs) through co-regulation of functional modules having three features: common coexpressed genes of lncRNA pairs, enrichment in the same functional category and close proximity within protein interaction networks. Applied to three cancers, we constructed cancer-specific LFSNs and found that they exhibit a scale free and modular architecture. In addition, cancer-associated lncRNAs tend to be hubs and are enriched within modules. Although there is little synergistic pairing of lncRNAs across cancers, lncRNA pairs involved in the same cancer hallmarks by regulating same or different biological processes. Finally, we identify prognostic biomarkers within cancer lncRNA expression datasets using modules derived from LFSNs. In summary, this proof-of-principle study indicates synergistic lncRNA pairs can be identified through integrative analysis of genome-wide expression data sets and functional information.

  14. Construction of HMI Network System for Individualized Maternity Intervention Service against Birth Defects in Community

    Institute of Scientific and Technical Information of China (English)

    Xu-huai HU

    2007-01-01

    The paper expounds the community maternity service system against birth defects,from the viewpoint of individualized service in family planning. We have utilized modern information technology to develop health management information (HMI) network with individualized maternity, and to establish the community service system for intervention of birth defects. The service system applied the concept of modern health management information to implementing informational management for screening,treatment, following up, outcome monitoring, so as to provide a base for promotion of health, diagnosis, treatment as well as scientific research, with the prenatal screening of Down's syndrome as a model. The introduction to informational network during the processes of service has been carried out with regards to its composition, function and application, while introducing the effects of computerized case record individualized in prevention, management and research of Down's syndrome.

  15. Analysis and Construction of Full-Diversity Joint Network-LDPC Codes for Cooperative Communications

    Directory of Open Access Journals (Sweden)

    Capirone Daniele

    2010-01-01

    Full Text Available Transmit diversity is necessary in harsh environments to reduce the required transmit power for achieving a given error performance at a certain transmission rate. In networks, cooperative communication is a well-known technique to yield transmit diversity and network coding can increase the spectral efficiency. These two techniques can be combined to achieve a double diversity order for a maximum coding rate on the Multiple-Access Relay Channel (MARC, where two sources share a common relay in their transmission to the destination. However, codes have to be carefully designed to obtain the intrinsic diversity offered by the MARC. This paper presents the principles to design a family of full-diversity LDPC codes with maximum rate. Simulation of the word error rate performance of the new proposed family of LDPC codes for the MARC confirms the full diversity.

  16. One-way hash function construction based on chaotic map network

    International Nuclear Information System (INIS)

    Yang Huaqian; Wong, K.-W.; Liao Xiaofeng; Wang Yong; Yang Degang

    2009-01-01

    A novel chaotic hash algorithm based on a network structure formed by 16 chaotic maps is proposed. The original message is first padded with zeros to make the length a multiple of four. Then it is divided into a number of blocks each contains 4 bytes. In the hashing process, the blocks are mixed together by the chaotic map network since the initial value and the control parameter of each tent map are dynamically determined by the output of its neighbors. To enhance the confusion and diffusion effect, the cipher block chaining (CBC) mode is adopted in the algorithm. Theoretic analyses and numerical simulations both show that the proposed hash algorithm possesses good statistical properties, strong collision resistance and high flexibility, as required by practical keyed hash functions.

  17. BIM in Europe: innovation networks in the construction sectors of Sweden, France and the UK

    OpenAIRE

    Davies, Richard; Crespin-Mazet, Florence; Linne, Ase; Pardo, Catherine; Havenvid, Malena Ingemannson; Harty, Chris; Ivory, Chris; Salle, Robert

    2015-01-01

    European countries are developing or implementing policies that promote or require the use of Building Information Modelling (BIM) and give BIM a central role in strategies for national sector-level transformation. It is necessary to understand BIM as a systemic innovation that is enacted and adopted by firms, projects and users but also by national actors. The Industrial Marketing and Purchasing (IMP) approach has shown how the evolution of innovations can be understood in terms of networks ...

  18. Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic

    Science.gov (United States)

    Lara-Rosano, Felipe

    1992-01-01

    In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.

  19. [Construction and evaluation of ecological network in Poyang Lake Eco-economic Zone, China.

    Science.gov (United States)

    Chen, Xiao Ping; Chen, Wen Bo

    2016-05-01

    Large-scale ecological patches play an important role in regional biodiversity conservation. However, with the rapid progress of China's urbanization, human disturbance on the environment is becoming stronger. Large-scale ecological patches will degrade not only in quantity, but also in quality, threatening the connections among them due to isolation and seriously affecting the biodiversity protection. Taking Poyang Lake Eco-economic Zone as a case, this paper established the potential ecological corridors by minimum cost model and GIS technique taking the impacts of landscape types, slope and human disturbance into consideration. Then, based on gravity quantitative model, we analyzed the intensity of ecological interactions between patches, and the potential ecological corridors were divided into two classes for sake of protection. Finally, the important ecological nodes and breaking points were identified, and the structure of the potential ecological network was analyzed. The results showed that forest and cropland were the main landscape types of ecological corridor composition, interaction between ecological patches differed obviously and the structure of the composed regional ecological network was complex with high connectivity and closure. It might provide a scientific basis for the protection of biodiversity and ecological network optimization in Poyang Lake Eco-economic Zone.

  20. Scalable Approach To Construct Free-Standing and Flexible Carbon Networks for Lithium–Sulfur Battery

    KAUST Repository

    Li, Mengliu

    2017-02-21

    Reconstructing carbon nanomaterials (e.g., fullerene, carbon nanotubes (CNTs), and graphene) to multidimensional networks with hierarchical structure is a critical step in exploring their applications. Herein, a sacrificial template method by casting strategy is developed to prepare highly flexible and free-standing carbon film consisting of CNTs, graphene, or both. The scalable size, ultralight and binder-free characteristics, as well as the tunable process/property are promising for their large-scale applications, such as utilizing as interlayers in lithium-sulfur battery. The capability of holding polysulfides (i.e., suppressing the sulfur diffusion) for the networks made from CNTs, graphene, or their mixture is pronounced, among which CNTs are the best. The diffusion process of polysulfides can be visualized in a specially designed glass tube battery. X-ray photoelectron spectroscopy analysis of discharged electrodes was performed to characterize the species in electrodes. A detailed analysis of lithium diffusion constant, electrochemical impedance, and elementary distribution of sulfur in electrodes has been performed to further illustrate the differences of different carbon interlayers for Li-S batteries. The proposed simple and enlargeable production of carbon-based networks may facilitate their applications in battery industry even as a flexible cathode directly. The versatile and reconstructive strategy is extendable to prepare other flexible films and/or membranes for wider applications.

  1. Construction of microscale structures in enclosed microfluidic networks by using a magnetic beads based method.

    Science.gov (United States)

    Wang, Zhenyu; Zhang, Xiaojuan; Yang, Jun; Yang, Zhong; Wan, Xiaoping; Hu, Ning; Zheng, Xiaolin

    2013-08-20

    A large number of microscale structures have been used to elaborate flowing control or complex biological and chemical reaction on microfluidic chips. However, it is still inconvenient to fabricate microstructures with different heights (or depths) on the same substrate. These kinds of microstructures can be fabricated by using the photolithography and wet-etching method step by step, but involves time-consuming design and fabrication process, as well as complicated alignment of different masters. In addition, few existing methods can be used to perform fabrication within enclosed microfluidic networks. It is also difficult to change or remove existing microstructures within these networks. In this study, a magnetic-beads-based approach is presented to build microstructures in enclosed microfluidic networks. Electromagnetic field generated by microfabricated conducting wires (coils) is used to manipulate and trap magnetic beads on the bottom surface of a microchannel. These trapped beads are accumulated to form a microscale pile with desired shape, which can adjust liquid flow, dock cells, modify surface, and do some other things as those fabricated microstructures. Once the electromagnetic field is changed, trapped beads may form new shapes or be removed by a liquid flow. Besides being used in microfabrication, this magnetic-beads-based method can be used for novel microfluidic manipulation. It has been validated by forming microscale dam structure for cell docking and modified surface for cell patterning, as well as guiding the growth of neurons. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

    Science.gov (United States)

    Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

    2018-04-16

    Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

  3. Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

    Directory of Open Access Journals (Sweden)

    Shehzad Khalid

    2014-01-01

    Full Text Available We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

  4. S-net : Construction of large scale seafloor observatory network for tsunamis and earthquakes along the Japan Trench

    Science.gov (United States)

    Mochizuki, M.; Uehira, K.; Kanazawa, T.; Shiomi, K.; Kunugi, T.; Aoi, S.; Matsumoto, T.; Sekiguchi, S.; Yamamoto, N.; Takahashi, N.; Nakamura, T.; Shinohara, M.; Yamada, T.

    2017-12-01

    NIED has launched the project of constructing a seafloor observatory network for tsunamis and earthquakes after the occurrence of the 2011 Tohoku Earthquake to enhance reliability of early warnings of tsunamis and earthquakes. The observatory network was named "S-net". The S-net project has been financially supported by MEXT.The S-net consists of 150 seafloor observatories which are connected in line with submarine optical cables. The total length of submarine optical cable is about 5,500 km. The S-net covers the focal region of the 2011 Tohoku Earthquake and its vicinity regions. Each observatory equips two units of a high sensitive pressure gauges as a tsunami meter and four sets of three-component seismometers. The S-net is composed of six segment networks. Five of six segment networks had been already installed. Installation of the last segment network covering the outer rise area have been finally finished by the end of FY2016. The outer rise segment has special features like no other five segments of the S-net. Those features are deep water and long distance. Most of 25 observatories on the outer rise segment are located at the depth of deeper than 6,000m WD. Especially, three observatories are set on the seafloor of deeper than about 7.000m WD, and then the pressure gauges capable of being used even at 8,000m WD are equipped on those three observatories. Total length of the submarine cables of the outer rise segment is about two times longer than those of the other segments. The longer the cable system is, the higher voltage supply is needed, and thus the observatories on the outer rise segment have high withstanding voltage characteristics. We employ a dispersion management line of a low loss formed by combining a plurality of optical fibers for the outer rise segment cable, in order to achieve long-distance, high-speed and large-capacity data transmission Installation of the outer rise segment was finished and then full-scale operation of S-net has started

  5. Step-by-Step Construction of Gene Co-expression Networks from High-Throughput Arabidopsis RNA Sequencing Data.

    Science.gov (United States)

    Contreras-López, Orlando; Moyano, Tomás C; Soto, Daniela C; Gutiérrez, Rodrigo A

    2018-01-01

    The rapid increase in the availability of transcriptomics data generated by RNA sequencing represents both a challenge and an opportunity for biologists without bioinformatics training. The challenge is handling, integrating, and interpreting these data sets. The opportunity is to use this information to generate testable hypothesis to understand molecular mechanisms controlling gene expression and biological processes (Fig. 1). A successful strategy to generate tractable hypotheses from transcriptomics data has been to build undirected network graphs based on patterns of gene co-expression. Many examples of new hypothesis derived from network analyses can be found in the literature, spanning different organisms including plants and specific fields such as root developmental biology.In order to make the process of constructing a gene co-expression network more accessible to biologists, here we provide step-by-step instructions using published RNA-seq experimental data obtained from a public database. Similar strategies have been used in previous studies to advance root developmental biology. This guide includes basic instructions for the operation of widely used open source platforms such as Bio-Linux, R, and Cytoscape. Even though the data we used in this example was obtained from Arabidopsis thaliana, the workflow developed in this guide can be easily adapted to work with RNA-seq data from any organism.

  6. Actor networks and the construction of applicable knowledge: the case of the Timbre Brownfield Prioritization Tool

    Czech Academy of Sciences Publication Activity Database

    Alexandrescu, F.; Klusáček, Petr; Bartke, S.; Osman, Robert; Frantál, Bohumil; Martinát, Stanislav; Kunc, Josef; Pizzol, L.; Zabeo, A.; Giubilato, E.; Critto, A.; Bleicher, A.

    2017-01-01

    Roč. 19, č. 5 (2017), s. 1323-1334 ISSN 1618-954X R&D Projects: GA MŠk(CZ) 7E11035; GA ČR(CZ) GA17-26934S Institutional support: RVO:68145535 Keywords : actor network theory * applicable knowledge * brownfield prioritization * four moments of translation * end-users Subject RIV: DE - Earth Magnetism, Geodesy, Geography OBOR OECD: Environmental sciences (social aspects) Impact factor: 3.331, year: 2016 https://link.springer.com/article/10.1007/s10098-016-1331-8

  7. Actor networks and the construction of applicable knowledge: the case of the Timbre Brownfield Prioritization Tool

    Czech Academy of Sciences Publication Activity Database

    Alexandrescu, F.; Klusáček, Petr; Bartke, S.; Osman, Robert; Frantál, Bohumil; Martinát, Stanislav; Kunc, Josef; Pizzol, L.; Zabeo, A.; Giubilato, E.; Critto, A.; Bleicher, A.

    2017-01-01

    Roč. 19, č. 5 (2017), s. 1323-1334 ISSN 1618-954X R&D Projects: GA MŠk(CZ) 7E11035; GA ČR(CZ) GA17-26934S Institutional support: RVO:68145535 Keywords : actor network theory * applicable knowledge * brownfield prioritization * four moments of translation * end-users Subject RIV: DE - Earth Magnetism, Geodesy, Geography OBOR OECD: Environment al sciences (social aspects) Impact factor: 3.331, year: 2016 https://link.springer.com/article/10.1007/s10098-016-1331-8

  8. Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China

    Directory of Open Access Journals (Sweden)

    Jingfeng Yuan

    2018-02-01

    Full Text Available The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers’ well-being should be highly addressed to improve construction workers’ efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC of construction workers has been considered as an effective approach to promote construction workers’ physical and mental health (P&M health, as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers’ efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers’ health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM was employed to test the three hypothetical relationships among construction workers’ P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers’ P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers’ social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers’ P&M health. Therefore, strategies for enhancing construction workers’ efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers.

  9. Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China.

    Science.gov (United States)

    Yuan, Jingfeng; Yi, Wen; Miao, Mengyi; Zhang, Lei

    2018-02-15

    The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers' well-being should be highly addressed to improve construction workers' efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC) of construction workers has been considered as an effective approach to promote construction workers' physical and mental health (P&M health), as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers' efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers' health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM) was employed to test the three hypothetical relationships among construction workers' P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers' P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers' social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers' P&M health. Therefore, strategies for enhancing construction workers' efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers.

  10. Evaluating the Impacts of Health, Social Network and Capital on Craft Efficiency and Productivity: A Case Study of Construction Workers in China

    Science.gov (United States)

    Yi, Wen; Miao, Mengyi; Zhang, Lei

    2018-01-01

    The construction industry has been recognized, for many years, as among those having a high likelihood of accidents, injuries and occupational illnesses. Such risks of construction workers can lead to low productivity and social problems. As a result, construction workers’ well-being should be highly addressed to improve construction workers’ efficiency and productivity. Meanwhile, the social support from a social network and capital (SNC) of construction workers has been considered as an effective approach to promote construction workers’ physical and mental health (P&M health), as well as their work efficiency and productivity. Based on a comprehensive literature review, a conceptual model, which aims to improve construction workers’ efficiency and productivity from the perspective of health and SNC, was proposed. A questionnaire survey was conducted to investigate the construction workers’ health, SNC and work efficiency and productivity in Nanjing, China. A structural equation model (SEM) was employed to test the three hypothetical relationships among construction workers’ P&M health, SNC and work efficiency and productivity. The results indicated that the direct impacts from construction workers’ P&M health on work efficiency and productivity were more significant than that from the SNC. In addition, the construction workers’ social capital and the network can indirectly influence the work efficiency and productivity by affecting the construction workers’ P&M health. Therefore, strategies for enhancing construction workers’ efficiency and productivity were proposed. Furthermore, many useable suggestions can be drawn from the research findings from the perspective of a government. The identified indicators and relationships would contribute to the construction work efficiency and productivity assessment and health management from the perspective of the construction workers. PMID:29462861

  11. Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

    Science.gov (United States)

    Zhang, Menghuan; Li, Hong; He, Ying; Sun, Han; Xia, Li; Wang, Lishun; Sun, Bo; Ma, Liangxiao; Zhang, Guoqing; Li, Jing; Li, Yixue; Xie, Lu

    2015-07-02

    Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

  12. Construction and comparison of gene co-expression networks shows complex plant immune responses

    Directory of Open Access Journals (Sweden)

    Luis Guillermo Leal

    2014-10-01

    Full Text Available Gene co-expression networks (GCNs are graphic representations that depict the coordinated transcription of genes in response to certain stimuli. GCNs provide functional annotations of genes whose function is unknown and are further used in studies of translational functional genomics among species. In this work, a methodology for the reconstruction and comparison of GCNs is presented. This approach was applied using gene expression data that were obtained from immunity experiments in Arabidopsis thaliana, rice, soybean, tomato and cassava. After the evaluation of diverse similarity metrics for the GCN reconstruction, we recommended the mutual information coefficient measurement and a clustering coefficient-based method for similarity threshold selection. To compare GCNs, we proposed a multivariate approach based on the Principal Component Analysis (PCA. Branches of plant immunity that were exemplified by each experiment were analyzed in conjunction with the PCA results, suggesting both the robustness and the dynamic nature of the cellular responses. The dynamic of molecular plant responses produced networks with different characteristics that are differentiable using our methodology. The comparison of GCNs from plant pathosystems, showed that in response to similar pathogens plants could activate conserved signaling pathways. The results confirmed that the closeness of GCNs projected on the principal component space is an indicative of similarity among GCNs. This also can be used to understand global patterns of events triggered during plant immune responses.

  13. Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

    International Nuclear Information System (INIS)

    Hargrave, C; Deegan, T; Gibbs, A; Poulsen, M; Moores, M; Harden, F; Mengersen, K

    2014-01-01

    A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

  14. Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

    Science.gov (United States)

    Hargrave, C.; Moores, M.; Deegan, T.; Gibbs, A.; Poulsen, M.; Harden, F.; Mengersen, K.

    2014-03-01

    A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

  15. Constructing rigorous and broad biosurveillance networks for detecting emerging zoonotic outbreaks.

    Directory of Open Access Journals (Sweden)

    Mac Brown

    Full Text Available Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic.

  16. POSSIBILITIES OF THE USE OF GRP PIPING IN THE CONSTRUCTION AND RECONSTRUCTION OF ENGINEERING NETWORKS

    Directory of Open Access Journals (Sweden)

    ikitina Irina Nikolaevna

    2015-12-01

    Full Text Available Today in modern construction new technologies and materials are used for the manufacture of pipelines for water supply and sanitation. They are supposed to operate for at least 50 years. Unlike plastic pipes, fiberglass ones may be made of larger sizes — up to 3700 mm in diameter. They are produced using the technology of optical fiber winding, which is carried out according to modern international standards of quality. The basic raw materials — fiberglass and resin — are produced in Russia, but their production is limited, so they are purchased abroad, which increases the cost of manufacture of this type of piping. However, due to the necessity of laying pipelines of large diameter, which cannot be made with plastic pipes, the manufacture of GRP pipes will increase. The experience of laying and constructing this type of pipelines, for example, in the areas of hot water supply allows concluding that they are able to withstand the temperatures of up to 150 °C, while their weight is four times less than the weight of steel pipes (they are easily installed with the help of small lifting equipment and by a team of six people. It should be noted that the use of fiberglass pipes helps to reduce the costs of system operation, because this type of piping is not subject to corrosion and encrustation of the inner surface, since it has a low level of roughness, which, for example, is 0.013 for a steel pipe, and 0.01 for fiberglass pipe. Thus, it is not necessary to put protective corrosion-resistant coatings and to provide an expensive protection against electrochemical corrosion. Piping made of fiberglass pipes can be designed as underground, above-ground with stacking or raised on poles. It is possible to combine these options.

  17. A Nonlinear Multiobjective Bilevel Model for Minimum Cost Network Flow Problem in a Large-Scale Construction Project

    Directory of Open Access Journals (Sweden)

    Jiuping Xu

    2012-01-01

    Full Text Available The aim of this study is to deal with a minimum cost network flow problem (MCNFP in a large-scale construction project using a nonlinear multiobjective bilevel model with birandom variables. The main target of the upper level is to minimize both direct and transportation time costs. The target of the lower level is to minimize transportation costs. After an analysis of the birandom variables, an expectation multiobjective bilevel programming model with chance constraints is formulated to incorporate decision makers’ preferences. To solve the identified special conditions, an equivalent crisp model is proposed with an additional multiobjective bilevel particle swarm optimization (MOBLPSO developed to solve the model. The Shuibuya Hydropower Project is used as a real-world example to verify the proposed approach. Results and analysis are presented to highlight the performances of the MOBLPSO, which is very effective and efficient compared to a genetic algorithm and a simulated annealing algorithm.

  18. Classified

    CERN Multimedia

    Computer Security Team

    2011-01-01

    In the last issue of the Bulletin, we have discussed recent implications for privacy on the Internet. But privacy of personal data is just one facet of data protection. Confidentiality is another one. However, confidentiality and data protection are often perceived as not relevant in the academic environment of CERN.   But think twice! At CERN, your personal data, e-mails, medical records, financial and contractual documents, MARS forms, group meeting minutes (and of course your password!) are all considered to be sensitive, restricted or even confidential. And this is not all. Physics results, in particular when being preliminary and pending scrutiny, are sensitive, too. Just recently, an ATLAS collaborator copy/pasted the abstract of an ATLAS note onto an external public blog, despite the fact that this document was clearly marked as an "Internal Note". Such an act was not only embarrassing to the ATLAS collaboration, and had negative impact on CERN’s reputation --- i...

  19. Polymorphous Supercapacitors Constructed from Flexible Three-Dimensional Carbon Network/Polyaniline/MnO2 Composite Textiles.

    Science.gov (United States)

    Wang, Jinjie; Dong, Liubing; Xu, Chengjun; Ren, Danyang; Ma, Xinpei; Kang, Feiyu

    2018-04-04

    Polymorphous supercapacitors were constructed from flexible three-dimensional carbon network/polyaniline (PANI)/MnO 2 composite textile electrodes. The flexible textile electrodes were fabricated through a layer-by-layer construction strategy: PANI, carbon nanotubes (CNTs), and MnO 2 were deposited on activated carbon fiber cloth (ACFC) in turn through an electropolymerization process, "dipping and drying" method, and in situ chemical reaction, respectively. In the fabricated ACFC/PANI/CNTs/MnO 2 textile electrodes, the ACFC/CNT hybrid framework serves as a porous and electrically conductive 3D network for the rapid transmission of electrons and electrolyte ions, where ACFC, PANI, and MnO 2 are high-performance supercapacitor electrode materials. In the electrolyte of H 2 SO 4 solution, the textile electrode-based symmetric supercapacitor delivers superior areal capacitance, energy density, and power density of 4615 mF cm -2 (for single electrode), 157 μW h cm -2 , and 10372 μW cm -2 , respectively, whereas asymmetric supercapacitor assembled with the prepared composite textile as the positive electrode and ACFC as the negative electrode exhibits an improved energy density of 413 μW h cm -2 and a power density of 16120 μW cm -2 . On the basis of the ACFC/PANI/CNTs/MnO 2 textile electrodes, symmetric and asymmetric solid-state textile supercapacitors with a PVA/H 2 SO 4 gel electrolyte were also produced. These solid-state textile supercapacitors exhibit good electrochemical performance and high flexibility. Furthermore, flexible solid-state fiber-like supercapacitors were prepared with fiber bundle electrodes dismantled from the above composite textiles. Overall, this work makes a meaningful exploration of the versatile applications of textile electrodes to produce polymorphous supercapacitors.

  20. Classifying Sluice Occurrences in Dialogue

    DEFF Research Database (Denmark)

    Baird, Austin; Hamza, Anissa; Hardt, Daniel

    2018-01-01

    perform manual annotation with acceptable inter-coder agreement. We build classifier models with Decision Trees and Naive Bayes, with accuracy of 67%. We deploy a classifier to automatically classify sluice occurrences in OpenSubtitles, resulting in a corpus with 1.7 million occurrences. This will support....... Despite this, the corpus can be of great use in research on sluicing and development of systems, and we are making the corpus freely available on request. Furthermore, we are in the process of improving the accuracy of sluice identification and annotation for the purpose of created a subsequent version...

  1. Classifying objects in LWIR imagery via CNNs

    Science.gov (United States)

    Rodger, Iain; Connor, Barry; Robertson, Neil M.

    2016-10-01

    The aim of the presented work is to demonstrate enhanced target recognition and improved false alarm rates for a mid to long range detection system, utilising a Long Wave Infrared (LWIR) sensor. By exploiting high quality thermal image data and recent techniques in machine learning, the system can provide automatic target recognition capabilities. A Convolutional Neural Network (CNN) is trained and the classifier achieves an overall accuracy of > 95% for 6 object classes related to land defence. While the highly accurate CNN struggles to recognise long range target classes, due to low signal quality, robust target discrimination is achieved for challenging candidates. The overall performance of the methodology presented is assessed using human ground truth information, generating classifier evaluation metrics for thermal image sequences.

  2. Ganoderma lucidum polysaccharides in human monocytic leukemia cells: from gene expression to network construction

    Directory of Open Access Journals (Sweden)

    Ou Chern-Han

    2007-11-01

    Full Text Available Abstract Background Ganoderma lucidum has been widely used as a herbal medicine for promoting health and longevity in China and other Asian countries. Polysaccharide extracts from Ganoderma lucidum have been reported to exhibit immuno-modulating and anti-tumor activities. In previous studies, F3, the active component of the polysaccharide extract, was found to activate various cytokines such as IL-1, IL-6, IL-12, and TNF-α. This gave rise to our investigation on how F3 stimulates immuno-modulating or anti-tumor effects in human leukemia THP-1 cells. Results Here, we integrated time-course DNA microarray analysis, quantitative PCR assays, and bioinformatics methods to study the F3-induced effects in THP-1 cells. Significantly disturbed pathways induced by F3 were identified with statistical analysis on microarray data. The apoptosis induction through the DR3 and DR4/5 death receptors was found to be one of the most significant pathways and play a key role in THP-1 cells after F3 treatment. Based on time-course gene expression measurements of the identified pathway, we reconstructed a plausible regulatory network of the involved genes using reverse-engineering computational approach. Conclusion Our results showed that F3 may induce death receptor ligands to initiate signaling via receptor oligomerization, recruitment of specialized adaptor proteins and activation of caspase cascades.

  3. Ganoderma lucidum polysaccharides in human monocytic leukemia cells: from gene expression to network construction.

    Science.gov (United States)

    Cheng, Kun-Chieh; Huang, Hsuan-Cheng; Chen, Jenn-Han; Hsu, Jia-Wei; Cheng, Hsu-Chieh; Ou, Chern-Han; Yang, Wen-Bin; Chen, Shui-Tein; Wong, Chi-Huey; Juan, Hsueh-Fen

    2007-11-09

    Ganoderma lucidum has been widely used as a herbal medicine for promoting health and longevity in China and other Asian countries. Polysaccharide extracts from Ganoderma lucidum have been reported to exhibit immuno-modulating and anti-tumor activities. In previous studies, F3, the active component of the polysaccharide extract, was found to activate various cytokines such as IL-1, IL-6, IL-12, and TNF-alpha. This gave rise to our investigation on how F3 stimulates immuno-modulating or anti-tumor effects in human leukemia THP-1 cells. Here, we integrated time-course DNA microarray analysis, quantitative PCR assays, and bioinformatics methods to study the F3-induced effects in THP-1 cells. Significantly disturbed pathways induced by F3 were identified with statistical analysis on microarray data. The apoptosis induction through the DR3 and DR4/5 death receptors was found to be one of the most significant pathways and play a key role in THP-1 cells after F3 treatment. Based on time-course gene expression measurements of the identified pathway, we reconstructed a plausible regulatory network of the involved genes using reverse-engineering computational approach. Our results showed that F3 may induce death receptor ligands to initiate signaling via receptor oligomerization, recruitment of specialized adaptor proteins and activation of caspase cascades.

  4. North/south relations: Representations of the other in the construction of transnational networks

    Directory of Open Access Journals (Sweden)

    Amanda Rueda

    2009-12-01

    Full Text Available In the modern-day process of cultural recomposition, cinema occupies an important position through the meeting spaces created by the North to help cinema in the South to develop. The increase in aid funding by the “countries of the North”, which explains the relative good health of cinema from the “South”, illustrates well the necessary internationalisation of networks, a factor that enables these films to be viewed abroad. Cinema is understood in this way, as a vector of identity and as a social and intercultural connection. As films circulate, and filmmakers and producers meet one another, an embryo of community is constituted; “a generator of exchanges in all senses and of all kinds”. Thus we are witnessing the emergence of new categories: that of the “South”, which conserves traces of an asymmetrical “geopolitics”, and that of independence, which refers to a more international “community” (and which even goes beyond national geographical limits of resistance to the “dominant” cinema. What is happening is that an intercultural connection is being created that mixes up the old centre/periphery relations. These spaces of exchange, while they enable, in effect, cinematographic production to develop in certain countries, represent repertories administrated by the “centre”. The discourse on the “Other” reveals a tormented cartography of world geopolitical relations.

  5. Construction of transport and energy networks in the Baltic region as an impetus for regional development

    Directory of Open Access Journals (Sweden)

    Kuznetsov Alexey

    2013-11-01

    Full Text Available In light of some new aspects of the EU functioning, particularly, the recovery from the 2008-2009 global crisis, transportation and energy development projects are coming to the forefront in the Baltic region. At the same time, there is a need to consider EU’s recent adoption of a common seven-year financial program (2014—2020, which serves, in effect, as the Union’s budget. Given that, one may conclude that the countries of the Baltic region are entering a new stage of development. We look at the role and significance of transportation and energy projects as an instrument of economic development. Having studied the largest transport and energy projects in the Baltic region, we were able to show that the new infrastructure networks supported the investment expansion of Swedish and Finnish companies into the post-communist countries of the Baltic Region. Which, in its turn, allowed the Nordic investors to expand their domestic markets. The analysis also shows that the experience of private businesses proves a recent theoretical concept — the pyramid of regional development factors. As a result, the actual regional policy of the EU cannot be considered in the narrow sense of the Cohesion Policy alone.

  6. Construction and simulation of the Bradyrhizobium diazoefficiens USDA110 metabolic network: a comparison between free-living and symbiotic states.

    Science.gov (United States)

    Yang, Yi; Hu, Xiao-Pan; Ma, Bin-Guang

    2017-02-28

    Bradyrhizobium diazoefficiens is a rhizobium able to convert atmospheric nitrogen into ammonium by establishing mutualistic symbiosis with soybean. It has been recognized as an important parent strain for microbial agents and is widely applied in agricultural and environmental fields. In order to study the metabolic properties of symbiotic nitrogen fixation and the differences between a free-living cell and a symbiotic bacteroid, a genome-scale metabolic network of B. diazoefficiens USDA110 was constructed and analyzed. The metabolic network, iYY1101, contains 1031 reactions, 661 metabolites, and 1101 genes in total. Metabolic models reflecting free-living and symbiotic states were determined by defining the corresponding objective functions and substrate input sets, and were further constrained by high-throughput transcriptomic and proteomic data. Constraint-based flux analysis was used to compare the metabolic capacities and the effects on the metabolic targets of genes and reactions between the two physiological states. The results showed that a free-living rhizobium possesses a steady state flux distribution for sustaining a complex supply of biomass precursors while a symbiotic bacteroid maintains a relatively condensed one adapted to nitrogen-fixation. Our metabolic models may serve as a promising platform for better understanding the symbiotic nitrogen fixation of this species.

  7. Systematic construction of spin liquids on the square lattice from tensor networks with SU(2) symmetry

    Science.gov (United States)

    Mambrini, Matthieu; Orús, Román; Poilblanc, Didier

    2016-11-01

    We elaborate a simple classification scheme of all rank-5 SU(2) spin rotational symmetric tensors according to (i) the onsite physical spin S , (ii) the local Hilbert space V⊗4 of the four virtual (composite) spins attached to each site, and (iii) the irreducible representations of the C4 v point group of the square lattice. We apply our scheme to draw a complete list of all SU(2)-symmetric translationally and rotationally invariant projected entangled pair states (PEPS) with bond dimension D ≤6 . All known SU(2)-symmetric PEPS on the square lattice are recovered and simple generalizations are provided in some cases. More generally, to each of our symmetry class can be associated a (D -1 )-dimensional manifold of spin liquids (potentially) preserving lattice symmetries and defined in terms of D -independent tensors of a given bond dimension D . In addition, generic (low-dimensional) families of PEPS explicitly breaking either (i) particular point-group lattice symmetries (lattice nematics) or (ii) time-reversal symmetry (chiral spin liquids) or (iii) SU(2) spin rotation symmetry down to U(1 ) (spin nematics or Néel antiferromagnets) can also be constructed. We apply this framework to search for new topological chiral spin liquids characterized by well-defined chiral edge modes, as revealed by their entanglement spectrum. In particular, we show how the symmetrization of a double-layer PEPS leads to a chiral topological state with a gapless edge described by a SU (2) 2 Wess-Zumino-Witten model.

  8. Exchanging expertise and constructing boundaries: The development of a transnational knowledge network around heroin-assisted treatment.

    Science.gov (United States)

    Duke, Karen

    2016-05-01

    Over the last 20 years, supervised injectable and inhalable heroin prescribing has been developed, tested and in some cases introduced as a second line treatment for limited groups of entrenched heroin users in a number of European countries and Canada. Based on documentary analyses and eleven key informant interviews, this paper investigates the growth of 'expertise' and the sharing of knowledge between scientific stakeholders from different countries involved in researching and developing this area of treatment. Drawing on Stone's concept of the 'knowledge network' (Stone, 2013) and Gieryn's theory of 'boundary-work' (Gieryn, 1983), the analysis demonstrates the collective power of this group of scientists in producing a particular form of knowledge and expertise which has accrued and been exchanged over time. It also illustrates the ways in which this type of science has gained credibility and authority and become legitimised, reinforced and reproduced by those who employ it in both scientific and political debates. Boundaries were constructed by the knowledge network between different types of professions/disciplines, different forms of science and between the production of science and its consumption by non-scientists. The uniformity of the knowledge network in terms of their professional and disciplinary backgrounds, methodological expertise and ideological perspectives has meant that alternative forms of knowledge and perspectives have been neglected. This limits the nature and scope of the scientific evidence on which to base policy and practice decisions impacting on the work of policy makers and practitioners as well as the experiences of those in treatment who are most affected by this research and policy development. Copyright © 2015 Elsevier B.V. All rights reserved.

  9. Classifying and Analyzing 3d Cell Motion in Jammed Microgels

    Science.gov (United States)

    Bhattacharjee, Tapomoy; Sawyer, W. Gregory; Angelini, Thomas

    Soft granular polyelectrolyte microgels swell in liquid cell growth media to form a continuous elastic solid that can easily transition between solid to fluid state under a low shear stress. Such Liquid-like solids (LLS) have recently been used to create 3D cellular constructs as well as to support, culture and harvest cells in 3D. Current understanding of cell migration mechanics in 3D was established from experiments performed in natural and synthetic polymer networks. Spatial variation in network structure and the transience of degradable gels limit their usefulness in quantitative cell mechanics studies. By contrast, LLS growth media approximates a homogeneous continuum, enabling tractable cell mechanics measurements to be performed in 3D. Here, we introduce a process to understand and classify cytotoxic T cell motion in 3D by studying cellular motility in LLS media. General classification of T cell motion can be achieved with a very traditional statistical approach: the cell's mean squared displacement (MSD) as a function of delay time. We will also use Langevin approaches combined with the constitutive equations of the LLS medium to predict the statistics of T cell motion. National Science Foundation under Grant No. DMR-1352043.

  10. Bayes classifiers for imbalanced traffic accidents datasets.

    Science.gov (United States)

    Mujalli, Randa Oqab; López, Griselda; Garach, Laura

    2016-03-01

    Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class (majority). This, however, supposes a challenging problem for classification algorithms and may cause obtaining a model that well cover the slight injuries instances whereas the killed or severe injuries instances are misclassified frequently. Based on traffic accidents data collected on urban and suburban roads in Jordan for three years (2009-2011); three different data balancing techniques were used: under-sampling which removes some instances of the majority class, oversampling which creates new instances of the minority class and a mix technique that combines both. In addition, different Bayes classifiers were compared for the different imbalanced and balanced data sets: Averaged One-Dependence Estimators, Weightily Average One-Dependence Estimators, and Bayesian networks in order to identify factors that affect the severity of an accident. The results indicated that using the balanced data sets, especially those created using oversampling techniques, with Bayesian networks improved classifying a traffic accident according to its severity and reduced the misclassification of killed and severe injuries instances. On the other hand, the following variables were found to contribute to the occurrence of a killed causality or a severe injury in a traffic accident: number of vehicles involved, accident pattern, number of directions, accident type, lighting, surface condition, and speed limit. This work, to the knowledge of the authors, is the first that aims at analyzing historical data records for traffic accidents occurring in Jordan and the first to apply balancing techniques to analyze injury severity of traffic accidents. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. An ensemble classifier to predict track geometry degradation

    International Nuclear Information System (INIS)

    Cárdenas-Gallo, Iván; Sarmiento, Carlos A.; Morales, Gilberto A.; Bolivar, Manuel A.; Akhavan-Tabatabaei, Raha

    2017-01-01

    Railway operations are inherently complex and source of several problems. In particular, track geometry defects are one of the leading causes of train accidents in the United States. This paper presents a solution approach which entails the construction of an ensemble classifier to forecast the degradation of track geometry. Our classifier is constructed by solving the problem from three different perspectives: deterioration, regression and classification. We considered a different model from each perspective and our results show that using an ensemble method improves the predictive performance. - Highlights: • We present an ensemble classifier to forecast the degradation of track geometry. • Our classifier considers three perspectives: deterioration, regression and classification. • We construct and test three models and our results show that using an ensemble method improves the predictive performance.

  12. Quantum ensembles of quantum classifiers.

    Science.gov (United States)

    Schuld, Maria; Petruccione, Francesco

    2018-02-09

    Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

  13. IAEA safeguards and classified materials

    International Nuclear Information System (INIS)

    Pilat, J.F.; Eccleston, G.W.; Fearey, B.L.; Nicholas, N.J.; Tape, J.W.; Kratzer, M.

    1997-01-01

    The international community in the post-Cold War period has suggested that the International Atomic Energy Agency (IAEA) utilize its expertise in support of the arms control and disarmament process in unprecedented ways. The pledges of the US and Russian presidents to place excess defense materials, some of which are classified, under some type of international inspections raises the prospect of using IAEA safeguards approaches for monitoring classified materials. A traditional safeguards approach, based on nuclear material accountancy, would seem unavoidably to reveal classified information. However, further analysis of the IAEA's safeguards approaches is warranted in order to understand fully the scope and nature of any problems. The issues are complex and difficult, and it is expected that common technical understandings will be essential for their resolution. Accordingly, this paper examines and compares traditional safeguards item accounting of fuel at a nuclear power station (especially spent fuel) with the challenges presented by inspections of classified materials. This analysis is intended to delineate more clearly the problems as well as reveal possible approaches, techniques, and technologies that could allow the adaptation of safeguards to the unprecedented task of inspecting classified materials. It is also hoped that a discussion of these issues can advance ongoing political-technical debates on international inspections of excess classified materials

  14. Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers.

    Science.gov (United States)

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

    2015-01-01

    In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs of genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) can establish coexpression through competition for a limited pool of the miRNA program. While the dynamics of ceRNA regulation among cellular conditions have been verified based on in silico and in vitro experiments, comprehensive investigation into the strength of ceRNA regulation in human datasets remains largely unexplored. Furthermore, pan-cancer analysis of ceRNA regulation, to our knowledge, has not been systematically investigated. In the present study we explored optimal conditions for ceRNA regulation, investigated functions governed by ceRNA regulation, and evaluated pan-cancer effects. We started by investigating how essential factors, such as the size of miRNA programs, the number of miRNA program binding sites, and expression levels of miRNA programs and ceRNAs affect the ceRNA regulation capacity in tumors derived from glioblastoma multiforme patients captured by The Cancer Genome Atlas (TCGA). We demonstrated that increased numbers of common targeting miRNAs as well as the abundance of binding sites enhance ceRNA regulation and strengthen coexpression of ceRNA pairs. Also, our investigation revealed that the strength of ceRNA regulation is dependent on expression levels of both miRNA programs and ceRNAs. Through functional annotation analysis, our results indicated that ceRNA regulation is highly associated with essential cellular functions and diseases including cancer. Furthermore, the highly intertwined ceRNA regulatory relationship enables constitutive and effective intra-function regulation of genes in diverse types of cancer. Using gene and microRNA expression datasets from TCGA, we successfully

  15. Construction of phosphorylation interaction networks by text mining of full-length articles using the eFIP system.

    Science.gov (United States)

    Tudor, Catalina O; Ross, Karen E; Li, Gang; Vijay-Shanker, K; Wu, Cathy H; Arighi, Cecilia N

    2015-01-01

    Protein phosphorylation is a reversible post-translational modification where a protein kinase adds a phosphate group to a protein, potentially regulating its function, localization and/or activity. Phosphorylation can affect protein-protein interactions (PPIs), abolishing interaction with previous binding partners or enabling new interactions. Extracting phosphorylation information coupled with PPI information from the scientific literature will facilitate the creation of phosphorylation interaction networks of kinases, substrates and interacting partners, toward knowledge discovery of functional outcomes of protein phosphorylation. Increasingly, PPI databases are interested in capturing the phosphorylation state of interacting partners. We have previously developed the eFIP (Extracting Functional Impact of Phosphorylation) text mining system, which identifies phosphorylated proteins and phosphorylation-dependent PPIs. In this work, we present several enhancements for the eFIP system: (i) text mining for full-length articles from the PubMed Central open-access collection; (ii) the integration of the RLIMS-P 2.0 system for the extraction of phosphorylation events with kinase, substrate and site information; (iii) the extension of the PPI module with new trigger words/phrases describing interactions and (iv) the addition of the iSimp tool for sentence simplification to aid in the matching of syntactic patterns. We enhance the website functionality to: (i) support searches based on protein roles (kinases, substrates, interacting partners) or using keywords; (ii) link protein entities to their corresponding UniProt identifiers if mapped and (iii) support visual exploration of phosphorylation interaction networks using Cytoscape. The evaluation of eFIP on full-length articles achieved 92.4% precision, 76.5% recall and 83.7% F-measure on 100 article sections. To demonstrate eFIP for knowledge extraction and discovery, we constructed phosphorylation-dependent interaction

  16. Classifying spaces of degenerating polarized Hodge structures

    CERN Document Server

    Kato, Kazuya

    2009-01-01

    In 1970, Phillip Griffiths envisioned that points at infinity could be added to the classifying space D of polarized Hodge structures. In this book, Kazuya Kato and Sampei Usui realize this dream by creating a logarithmic Hodge theory. They use the logarithmic structures begun by Fontaine-Illusie to revive nilpotent orbits as a logarithmic Hodge structure. The book focuses on two principal topics. First, Kato and Usui construct the fine moduli space of polarized logarithmic Hodge structures with additional structures. Even for a Hermitian symmetric domain D, the present theory is a refinem

  17. Cubical sets as a classifying topos

    DEFF Research Database (Denmark)

    Spitters, Bas

    Coquand’s cubical set model for homotopy type theory provides the basis for a computational interpretation of the univalence axiom and some higher inductive types, as implemented in the cubical proof assistant. We show that the underlying cube category is the opposite of the Lawvere theory of De...... Morgan algebras. The topos of cubical sets itself classifies the theory of ‘free De Morgan algebras’. This provides us with a topos with an internal ‘interval’. Using this interval we construct a model of type theory following van den Berg and Garner. We are currently investigating the precise relation...

  18. Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

    Science.gov (United States)

    Kasiviswanathan, K.; Sudheer, K.

    2013-05-01

    Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

  19. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  20. Protein-protein networks construction and their relevance measurement based on multi-epitope-ligand-kartographie and gene ontology data of T-cell surface proteins for polymyositis.

    Science.gov (United States)

    Li, Fang-Zhen; Gao, Feng

    2012-08-01

    Polymyositis is an inflammatory myopathy characterized by muscle invasion of T-cells penetrating the basal lamina and displacing the plasma membrane of normal muscle fibers. In order to understand the different adhesive mechanisms at the T-cell surface, Schubert randomly selected 19 proteins expressed at the T-cell surface and studied them using MELK technique [4], among which 15 proteins are picked up for further study by us. Two types of functional similarity networks are constructed for these proteins. The first type is MELK similarity network, which is constructed based on their MELK data by using the McNemar's test [24]. The second type is GO similarity network, which is constructed based on their GO annotation data by using the RSS method to measuring functional similarity. Then the subset surprisology theory is employed to measure the degree of similarity between two networks. Our computing results show that these two types of networks are high related. This conclusion added new values on MELK technique and expanded its applications greatly.

  1. Development of a skateboarding trick classifier using accelerometry and machine learning

    Directory of Open Access Journals (Sweden)

    Nicholas Kluge Corrêa

    Full Text Available Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN. Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE. The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation. Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.

  2. Comparison of artificial intelligence classifiers for SIP attack data

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2016-05-01

    Honeypot application is a source of valuable data about attacks on the network. We run several SIP honeypots in various computer networks, which are separated geographically and logically. Each honeypot runs on public IP address and uses standard SIP PBX ports. All information gathered via honeypot is periodically sent to the centralized server. This server classifies all attack data by neural network algorithm. The paper describes optimizations of a neural network classifier, which lower the classification error. The article contains the comparison of two neural network algorithm used for the classification of validation data. The first is the original implementation of the neural network described in recent work; the second neural network uses further optimizations like input normalization or cross-entropy cost function. We also use other implementations of neural networks and machine learning classification algorithms. The comparison test their capabilities on validation data to find the optimal classifier. The article result shows promise for further development of an accurate SIP attack classification engine.

  3. Exploring the acquisition and production of grammatical constructions through human-robot interaction with echo state networks.

    Science.gov (United States)

    Hinaut, Xavier; Petit, Maxime; Pointeau, Gregoire; Dominey, Peter Ford

    2014-01-01

    One of the principal functions of human language is to allow people to coordinate joint action. This includes the description of events, requests for action, and their organization in time. A crucial component of language acquisition is learning the grammatical structures that allow the expression of such complex meaning related to physical events. The current research investigates the learning of grammatical constructions and their temporal organization in the context of human-robot physical interaction with the embodied sensorimotor humanoid platform, the iCub. We demonstrate three noteworthy phenomena. First, a recurrent network model is used in conjunction with this robotic platform to learn the mappings between grammatical forms and predicate-argument representations of meanings related to events, and the robot's execution of these events in time. Second, this learning mechanism functions in the inverse sense, i.e., in a language production mode, where rather than executing commanded actions, the robot will describe the results of human generated actions. Finally, we collect data from naïve subjects who interact with the robot via spoken language, and demonstrate significant learning and generalization results. This allows us to conclude that such a neural language learning system not only helps to characterize and understand some aspects of human language acquisition, but also that it can be useful in adaptive human-robot interaction.

  4. Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks

    International Nuclear Information System (INIS)

    Akkoyun, Serkan; Yildiz, Nihat

    2012-01-01

    The gamma-ray tracking technique is a highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being tested. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. In this paper, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1–5 MeV neutrons were first obtained by simulation experiments. Secondly, as a novel approach, for these highly nonlinear detector responses of recoiling germanium nuclei, consistent empirical physical formulas (EPFs) were constructed by appropriate feedforward neural networks (LFNNs). The LFNN-EPFs are of explicit mathematical functional form. Therefore, the LFNN-EPFs can be used to derive further physical functions which could be potentially relevant for the determination of neutron interactions in gamma-ray tracking process.

  5. Knowledge Uncertainty and Composed Classifier

    Czech Academy of Sciences Publication Activity Database

    Klimešová, Dana; Ocelíková, E.

    2007-01-01

    Roč. 1, č. 2 (2007), s. 101-105 ISSN 1998-0140 Institutional research plan: CEZ:AV0Z10750506 Keywords : Boosting architecture * contextual modelling * composed classifier * knowledge management, * knowledge * uncertainty Subject RIV: IN - Informatics, Computer Science

  6. Correlation Dimension-Based Classifier

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Jiřina jr., M.

    2014-01-01

    Roč. 44, č. 12 (2014), s. 2253-2263 ISSN 2168-2267 R&D Projects: GA MŠk(CZ) LG12020 Institutional support: RVO:67985807 Keywords : classifier * multidimensional data * correlation dimension * scaling exponent * polynomial expansion Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.469, year: 2014

  7. Método para classificação de tipos de erros humanos: estudo de caso em acidentes em canteiros de obras An algorithm for classifying error types of front-line workers: a case study in accidents in construction sites

    Directory of Open Access Journals (Sweden)

    Tarcisio Abreu Saurin

    2012-04-01

    Full Text Available Este trabalho tem como objetivo principal desenvolver melhorias em um método de classificação de tipos de erros humanos de operadores de linha de frente. Tais melhorias foram desenvolvidas com base no teste do método em canteiros de obras, um ambiente no qual ele ainda não havia sido aplicado. Assim, foram investigados 19 acidentes de trabalho ocorridos em uma construtora de pequeno porte, sendo classificados os tipos de erros dos trabalhadores lesionados e de colegas de equipe que se encontravam no cenário do acidente. Os resultados indicaram que não houve nenhum erro em 70,5% das 34 vezes em que o método foi aplicado, evidenciando que as causas dos acidentes estavam fortemente associadas a fatores organizacionais. O estudo apresenta ainda recomendações para a interpretação das perguntas que constituem o método, bem como modificações em algumas dessas perguntas em comparação às versões anteriores.The objective of this study is to propose improvements in the algorithm for classifying error types of front-line workers. The improvements have been identified on the basis of testing the algorithm in construction sites, an environment where it had not been implemented it. To this end, 19 occupational accidents which occurred in a small construction company were investigated, and the error types of both injured workers and team members were classified. The results indicated that there was no error in 70.5% of the 34 times the algorithm was applied, providing evidence that the causes were strongly linked to organizational factors. Moreover, the study presents not only recommendations to facilitate the interpretation of the questions that constitute the algorithm, but also changes in some questions in comparison to the previous versions of the tool.

  8. Ship localization in Santa Barbara Channel using machine learning classifiers.

    Science.gov (United States)

    Niu, Haiqiang; Ozanich, Emma; Gerstoft, Peter

    2017-11-01

    Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

  9. Perspectives of construction robots

    Science.gov (United States)

    Stepanov, M. A.; Gridchin, A. M.

    2018-03-01

    This article is an overview of construction robots features, based on formulating the list of requirements for different types of construction robots in relation to different types of construction works.. It describes a variety of construction works and ways to construct new or to adapt existing robot designs for a construction process. Also, it shows the prospects of AI-controlled machines, implementation of automated control systems and networks on construction sites. In the end, different ways to develop and improve, including ecological aspect, the construction process through the wide robotization, creating of data communication networks and, in perspective, establishing of fully AI-controlled construction complex are formulated.

  10. Implications of physical symmetries in adaptive image classifiers

    DEFF Research Database (Denmark)

    Sams, Thomas; Hansen, Jonas Lundbek

    2000-01-01

    It is demonstrated that rotational invariance and reflection symmetry of image classifiers lead to a reduction in the number of free parameters in the classifier. When used in adaptive detectors, e.g. neural networks, this may be used to decrease the number of training samples necessary to learn...... a given classification task, or to improve generalization of the neural network. Notably, the symmetrization of the detector does not compromise the ability to distinguish objects that break the symmetry. (C) 2000 Elsevier Science Ltd. All rights reserved....

  11. Classified facilities for environmental protection

    International Nuclear Information System (INIS)

    Anon.

    1993-02-01

    The legislation of the classified facilities governs most of the dangerous or polluting industries or fixed activities. It rests on the law of 9 July 1976 concerning facilities classified for environmental protection and its application decree of 21 September 1977. This legislation, the general texts of which appear in this volume 1, aims to prevent all the risks and the harmful effects coming from an installation (air, water or soil pollutions, wastes, even aesthetic breaches). The polluting or dangerous activities are defined in a list called nomenclature which subjects the facilities to a declaration or an authorization procedure. The authorization is delivered by the prefect at the end of an open and contradictory procedure after a public survey. In addition, the facilities can be subjected to technical regulations fixed by the Environment Minister (volume 2) or by the prefect for facilities subjected to declaration (volume 3). (A.B.)

  12. Energy-Efficient Neuromorphic Classifiers.

    Science.gov (United States)

    Martí, Daniel; Rigotti, Mattia; Seok, Mingoo; Fusi, Stefano

    2016-10-01

    Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of the brain. The energy consumptions promised by neuromorphic engineering are extremely low, comparable to those of the nervous system. Until now, however, the neuromorphic approach has been restricted to relatively simple circuits and specialized functions, thereby obfuscating a direct comparison of their energy consumption to that used by conventional von Neumann digital machines solving real-world tasks. Here we show that a recent technology developed by IBM can be leveraged to realize neuromorphic circuits that operate as classifiers of complex real-world stimuli. Specifically, we provide a set of general prescriptions to enable the practical implementation of neural architectures that compete with state-of-the-art classifiers. We also show that the energy consumption of these architectures, realized on the IBM chip, is typically two or more orders of magnitude lower than that of conventional digital machines implementing classifiers with comparable performance. Moreover, the spike-based dynamics display a trade-off between integration time and accuracy, which naturally translates into algorithms that can be flexibly deployed for either fast and approximate classifications, or more accurate classifications at the mere expense of longer running times and higher energy costs. This work finally proves that the neuromorphic approach can be efficiently used in real-world applications and has significant advantages over conventional digital devices when energy consumption is considered.

  13. 76 FR 34761 - Classified National Security Information

    Science.gov (United States)

    2011-06-14

    ... MARINE MAMMAL COMMISSION Classified National Security Information [Directive 11-01] AGENCY: Marine... Commission's (MMC) policy on classified information, as directed by Information Security Oversight Office... of Executive Order 13526, ``Classified National Security Information,'' and 32 CFR part 2001...

  14. DETECTION OF CLAMPING FORCES ON MOUNTING A CONSTRUCTION VIA NEURAL NETWORK FOR THE FINITE-ELEMENT MODEL OF COMPRESSOR-CONDENSING UNIT

    Directory of Open Access Journals (Sweden)

    S. V. Krasnovskaya

    2017-01-01

    Full Text Available The article provides a brief review of a condensing unit and problems of mathematic simulation. It examines the influence of pretension on the strain-stress state of a construction by means of finiteelement modeling. The arrangement of a set of input-output data for neural network is also considered. The article investigates a possibility to predict mounting precision via neural networks; by analogy with the above calculations it examines the option to detect clamping forces on mounting compressorcondensing unit. 

  15. A deep learning method for classifying mammographic breast density categories.

    Science.gov (United States)

    Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

    2018-01-01

    Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples

  16. Two channel EEG thought pattern classifier.

    Science.gov (United States)

    Craig, D A; Nguyen, H T; Burchey, H A

    2006-01-01

    This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for the control of a powered wheelchair has a very fast response. It can detect changes in the user's thought pattern in 1 second. Using only two EEG electrodes at positions O(1) and C(4) the system can classify three mental commands (forward, left and right) with an accuracy of more than 79 %

  17. STATISTICAL TOOLS FOR CLASSIFYING GALAXY GROUP DYNAMICS

    International Nuclear Information System (INIS)

    Hou, Annie; Parker, Laura C.; Harris, William E.; Wilman, David J.

    2009-01-01

    The dynamical state of galaxy groups at intermediate redshifts can provide information about the growth of structure in the universe. We examine three goodness-of-fit tests, the Anderson-Darling (A-D), Kolmogorov, and χ 2 tests, in order to determine which statistical tool is best able to distinguish between groups that are relaxed and those that are dynamically complex. We perform Monte Carlo simulations of these three tests and show that the χ 2 test is profoundly unreliable for groups with fewer than 30 members. Power studies of the Kolmogorov and A-D tests are conducted to test their robustness for various sample sizes. We then apply these tests to a sample of the second Canadian Network for Observational Cosmology Redshift Survey (CNOC2) galaxy groups and find that the A-D test is far more reliable and powerful at detecting real departures from an underlying Gaussian distribution than the more commonly used χ 2 and Kolmogorov tests. We use this statistic to classify a sample of the CNOC2 groups and find that 34 of 106 groups are inconsistent with an underlying Gaussian velocity distribution, and thus do not appear relaxed. In addition, we compute velocity dispersion profiles (VDPs) for all groups with more than 20 members and compare the overall features of the Gaussian and non-Gaussian groups, finding that the VDPs of the non-Gaussian groups are distinct from those classified as Gaussian.

  18. Classification of wheat varieties: Use of two-dimensional gel electrophoresis for varieties that can not be classified by matrix assisted laser desorption/ionization-time of flight-mass spectrometry and an artificial neural network

    DEFF Research Database (Denmark)

    Jacobsen, Susanne; Nesic, Ljiljana; Petersen, Marianne Kjerstine

    2001-01-01

    Analyzing a gliadin extract by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI- TOF-MS) combined with an artificial neural network (ANN) is a suitable method for identification of wheat varieties. However, the ANN can not distinguish between all different wheat...

  19. CLASSIFYING BENIGN AND MALIGNANT MASSES USING STATISTICAL MEASURES

    Directory of Open Access Journals (Sweden)

    B. Surendiran

    2011-11-01

    Full Text Available Breast cancer is the primary and most common disease found in women which causes second highest rate of death after lung cancer. The digital mammogram is the X-ray of breast captured for the analysis, interpretation and diagnosis. According to Breast Imaging Reporting and Data System (BIRADS benign and malignant can be differentiated using its shape, size and density, which is how radiologist visualize the mammograms. According to BIRADS mass shape characteristics, benign masses tend to have round, oval, lobular in shape and malignant masses are lobular or irregular in shape. Measuring regular and irregular shapes mathematically is found to be a difficult task, since there is no single measure to differentiate various shapes. In this paper, the malignant and benign masses present in mammogram are classified using Hue, Saturation and Value (HSV weight function based statistical measures. The weight function is robust against noise and captures the degree of gray content of the pixel. The statistical measures use gray weight value instead of gray pixel value to effectively discriminate masses. The 233 mammograms from the Digital Database for Screening Mammography (DDSM benchmark dataset have been used. The PASW data mining modeler has been used for constructing Neural Network for identifying importance of statistical measures. Based on the obtained important statistical measure, the C5.0 tree has been constructed with 60-40 data split. The experimental results are found to be encouraging. Also, the results will agree to the standard specified by the American College of Radiology-BIRADS Systems.

  20. Constructing a two bands optical code-division multiple-access network of bipolar optical access codecs using Walsh-coded liquid crystal modulators

    Science.gov (United States)

    Yen, Chih-Ta; Huang, Jen-Fa; Chih, Ping-En

    2014-08-01

    We propose and experimentally demonstrated the two bands optical code-division multiple-access (OCDMA) network over bipolar Walsh-coded liquid-crystal modulators (LCMs) and driven by green light and red light lasers. Achieving system performance depends on the construction of a decoder that implements a true bipolar correlation using only unipolar signals and intensity detection for each band. We took advantage of the phase delay characteristics of LCMs to construct a prototype optical coder/decoder (codec). Matched and unmatched Walsh signature codes were evaluated to detect correlations among multiuser data in the access network. By using LCMs, a red and green laser light source was spectrally encoded and the summed light dots were complementary decoded. Favorable contrast on auto- and cross-correlations indicates that binary information symbols can be properly recovered using a balanced photodetector.

  1. The networked student: A design-based research case study of student constructed personal learning environments in a middle school science course

    Science.gov (United States)

    Drexler, Wendy

    This design-based research case study applied a networked learning approach to a seventh grade science class at a public school in the southeastern United States. Students adapted emerging Web applications to construct personal learning environments for in-depth scientific inquiry of poisonous and venomous life forms. The personal learning environments constructed used Application Programming Interface (API) widgets to access, organize, and synthesize content from a number of educational Internet resources and social network connections. This study examined the nature of personal learning environments; the processes students go through during construction, and patterns that emerged. The project was documented from both an instructional and student-design perspective. Findings revealed that students applied the processes of: practicing digital responsibility; practicing digital literacy; organizing content; collaborating and socializing; and synthesizing and creating. These processes informed a model of the networked student that will serve as a framework for future instructional designs. A networked learning approach that incorporates these processes into future designs has implications for student learning, teacher roles, professional development, administrative policies, and delivery. This work is significant in that it shifts the focus from technology innovations based on tools to student empowerment based on the processes required to support learning. It affirms the need for greater attention to digital literacy and responsibility in K12 schools as well as consideration for those skills students will need to achieve success in the 21st century. The design-based research case study provides a set of design principles for teachers to follow when facilitating student construction of personal learning environments.

  2. Diagnosis of Broiler Livers by Classifying Image Patches

    DEFF Research Database (Denmark)

    Jørgensen, Anders; Fagertun, Jens; Moeslund, Thomas B.

    2017-01-01

    The manual health inspection are becoming the bottleneck at poultry processing plants. We present a computer vision method for automatic diagnosis of broiler livers. The non-rigid livers, of varying shape and sizes, are classified in patches by a convolutional neural network, outputting maps...

  3. The Closing of the Classified Catalog at Boston University

    Science.gov (United States)

    Hazen, Margaret Hindle

    1974-01-01

    Although the classified catalog at Boston University libraries has been a useful research tool, it has proven too expensive to keep current. The library has converted to a traditional alphabetic subject catalog and will recieve catalog cards from the Ohio College Library Center through the New England Library Network. (Author/LS)

  4. [Introduction of computerized anesthesia-recording systems and construction of comprehensive medical information network for patients undergoing surgery in the University of Tokyo Hospital].

    Science.gov (United States)

    Kitamura, Takayuki; Hoshimoto, Hiroyuki; Yamada, Yoshitsugu

    2009-10-01

    The computerized anesthesia-recording systems are expensive and the introduction of the systems takes time and requires huge effort. Generally speaking, the efficacy of the computerized anesthesia-recording systems on the anesthetic managements is focused on the ability to automatically input data from the monitors to the anesthetic records, and tends to be underestimated. However, once the computerized anesthesia-recording systems are integrated into the medical information network, several features, which definitely contribute to improve the quality of the anesthetic management, can be developed; for example, to prevent misidentification of patients, to prevent mistakes related to blood transfusion, and to protect patients' personal information. Here we describe our experiences of the introduction of the computerized anesthesia-recording systems and the construction of the comprehensive medical information network for patients undergoing surgery in The University of Tokyo Hospital. We also discuss possible efficacy of the comprehensive medical information network for patients during surgery under anesthetic managements.

  5. 29 CFR 1926.407 - Hazardous (classified) locations.

    Science.gov (United States)

    2010-07-01

    ...) locations, unless modified by provisions of this section. (b) Electrical installations. Equipment, wiring..., DEPARTMENT OF LABOR (CONTINUED) SAFETY AND HEALTH REGULATIONS FOR CONSTRUCTION Electrical Installation Safety... electric equipment and wiring in locations which are classified depending on the properties of the...

  6. Waste classifying and separation device

    International Nuclear Information System (INIS)

    Kakiuchi, Hiroki.

    1997-01-01

    A flexible plastic bags containing solid wastes of indefinite shape is broken and the wastes are classified. The bag cutting-portion of the device has an ultrasonic-type or a heater-type cutting means, and the cutting means moves in parallel with the transferring direction of the plastic bags. A classification portion separates and discriminates the plastic bag from the contents and conducts classification while rotating a classification table. Accordingly, the plastic bag containing solids of indefinite shape can be broken and classification can be conducted efficiently and reliably. The device of the present invention has a simple structure which requires small installation space and enables easy maintenance. (T.M.)

  7. Defining and Classifying Interest Groups

    DEFF Research Database (Denmark)

    Baroni, Laura; Carroll, Brendan; Chalmers, Adam

    2014-01-01

    The interest group concept is defined in many different ways in the existing literature and a range of different classification schemes are employed. This complicates comparisons between different studies and their findings. One of the important tasks faced by interest group scholars engaged...... in large-N studies is therefore to define the concept of an interest group and to determine which classification scheme to use for different group types. After reviewing the existing literature, this article sets out to compare different approaches to defining and classifying interest groups with a sample...... in the organizational attributes of specific interest group types. As expected, our comparison of coding schemes reveals a closer link between group attributes and group type in narrower classification schemes based on group organizational characteristics than those based on a behavioral definition of lobbying....

  8. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  9. Three-dimensional fabrication of thick and densely populated soft constructs with complex and actively perfused channel network

    DEFF Research Database (Denmark)

    Pimentel, C Rodrigo; Ko, Suk Kyu; Caviglia, Claudia

    2017-01-01

    One of the fundamental steps needed to design functional tissues and, ultimately organs is the ability to fabricate thick and densely populated tissue constructs with controlled vasculature and microenvironment. To date, bioprinting methods have been employed to manufacture tissue constructs with...

  10. Constructing disease-specific gene networks using pair-wise relevance metric: Application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements

    Directory of Open Access Journals (Sweden)

    Jiang Wei

    2008-08-01

    Full Text Available Abstract Background With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. Results The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8 (p ≈ 0, desmin (DES (p = 2.71 × 10-6 and enolase 1 (ENO1 (p = 4.19 × 10-5], while two novel hub genes [RNA binding motif protein 9 (RBM9 (p = 1.50 × 10-4 and ribosomal protein L30 (RPL30 (p = 1.50 × 10-4] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO based analysis of the colon cancer-specific gene network and

  11. Constructing disease-specific gene networks using pair-wise relevance metric: application to colon cancer identifies interleukin 8, desmin and enolase 1 as the central elements.

    Science.gov (United States)

    Jiang, Wei; Li, Xia; Rao, Shaoqi; Wang, Lihong; Du, Lei; Li, Chuanxing; Wu, Chao; Wang, Hongzhi; Wang, Yadong; Yang, Baofeng

    2008-08-10

    With the advance of large-scale omics technologies, it is now feasible to reversely engineer the underlying genetic networks that describe the complex interplays of molecular elements that lead to complex diseases. Current networking approaches are mainly focusing on building genetic networks at large without probing the interaction mechanisms specific to a physiological or disease condition. The aim of this study was thus to develop such a novel networking approach based on the relevance concept, which is ideal to reveal integrative effects of multiple genes in the underlying genetic circuit for complex diseases. The approach started with identification of multiple disease pathways, called a gene forest, in which the genes extracted from the decision forest constructed by supervised learning of the genome-wide transcriptional profiles for patients and normal samples. Based on the newly identified disease mechanisms, a novel pair-wise relevance metric, adjusted frequency value, was used to define the degree of genetic relationship between two molecular determinants. We applied the proposed method to analyze a publicly available microarray dataset for colon cancer. The results demonstrated that the colon cancer-specific gene network captured the most important genetic interactions in several cellular processes, such as proliferation, apoptosis, differentiation, mitogenesis and immunity, which are known to be pivotal for tumourigenesis. Further analysis of the topological architecture of the network identified three known hub cancer genes [interleukin 8 (IL8) (p approximately 0), desmin (DES) (p = 2.71 x 10(-6)) and enolase 1 (ENO1) (p = 4.19 x 10(-5))], while two novel hub genes [RNA binding motif protein 9 (RBM9) (p = 1.50 x 10(-4)) and ribosomal protein L30 (RPL30) (p = 1.50 x 10(-4))] may define new central elements in the gene network specific to colon cancer. Gene Ontology (GO) based analysis of the colon cancer-specific gene network and the sub-network that

  12. Feature genes in metastatic breast cancer identified by MetaDE and SVM classifier methods.

    Science.gov (United States)

    Tuo, Youlin; An, Ning; Zhang, Ming

    2018-03-01

    The aim of the present study was to investigate the feature genes in metastatic breast cancer samples. A total of 5 expression profiles of metastatic breast cancer samples were downloaded from the Gene Expression Omnibus database, which were then analyzed using the MetaQC and MetaDE packages in R language. The feature genes between metastasis and non‑metastasis samples were screened under the threshold of PSVM) classifier training and verification. The accuracy of the SVM classifier was then evaluated using another independent dataset from The Cancer Genome Atlas database. Finally, function and pathway enrichment analyses for genes in the SVM classifier were performed. A total of 541 feature genes were identified between metastatic and non‑metastatic samples. The top 10 genes with the highest betweenness centrality values in the PPI network of feature genes were Nuclear RNA Export Factor 1, cyclin‑dependent kinase 2 (CDK2), myelocytomatosis proto‑oncogene protein (MYC), Cullin 5, SHC Adaptor Protein 1, Clathrin heavy chain, Nucleolin, WD repeat domain 1, proteasome 26S subunit non‑ATPase 2 and telomeric repeat binding factor 2. The cyclin‑dependent kinase inhibitor 1A (CDKN1A), E2F transcription factor 1 (E2F1), and MYC interacted with CDK2. The SVM classifier constructed by the top 30 feature genes was able to distinguish metastatic samples from non‑metastatic samples [correct rate, specificity, positive predictive value and negative predictive value >0.89; sensitivity >0.84; area under the receiver operating characteristic curve (AUROC) >0.96]. The verification of the SVM classifier in an independent dataset (35 metastatic samples and 143 non‑metastatic samples) revealed an accuracy of 94.38% and AUROC of 0.958. Cell cycle associated functions and pathways were the most significant terms of the 30 feature genes. A SVM classifier was constructed to assess the possibility of breast cancer metastasis, which presented high accuracy in several

  13. Gene Network Construction from Microarray Data Identifies a Key Network Module and Several Candidate Hub Genes in Age-Associated Spatial Learning Impairment.

    Science.gov (United States)

    Uddin, Raihan; Singh, Shiva M

    2017-01-01

    As humans age many suffer from a decrease in normal brain functions including spatial learning impairments. This study aimed to better understand the molecular mechanisms in age-associated spatial learning impairment (ASLI). We used a mathematical modeling approach implemented in Weighted Gene Co-expression Network Analysis (WGCNA) to create and compare gene network models of young (learning unimpaired) and aged (predominantly learning impaired) brains from a set of exploratory datasets in rats in the context of ASLI. The major goal was to overcome some of the limitations previously observed in the traditional meta- and pathway analysis using these data, and identify novel ASLI related genes and their networks based on co-expression relationship of genes. This analysis identified a set of network modules in the young, each of which is highly enriched with genes functioning in broad but distinct GO functional categories or biological pathways. Interestingly, the analysis pointed to a single module that was highly enriched with genes functioning in "learning and memory" related functions and pathways. Subsequent differential network analysis of this "learning and memory" module in the aged (predominantly learning impaired) rats compared to the young learning unimpaired rats allowed us to identify a set of novel ASLI candidate hub genes. Some of these genes show significant repeatability in networks generated from independent young and aged validation datasets. These hub genes are highly co-expressed with other genes in the network, which not only show differential expression but also differential co-expression and differential connectivity across age and learning impairment. The known function of these hub genes indicate that they play key roles in critical pathways, including kinase and phosphatase signaling, in functions related to various ion channels, and in maintaining neuronal integrity relating to synaptic plasticity and memory formation. Taken together, they

  14. Classifying Transition Behaviour in Postural Activity Monitoring

    Directory of Open Access Journals (Sweden)

    James BRUSEY

    2009-10-01

    Full Text Available A few accelerometers positioned on different parts of the body can be used to accurately classify steady state behaviour, such as walking, running, or sitting. Such systems are usually built using supervised learning approaches. Transitions between postures are, however, difficult to deal with using posture classification systems proposed to date, since there is no label set for intermediary postures and also the exact point at which the transition occurs can sometimes be hard to pinpoint. The usual bypass when using supervised learning to train such systems is to discard a section of the dataset around each transition. This leads to poorer classification performance when the systems are deployed out of the laboratory and used on-line, particularly if the regimes monitored involve fast paced activity changes. Time-based filtering that takes advantage of sequential patterns is a potential mechanism to improve posture classification accuracy in such real-life applications. Also, such filtering should reduce the number of event messages needed to be sent across a wireless network to track posture remotely, hence extending the system’s life. To support time-based filtering, understanding transitions, which are the major event generators in a classification system, is a key. This work examines three approaches to post-process the output of a posture classifier using time-based filtering: a naïve voting scheme, an exponentially weighted voting scheme, and a Bayes filter. Best performance is obtained from the exponentially weighted voting scheme although it is suspected that a more sophisticated treatment of the Bayes filter might yield better results.

  15. Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

    Science.gov (United States)

    Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

    2014-01-01

    A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

  16. The Threats Model Construction by means of Fuzzy Cognitive Map on the basis of the Networks Security Policy

    Directory of Open Access Journals (Sweden)

    M. B. Guzairov

    2011-06-01

    Full Text Available The threats matrix construction on the basis of the access matrixes is discussed. Development of threats model on the basis of fuzzy cognitive maps displaying the threats spreading pathways from attack sources to objects is described.

  17. Alignment of Partnering with Construction IT : Exploration and Synthesis of network strategies to integrate BIM-enabled Supply Chains

    NARCIS (Netherlands)

    Papadonikolaki, E.

    2016-01-01

    Supply Chain Management (SCM) and Building Information Modelling (BIM) are seen as innovations that can manage complexities in construction by focusing on integrating processes and products respectively. Whereas these two innovations have been considered compatible, their practical combination has

  18. Development of the business area construction and energy of EnergieRegion Nuernberg. Transfer from project management to a regional network

    International Nuclear Information System (INIS)

    Seiverth, A.

    2006-01-01

    The association EnergieRegion Nuernberg is a regional authority network, which is employed with the promotion of sustainable handling of the factor energy in the region Nuernberg and with the proliferation of this region as internationally recognized location for energy engineering, energy industry and energy science. The intention is to use the important industrial, service-oriented and scientific potential optimally. For this reason a functional co-ordination and communication platform had to be created for the cross-linking of the appropriate participants from economics, research and public administration. Therefore, the author of the contribution under consideration accompanies the development process of the business field construction and energy of this association in the background of the current trends in the construction and energy sector in the region Nuernberg. Under this aspect, the author reports on the following aspects: (a) Success factors of the project management in a regional network; (b) Operationalisation of the success of the project by means of a model; (c) Analysis of the different aspects of energetic measures; (d) Determination of chances and risks of the range building and energy in the region Nuernberg; (e) Comparison of the success of the model projects with the model for the determination of project success; (f) Determination of strengths and weaknesses of the project management in the business field construction and energy of the energy region Nuernberg

  19. Monitoring and assessing the effects from Metro networks construction on the urban acoustic environment: The Athens Metro Line 3 Extension.

    Science.gov (United States)

    Vogiatzis, Konstantinos; Zafiropoulou, Vassiliki; Mouzakis, Haralampos

    2018-10-15

    The Line 3 Extension from Aghia Marina to Piraeus constitutes one of the most significant construction projects in full development in Athens Greater area. For the management and abatement of the air borne noise generated from surface, and/or underground construction activities, relevant machinery operation, and trucks movements at open worksites and the tunnel, a continuous monthly noise and vibration monitoring program is enforced in order to assess any potential intrusion of the acoustic environment. On basis of measured 24 hour L eq noise levels, both L den and L night EU indices were assessed along with vibration velocity for every worksite and tunnel construction activity. The existing environmental noise background generated mainly from road traffic was assessed in order to evaluate potential effects on both air borne noise from construction activities. This comprehensive monitoring program aims to protect the inhabitants in the vicinity of worksites and the tunnel surrounding from construction noise and vibration processing and evaluating all necessary mitigation measures. Especially, for the protection of sensitive receptors, this program may serve as a tool ensuring a successful management of both noise and vibration levels emitted from open air construction activities and (Tunnel Boring Machine) TBM or hammer/pilling operation by implementing mitigation measures where necessary. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. Optimal source coding, removable noise elimination, and natural coordinate system construction for general vector sources using replicator neural networks

    Science.gov (United States)

    Hecht-Nielsen, Robert

    1997-04-01

    A new universal one-chart smooth manifold model for vector information sources is introduced. Natural coordinates (a particular type of chart) for such data manifolds are then defined. Uniformly quantized natural coordinates form an optimal vector quantization code for a general vector source. Replicator neural networks (a specialized type of multilayer perceptron with three hidden layers) are the introduced. As properly configured examples of replicator networks approach minimum mean squared error (e.g., via training and architecture adjustment using randomly chosen vectors from the source), these networks automatically develop a mapping which, in the limit, produces natural coordinates for arbitrary source vectors. The new concept of removable noise (a noise model applicable to a wide variety of real-world noise processes) is then discussed. Replicator neural networks, when configured to approach minimum mean squared reconstruction error (e.g., via training and architecture adjustment on randomly chosen examples from a vector source, each with randomly chosen additive removable noise contamination), in the limit eliminate removable noise and produce natural coordinates for the data vector portions of the noise-corrupted source vectors. Consideration regarding selection of the dimension of a data manifold source model and the training/configuration of replicator neural networks are discussed.

  1. Aggregation Operator Based Fuzzy Pattern Classifier Design

    DEFF Research Database (Denmark)

    Mönks, Uwe; Larsen, Henrik Legind; Lohweg, Volker

    2009-01-01

    This paper presents a novel modular fuzzy pattern classifier design framework for intelligent automation systems, developed on the base of the established Modified Fuzzy Pattern Classifier (MFPC) and allows designing novel classifier models which are hardware-efficiently implementable....... The performances of novel classifiers using substitutes of MFPC's geometric mean aggregator are benchmarked in the scope of an image processing application against the MFPC to reveal classification improvement potentials for obtaining higher classification rates....

  2. Well-Constructed Single-Layer Molybdenum Disulfide Nanorose Cross-Linked by Three Dimensional-Reduced Graphene Oxide Network for Superior Water Splitting and Lithium Storage Property

    Science.gov (United States)

    Zhao, Yanyan; Kuai, Long; Liu, Yanguo; Wang, Pengpeng; Arandiyan, Hamidreza; Cao, Sufeng; Zhang, Jie; Li, Fengyun; Wang, Qing; Geng, Baoyou; Sun, Hongyu

    2015-01-01

    A facile one-step solution reaction route for growth of novel MoS2 nanorose cross-linked by 3D rGO network, in which the MoS2 nanorose is constructed by single-layered or few-layered MoS2 nanosheets, is presented. Due to the 3D assembled hierarchical architecture of the ultrathin MoS2 nanosheets and the interconnection of 3D rGO network, as well as the synergetic effects of MoS2 and rGO, the as-prepared MoS2-NR/rGO nanohybrids delivered high specific capacity, excellent cycling and good rate performance when evaluated as an anode material for lithium-ion batteries. Moreover, the nanohybrids also show excellent hydrogen-evolution catalytic activity and durability in an acidic medium, which is superior to MoS2 nanorose and their nanoparticles counterparts. PMID:25735416

  3. GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

    Science.gov (United States)

    Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

    2014-09-01

    Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. APPLICATION OF METHODS OF LOGISTICS AND PROJECT MANAGEMENT FOR THE CONSTRUCTION OF MANAGEMENT MODEL OF BUSINESS PROCESSES IN THE NETWORK

    Directory of Open Access Journals (Sweden)

    Наталія Іванівна ЧУХРАЙ

    2016-02-01

    Full Text Available In terms of the dynamic development of network economy for effective decision-making managers of enterprises should be combined methods of logistics and project management to obtain the positive synergistic effect. It is shown that the basis of objective measures aimed at minimizing transaction costs. Solving this problem is associated with the development of the structural shell of business enterprises, which continue to evolve rapidly. Organization joint coordinated work in the same virtual information field together geographically separated users opens up entirely new possibilities for improving the mechanisms of project management and logistics. It was reviewed the evolution tool of business process and identified key business processes in networks. The analysis of support for business processes in logistics networks contains a list of basic management mechanisms. It was developed the model of economic and mathematical business process management in structural shell business. The semantic content of the objective function is to minimize transaction costs.

  5. 15 CFR 4.8 - Classified Information.

    Science.gov (United States)

    2010-01-01

    ... 15 Commerce and Foreign Trade 1 2010-01-01 2010-01-01 false Classified Information. 4.8 Section 4... INFORMATION Freedom of Information Act § 4.8 Classified Information. In processing a request for information..., the information shall be reviewed to determine whether it should remain classified. Ordinarily the...

  6. Neural network construction of flow of a viscoelastic fluid of a second order between two eccentric spheres

    International Nuclear Information System (INIS)

    Elbakry, M.Y.; El-Helly, M.; Elbakry, M.Y.

    2010-01-01

    Neural networks are widely for solving many scientific linear and non-linear problems. In this work ,we used the artificial neural network (ANN) to simulate and predict the torque and force acting on the outer stationary sphere due to steady state motion of the second order fluid between two eccentric spheres by a rotating inner sphere with an angular velocity Ω. the (ANN) model has been trained based on the experimental data to produce the torque and force at different eccentricities. The experimental and trained torque and force are compared. The designed ANN shows a good match to the experimental data.

  7. Constructive episodic simulation of the future and the past: distinct subsystems of a core brain network mediate imagining and remembering.

    Science.gov (United States)

    Addis, Donna Rose; Pan, Ling; Vu, Mai-Anh; Laiser, Noa; Schacter, Daniel L

    2009-09-01

    Recent neuroimaging studies demonstrate that remembering the past and imagining the future rely on the same core brain network. However, findings of common core network activity during remembering and imagining events and increased activity during future event simulation could reflect the recasting of past events as future events. We experimentally recombined event details from participants' own past experiences, thus preventing the recasting of past events as imagined events. Moreover, we instructed participants to imagine both future and past events in order to disambiguate whether future-event-specific activity found in previous studies is related specifically to prospection or a general demand of imagining episodic events. Using spatiotemporal partial-least-squares (PLS), a conjunction contrast confirmed that even when subjects are required to recombine details into imagined events (and prevented from recasting events), significant neural overlap between remembering and imagining events is evident throughout the core network. However, the PLS analysis identified two subsystems within the core network. One extensive subsystem was preferentially associated with imagining both future and past events. This finding suggests that regions previously associated with future events, such as anterior hippocampus, medial prefrontal cortex and inferior frontal gyrus, support processes general to imagining events rather than specific to prospection. This PLS analysis also identified a subsystem, including hippocampus, parahippocampal gyrus and extensive regions of posterior visual cortex that was preferentially engaged when remembering past events rich in contextual and visuospatial detail.

  8. Understanding the Construction of Personal Learning Networks to Support Non-Formal Workplace Learning of Training Professionals

    Science.gov (United States)

    Manning, Christin

    2013-01-01

    Workers in the 21st century workplace are faced with rapid and constant developments that place a heavy demand on them to continually learn beyond what the Human Resources and Training groups can meet. As a consequence, professionals must rely on non-formal learning approaches through the development of a personal learning network to keep…

  9. A History of Classified Activities at Oak Ridge National Laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Quist, A.S.

    2001-01-30

    The facilities that became Oak Ridge National Laboratory (ORNL) were created in 1943 during the United States' super-secret World War II project to construct an atomic bomb (the Manhattan Project). During World War II and for several years thereafter, essentially all ORNL activities were classified. Now, in 2000, essentially all ORNL activities are unclassified. The major purpose of this report is to provide a brief history of ORNL's major classified activities from 1943 until the present (September 2000). This report is expected to be useful to the ORNL Classification Officer and to ORNL's Authorized Derivative Classifiers and Authorized Derivative Declassifiers in their classification review of ORNL documents, especially those documents that date from the 1940s and 1950s.

  10. Statistical text classifier to detect specific type of medical incidents.

    Science.gov (United States)

    Wong, Zoie Shui-Yee; Akiyama, Masanori

    2013-01-01

    WHO Patient Safety has put focus to increase the coherence and expressiveness of patient safety classification with the foundation of International Classification for Patient Safety (ICPS). Text classification and statistical approaches has showed to be successful to identifysafety problems in the Aviation industryusing incident text information. It has been challenging to comprehend the taxonomy of medical incidents in a structured manner. Independent reporting mechanisms for patient safety incidents have been established in the UK, Canada, Australia, Japan, Hong Kong etc. This research demonstrates the potential to construct statistical text classifiers to detect specific type of medical incidents using incident text data. An illustrative example for classifying look-alike sound-alike (LASA) medication incidents using structured text from 227 advisories related to medication errors from Global Patient Safety Alerts (GPSA) is shown in this poster presentation. The classifier was built using logistic regression model. ROC curve and the AUC value indicated that this is a satisfactory good model.

  11. Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs.

    NARCIS (Netherlands)

    Forkmann, T.; Teismann, T.; Stenzel, J.S.; Glaesmer, H.; Beurs, D. de

    2018-01-01

    Background: Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic

  12. Pap-smear Classification Using Efficient Second Order Neural Network Training Algorithms

    DEFF Research Database (Denmark)

    Ampazis, Nikolaos; Dounias, George; Jantzen, Jan

    2004-01-01

    In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier. The alg......In this paper we make use of two highly efficient second order neural network training algorithms, namely the LMAM (Levenberg-Marquardt with Adaptive Momentum) and OLMAM (Optimized Levenberg-Marquardt with Adaptive Momentum), for the construction of an efficient pap-smear test classifier...

  13. Classifier fusion for VoIP attacks classification

    Science.gov (United States)

    Safarik, Jakub; Rezac, Filip

    2017-05-01

    SIP is one of the most successful protocols in the field of IP telephony communication. It establishes and manages VoIP calls. As the number of SIP implementation rises, we can expect a higher number of attacks on the communication system in the near future. This work aims at malicious SIP traffic classification. A number of various machine learning algorithms have been developed for attack classification. The paper presents a comparison of current research and the use of classifier fusion method leading to a potential decrease in classification error rate. Use of classifier combination makes a more robust solution without difficulties that may affect single algorithms. Different voting schemes, combination rules, and classifiers are discussed to improve the overall performance. All classifiers have been trained on real malicious traffic. The concept of traffic monitoring depends on the network of honeypot nodes. These honeypots run in several networks spread in different locations. Separation of honeypots allows us to gain an independent and trustworthy attack information.

  14. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  15. Wavelet classifier used for diagnosing shock absorbers in cars

    Directory of Open Access Journals (Sweden)

    Janusz GARDULSKI

    2007-01-01

    Full Text Available The paper discusses some commonly used methods of hydraulic absorbertesting. Disadvantages of the methods are described. A vibro-acoustic method is presented and recommended for practical use on existing test rigs. The method is based on continuous wavelet analysis combined with neural classifier and 25-neuron, one-way, three-layer back propagation network. The analysis satisfies the intended aim.

  16. Measurement of the Constructs of Health Belief Model related to Self-care during Pregnancy in Women Referred to South Tehran Health Network

    Directory of Open Access Journals (Sweden)

    Yalda Soleiman Ekhtiari

    2016-03-01

    Full Text Available Background and Objective: Self-care activities during pregnancy can be effective in reducing adverse pregnancy outcomes. Health Belief Model (HBM is one of the most applicable models in educational need assessment for planning and implementation of educational interventions. The purpose of this study was to measurement of the constructs of HBM related to self-care during pregnancy in women referred to South Tehran health network.Materials and Methods: In this cross-sectional study 270 pregnant women who referred to health centers of South Tehran Health Networks participated. Demographic, knowledge and attitude questionnaires based on constructs of HBM was used to measure the status of knowledge and attitude of women. Data were analyzed using statistical software SPSS18.Results: Results showed that 92.2% of women had the knowledge scores in good level. The scores of perceived severity, perceived self-efficacy and cues to action were in good level in almost of women but almost of women obtained weak point in perceived susceptibility, perceived benefits and barriersConclusion: HBM can be used as an appropriate tool for assessment the status of pregnant women in the field of self-care behaviors during pregnancy and planning and implementation of educational interventions.

  17. Uncovering and Managing the Impact of Methodological Choices for the Computational Construction of Socio-Technical Networks from Texts

    Science.gov (United States)

    2012-09-01

    results? In the field of network analysis, people have developed methods, metrics and theories that help to address these questions ( Brandes & Erlebach...properties have shown to foster the development of strategic alliances (Fitzmaurice, 2000). For situations in which groups need to balance...gold standard test, REX outputs can be assessed by subject matter experts ( SMEs ). The SMEs examine how closely the extracted data resemble the actual

  18. Current Directional Protection of Series Compensated Line Using Intelligent Classifier

    Directory of Open Access Journals (Sweden)

    M. Mollanezhad Heydarabadi

    2016-12-01

    Full Text Available Current inversion condition leads to incorrect operation of current based directional relay in power system with series compensated device. Application of the intelligent system for fault direction classification has been suggested in this paper. A new current directional protection scheme based on intelligent classifier is proposed for the series compensated line. The proposed classifier uses only half cycle of pre-fault and post fault current samples at relay location to feed the classifier. A lot of forward and backward fault simulations under different system conditions upon a transmission line with a fixed series capacitor are carried out using PSCAD/EMTDC software. The applicability of decision tree (DT, probabilistic neural network (PNN and support vector machine (SVM are investigated using simulated data under different system conditions. The performance comparison of the classifiers indicates that the SVM is a best suitable classifier for fault direction discriminating. The backward faults can be accurately distinguished from forward faults even under current inversion without require to detect of the current inversion condition.

  19. Dynamic cluster generation for a fuzzy classifier with ellipsoidal regions.

    Science.gov (United States)

    Abe, S

    1998-01-01

    In this paper, we discuss a fuzzy classifier with ellipsoidal regions that dynamically generates clusters. First, for the data belonging to a class we define a fuzzy rule with an ellipsoidal region. Namely, using the training data for each class, we calculate the center and the covariance matrix of the ellipsoidal region for the class. Then we tune the fuzzy rules, i.e., the slopes of the membership functions, successively until there is no improvement in the recognition rate of the training data. Then if the number of the data belonging to a class that are misclassified into another class exceeds a prescribed number, we define a new cluster to which those data belong and the associated fuzzy rule. Then we tune the newly defined fuzzy rules in the similar way as stated above, fixing the already obtained fuzzy rules. We iterate generation of clusters and tuning of the newly generated fuzzy rules until the number of the data belonging to a class that are misclassified into another class does not exceed the prescribed number. We evaluate our method using thyroid data, Japanese Hiragana data of vehicle license plates, and blood cell data. By dynamic cluster generation, the generalization ability of the classifier is improved and the recognition rate of the fuzzy classifier for the test data is the best among the neural network classifiers and other fuzzy classifiers if there are no discrete input variables.

  20. Young module multiplicities and classifying the indecomposable Young permutation modules

    OpenAIRE

    Gill, Christopher C.

    2012-01-01

    We study the multiplicities of Young modules as direct summands of permutation modules on cosets of Young subgroups. Such multiplicities have become known as the p-Kostka numbers. We classify the indecomposable Young permutation modules, and, applying the Brauer construction for p-permutation modules, we give some new reductions for p-Kostka numbers. In particular we prove that p-Kostka numbers are preserved under multiplying partitions by p, and strengthen a known reduction given by Henke, c...

  1. Maximum margin classifier working in a set of strings.

    Science.gov (United States)

    Koyano, Hitoshi; Hayashida, Morihiro; Akutsu, Tatsuya

    2016-03-01

    Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem for a consensus sequence of strings demonstrated by one of the authors and co-workers in a previous study. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein-protein interactions using amino acid sequences and classifying RNAs by the secondary structure using nucleotide sequences.

  2. Binary conductive network for construction of Si/Ag nanowires/rGO integrated composite film by vacuum-filtration method and their application for lithium ion batteries

    International Nuclear Information System (INIS)

    Tang, H.; Xia, X.H.; Zhang, Y.J.; Tong, Y.Y.; Wang, X.L.; Gu, C.D.; Tu, J.P.

    2015-01-01

    Construction of high-capacity anode is highly important for the development of next-generation high-performance lithium ion batteries (LIBs). Herein we fabricate Si/Ag nanowires/reduced graphene oxide (Si/Ag NWs/rGO) integrated composite film by introducing binary conductive networks (Ag NWs and rGO) into Si active materials with the help of a facile vacuum-filtration method. Active Si nanoparticles are homogeneously encapsulated by binary Ag NWs-rGO conductive network, in which Ag NWs are interwoven among the rGO sheets. The electrochemical properties of the integrated Si/Ag NWs/rGO composite film are thoroughly characterized as anode of LIBs. Compared to the Si/rGO composite film, the integrated Si/Ag NWs/rGO composite film exhibits enhanced electrochemical performances with higher capacity, better high-rate capability and cycling stability (1269 mAh g"−"1 at 50 mA g"−"1 up to 50 cycles). The binary conductive network plays a positive role in the enhancement of performance due to its faster ion/electron transfer, and better anti-structure degradation caused by volume expansion during the cycling process.

  3. Construction of multi-agent mobile robots control system in the problem of persecution with using a modified reinforcement learning method based on neural networks

    Science.gov (United States)

    Patkin, M. L.; Rogachev, G. N.

    2018-02-01

    A method for constructing a multi-agent control system for mobile robots based on training with reinforcement using deep neural networks is considered. Synthesis of the management system is proposed to be carried out with reinforcement training and the modified Actor-Critic method, in which the Actor module is divided into Action Actor and Communication Actor in order to simultaneously manage mobile robots and communicate with partners. Communication is carried out by sending partners at each step a vector of real numbers that are added to the observation vector and affect the behaviour. Functions of Actors and Critic are approximated by deep neural networks. The Critics value function is trained by using the TD-error method and the Actor’s function by using DDPG. The Communication Actor’s neural network is trained through gradients received from partner agents. An environment in which a cooperative multi-agent interaction is present was developed, computer simulation of the application of this method in the control problem of two robots pursuing two goals was carried out.

  4. A support vector machine and a random forest classifier indicates a 15-miRNA set related to osteosarcoma recurrence

    Directory of Open Access Journals (Sweden)

    He Y

    2018-01-01

    Full Text Available Yunfei He,1,2,* Jun Ma,1,* An Wang,1,3,* Weiheng Wang,1 Shengchang Luo,1 Yaoming Liu,2 Xiaojian Ye1 1Department of Orthopaedics, Changzheng Hospital Affiliated with Second Military Medical University, Shanghai, 2Department of Orthopaedics, Lanzhou General Hospital of Lanzhou Military Command Region, Lanzhou, 3Department of Orthopaedics, Shanghai Armed Police Force Hospital, Shanghai, People’s Republic of China *These authors contributed equally to this work Background: Osteosarcoma, which originates in the mesenchymal tissue, is the prevalent primary solid malignancy of the bone. It is of great importance to explore the mechanisms of metastasis and recurrence, which are two primary reasons accounting for the high death rate in osteosarcoma. Data and methods: Three miRNA expression profiles related to osteosarcoma were downloaded from GEO DataSets. Differentially expressed miRNAs (DEmiRs were screened using MetaDE.ES of the MetaDE package. A support vector machine (SVM classifier was constructed using optimal miRNAs, and its prediction efficiency for recurrence was detected in independent datasets. Finally, a co-expression network was constructed based on the DEmiRs and their target genes. Results: In total, 78 significantly DEmiRs were screened. The SVM classifier constructed by 15 miRNAs could accurately classify 58 samples in 65 samples (89.2% in the GSE39040 database, which was validated in another two databases, GSE39052 (84.62%, 22/26 and GSE79181 (91.3%, 21/23. Cox regression showed that four miRNAs, including hsa-miR-10b, hsa-miR-1227, hsa-miR-146b-3p, and hsa-miR-873, significantly correlated with tumor recurrence time. There were 137, 147, 145, and 77 target genes of the above four miRNAs, respectively, which were assigned to 17 gene ontology functionally annotated terms and 14 Kyoto Encyclopedia of Genes and Genomes pathways. Among them, the “Osteoclast differentiation” pathway contained a total of seven target genes and was

  5. Identification of flooded area from satellite images using Hybrid Kohonen Fuzzy C-Means sigma classifier

    Directory of Open Access Journals (Sweden)

    Krishna Kant Singh

    2017-06-01

    Full Text Available A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

  6. Error minimizing algorithms for nearest eighbor classifiers

    Energy Technology Data Exchange (ETDEWEB)

    Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Zimmer, G. Beate [TEXAS A& M

    2011-01-03

    Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

  7. Artificial Neural Network-Based Constitutive Relationship of Inconel 718 Superalloy Construction and Its Application in Accuracy Improvement of Numerical Simulation

    Directory of Open Access Journals (Sweden)

    Junya Lv

    2017-01-01

    Full Text Available The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which play critical roles in process design and optimization. In this investigation, the true stress-strain data of an Inconel 718 superalloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1153–1353 K and strain rate range of 0.01–10 s−1 on a Gleeble 3500 testing machine (DSI, St. Paul, DE, USA. Then the constitutive relationship was modeled by an optimally-constructed and well-trained back-propagation artificial neural network (ANN. The evaluation of the ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of Inconel 718 superalloy. Consequently, the developed ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the continuous mapping relationship for temperature, strain rate, strain and stress. Finally, the constructed ANN was implanted in a finite element solver though the interface of “URPFLO” subroutine to simulate the isothermal compression tests. The results show that the integration of finite element method with ANN model can significantly promote the accuracy improvement of numerical simulations for hot forming processes.

  8. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

    Science.gov (United States)

    Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

    2015-01-01

    The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

  9. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Directory of Open Access Journals (Sweden)

    Seung Seog Han

    Full Text Available Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively, 125 images from Hallym University (C dataset, and 939 images from Seoul National University (D dataset. The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98, (82.7 / 96.7 / 0.95, (92.3 / 79.3 / 0.93, (87.7 / 69.3 / 0.82 for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01 higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  10. Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network.

    Science.gov (United States)

    Han, Seung Seog; Park, Gyeong Hun; Lim, Woohyung; Kim, Myoung Shin; Na, Jung Im; Park, Ilwoo; Chang, Sung Eun

    2018-01-01

    Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.

  11. Evolutionary signatures amongst disease genes permit novel methods for gene prioritization and construction of informative gene-based networks.

    Directory of Open Access Journals (Sweden)

    Nolan Priedigkeit

    2015-02-01

    Full Text Available Genes involved in the same function tend to have similar evolutionary histories, in that their rates of evolution covary over time. This coevolutionary signature, termed Evolutionary Rate Covariation (ERC, is calculated using only gene sequences from a set of closely related species and has demonstrated potential as a computational tool for inferring functional relationships between genes. To further define applications of ERC, we first established that roughly 55% of genetic diseases posses an ERC signature between their contributing genes. At a false discovery rate of 5% we report 40 such diseases including cancers, developmental disorders and mitochondrial diseases. Given these coevolutionary signatures between disease genes, we then assessed ERC's ability to prioritize known disease genes out of a list of unrelated candidates. We found that in the presence of an ERC signature, the true disease gene is effectively prioritized to the top 6% of candidates on average. We then apply this strategy to a melanoma-associated region on chromosome 1 and identify MCL1 as a potential causative gene. Furthermore, to gain global insight into disease mechanisms, we used ERC to predict molecular connections between 310 nominally distinct diseases. The resulting "disease map" network associates several diseases with related pathogenic mechanisms and unveils many novel relationships between clinically distinct diseases, such as between Hirschsprung's disease and melanoma. Taken together, these results demonstrate the utility of molecular evolution as a gene discovery platform and show that evolutionary signatures can be used to build informative gene-based networks.

  12. Oblique decision trees using embedded support vector machines in classifier ensembles

    NARCIS (Netherlands)

    Menkovski, V.; Christou, I.; Efremidis, S.

    2008-01-01

    Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel)

  13. Adaptation in P300 braincomputer interfaces: A two-classifier cotraining approach

    DEFF Research Database (Denmark)

    Panicker, Rajesh C.; Sun, Ying; Puthusserypady, Sadasivan

    2010-01-01

    A cotraining-based approach is introduced for constructing high-performance classifiers for P300-based braincomputer interfaces (BCIs), which were trained from very little data. It uses two classifiers: Fishers linear discriminant analysis and Bayesian linear discriminant analysis progressively...

  14. Constructing "Authentic" Science: Results from a University/High School Collaboration Integrating Digital Storytelling and Social Networking

    Science.gov (United States)

    Olitsky, Stacy; Becker, Elizabeth A.; Jayo, Ignacio; Vinogradov, Philip; Montcalmo, Joseph

    2018-02-01

    This study explores the implications of a redesign of a college course that entailed a new partnership between a college neuroscience classroom and a high school. In this course, the college students engaged in original research projects which included conducting brain surgery and behavioural tests on rats. They used digital storytelling and social networking to communicate with high school students and were visited by the students during the semester. The aims of the redesign were to align the course with science conducted in the field and to provide opportunities to disseminate scientific knowledge through emerging technologies. This study investigates the impact of these innovations on the college and high school students' perceptions of authentic science, including their relationship with science-centred communities. We found that these collaborative tools increased college students' perceptions that authentic science entailed communication with the general public, in addition to supporting prior perceptions of the importance of conducting experiments and presenting results to experts. In addition, the view of science as high-status knowledge was attenuated as students integrated non-formal communication practices into presentations, showing the backstage process of learning, incorporating music and youth discourse styles, and displaying emotional engagement. An impact of these hybrid presentation approaches was an increase in the high school students' perceptions of the accessibility of laboratory science. We discuss how the use of technologies that are familiar to youth, such as iPads, social networking sites, and multimedia presentations, has the potential to prioritize students' voices and promote a more inclusive view of science.

  15. Hierarchical mixtures of naive Bayes classifiers

    NARCIS (Netherlands)

    Wiering, M.A.

    2002-01-01

    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical

  16. Comparing classifiers for pronunciation error detection

    NARCIS (Netherlands)

    Strik, H.; Truong, K.; Wet, F. de; Cucchiarini, C.

    2007-01-01

    Providing feedback on pronunciation errors in computer assisted language learning systems requires that pronunciation errors be detected automatically. In the present study we compare four types of classifiers that can be used for this purpose: two acoustic-phonetic classifiers (one of which employs

  17. Feature extraction for dynamic integration of classifiers

    NARCIS (Netherlands)

    Pechenizkiy, M.; Tsymbal, A.; Puuronen, S.; Patterson, D.W.

    2007-01-01

    Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique

  18. Speaker emotion recognition: from classical classifiers to deep neural networks

    Science.gov (United States)

    Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

    2018-04-01

    Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

  19. Evaluating Machine Learning Classifiers for Hybrid Network Intrusion Detection Systems

    Science.gov (United States)

    2015-03-26

    and the value-focused method. Comparing results from the two evaluation methods, fallacies are revealed with 2 of the 5 notional weighting schemes...for them, because of their relentless support, love , and encouragement. I give a sincere thank you to my research advisor, Dr. Robert Mills, for his...though Ad- aBoost.BayesNet dominated the traditional PR space using a single curve approach. This evaluation fallacy has not been demonstrated prior to

  20. Using Conjugate Gradient Network to Classify Stress Level of Patients.

    Directory of Open Access Journals (Sweden)

    Er. S. Pawar

    2013-02-01

    Full Text Available Diagnosis of stress is important because it can cause many diseases e.g., heart disease, headache, migraine, sleep problems, irritability etc. Diagnosis of stress in patients often involves acquisition of biological signals for example heart rate, electrocardiogram (ECG, electromyography signals (EMG etc. Stress diagnosis using biomedical signals is difficult and since the biomedical signals are too complex to generate any rule an experienced person or expert is needed to determine stress levels. Also, it is not feasible to use all the features that are available or possible to extract from the signal. So, relevant features should be chosen from the extracted features that are capable to diagnose stress. Electronics devices are increasingly being seen in the field of medicine for diagnosis, therapy, checking of stress levels etc. The research and development work of medical electronics engineers leads to the manufacturing of sophisticated diagnostic medical equipment needed to ensure good health care. Biomedical engineering combines the design and problem solving skills of engineering with medical and biological sciences to improve health care diagnosis and treatment.