WorldWideScience

Sample records for pruned extreme learning

  1. Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP Model Using Degradation Data

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    2017-11-01

    Full Text Available The insulated gate bipolar transistor (IGBT is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN and least squares estimation (LSE method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

  2. REM sleep selectively prunes and maintains new synapses in development and learning.

    Science.gov (United States)

    Li, Wei; Ma, Lei; Yang, Guang; Gan, Wen-Biao

    2017-03-01

    The functions and underlying mechanisms of rapid eye movement (REM) sleep remain unclear. Here we show that REM sleep prunes newly formed postsynaptic dendritic spines of layer 5 pyramidal neurons in the mouse motor cortex during development and motor learning. This REM sleep-dependent elimination of new spines facilitates subsequent spine formation during development and when a new motor task is learned, indicating a role for REM sleep in pruning to balance the number of new spines formed over time. Moreover, REM sleep also strengthens and maintains newly formed spines, which are critical for neuronal circuit development and behavioral improvement after learning. We further show that dendritic calcium spikes arising during REM sleep are important for pruning and strengthening new spines. Together, these findings indicate that REM sleep has multifaceted functions in brain development, learning and memory consolidation by selectively eliminating and maintaining newly formed synapses via dendritic calcium spike-dependent mechanisms.

  3. Pruning Shrubs

    OpenAIRE

    French, Sue (Sue C.); Appleton, Bonnie Lee, 1948-2012

    2009-01-01

    Understanding the "natural habit" or "shape" of shrubs will help you determine how to prune them. This publication explores how and when to prune, maintenance and rejuvenation pruning, and the growth habit of shrubs.

  4. Online transfer learning with extreme learning machine

    Science.gov (United States)

    Yin, Haibo; Yang, Yun-an

    2017-05-01

    In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

  5. An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity

    DEFF Research Database (Denmark)

    Fu, Bin; Wang, Zhihai; Pan, Rong

    2013-01-01

    be further considered while designing a pruning criterion is presented, and then an effective definition of diversity is proposed. The experimental results have validated that the given pruning criterion could single out the subset of classifiers that show better performance in the process of hill...

  6. An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

    Science.gov (United States)

    Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

    2017-02-01

    Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

  7. BELM: Bayesian extreme learning machine.

    Science.gov (United States)

    Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

    2011-03-01

    The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

  8. Extreme learning machines 2013 algorithms and applications

    CERN Document Server

    Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

    2014-01-01

    In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

  9. Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis.

    Science.gov (United States)

    Samala, Ravi K; Chan, Heang-Ping; Hadjiiski, Lubomir M; Helvie, Mark A; Richter, Caleb; Cha, Kenny

    2018-05-01

    Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

  10. Evolutionary pruning of transfer learned deep convolutional neural network for breast cancer diagnosis in digital breast tomosynthesis

    Science.gov (United States)

    Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir M.; Helvie, Mark A.; Richter, Caleb; Cha, Kenny

    2018-05-01

    Deep learning models are highly parameterized, resulting in difficulty in inference and transfer learning for image recognition tasks. In this work, we propose a layered pathway evolution method to compress a deep convolutional neural network (DCNN) for classification of masses in digital breast tomosynthesis (DBT). The objective is to prune the number of tunable parameters while preserving the classification accuracy. In the first stage transfer learning, 19 632 augmented regions-of-interest (ROIs) from 2454 mass lesions on mammograms were used to train a pre-trained DCNN on ImageNet. In the second stage transfer learning, the DCNN was used as a feature extractor followed by feature selection and random forest classification. The pathway evolution was performed using genetic algorithm in an iterative approach with tournament selection driven by count-preserving crossover and mutation. The second stage was trained with 9120 DBT ROIs from 228 mass lesions using leave-one-case-out cross-validation. The DCNN was reduced by 87% in the number of neurons, 34% in the number of parameters, and 95% in the number of multiply-and-add operations required in the convolutional layers. The test AUC on 89 mass lesions from 94 independent DBT cases before and after pruning were 0.88 and 0.90, respectively, and the difference was not statistically significant (p  >  0.05). The proposed DCNN compression approach can reduce the number of required operations by 95% while maintaining the classification performance. The approach can be extended to other deep neural networks and imaging tasks where transfer learning is appropriate.

  11. International Conference on Extreme Learning Machines 2014

    CERN Document Server

    Mao, Kezhi; Cambria, Erik; Man, Zhihong; Toh, Kar-Ann

    2015-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.  

  12. International Conference on Extreme Learning Machine 2015

    CERN Document Server

    Mao, Kezhi; Wu, Jonathan; Lendasse, Amaury; ELM 2015; Theory, Algorithms and Applications (I); Theory, Algorithms and Applications (II)

    2016-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .

  13. Prune belly syndrome associated with incomplete VACTERL

    Directory of Open Access Journals (Sweden)

    Ghritlaharey R

    2007-01-01

    Full Text Available A Prune Belly syndrome with VATER/VACTERL association is an extremely rare. They are either stillborn or die within few days of life, only few such cases have been reported in literature. We are presenting here a male neonate of Prune Belly syndrome associated with incomplete VACTERL with brief review of literature.

  14. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  15. Two-Dimensional Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Bo Jia

    2015-01-01

    (BP networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM based on the very natural idea to deal with matrix data directly. Unlike original ELM which handles vectors, 2DELM take the matrices as input features without vectorization. Empirical studies on several real image datasets show the efficiency and effectiveness of the algorithm.

  16. Robust Matching Pursuit Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Zejian Yuan

    2018-01-01

    Full Text Available Extreme learning machine (ELM is a popular learning algorithm for single hidden layer feedforward networks (SLFNs. It was originally proposed with the inspiration from biological learning and has attracted massive attentions due to its adaptability to various tasks with a fast learning ability and efficient computation cost. As an effective sparse representation method, orthogonal matching pursuit (OMP method can be embedded into ELM to overcome the singularity problem and improve the stability. Usually OMP recovers a sparse vector by minimizing a least squares (LS loss, which is efficient for Gaussian distributed data, but may suffer performance deterioration in presence of non-Gaussian data. To address this problem, a robust matching pursuit method based on a novel kernel risk-sensitive loss (in short KRSLMP is first proposed in this paper. The KRSLMP is then applied to ELM to solve the sparse output weight vector, and the new method named the KRSLMP-ELM is developed for SLFN learning. Experimental results on synthetic and real-world data sets confirm the effectiveness and superiority of the proposed method.

  17. Trends in extreme learning machines: a review.

    Science.gov (United States)

    Huang, Gao; Huang, Guang-Bin; Song, Shiji; You, Keyou

    2015-01-01

    Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

  18. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  19. Better marking means cheaper pruning.

    Science.gov (United States)

    Kenneth R. Eversole

    1953-01-01

    Careful selection of trees to be pruned can make the difference between profit and loss on the pruning investment, especially in stands where no thinning is contemplated. Expert marking is required to make sure that the pruned trees will grow rapidly. The most important variable influencing the cost of clear wood produced by pruning is growth rate. For example, at 3...

  20. Prune belly syndrome

    Science.gov (United States)

    Eagle-Barrett syndrome; Triad syndrome ... The exact causes of prune belly syndrome are unknown. The condition affects mostly boys. While in the womb, the developing baby's abdomen swells with fluid. Often, the cause is ...

  1. Pruning devices in 1995

    International Nuclear Information System (INIS)

    Mutikainen, A.

    1995-01-01

    This bulletin describes the market situation in April 1995 in Finland concerning devices suitable for silvicultural pruning in forestry. The review is based on the responses to a questionnaire sent to manufacturers and importers. Manually operated pruning devices, relying entirely on muscle power, were manufactured by six companies. There were four models each of branch saws and branch cutters and two models of branch blades. Motorised pruning devices, with the branch-severing power supplied by a combustion engine, battery or a power unit were manufactured by five companies. There were twelve models in all. The amount of pruning done in Finland has diminished year by year from the peak years of 1988-1989 when ca. 13000 hectares were pruned. In 1993 the corresponding figure was 5290 hectares of which 3930 hectares applied to private, non-industrial forestry. One contributing factor to this fall may be seen in the changes that have occurred in forest improvement regulations. The annual target set in the Forest 2000 program is for 20000 hectares to be pruned. (author)

  2. 7 CFR 993.7 - French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false French prunes. 993.7 Section 993.7 Agriculture... Order Regulating Handling Definitions § 993.7 French prunes. French prunes means: (a) Prunes produced from plums of the following varieties of plums: French (Prune d'Agen, Petite Prune d'Agen), Coates (Cox...

  3. Prune Belly Syndrome

    Directory of Open Access Journals (Sweden)

    Koyye Ravindranath Tagore

    2011-01-01

    Full Text Available Prune belly syndrome is a rare congenital disorder of the urinary system, characterized by a triad of abnormalities. The aetiology is not known. Many infants are either stillborn or die within the first few weeks of life from severe lung or kidney problems, or a combination of congenital anomalies.

  4. Improved Extreme Learning Machine based on the Sensitivity Analysis

    Science.gov (United States)

    Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

    2018-03-01

    Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

  5. Fast Parsing using Pruning and Grammar Specialization

    OpenAIRE

    Rayner, Manny; Carter, David

    1996-01-01

    We show how a general grammar may be automatically adapted for fast parsing of utterances from a specific domain by means of constituent pruning and grammar specialization based on explanation-based learning. These methods together give an order of magnitude increase in speed, and the coverage loss entailed by grammar specialization is reduced to approximately half that reported in previous work. Experiments described here suggest that the loss of coverage has been reduced to the point where ...

  6. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  7. Study of Environmental Data Complexity using Extreme Learning Machine

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2017-04-01

    The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

  8. A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles.

    Science.gov (United States)

    Wang, Ning; Sun, Jing-Chao; Er, Meng Joo; Liu, Yan-Cheng

    2016-05-01

    In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

  9. Adaptive learning by extremal dynamics and negative feedback

    International Nuclear Information System (INIS)

    Bak, Per; Chialvo, Dante R.

    2001-01-01

    We describe a mechanism for biological learning and adaptation based on two simple principles: (i) Neuronal activity propagates only through the network's strongest synaptic connections (extremal dynamics), and (ii) the strengths of active synapses are reduced if mistakes are made, otherwise no changes occur (negative feedback). The balancing of those two tendencies typically shapes a synaptic landscape with configurations which are barely stable, and therefore highly flexible. This allows for swift adaptation to new situations. Recollection of past successes is achieved by punishing synapses which have once participated in activity associated with successful outputs much less than neurons that have never been successful. Despite its simplicity, the model can readily learn to solve complicated nonlinear tasks, even in the presence of noise. In particular, the learning time for the benchmark parity problem scales algebraically with the problem size N, with an exponent k∼1.4

  10. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    Wojciech M. Czarnecki

    2015-11-01

    Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  11. DANNP: an efficient artificial neural network pruning tool

    KAUST Repository

    Alshahrani, Mona

    2017-11-06

    Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly

  12. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

    Science.gov (United States)

    Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

    2017-09-01

    The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

  13. Semi-supervised and unsupervised extreme learning machines.

    Science.gov (United States)

    Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

    2014-12-01

    Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

  14. Extreme learning machine for ranking: generalization analysis and applications.

    Science.gov (United States)

    Chen, Hong; Peng, Jiangtao; Zhou, Yicong; Li, Luoqing; Pan, Zhibin

    2014-05-01

    The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Classifying BCI signals from novice users with extreme learning machine

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bermúdez Germán

    2017-07-01

    Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  16. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  17. Bidirectional extreme learning machine for regression problem and its learning effectiveness.

    Science.gov (United States)

    Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

    2012-09-01

    It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

  18. Sample-Based Extreme Learning Machine with Missing Data

    Directory of Open Access Journals (Sweden)

    Hang Gao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information. However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

  19. NMF-Based Image Quality Assessment Using Extreme Learning Machine.

    Science.gov (United States)

    Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

    2017-01-01

    Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

  20. Estimating building energy consumption using extreme learning machine method

    International Nuclear Information System (INIS)

    Naji, Sareh; Keivani, Afram; Shamshirband, Shahaboddin; Alengaram, U. Johnson; Jumaat, Mohd Zamin; Mansor, Zulkefli; Lee, Malrey

    2016-01-01

    The current energy requirements of buildings comprise a large percentage of the total energy consumed around the world. The demand of energy, as well as the construction materials used in buildings, are becoming increasingly problematic for the earth's sustainable future, and thus have led to alarming concern. The energy efficiency of buildings can be improved, and in order to do so, their operational energy usage should be estimated early in the design phase, so that buildings are as sustainable as possible. An early energy estimate can greatly help architects and engineers create sustainable structures. This study proposes a novel method to estimate building energy consumption based on the ELM (Extreme Learning Machine) method. This method is applied to building material thicknesses and their thermal insulation capability (K-value). For this purpose up to 180 simulations are carried out for different material thicknesses and insulation properties, using the EnergyPlus software application. The estimation and prediction obtained by the ELM model are compared with GP (genetic programming) and ANNs (artificial neural network) models for accuracy. The simulation results indicate that an improvement in predictive accuracy is achievable with the ELM approach in comparison with GP and ANN. - Highlights: • Buildings consume huge amounts of energy for operation. • Envelope materials and insulation influence building energy consumption. • Extreme learning machine is used to estimate energy usage of a sample building. • The key effective factors in this study are insulation thickness and K-value.

  1. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2014-05-01

    In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

  2. Spatial extreme learning machines: An application on prediction of disease counts.

    Science.gov (United States)

    Prates, Marcos O

    2018-01-01

    Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

  3. Visual memory and learning in extremely low-birth-weight/extremely preterm adolescents compared with controls: a geographic study.

    Science.gov (United States)

    Molloy, Carly S; Wilson-Ching, Michelle; Doyle, Lex W; Anderson, Vicki A; Anderson, Peter J

    2014-04-01

    Contemporary data on visual memory and learning in survivors born extremely preterm (EP; Visual learning and memory data were available for 221 (74.2%) EP/ELBW subjects and 159 (60.7%) controls. EP/ELBW adolescents exhibited significantly poorer performance across visual memory and learning variables compared with controls. Visual learning and delayed visual memory were particularly problematic and remained so after controlling for visual-motor integration and visual perception and excluding adolescents with neurosensory disability, and/or IQ visual memory and learning outcomes compared with controls, which cannot be entirely explained by poor visual perceptual or visual constructional skills or intellectual impairment.

  4. Development of a Grapevine Pruning Algorithm for Using in Pruning

    Directory of Open Access Journals (Sweden)

    S. M Hosseini

    2017-10-01

    Full Text Available Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known as the most laborious jobs in the farm. Some of the grape producing countries use various mechanical machines to prune the grapevines, but in most cases, these machines do not have a good performance. Therefore intelligent pruning machine seems to be necessary in this regard and this intelligent pruning machines can reduce the labor required to prune the vineyards. It this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut. Stereo vision technique was used to obtain three dimensional images from the bare bushes whose leaves were fallen in autumn. Stereo vision systems are used to determine the depth from two images taken at the same time but from slightly different viewpoints using two cameras. Each pair of images of a common scene is related by a popular geometry, and corresponding points in the images pairs are constrained to lie on pairs of conjugate popular lines. Materials and Methods Photos were taken from gardens of the Research Center for Agriculture and Natural Resources of Fars province, Iran. At first, the distance between the plants and the cameras should be determined. The distance between the plants and cameras can be obtained by using the stereo vision techniques. Therefore, this method was used in this paper by two pictures taken from each plant with the left and right cameras. The algorithm was written in MATLAB. To facilitate the segmentation of the branches from the rows at the back, a blue plate with dimensions of 2×2 m2 were used at the background. After invoking the images, branches were segmented from the background to produce the binary

  5. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  6. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified......Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

  7. Application of Extreme Learning Machines to inverse neutron kinetics

    International Nuclear Information System (INIS)

    Picca, Paolo; Furfaro, Roberto

    2017-01-01

    Highlights: • The paper applies the Extreme Learning Machines (ELMs) to inverse reactor problems. • Multi-group transport model is used for the inversion as opposed to point kinetics. • ELMs are compared against Artificial Neural Networks (ANNs). • Various options are tested to improve the reliability of the estimation. • Results highlight the potential of the ELM approach. - Abstract: The paper presents the application of Extreme Leaning Machines (ELMs) for inverse reactor kinetic applications. ELMs were proposed by Huang and co-workers (2004, 2006a,b, 2015), which showed their enhances capabilities in terms of training speed and generalization with respect to classical Artificial Neural Networks (ANNs). ELMs are here implemented for reactivity determination as an alternative to ANNs (e.g. Picca et al. (2008)) and Gaussian Processes (Picca and Furfaro, 2012). After a review of the main features of ELMs, their application to inverse kinetic problems is proposed. The ELMs performance is tested on a typical accelerator drive system configuration (Yalina reactor) and the inversion is carried out on an accurate kinetic model (multi-group transport).

  8. Prune-Belly Sendromu: Olgu Sunumu

    OpenAIRE

    BELET, N.; PAKSU, Ş.; BELET, Ü.; KÜÇÜKÖDÜK, Ş.

    2010-01-01

    Prune belly sendromu abdominal kasların yokluğu, kriptorşidizm ve obstruktif üropati tri-adından oluşur. Bu yazıda nadir görülmesi nedeniyle prune belly sendromlu bir hasta sunulmuş, literatür bilgileri gözden geçirilmiştir. The Prune-Belly Syndrome: A Case Report The Prune belly syndrome consists of the triad absence of abdominal muscles, cryptorchidism and obstructive uropathy. A rare case of Prune belly syndrome was presented and the literature was reviewed.

  9. Parallel multiple instance learning for extremely large histopathology image analysis.

    Science.gov (United States)

    Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

    2017-08-03

    Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

  10. Prediction of length-of-day using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Yu Lei

    2015-03-01

    Full Text Available Traditional artificial neural networks (ANN such as back-propagation neural networks (BPNN provide good predictions of length-of-day (LOD. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM, to improve the efficiency of LOD predictions. Earth orientation parameters (EOP C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS, which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN, and adaptive network-based fuzzy inference systems (ANFIS. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

  11. Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Ying Yin

    2016-05-01

    Full Text Available Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1 the user-specific parameter for the number of clusters may incur the effective problem; (2 SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML-extreme learning machine (ELM, to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

  12. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    OpenAIRE

    Mao, Li; Zhang, Lidong; Liu, Xingyang; Li, Chaofeng; Yang, Hong

    2014-01-01

    Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting...

  13. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad

    2016-12-09

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  14. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad; Shafait, Faisal; Ghanem, Bernard; Mian, Ajmal

    2016-01-01

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  15. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  16. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    Directory of Open Access Journals (Sweden)

    Li Mao

    2014-01-01

    Full Text Available Extreme learning machine (ELM is a new class of single-hidden layer feedforward neural network (SLFN, which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted ELM, and propose a modified (weighted extreme learning machine (M-ELM and M-WELM. Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

  17. 7 CFR 993.6 - Non-French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Non-French prunes. 993.6 Section 993.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Order Regulating Handling Definitions § 993.6 Non-French prunes. Non-French prunes means prunes commonly...

  18. Countering the Pedagogy of Extremism: Reflective Narratives and Critiques of Problem-Based Learning

    Science.gov (United States)

    Woo, Chris W. H.; Laxman, Kumar

    2013-01-01

    This paper is a critique against "purist" pedagogies found in the literature of student-centred learning. The article reproves extremism in education and questions the absolutism and teleological truths expounded in exclusive problem-based learning. The paper articulates the framework of a unifying pedagogical practice through Eve…

  19. A review of pruning fruit trees

    Science.gov (United States)

    Zhang, L.; Koc, A. B.; Wang, X. N.; Jiang, Y. X.

    2018-05-01

    The focus of this review is to present the results of studies and articles about ways to prune fruit trees. Pruning should be done in late winter or early spring so that the infection risk can be significantly decreased. This review will also offer an overview of methods to prevent infections and speed up recovery on the trees. The following is an interpretation of why high-power ultrasonic assisted pruning in the fruits trees is needed and will elaborate on the efficiency, labor costs, and safety, as well as space, location, and some environmental issues.

  20. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  1. An extreme case of action learning at BAT Niemeyer

    NARCIS (Netherlands)

    Eckstein, E.; Veenhoven, G.; De Loo, I.G.M.

    2009-01-01

    Becoming a 'winning organization' when one currently is an 'ugly ducking' can be a difficult and strenuous task. BAT Niemeyer in the Netherlands succeeded in making such a transformation over the course of four years. Action learning was used, among other methods, to steer part of this

  2. An Extreme Case of Action Learning at BAT Niemeyer

    Science.gov (United States)

    Eckstein, Emiel; Veenhoven, Gert; De Loo, Ivo

    2009-01-01

    Becoming a "winning organization" when one currently is an "ugly ducking" can be a difficult and strenuous task. BAT Niemeyer in the Netherlands succeeded in making such a transformation over the course of four years. Action learning was used, among other methods, to steer part of this transformation, in which employee…

  3. Prune belly syndrome. A focused physical assessment.

    Science.gov (United States)

    Woods, Amanda G; Brandon, Debra H

    2007-06-01

    Prune belly syndrome, a rare congenital anomaly, exists almost exclusively in males and consists of genital and urinary abnormalities with partial or complete absence of abdominal wall musculature. The syndrome, caused by urethral obstruction early in development, is the result of massive bladder distention and urinary ascites, leading to degeneration of the abdominal wall musculature and failure of testicular descent. The impaired elimination of urine from the bladder leads to oligohydramnios, pulmonary hypoplasia, and Potter's facies. The syndrome has a broad spectrum of affected anatomy with different levels of severity. The exact etiology of prune belly syndrome is unknown, although several embryologic theories attempt to explain the anomaly. With advances in prenatal screening techniques, the diagnosis and possible treatment of prune belly syndrome can occur before birth, although controversy exists on the proper management of prune belly syndrome. This article explores the theories behind the pathophysiology and embryology of prune belly syndrome and its multisystemic effects on the newborn infant. Specific attention is paid to presentation, clinical features, head-to-toe physical assessment, family support, and nursing care of infants with prune belly syndrome.

  4. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach

    Science.gov (United States)

    Tiun, Sabrina; AL-Dhief, Fahad Taha; Sammour, Mahmoud A. M.

    2018-01-01

    Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%. PMID:29672546

  5. Spoken language identification based on the enhanced self-adjusting extreme learning machine approach.

    Science.gov (United States)

    Albadr, Musatafa Abbas Abbood; Tiun, Sabrina; Al-Dhief, Fahad Taha; Sammour, Mahmoud A M

    2018-01-01

    Spoken Language Identification (LID) is the process of determining and classifying natural language from a given content and dataset. Typically, data must be processed to extract useful features to perform LID. The extracting features for LID, based on literature, is a mature process where the standard features for LID have already been developed using Mel-Frequency Cepstral Coefficients (MFCC), Shifted Delta Cepstral (SDC), the Gaussian Mixture Model (GMM) and ending with the i-vector based framework. However, the process of learning based on extract features remains to be improved (i.e. optimised) to capture all embedded knowledge on the extracted features. The Extreme Learning Machine (ELM) is an effective learning model used to perform classification and regression analysis and is extremely useful to train a single hidden layer neural network. Nevertheless, the learning process of this model is not entirely effective (i.e. optimised) due to the random selection of weights within the input hidden layer. In this study, the ELM is selected as a learning model for LID based on standard feature extraction. One of the optimisation approaches of ELM, the Self-Adjusting Extreme Learning Machine (SA-ELM) is selected as the benchmark and improved by altering the selection phase of the optimisation process. The selection process is performed incorporating both the Split-Ratio and K-Tournament methods, the improved SA-ELM is named Enhanced Self-Adjusting Extreme Learning Machine (ESA-ELM). The results are generated based on LID with the datasets created from eight different languages. The results of the study showed excellent superiority relating to the performance of the Enhanced Self-Adjusting Extreme Learning Machine LID (ESA-ELM LID) compared with the SA-ELM LID, with ESA-ELM LID achieving an accuracy of 96.25%, as compared to the accuracy of SA-ELM LID of only 95.00%.

  6. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  7. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  8. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    OpenAIRE

    Fu Yu; Mu Jiong; Duan Xu Liang

    2016-01-01

    By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research...

  9. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2016-01-01

    Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

  10. Abdominoplasty in Prune Belly Syndrome.

    Science.gov (United States)

    Dénes, F T; Park, R; Lopes, R I; Moscardi, P R M; Srougi, M

    2015-10-01

    Many patients with Prune Belly Syndrome (PBS) require abdominoplasty alone or in combination with correction of any urogenital abnormalities. This video presents a simplified technique with which to treat the abdominal flaccidity in PBS. A longitudinal xypho-pubic fusiform figure is drawn on the abdomen, based on the area of skin and subcutaneous tissue to be removed. This is performed with preservation of the musculo-fascial layer and the umbilicus. A lateral elliptical single xypho-pubic line is drawn in the most lax side of the fascia, which is incised along this line. After urinary tract reconstruction and orchidopexy, closure is initiated by suturing the medial edge of the wider fascial flap laterally to the peritoneal side of the contralateral flap. Next, the now outer fascial flap is laid over the inner flap, and a buttonhole is made to expose the umbilicus. The subcutaneous tissue of the inner flap is laterally undermined to gain extra distance for the suture of the outer flap over the inner flap. The subcutaneous tissue and skin are sutured in the midline, incorporating the umbilicus. In a 30-year period, 43 PBS patients underwent this procedure with good cosmetic and long-term functional results. This abdominoplasty technique is simple and presents good functional and cosmetic results in PBS patients. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

  11. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    Directory of Open Access Journals (Sweden)

    Fu Yu

    2016-01-01

    Full Text Available By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research thinking concerning extreme learning machine into the economics classification area so as to fulfill the purpose of computerizing the speedy but effective evaluation of massive financial statements of listed companies pertain to different classes

  12. Learning disabilities among extremely preterm children without neurosensory impairment: Comorbidity, neuropsychological profiles and scholastic outcomes.

    Science.gov (United States)

    Johnson, Samantha; Strauss, Victoria; Gilmore, Camilla; Jaekel, Julia; Marlow, Neil; Wolke, Dieter

    2016-12-01

    Children born extremely preterm are at high risk for intellectual disability, learning disabilities, executive dysfunction and special educational needs, but little is understood about the comorbidity of intellectual and learning disabilities in this population. This study explored comorbidity in intellectual disability (ID) and learning disabilities (LD) in children born extremely preterm (EP; disabilities. LD were associated with a 3 times increased risk for SEN. However, EP children with ID alone had poorer neuropsychological abilities and curriculum-based attainment than children with no disabilities, yet there was no increase in SEN provision among this group. EP children are at high risk for comorbid intellectual and learning disabilities. Education professionals should be aware of the complex nature of EP children's difficulties and the need for multi-domain assessments to guide intervention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  13. Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion

    OpenAIRE

    Le Cadre , Hélène; Aravena , Ignacio; Papavasiliou , Anthony

    2015-01-01

    International audience; We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate ...

  14. Solar PV power forecasting using extreme machine learning and experts advice fusion

    OpenAIRE

    Le Cadre, Hélène; Aravena Solís, Ignacio Andrés; Papavasiliou, Anthony; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

    2015-01-01

    We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the aggregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d’Azur, to evaluate the algorithm performance...

  15. Prune belly syndrome with pouch colon with scaphoid megalourethra

    African Journals Online (AJOL)

    We here report a rare association of megalourethra with pouch colon with prune belly syndrome. We also provide a newer embryological and prognostic perspective to this association. Keywords: megalourethra, prune belly syndrome pouch colon, scaphoid ...

  16. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms

    International Nuclear Information System (INIS)

    Liu, Hui; Tian, Hong-qi; Li, Yan-fei

    2015-01-01

    Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions

  17. Composability-Centered Convolutional Neural Network Pruning

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Xipeng [North Carolina State University; Guan, Hui [North Carolina State University; Lim, Seung-Hwan [ORNL; Patton, Robert M. [ORNL

    2018-02-01

    This work studies the composability of the building blocks ofstructural CNN models (e.g., GoogleLeNet and Residual Networks) in thecontext of network pruning. We empirically validate that a networkcomposed of pre-trained building blocks (e.g. residual blocks andInception modules) not only gives a better initial setting fortraining, but also allows the training process to converge at asignificantly higher accuracy in much less time. Based on thatinsight, we propose a {\\em composability-centered} design for CNNnetwork pruning. Experiments show that this new scheme shortens theconfiguration process in CNN network pruning by up to 186.8X forResNet-50 and up to 30.2X for Inception-V3, and meanwhile, the modelsit finds that meet the accuracy requirement are significantly morecompact than those found by default schemes.

  18. Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

    Directory of Open Access Journals (Sweden)

    Dazi Li

    2015-01-01

    Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

  19. Pruning Boltzmann networks and hidden Markov models

    DEFF Research Database (Denmark)

    Pedersen, Morten With; Stork, D.

    1996-01-01

    We present sensitivity-based pruning algorithms for general Boltzmann networks. Central to our methods is the efficient calculation of a second-order approximation to the true weight saliencies in a cross-entropy error. Building upon previous work which shows a formal correspondence between linear...... Boltzmann chains and hidden Markov models (HMMs), we argue that our method can be applied to HMMs as well. We illustrate pruning on Boltzmann zippers, which are equivalent to two HMMs with cross-connection links. We verify that our second-order approximation preserves the rank ordering of weight saliencies...

  20. A Case of Prune Belly Syndrome

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2015-06-01

    Full Text Available Prune belly syndrome (PBS is a rare congenital disorder characterized by deficient abdominal wall muscles, urinary tract malformation, and, in males, cryptorchidism. We present a case of PBS in China. The patient was a newborn baby boy who had wrinkled, “prune-like” abdominal skin, bilateral cryptorchidism, and urinary system malformation, complicated with hypoplasia of the lung and branch of the coronary artery–right ventricular fistula. His kidney function was inadequate. The patient subsequently died at age 28 days due to septicemia from a severe urinary tract infection.

  1. A case of prune belly syndrome.

    Science.gov (United States)

    Xu, Wei; Wu, Hui; Wang, Dong-Xuan; Mu, Zhi-Hong

    2015-06-01

    Prune belly syndrome (PBS) is a rare congenital disorder characterized by deficient abdominal wall muscles, urinary tract malformation, and, in males, cryptorchidism. We present a case of PBS in China. The patient was a newborn baby boy who had wrinkled, "prune-like" abdominal skin, bilateral cryptorchidism, and urinary system malformation, complicated with hypoplasia of the lung and branch of the coronary artery-right ventricular fistula. His kidney function was inadequate. The patient subsequently died at age 28 days due to septicemia from a severe urinary tract infection. Copyright © 2013. Published by Elsevier B.V.

  2. 21 CFR 146.187 - Canned prune juice.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 2 2010-04-01 2010-04-01 false Canned prune juice. 146.187 Section 146.187 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR... Beverages § 146.187 Canned prune juice. (a) Canned prune juice is the food prepared from a water extract of...

  3. 7 CFR 993.149 - Receiving of prunes by handlers.

    Science.gov (United States)

    2010-01-01

    ... SERVICE (Marketing Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE DRIED... certifies that the dehydration process of the prunes being certified resulted in prunes eligible to be... conditioning by further drying or dehydration: Provided, That such prunes shall be identified and kept separate...

  4. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. 21 CFR 145.190 - Canned prunes.

    Science.gov (United States)

    2010-04-01

    ... of a mixture of brown sugar and honey, an appropriate statement would be “___ sirup of brown sugar... 21 Food and Drugs 2 2010-04-01 2010-04-01 false Canned prunes. 145.190 Section 145.190 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN...

  6. Enhanced Context Recognition by Sensitivity Pruned Vocabularies

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2004-01-01

    a latent semantic indexing representation and a probabilistic neural network classifier. Pruning the vocabularies to approximately 20% of the original size, we find consistent context recognition enhancement for two mid size data-sets for a range of training set sizes. We also study the applicability...

  7. 7 CFR 993.5 - Prunes.

    Science.gov (United States)

    2010-01-01

    ... the Food Technology Division, College of Agriculture, University of California, for the specialty pack... 7 Agriculture 8 2010-01-01 2010-01-01 false Prunes. 993.5 Section 993.5 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements and...

  8. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  9. Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

    OpenAIRE

    Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

    2015-01-01

    Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

  10. A Deep Learning Prediction Model Based on Extreme-Point Symmetric Mode Decomposition and Cluster Analysis

    OpenAIRE

    Li, Guohui; Zhang, Songling; Yang, Hong

    2017-01-01

    Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extreme-point symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy c-means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed ...

  11. A Comparative Classification of Wheat Grains for Artificial Neural Network and Extreme Learning Machine

    OpenAIRE

    ASLAN, Muhammet Fatih; SABANCI, Kadir; YİĞİT, Enes; KAYABAŞI, Ahmet; TOKTAŞ, Abdurrahim; DUYSAK, Hüseyin

    2018-01-01

    In this study, classification of two types of wheat grainsinto bread and durum was carried out. The species of wheat grains in thisdataset are bread and durum and these species have equal samples in the datasetas 100 instances. Seven features, including width, height, area, perimeter,roundness, width and perimeter/area were extracted from each wheat grains. Classificationwas separately conducted by Artificial Neural Network (ANN) and Extreme Learning Machine (ELM)artificial intelligence techn...

  12. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

    Directory of Open Access Journals (Sweden)

    Jiangshe Zhang

    2017-01-01

    Full Text Available With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  13. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

    Science.gov (United States)

    Zhang, Jiangshe; Ding, Weifu

    2017-01-24

    With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  14. Learning and Memory in Adolescent Moderate, Binge, and Extreme-Binge Drinkers.

    Science.gov (United States)

    Nguyen-Louie, Tam T; Tracas, Ashley; Squeglia, Lindsay M; Matt, Georg E; Eberson-Shumate, Sonja; Tapert, Susan F

    2016-09-01

    Binge drinking has been linked to neurocognitive disadvantages in youth, but it is unclear whether drinking at particularly heavy levels uniquely affects neurocognitive performance. This study prospectively examined (1) whether initiating moderate, binge, or extreme-binge drinking in adolescence differentially influences subsequent learning and memory performances, and (2) whether dosage of alcohol consumption is linearly associated with changes in learning and memory over 6 years of adolescence. Participants, who later transitioned into drinking, were administered verbal learning and memory (VLM) assessments at project intake prior to the onset of substance use (age 12 to 16 years), and at follow-up approximately 6 years later (N = 112). Participants were grouped based on alcohol involvement at follow-up as follows: moderate (≤4 drinks per occasion), binge (5+ drinks per occasion), or extreme-binge (10+ drinks per occasion) drinkers. Despite equivalent performances prior to onset of drinking, extreme-binge drinkers performed worse than moderate drinkers on verbal learning, and cued and free short delayed recall (ps learning (β^ = -0.24), and immediate (β^ = -0.27), short delay free (β^ = -0.28) and cued (β^ = -0.30), and long delay free (β^ = -0.24) and cued (β^ = -0.27) recall (ps < 0.05). Drinking quantity during adolescence appears to adversely affect VLM in a dose-dependent manner. The acquisition of new verbal information may be particularly affected, notably for those who initiated drinking 10+ drinks in an occasion. Although classification of drinkers into categories remains critical in the study of alcohol, it is important to consider that subtle differences may exist within drinking categories. Copyright © 2016 by the Research Society on Alcoholism.

  15. Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

    Science.gov (United States)

    Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

    2017-12-01

    In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

  16. Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

    Science.gov (United States)

    Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

    2018-01-10

    A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

  17. Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

    KAUST Repository

    AlShahrani, Mona

    2015-01-01

    Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

  18. Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

    KAUST Repository

    AlShahrani, Mona

    2015-05-24

    Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

  19. Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm

    International Nuclear Information System (INIS)

    Sánchez-Oro, J.; Duarte, A.; Salcedo-Sanz, S.

    2016-01-01

    Highlights: • The total energy demand in Spain is estimated with a Variable Neighborhood algorithm. • Socio-economic variables are used, and one year ahead prediction horizon is considered. • Improvement of the prediction with an Extreme Learning Machine network is considered. • Experiments are carried out in real data for the case of Spain. - Abstract: Energy demand prediction is an important problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this paper, the performance of a robust hybrid approach, composed of a Variable Neighborhood Search algorithm and a new class of neural network called Extreme Learning Machine, is discussed. The Variable Neighborhood Search algorithm is focused on obtaining the most relevant features among the set of initial ones, by including an exponential prediction model. While previous approaches consider that the number of macroeconomic variables used for prediction is a parameter of the algorithm (i.e., it is fixed a priori), the proposed Variable Neighborhood Search method optimizes both: the number of variables and the best ones. After this first step of feature selection, an Extreme Learning Machine network is applied to obtain the final energy demand prediction. Experiments in a real case of energy demand estimation in Spain show the excellent performance of the proposed approach. In particular, the whole method obtains an estimation of the energy demand with an error lower than 2%, even when considering the crisis years, which are a real challenge.

  20. 7 CFR 993.109 - Modified definition of non-French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Modified definition of non-French prunes. 993.109... definition of non-French prunes. The definition of non-French prunes set forth in § 993.6 is modified to read as follows: Non-French Prunes means prunes commonly known as Imperial, Sugar, Robe de Sargent, Burton...

  1. Vocabulary Pruning for Improved Context Recognition

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2004-01-01

    Language independent `bag-of-words' representations are surprisingly effective for text classification. The representation is high dimensional though, containing many non-consistent words for text categorization. These non-consistent words result in reduced generalization performance of subsequent...... of term relevancy, when pruning the vocabularies. With reduced vocabularies documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. Reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two...

  2. Robust representation and recognition of facial emotions using extreme sparse learning.

    Science.gov (United States)

    Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Nandakumar, Karthik; Li, Jun; Teoh, Eam Khwang

    2015-07-01

    Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

  3. An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

    Directory of Open Access Journals (Sweden)

    Senyue Zhang

    2016-01-01

    Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

  4. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

    Science.gov (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

    2017-12-01

    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  5. Prune Belly Syndrome in Adolescence: A Case Report

    Directory of Open Access Journals (Sweden)

    Prasad Mylarappa

    2015-01-01

    Full Text Available The Prune Belly syndrome also known as Eagle Barret syndrome is a rare disorder. We report a rare case of Prune Belly syndrome in 17 year old boy. Patient presented with complains of absence of both testis in scrotum since birth. On examination patient was found to have lax abdominal wall. Patient was further evaluated and found to have shrunken small right kidney and left hydroureteronephrosis and the diagnosis of Prune Belly Syndrome was made. Prune Belly Syndrome represents a wide spectrum of disease. Each patient must be dealt with on an individual basis. A course of watchful waiting with selective surgical intervention has also been successful.

  6. Community detection in complex networks using deep auto-encoded extreme learning machine

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-06-01

    Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

  7. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2017-01-01

    Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

  8. Virtual screening by a new Clustering-based Weighted Similarity Extreme Learning Machine approach.

    Science.gov (United States)

    Pasupa, Kitsuchart; Kudisthalert, Wasu

    2018-01-01

    Machine learning techniques are becoming popular in virtual screening tasks. One of the powerful machine learning algorithms is Extreme Learning Machine (ELM) which has been applied to many applications and has recently been applied to virtual screening. We propose the Weighted Similarity ELM (WS-ELM) which is based on a single layer feed-forward neural network in a conjunction of 16 different similarity coefficients as activation function in the hidden layer. It is known that the performance of conventional ELM is not robust due to random weight selection in the hidden layer. Thus, we propose a Clustering-based WS-ELM (CWS-ELM) that deterministically assigns weights by utilising clustering algorithms i.e. k-means clustering and support vector clustering. The experiments were conducted on one of the most challenging datasets-Maximum Unbiased Validation Dataset-which contains 17 activity classes carefully selected from PubChem. The proposed algorithms were then compared with other machine learning techniques such as support vector machine, random forest, and similarity searching. The results show that CWS-ELM in conjunction with support vector clustering yields the best performance when utilised together with Sokal/Sneath(1) coefficient. Furthermore, ECFP_6 fingerprint presents the best results in our framework compared to the other types of fingerprints, namely ECFP_4, FCFP_4, and FCFP_6.

  9. Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

    Science.gov (United States)

    Khellal, Atmane; Ma, Hongbin; Fei, Qing

    2018-05-09

    The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

  10. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

    Directory of Open Access Journals (Sweden)

    Zhi-Xin Yang

    2016-05-01

    Full Text Available Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL, the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

  11. Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

    Science.gov (United States)

    Arenas, Ana M.; Sun, Tingxiao

    2018-01-01

    Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. PMID:29401754

  12. Solar photovoltaic power forecasting using optimized modified extreme learning machine technique

    Directory of Open Access Journals (Sweden)

    Manoja Kumar Behera

    2018-06-01

    Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network

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

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

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

  14. Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

    Directory of Open Access Journals (Sweden)

    Ho Chit Siu

    2018-02-01

    Full Text Available Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG, but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

  15. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    International Nuclear Information System (INIS)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L

    2015-01-01

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy

  16. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

    2015-06-15

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

  17. Facilitating mathematics learning for students with upper extremity disabilities using touch-input system.

    Science.gov (United States)

    Choi, Kup-Sze; Chan, Tak-Yin

    2015-03-01

    To investigate the feasibility of using tablet device as user interface for students with upper extremity disabilities to input mathematics efficiently into computer. A touch-input system using tablet device as user interface was proposed to assist these students to write mathematics. User-switchable and context-specific keyboard layouts were designed to streamline the input process. The system could be integrated with conventional computer systems only with minor software setup. A two-week pre-post test study involving five participants was conducted to evaluate the performance of the system and collect user feedback. The mathematics input efficiency of the participants was found to improve during the experiment sessions. In particular, their performance in entering trigonometric expressions by using the touch-input system was significantly better than that by using conventional mathematics editing software with keyboard and mouse. The participants rated the touch-input system positively and were confident that they could operate at ease with more practice. The proposed touch-input system provides a convenient way for the students with hand impairment to write mathematics and has the potential to facilitate their mathematics learning. Implications for Rehabilitation Students with upper extremity disabilities often face barriers to learning mathematics which is largely based on handwriting. Conventional computer user interfaces are inefficient for them to input mathematics into computer. A touch-input system with context-specific and user-switchable keyboard layouts was designed to improve the efficiency of mathematics input. Experimental results and user feedback suggested that the system has the potential to facilitate mathematics learning for the students.

  18. Síndrome de Prune Belly

    Directory of Open Access Journals (Sweden)

    Roni Leonardo Teixeira

    Full Text Available Prune Belly Syndrome is a fetal uropathy of unknown etiology with incidence of 1/35000 to 1/50000 alive been born, characterized by a classical triad: abdominal musculature congenital deficiency, bilateral criptorquidia and urinary tract malformations. The authors present a case of this rare pathology associated with a patent urachus. After complementary exams confirmed urinary tract alterations (bilateral ureterohidronefrosis and vesicoureteral reflux degree 5, besides urinary infection, the surgical approach was vesicostomy to decrease urinary infections and sepsis. Definitve surgery should be accomplished around the 12th month of life. Nowadays, the child is asymptomatic , with follow-up every two months, with return consultation bimonthly.

  19. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

    Science.gov (United States)

    Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

    2017-06-19

    A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

  20. RECONSTRUCTION OF THE URINARY TRACT IN PRUNE BELLY SYNDROME

    OpenAIRE

    竹内, 秀雄; 小松, 洋輔; 友吉, 唯夫; 吉田, 修

    1981-01-01

    Three of five patients with prune belly syndrome which we experienced had constructive surgery of the urinary tract and had good results. In the Japanese literatures, 23 of 56 cases which had been reported up to date died. More proper treatment should be done in prune belly syndrome.

  1. Respiratory function in the prune-belly syndrome.

    Science.gov (United States)

    Crompton, C H; MacLusky, I B; Geary, D F

    1993-01-01

    Respiratory function was evaluated in 11 patients with prune-belly syndrome. Nine had evidence of gas trapping and six of restrictive lung disease. These abnormalities of lung function appear to be secondary to the musculoskeletal disorder associated with prune-belly syndrome rather than parenchymal lung disease. PMID:8503677

  2. Respiratory function in the prune-belly syndrome.

    OpenAIRE

    Crompton, C H; MacLusky, I B; Geary, D F

    1993-01-01

    Respiratory function was evaluated in 11 patients with prune-belly syndrome. Nine had evidence of gas trapping and six of restrictive lung disease. These abnormalities of lung function appear to be secondary to the musculoskeletal disorder associated with prune-belly syndrome rather than parenchymal lung disease.

  3. Prune Belly Syndrome | Hammond | South African Medical Journal

    African Journals Online (AJOL)

    Two cases of prune belly syndrome in Black infants are presented. The prune belly syndrome, or congenital absence of abdominal muscles, is accompanied by hydro-ureter, hydronephrosis, megalocystis and usually undescended testes. Other associated congenital defects occur, of which orthopaedic defects appear to be ...

  4. 7 CFR 52.3182 - Varietal types of dried prunes.

    Science.gov (United States)

    2010-01-01

    ... PROCESSED FOOD PRODUCTS 1 United States Standards for Grades of Dried Prunes Product Description, Varietal Types, Sizes, Grades § 52.3182 Varietal types of dried prunes. (a) Type I. French; or Robe; or a mixture of French and Robe. (b) Type II. Italian. (c) Type III. Imperial; or Sugar; or a mixture of Imperial...

  5. An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

    Science.gov (United States)

    Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

    2015-01-01

    Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age. PMID:26402795

  6. Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

    Directory of Open Access Journals (Sweden)

    Anggraeny Fetty Tri

    2016-01-01

    Full Text Available Extreme Learning Machine (ELM is a method of learning feed forward neural network quickly and has a fairly good accuracy. This method is devoted to a feed forward neural network with one hidden layer where the parameters (i.e. weight and bias are adjusted one time randomly at the beginning of the learning process. In neural network, the input layer is connected to all characteristics/features, and the output layer is connected to all classes of species. This research used three datasets from UCI database, which were Iris, Breast Wisconsin, and Dermatology, with each dataset having several features. Each characteristic/feature of the data has a role in the process of classification levels, starting from the most influencing role to non-influencing at all. Gain ratio was used to extract each feature role on each datasets. Gain ratio is a method to extract feature role in order to develop a decision tree structure. In this study, ELM structure has been modified, where the random weights of the hidden layer were adjusted to the level of each feature role in determining the species class, so as to improve the level of training and testing accuracy. The proposed method has higher classification accuracy rate than basic ELM on all three datasets, which were 99%, 96%, and 82%, respectively.

  7. An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

    Science.gov (United States)

    Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

    2015-01-01

    Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM) is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP) and artificial neural networks (ANNs) models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

  8. A comparative analysis of support vector machines and extreme learning machines.

    Science.gov (United States)

    Liu, Xueyi; Gao, Chuanhou; Li, Ping

    2012-09-01

    The theory of extreme learning machines (ELMs) has recently become increasingly popular. As a new learning algorithm for single-hidden-layer feed-forward neural networks, an ELM offers the advantages of low computational cost, good generalization ability, and ease of implementation. Hence the comparison and model selection between ELMs and other kinds of state-of-the-art machine learning approaches has become significant and has attracted many research efforts. This paper performs a comparative analysis of the basic ELMs and support vector machines (SVMs) from two viewpoints that are different from previous works: one is the Vapnik-Chervonenkis (VC) dimension, and the other is their performance under different training sample sizes. It is shown that the VC dimension of an ELM is equal to the number of hidden nodes of the ELM with probability one. Additionally, their generalization ability and computational complexity are exhibited with changing training sample size. ELMs have weaker generalization ability than SVMs for small sample but can generalize as well as SVMs for large sample. Remarkably, great superiority in computational speed especially for large-scale sample problems is found in ELMs. The results obtained can provide insight into the essential relationship between them, and can also serve as complementary knowledge for their past experimental and theoretical comparisons. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. Iterative Mixture Component Pruning Algorithm for Gaussian Mixture PHD Filter

    Directory of Open Access Journals (Sweden)

    Xiaoxi Yan

    2014-01-01

    Full Text Available As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the posterior probability density of the mixture weights. The entropy distribution of the mixture weights is adopted as the prior distribution of mixture component parameters. The iterative update formulations of the mixture weights are derived by Lagrange multiplier and Lambert W function. Mixture components, whose weights become negative during iterative procedure, are pruned by setting corresponding mixture weights to zeros. In addition, multiple mixture components with similar parameters describing the same PHD peak can be merged into one mixture component in the algorithm. Simulation results show that the proposed iterative mixture component pruning algorithm is superior to the typical pruning algorithm based on thresholds.

  10. Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Xue-cun Yang

    2015-01-01

    Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

  11. Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

    Directory of Open Access Journals (Sweden)

    Zheng Chang

    2015-01-01

    Full Text Available Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements and the extreme learning machine (constructed as the classification artificial neural network of body movements, the paper applies the method to the machine vision of the body movement trajectory. In detail, the paper gives a detailed introduction about the algorithm and realization scheme of the body movement trajectory recognition based on the three-dimensional motion history image and the extreme learning machine. Finally, by comparing with the results of the recognition experiments, it attempts to verify that the method of body movement trajectory recognition technology based on the three-dimensional motion history image and extreme learning machine has a more accurate recognition rate and better robustness.

  12. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

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    Xiguang Li

    2017-01-01

    Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.

  13. Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

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    Guangyong Gao

    2015-01-01

    Full Text Available Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE technique. Firstly, the extreme learning machine (ELM with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED predictor and gradient-adjusted predictor (GAP, the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.

  14. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

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    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  15. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

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    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  16. Extreme learning machine for reduced order modeling of turbulent geophysical flows

    Science.gov (United States)

    San, Omer; Maulik, Romit

    2018-04-01

    We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

  17. Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters

    Science.gov (United States)

    Sargent, Garrett C.; Ratliff, Bradley M.; Asari, Vijayan K.

    2017-08-01

    The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

  18. Prediction of laser cutting heat affected zone by extreme learning machine

    Science.gov (United States)

    Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

    2017-01-01

    Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

  19. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

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    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  20. A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

    Science.gov (United States)

    Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua

    2016-05-01

    Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Learning Disabilities in Extremely Low Birth Weight Children and Neurodevelopmental Profiles at Preschool Age.

    Science.gov (United States)

    Squarza, Chiara; Picciolini, Odoardo; Gardon, Laura; Giannì, Maria L; Murru, Alessandra; Gangi, Silvana; Cortinovis, Ivan; Milani, Silvano; Mosca, Fabio

    2016-01-01

    At school age extremely low birth weight (ELBW) and extremely low gestational age (ELGAN) children are more likely to show Learning Disabilities (LDs) and difficulties in emotional regulation. The aim of this study was to investigate the incidence of LDs at school age and to detect neurodevelopmental indicators of risk for LDs at preschool ages in a cohort of ELBW/ELGAN children with broadly average intelligence. All consecutively newborns 2001-2006 admitted to the same Institution entered the study. Inclusion criteria were BW disabilities, genetic abnormalities, and/or a Developmental Quotient below normal limits (learning disabilities at school age was investigated through a parent-report questionnaire at children's age range 9-10 years. Neurodevelopmental profiles were assessed through the Griffiths Mental Development Scales at 1 and 2 years of corrected age and at 3, 4, 5, and 6 years of chronological age and were analyzed comparing two groups of children: those with LDs and those without. At school age 24 on 102 (23.5%) of our ELBW/ELGAN children met criteria for LDs in one or more areas, with 70.8% comorbidity with emotional/attention difficulties. Children with LDs scored significantly lower in the Griffiths Locomotor and Language subscales at 2 years of corrected age and in the Personal-social, Performance and Practical Reasoning subscales at 5 years of chronological age. Our findings suggest that, among the early developmental indicators of adverse school outcome, there is a poor motor experimentation, language delay, and personal-social immaturity. Cognitive rigidity and poor ability to manage practical situations also affect academic attainment. Timely detection of these early indicators of risk is crucial to assist the transition to school.

  2. Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.

    Directory of Open Access Journals (Sweden)

    Huiling Chen

    Full Text Available The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC curve criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01. According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.

  3. Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

    Science.gov (United States)

    Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

    2018-02-01

    The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

  4. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

  5. [Prune Belly syndrome: epidemiologic, clinic and therapeutic aspects].

    Science.gov (United States)

    Diao, B; Diallo, Y; Fall, P A; Ngom, G; Fall, B; Ndoye, A K; Fall, I; Ba, M; Ndoye, M; Diagne, B A

    2008-07-01

    Prune Belly syndrome (PBS) is a rare complex malformation with male predominance. His pathogeny is not yet completely elucidated. The goal of this work is to analyze the epidemiological, anatomoclinical and treatment aspects of a retrospective trial in Aristide-Le-Dantec Hospital. We carried out a retrospective study about 22 cases collected in the departments of urology-andrology and pediatric surgery in Aristide-Le-Dantec Hospital between April 1995 and November 2004. The mean age of the patients was 15 months with extremes of one day and 10 years. The somatic examination revealed 20 cases of complete abdominal muscle aplasia, one right partial form and the last case had a left partial form. Nineteen patients were managed with conservative treatment and three patients benefited a surgical act for urinary abnormalities. The Montfort intervention was performed in two patients respectively aged eight and 10 years. The orchidopexy, stage 1, by Fowler-Stephens technique was performed in 13 cases. Five cases of death and nine cases of testicular atrophy after orchidopexy occurred. The followings were satisfactory in the three operated patients for urinary abnormalities. The renal failure is the main cause of death. The management of the urinary tract abnormalities must be performed individually. The testis descending should be performed in newborn period to enhance the fertility chances. The abdominoplasty also should be done early for aesthetic reason and to improve pulmonary, defecation, and voiding functions.

  6. Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Iman Mazinani

    2016-04-01

    Full Text Available This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP and Artificial Neural Networks (ANNs models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.

  7. Online sparse Gaussian process based human motion intent learning for an electrically actuated lower extremity exoskeleton.

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Chen, Chao-Feng; Dong, Wei; Wang, Wei-Dong

    2017-07-01

    The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems. GP regression is restrictive for large data sets due to its computation complexity. In this paper, an online sparse GP algorithm is constructed to learn the HMI. The original training dataset is collected when the user wears the exoskeleton system with friction compensation to perform unconstrained movement as far as possible. The dataset has two kinds of data, i.e., (1) physical HRI, which is collected by torque sensors placed at the interaction cuffs for the active joints, i.e., knee joints; (2) joint angular position, which is measured by optical position sensors. To reduce the computation complexity of GP, grey relational analysis (GRA) is utilized to specify the original dataset and provide the final training dataset. Those hyper-parameters are optimized offline by maximizing marginal likelihood and will be applied into online GP regression algorithm. The HMI, i.e., angular position of human joints, will be regarded as the reference trajectory for the mechanical legs. To verify the effectiveness of the proposed algorithm, experiments are performed on a subject at a natural speed. The experimental results show the HMI can be obtained in real time, which can be extended and employed in the similar exoskeleton systems.

  8. Prune Belly syndrome: A rare case report.

    Science.gov (United States)

    Samal, Sunil Kumar; Rathod, Setu

    2015-01-01

    Prune Belly syndrome (PBS) is a rare congenital anomaly of uncertain etiology almost exclusive to males. We report a case of term male baby born to a 39-year-old grand multipara with previous four normal vaginal births. There was no history of genetic or congenital anomaly in her family. Examination of the baby revealed hypotonia, deficient abdominal muscle, cryptorchidism, palpable kidney, and bladder. Ultrasound examination of the abdomen revealed bilateral gross hydronephrosis and megaureter. Provisional diagnosis of PBS was made and the baby was admitted in neonatal intensive care units for further management. Routine antenatal care with ultrasonography will help in detecting renal anomalies, which can be followed postnatally. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  9. Rare association of prune belly syndrome with pouch colon

    Directory of Open Access Journals (Sweden)

    M Ragavan

    2011-01-01

    Full Text Available M Ragavan1, U Haripriya1, PV Pradeep1, J Sarvavinothini21Department of Endocrine Surgery, 2Department of Anaesthesia, Narayana Medical College and Superspeciality Hospital, Nellore, Andhra Pradesh, IndiaAbstract: Prune belly syndrome is a triad characterized by abdominal wall musculature deficiency, cryptorchidism and urinary tract abnormalities, and is often associated with other anomalies. Although associated anorectal anomalies have been reported with this syndrome, only two cases of pouch colon, a rare type of anorectal malformation, have been reported. We report a case of prune belly syndrome with pouch colon presenting with retention of urine.Keywords: prune belly, triad syndrome, pouch colon, anorectal malformation

  10. Upper Extremity Motor Learning among Individuals with Parkinson's Disease: A Meta-Analysis Evaluating Movement Time in Simple Tasks

    Directory of Open Access Journals (Sweden)

    K. Felix

    2012-01-01

    Full Text Available Motor learning has been found to occur in the rehabilitation of individuals with Parkinson's disease (PD. Through repetitive structured practice of motor tasks, individuals show improved performance, confirming that motor learning has probably taken place. Although a number of studies have been completed evaluating motor learning in people with PD, the sample sizes were small and the improvements were variable. The purpose of this meta-analysis was to determine the ability of people with PD to learn motor tasks. Studies which measured movement time in upper extremity reaching tasks and met the inclusion criteria were included in the analysis. Results of the meta-analysis indicated that people with PD and neurologically healthy controls both demonstrated motor learning, characterized by a decrease in movement time during upper extremity movements. Movement time improvements were greater in the control group than in individuals with PD. These results support the findings that the practice of upper extremity reaching tasks is beneficial in reducing movement time in persons with PD and has important implications for rehabilitation.

  11. Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E-Nose Systems

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    Zhiyuan Ma

    2018-01-01

    Full Text Available Machine learning approaches have been widely used to tackle the problem of sensor array drift in E-Nose systems. However, labeled data are rare in practice, which makes supervised learning methods hard to be applied. Meanwhile, current solutions require updating the analytical model in an offline manner, which hampers their uses for online scenarios. In this paper, we extended Target Domain Adaptation Extreme Learning Machine (DAELM_T to achieve high accuracy with less labeled samples by proposing a Weighted Domain Transfer Extreme Learning Machine, which uses clustering information as prior knowledge to help select proper labeled samples and calculate sensitive matrix for weighted learning. Furthermore, we converted DAELM_T and the proposed method into their online learning versions under which scenario the labeled data are selected beforehand. Experimental results show that, for batch learning version, the proposed method uses around 20% less labeled samples while achieving approximately equivalent or better accuracy. As for the online versions, the methods maintain almost the same accuracies as their offline counterparts do, but the time cost remains around a constant value while that of offline versions grows with the number of samples.

  12. Prune belly syndrome in an adult Nigerian: case report.

    Science.gov (United States)

    Salako, A A; Takure, A O; Olajide, A O; Aarowolo, O A; Egberongbe, A A

    2009-12-01

    Prune Belly Syndrome is a rare congenital anomaly characterized by deficient anterior abdominal wall musculature, bilateral cryptorchidism, bilateral megaureters and often unilateral or bilateral vesico-ureteric junction obstruction. The report of prune belly syndrome in the adult is scanty. We report a case of prune belly syndrome in a 24 year old Nigerian who presented with 3 year history of recurrent right loin pain. Examination showed wrinkled abdominal skin, bilateral undescended testes and an hypoplastic rectus abdominis, below the umbilicus. Further evaluation revealed enlarged bladder, bilateral megaureters and right intra-abdominal testis. A diagnosis of Prune Belly Syndrome was made. The challenges in the diagnosis and management of this rare condition are highlighted in this presentation.

  13. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  14. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

    Directory of Open Access Journals (Sweden)

    Yanjiao Li

    2017-08-01

    Full Text Available Gas utilization ratio (GUR is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs. In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF, depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

  15. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.

    Science.gov (United States)

    Li, Yanjiao; Zhang, Sen; Yin, Yixin; Xiao, Wendong; Zhang, Jie

    2017-08-10

    Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

  16. Digital Learning Network Education Events of NASA's Extreme Environments Mission Operations

    Science.gov (United States)

    Paul, Heather; Guillory, Erika

    2007-01-01

    NASA's Digital Learning Network (DLN) reaches out to thousands of students each year through video conferencing and web casting. The DLN has created a series of live education videoconferences connecting NASA s Extreme Environment Missions Operations (NEEMO) team to students across the United States. The programs are also extended to students around the world live web casting. The primary focus of the events is the vision for space exploration. During the programs, NEEMO Crewmembers including NASA astronauts, engineers and scientists inform and inspire students about the importance of exploration and share the impact of the project as it correlates with plans to return to the moon and explore the planet Mars. These events highlight interactivity. Students talk live with the aquanauts in Aquarius, the National Oceanic and Atmospheric Administration s underwater laboratory. With this program, NASA continues the Agency s tradition of investing in the nation's education programs. It is directly tied to the Agency's major education goal of attracting and retaining students in science, technology, and engineering disciplines. Before connecting with the aquanauts, the students conduct experiments of their own designed to coincide with mission objectives. This paper describes the events that took place in September 2006.

  17. A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

    Science.gov (United States)

    Lu, Siyuan; Qiu, Xin; Shi, Jianping; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

    2017-01-01

    It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  18. Combining extreme learning machines using support vector machines for breast tissue classification.

    Science.gov (United States)

    Daliri, Mohammad Reza

    2015-01-01

    In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. The feature extraction phase derives the features from the electrical impedance spectra. The extracted features consist of the impedivity at zero frequency (I0), the phase angle at 500 KHz, the high-frequency slope of phase angle, the impedance distance between spectral ends, the area under spectrum, the normalised area, the maximum of the spectrum, the distance between impedivity at I0 and the real part of the maximum frequency point and the length of the spectral curve. The system uses the information theoretic criterion as a strategy for feature selection and the combining extreme learning machines (ELMs) for the classification phase. The results of several ELMs are combined using the support vector machines classifier, and the result of classification is reported as a measure of the performance of the system. The results indicate that the proposed system achieves high accuracy in classification of breast tissues using the electrical impedance spectroscopy.

  19. Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2015-01-01

    Full Text Available With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier.

  20. Extreme learning machine based optimal embedding location finder for image steganography.

    Directory of Open Access Journals (Sweden)

    Hayfaa Abdulzahra Atee

    Full Text Available In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM index, fusion matrices, and mean square error (MSE. The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

  1. An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Abdu Gumaei

    2018-01-01

    Full Text Available Multispectral palmprint recognition system (MPRS is an essential technology for effective human identification and verification tasks. To improve the accuracy and performance of MPRS, a novel approach based on autoencoder (AE and regularized extreme learning machine (RELM is proposed in this paper. The proposed approach is intended to make the recognition faster by reducing the number of palmprint features without degrading the accuracy of classifier. To achieve this objective, first, the region of interest (ROI from palmprint images is extracted by David Zhang’s method. Second, an efficient normalized Gist (NGist descriptor is used for palmprint feature extraction. Then, the dimensionality of extracted features is reduced using optimized AE. Finally, the reduced features are fed to the RELM for classification. A comprehensive set of experiments are conducted on the benchmark MS-PolyU dataset. The results were significantly high compared to the state-of-the-art approaches, and the robustness and efficiency of the proposed approach are revealed.

  2. Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Nibaldo Rodriguez

    2017-10-01

    Full Text Available The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE, aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE and decimated wavelet packet singular entropy (DWPSE. The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster.

  3. Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

    Science.gov (United States)

    Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

    2018-04-01

    The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

  4. Color Face Recognition Based on Steerable Pyramid Transform and Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Ayşegül Uçar

    2014-01-01

    Full Text Available This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT to the local face regions. In this paper, the new three hybrid color spaces, YSCr, ZnSCr, and BnSCr, are firstly constructed using the Cb and Cr component images of the YCbCr color space, the S color component of the HSV color spaces, and the Zn and Bn color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.

  5. Volatility forecasting for interbank offered rate using grey extreme learning machine: The case of China

    International Nuclear Information System (INIS)

    Liu, Xiaoyong; Fu, Hui

    2016-01-01

    Interbank Offered rate is the only direct market rate in China’s currency market. Volatility forecasting of China Interbank Offered Rate (IBOR) has a very important theoretical and practical significance for financial asset pricing and financial risk measure or management. However, IBOR is a dynamics and non-steady time series whose developmental changes have stronger random fluctuation, so it is difficult to forecast the volatility of IBOR. This paper offers a hybrid algorithm using grey model and extreme learning machine (ELM) to forecast volatility of IBOR. The proposed algorithm is composed of three phases. In the first, grey model is used to deal with the original IBOR time series by accumulated generating operation (AGO) and weaken the stochastic volatility in original series. And then, a forecasting model is founded by using ELM to analyze the new IBOR series. Lastly, the predictive value of the original IBOR series can be obtained by inverse accumulated generating operation (IAGO). The new model is applied to forecasting Interbank Offered Rate of China. Compared with the forecasting results of BP and classical ELM, the new model is more efficient to forecasting short- and middle-term volatility of IBOR.

  6. Assessing the suitability of extreme learning machines (ELM for groundwater level prediction

    Directory of Open Access Journals (Sweden)

    Yadav Basant

    2017-03-01

    Full Text Available Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM and support vector machine (SVM to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

  7. Tensor manifold-based extreme learning machine for 2.5-D face recognition

    Science.gov (United States)

    Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

    2018-01-01

    We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

  8. The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM

    Directory of Open Access Journals (Sweden)

    Josphat Igadwa Mwasiagi

    2017-03-01

    Full Text Available The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya.The collected samples were tested under standard testing conditions. Cotton lint parameters, machine parameters and yarn elongation were used to design yarn elongation prediction models. The elongation prediction models used three network training algorithms, including backpropagation (BP, an extreme learning machine (ELM, and a hybrid of differential evolution (DE and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DEELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm.

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

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

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

  10. An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

    Science.gov (United States)

    Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

    2018-04-01

    Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic

  11. The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

    KAUST Repository

    Prasetyo Utomo, Chandra

    2011-06-01

    Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.

  12. Modified abdominoplasty for patients with the Prune Belly syndrome.

    Science.gov (United States)

    Dénes, Francisco Tibor; Lopes, Roberto Iglesias; Oliveira, Lorena Marçalo; Tavares, Alessandro; Srougi, Miguel

    2014-02-01

    To present the results of a new technique for abdominoplasty in patients with the Prune Belly syndrome (PBS). Since 1985, 46 children with PBS underwent surgical treatment that included urinary tract reconstruction (UTR), orchidopexy, and abdominoplasty. In 41 patients, we performed the abdominoplasty as follows: (1) fusiform longitudinal resection of the mid-abdominal skin and subcutaneous tissue, with preservation of the musculo-aponeurotic fascia (MAF) and umbilicus, (2) ellipsoid unilateral longitudinal incision of the MAF in the most weakened side of the abdomen, producing 2 flaps, with the umbilicus being kept intact in the widest flap, (3) after UTR and bilateral orchiopexy, suture fixation of the widest MAF layer to the inner side of the contralateral abdominal wall, creating an inner MAF layer, (4) lateral suture fixation of the other flap over the inner layer, creating an outer MAF layer with a buttonhole exposing the umbilicus, that is sutured to the outer layer, and (5) approximation of the skin edges with incorporation of the umbilicus in the suture. Skin coaptation was excellent in all patients, and no trimming was necessary in incision extremities. There was no dehiscence or skin necrosis and all patients presented immediate improvement of the abdominal tonus and appearance. Further improvement with growth was observed in all except 4 patients, 2 requiring secondary abdominoplasties. We conclude that this technique is applicable in all forms of weakened abdomen typical of PBS, even in asymmetrical cases, requiring only 1 MAF incision, with good cosmetic and functional results. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    Science.gov (United States)

    Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

    2013-06-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  14. A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

    International Nuclear Information System (INIS)

    Aziz, Nur Liyana Afiqah Abdul; Yap, Keem Siah; Bunyamin, Muhammad Afif

    2013-01-01

    This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of c omputing the word . The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

  15. A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM

    OpenAIRE

    KAYA, Yılmaz

    2014-01-01

    Breast cancer is one of the leading causes of death among women all around the world. Therefore, true and early diagnosis of breast cancer is an important problem. The rough set (RS) and extreme learning machine (ELM) methods were used collectively in this study for the diagnosis of breast cancer. The unnecessary attributes were discarded from the dataset by means of the RS approach. The classification process by means of ELM was performed using the remaining attributes. The Wisconsin B...

  16. Energy potential of fruit tree pruned biomass in Croatia

    Energy Technology Data Exchange (ETDEWEB)

    Bilandzija, N.; Voca, N.; Kricka, T.; Martin, A.; Jurisic, V.

    2012-11-01

    The world's most developed countries and the European Union (EU) deem that the renewable energy sources should partly substitute fossil fuels and become a bridge to the utilization of other energy sources of the future. This paper will present the possibility of using pruned biomass from fruit cultivars. It will also present the calculation of potential energy from the mentioned raw materials in order to determine the extent of replacement of non-renewable sources with these types of renewable energy. One of the results of the intensive fruit-growing process, in post pruning stage, is large amount of pruned biomass waste. Based on the calculated biomass (kg ha{sup 1}) from intensively grown woody fruit crops that are most grown in Croatia (apple, pear, apricots, peach and nectarine, sweet cherry, sour cherry, prune, walnut, hazelnut, almond, fig, grapevine, and olive) and the analysis of combustible (carbon 45.55-49.28%, hydrogen 5.91-6.83%, and sulphur 0.18-0.21%) and non-combustible matters (oxygen 43.34-46.6%, nitrogen 0.54-1.05%, moisture 3.65-8.83%, ashes 1.52-5.39%) with impact of lowering the biomass heating value (15.602-17.727 MJ kg{sup 1}), the energy potential of the pruned fruit biomass is calculated at 4.21 PJ. (Author) 31 refs.

  17. DANNP: an efficient artificial neural network pruning tool

    KAUST Repository

    AlShahrani, Mona; Soufan, Othman; Magana-Mora, Arturo; Bajic, Vladimir B.

    2017-01-01

    Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly available at www.cbrc.kaust.edu.sa/dannp) is the only available and on-line accessible tool that provides multiple parallelized ANN pruning options. Datasets and DANNP code can be obtained at www.cbrc.kaust.edu.sa/dannp/data.php and https://doi.org/10.5281/zenodo.1001086.

  18. [Prune-Belly Syndrome: a case report].

    Science.gov (United States)

    Tattoli, Fabio; De Prisco, Ornella; Gherzi, Maurizio; Falconi, Daniela; Marazzi, Federico; Marengo, Marita; Serra, Ilaria; Tamagnone, Michela; Formica, Marco

    2014-01-01

    Prune-Belly Syndrome (PBS) is a rare congenital syndrome characterized by the absence of abdominal muscles, anomalies in the urinary tract, megaureter, cryptorchidism or testicular agenesis, hypertension and worsening chronic kidney disease (CKD). The incidence is estimated between 1 out of 35,000 and 1 out of 50,000 born alive, and it affects males in prevalence (97%). In the present study we describe the case of a 38 year old male patient (followed since May 2011) affected by PBS, CKD, one functional kidney at the scintigraphy, pediatric testicular implants, bladder surgery and correction of pectus excavatum. At the beginning of the observation, renal function was deteriorated, with a creatinine 3.3 mg/dl, GFR calculated at MDRD 23 ml/min, proteinuria in nephrotic range (4 g/day), high blood pressure, anemia and hyperparathyroidism. In the following examinations renal function framework worsened, despite the adoption of a low-protein diet. Due to the functional trend, the patient was prescribed hemodialysis as substitute treatment. In January 2013 a first attempt of artero-venous fistula (AVF) did not succeed, while a new AVF in March 2013 resulted effective. In July hemodialysis was started. In the future, we expect to insert the patient in the Kidney Transplant List (since surgical feasibility has already been positively evaluated). Our case is quite peculiar due to the late beginning of substitute treatment. Further, SPB represents a challenge that, in the absence of a prompt and effective treatment, inevitably it leads to terminal uremia; nevertheless, given a proper treatment, a transplant with good chances of success can be envisaged.

  19. Effect of Time and Level of Pruning on Vegetative Growth, Flowering, Yield, and Quality of Guava

    DEFF Research Database (Denmark)

    Adhikari, Shiva; Kandel, Tanka Prasad

    2015-01-01

    Poor quality fruit production in the rainy season and failure to manipulate production periods are common problems for guava production in India and Nepal. As a possible management to overcome these problems, a field experiment was conducted to understand the effect of time and level of pruning...... (%) of fruits increased with the increased level of pruning in both seasons irrespective of timing of pruning, but fruit acidity was not affected by both treatments. In conclusion, pruning plants at a 20 cm pruning level in early May was the most effective management to reduce yield in the rainy season...... on growth, flowering, yield, and quality of guava. An experiment was laid out with split-pot design allocating three pruning times (mid-April, early May, and mid-May) and four pruning levels (0-, 10-, 20-, and 30-cm tip removal) with three replications in each treatment. Increased level of pruning in early...

  20. Tales from the Paleoclimate Underground: Lessons Learned from Reconstructing Extreme Events

    Science.gov (United States)

    Frappier, A. E.

    2017-12-01

    Tracing patterns of paleoclimate extremes over the past two millennia is becoming ever more important in the effort to understand and predict costly weather hazards and their varied societal impacts. I present three paleoclimate vignettes from the past ten years of different paleotempestology projects I have worked on closely, illustrating our collective challenges and productive pathways in reconstructing rainfall extremes: temporal, spatial, and combining information from disparate proxies. Finally, I aim to share new results from modeling multiple extremes and hazards in Yucatan, a climate change hotspot.

  1. Learning from today's extreme weather events to increase our resilience to climate change

    Science.gov (United States)

    Ruin, I.; Lutoff, C.; Borga, M.; Creutin, J.-D.; Anquetin, S.; Gruntfest, E.; Scolobig, A.

    2009-04-01

    According to the IPCC, flooding is the most widespread serious potential impact of climate change on human settlement. Vulnerability to floods can be thought as a function of exposure and adaptive capacity, and all three entities have been increasing in many areas. Therefore, in order to inform decision-makers, it is crucial to better understand what are the vulnerability factors but also to what extend individuals and societies are capable to adapt their way of life to their changing environment. In this perspective, flash flood events offer a good example of the kind of extremes that our societies may have to face more often in the future. Characterized by their suddenness, fast and violent movement, rarity and small scale, they are particularly difficult to forecast accurately and leave very little lead-time for warnings. In this context, our interdisciplinary team conducts research focusing on individual and human organization responses to warning and crisis situations by using a comprehensive, coupled natural—human system approach over time and space scales. The objective is to understand i) what cognitive and situational factors help individuals and communities to shift from normal daily activities to adapted crisis response and ii) what is the dynamic of this process compared to the one of the natural phenomenon. In this regard, our research learned both from individual perception and behavioral intent survey ("what if" type of survey) than from actual behavioral data gathered in a context of post-event investigations. The review of the literature shows that behavioral intent surveys do not accurately predict warning and crisis response as well as behavioral data do. Knowing that, the difficulty is to obtain consistent and accurate spatio-temporal behavioral data. According to our experience, this is particularly difficult in the context of crisis situations. Behavioral verification requires real-time observations and data collection of indicators

  2. Grafting, pruning, and the antipodal map on measured laminations

    OpenAIRE

    Dumas, David

    2006-01-01

    Grafting a measured lamination on a hyperbolic surface defines a self-map of Teichmuller space, which is a homeomorphism by a result of Scannell and Wolf. In this paper we study the large-scale behavior of pruning, which is the inverse of grafting. Specifically, for each conformal structure $X \\in \\T(S)$, pruning $X$ gives a map $\\ML(S) \\to \\T(S)$. We show that this map extends to the Thurston compactification of $\\T(S)$, and that its boundary values are the natural antipodal involution relat...

  3. Partial prune belly syndrome: A rare case report

    Directory of Open Access Journals (Sweden)

    Aditya Pratap Singh

    2017-01-01

    Full Text Available Prune belly syndrome (PBS is characterized by deficient development of abdominal muscles that causes the skin of the abdomen to wrinkle like a prune, bilateral cryptorchidism, abnormalities of the urinary tract. The etiology of PBS is unclear and possible familial genetic inheritance was reported in some of the studies. We are presenting here a case with the absence of the muscle in the right side of the abdomen as hernia, thinning of the muscle on left side with bilateral cryptorchidism, and abnormalities of the urinary tract. It is the partial presentation of the PBS.

  4. Influence of coffee pruning on the severity of frost damage

    OpenAIRE

    Androcioli Filho,Armando; Caramori,Paulo Henrique

    2000-01-01

    Frost damages in a field experiment of pruning types and systems for the cultivars of Coffea arabica Catuaí and Mundo Novo, were evaluated at Londrina (23º22’S, 52º10´W), State of Parana, southern Brazil, during the winter of 1990 and 1994. Pruning types evaluated were ‘esqueletamento’ (cutting off all plagiotropic branches at 20-30 cm from the orthotropic branch), ‘decote’ (cutting off the orthotropic branch at 1.5 m and 2.0 m above ground) and ‘recepa’ (cutting off the orthotropic branch at...

  5. Long-term follow-up of total abdominal wall reconstruction for prune belly syndrome.

    Science.gov (United States)

    Lesavoy, Malcolm A; Chang, Eric I; Suliman, Ahmed; Taylor, Jason; Taylor, James; Kim, Sara E; Ehrlich, Richard M

    2012-01-01

    Prune belly syndrome is a rare, congenital condition that consists of a major deficiency or hypoplasia of the abdominal wall musculature, bilateral cryptorchidism, and genitourinary tract malformations. Reconstruction of the abdominal wall in these patients has presented a challenge to plastic surgeons throughout the years. The authors previously described a technique for total abdominal wall reconstruction that permitted simultaneous urinary tract reconstruction and bilateral orchiopexy. This innovative procedure used medial advancement of the fascia in a "double-breasted" fashion with preservation of the umbilicus. The authors reviewed their experience with this particular technique in one of the largest series of patients in the literature and the series with the longest follow-up. Twenty patients underwent total abdominal wall reconstruction with simultaneous urinary tract reconstruction and orchiopexy with a mean follow-up of 20.4 years. There were no major complications noted during this period, and all patients were extremely satisfied with their postoperative result. Total abdominal wall reconstruction using the double-breasted technique in patients with prune belly syndrome is a safe and durable procedure that achieves excellent cosmetic results. Therapeutic, IV.

  6. Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization – Extreme learning machine approach

    International Nuclear Information System (INIS)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Prieto, L.; Blanco-Aguilera, A.; García-Herrera, R.

    2014-01-01

    Highlights: • A novel approach for short-term wind speed prediction is presented. • The system is formed by a coral reefs optimization algorithm and an extreme learning machine. • Feature selection is carried out with the CRO to improve the ELM performance. • The method is tested in real wind farm data in USA, for the period 2007–2008. - Abstract: This paper presents a novel approach for short-term wind speed prediction based on a Coral Reefs Optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks’ training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO–ELM approach in this FSP wind speed prediction problem

  7. Should Pruning be a Pre-Processor of any Linear System?

    Science.gov (United States)

    Sen, Syamal K.; Ramakrishnan, Suja; Agarwal, Ravi P.; Shaykhian, Gholam Ali

    2011-01-01

    measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The . error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning.

  8. A Large-Scale Multi-Hop Localization Algorithm Based on Regularized Extreme Learning for Wireless Networks.

    Science.gov (United States)

    Zheng, Wei; Yan, Xiaoyong; Zhao, Wei; Qian, Chengshan

    2017-12-20

    A novel large-scale multi-hop localization algorithm based on regularized extreme learning is proposed in this paper. The large-scale multi-hop localization problem is formulated as a learning problem. Unlike other similar localization algorithms, the proposed algorithm overcomes the shortcoming of the traditional algorithms which are only applicable to an isotropic network, therefore has a strong adaptability to the complex deployment environment. The proposed algorithm is composed of three stages: data acquisition, modeling and location estimation. In data acquisition stage, the training information between nodes of the given network is collected. In modeling stage, the model among the hop-counts and the physical distances between nodes is constructed using regularized extreme learning. In location estimation stage, each node finds its specific location in a distributed manner. Theoretical analysis and several experiments show that the proposed algorithm can adapt to the different topological environments with low computational cost. Furthermore, high accuracy can be achieved by this method without setting complex parameters.

  9. An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

    International Nuclear Information System (INIS)

    Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo

    2017-01-01

    Highlights: • A secondary decomposition approach is applied in the data pre-processing. • The empirical mode decomposition is used to decompose the original time series. • IMF1 continues to be decomposed by applying wavelet packet decomposition. • Crisscross optimization algorithm is applied to train extreme learning machine. • The proposed SHD-CSO-ELM outperforms other pervious methods in the literature. - Abstract: Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

  10. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

    Science.gov (United States)

    Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

    2018-01-01

    Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  11. Familial prune belly syndrome in a Nigerian family.

    Science.gov (United States)

    Ibadin, Michael Okoeguale; Ademola, Ade Adeyekun; Ofovwe, Gabriel Egberue

    2012-03-01

    A case of Prune Belly Syndrome in an infant, the second in a middle class family with both parents in their late thirties, is presented because of its rarity. Constraints in the management are discussed and relevant literature reviewed. This is intended to awaken interest and sharpen indices of suspicion that would facilitate early diagnosis, enhance management, and mitigate prejudices.

  12. Prune belly syndrome: A report of 15 cases from Sudan.

    Science.gov (United States)

    Kheir, Abdelmoneim E M; Ali, Eltigani M A; Medani, Safaa A; Maaty, Huda S

    2017-01-01

    Prune belly syndrome is a rare congenital malformation of unknown aetiology, composed of a triad of deficient abdominal wall muscle, cryptorchidism and urinary tract anomalies. The majority of patients have associated pulmonary, skeletal, cardiac, and gastrointestinal defects. This was a prospective, case finding study that was conducted in the main paediatric hospitals in Khartoum state, during the period December 2015 to September 2016. A total of 15 patients with prune belly syndrome were collected. Patients' characteristics were noted including socio-demographic data, laboratory and radiological investigations and any medical or surgical intervention. There were 12 males and 3 females with a male to female ratio of 4:1. Most of the patients (80%) had hydronephrosis and hydroureter. The study revealed that 60% of the patients had associated anomalies, there were 4 (26.6%) with cardiac defects, 3 (20%) with orthopaedic defects one patient with small bowel volvulus and one patient with cleft lip. 6 (40%) patients received medical intervention and 8 (53%) patients underwent surgical procedures. At the last follow up visit, 2 (13.4%) patients had normal renal function tests, 8 (53.3%) ended with chronic kidney disease, and 5 died with a mortality rate of 33.3%. Prune belly syndrome is a rare entity with wide variability in severity and clinical manifestations. The mortality in prune belly syndrome remains high despite medical and surgical interventions.

  13. Prune Belly Syndrome: A case Report | Ezeaka | Nigerian Quarterly ...

    African Journals Online (AJOL)

    The Prune Belly Syndrome (PBS) is a anomaly. It comprises of a lax abdominal wall musculature, urinary tract anomalies, and cryptorchidism. Our patients had urinary tract infection and renal failure. These are well described consequences of the syndrome and are poor prognostic indices. This case report was undertaken ...

  14. Prune belly syndrome: Early management outcome of nine ...

    African Journals Online (AJOL)

    Background: Prune belly syndrome (PBS) is a rare congenital malformation of unclear etiology. The disease progress and outcome in developing countries are not clear as most reports are isolated case reports. Materials and Methods: A review of 9 patients managed for PBS in 5 years. Results: There were 7 males and 2 ...

  15. On the use of a pruning prior for neural networks

    DEFF Research Database (Denmark)

    Goutte, Cyril

    1996-01-01

    We address the problem of using a regularization prior that prunes unnecessary weights in a neural network architecture. This prior provides a convenient alternative to traditional weight-decay. Two examples are studied to support this method and illustrate its use. First we use the sunspots...

  16. 7 CFR 52.3188 - Work sheet for dried prunes.

    Science.gov (United States)

    2010-01-01

    ... for dried prunes. Size and kind of container Container mark or identification Label or brand Varietal... fermentation, scars, heat damage, insect injury, other means, mold, dirt, foreign material, insect infestation... fermentation, scars, heat damage, insect injury, other means, mold, dirt, foreign material, insect infestation...

  17. 7 CFR 457.133 - Prune crop insurance provisions.

    Science.gov (United States)

    2010-01-01

    ... roadside stand, farmer's market, and permitting the general public to enter the field for the purpose of... (except where otherwise provided in the Special Provisions); (d) That are grown in an orchard that, if... not been controlled or pruning debris has not been removed from the orchard; (3) Wildlife; (4...

  18. Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Becerra-Alonso, David

    2012-01-01

    Full Text Available The level of default in financial institutions is a key piece of information in the activity of these organizations and reveals their level of risk. This in turn explains the growing attention given to variables of this kind, during the crisis of these last years. This paper presents a method to estimate the default rate using the non-linear model defined by standard Multilayer Perceptron (MLP neural networks trained with a novel methodology called Extreme Learning Machine (ELM. The experimental results are promising, and show a good performance when comparing the MLP model trained with the Leverberg-Marquard algorithm. || La morosidad en las entidades financieras es un dato muy importante de la actividad de estas instituciones pues permite conocer el nivel de riesgo asumido por éstas. Esto a su vez explica la creciente atención otorgada a esta variable, especialmente en los últimos años de crisis. Este artículo presenta un método para estimar el nivel de la tasa de morosidad por medio de un modelo no lineal definido por la red neuronal Multilayer Perceptron (MLP entrenada con una nueva metodología llamada Extreme Learning Machine (ELM. Los resultados experimentales son prometedores, mostrando un buen resultado si se compara con el modelo MLP entrenado con el algoritmo de Leverberg-Marquard.

  19. TREE CANOPY PRUNING DOES NOT REGULATE BIENNIAL BEARING IN ”ELSTAR” APPLE (Malus domestica Borkh.

    Directory of Open Access Journals (Sweden)

    Nikola Pavičić

    2004-12-01

    Full Text Available Four alternative pruning strategies (A– 25 generative buds, B– 50 generative buds, C– 75 generative buds and D–100 generative buds per tree for Elstar apple cultivar and their possible impact on improvement in productivity were examined in 1999 and 2000. Year was significant factor for all traits, except yield. The pruning strategy is significant for number of fruits per flower cluster and fruit mass. Interaction year and pruning strategy is significant only for number of fruits per flower cluster. Fruit mass was larger for pruning strategy A compared to the pruning strategies C and D. Yield efficiency and biennial bearing index were not affected by pruning strategies. The biennial bearing index variance was the lowest for the pruning strategy B. Trunk cross sectional area (TCSA had negative impact on fruit mass in pruning strategy C. Correlation between the flower and crop density was positive in pruning strategy A. Flower density was in positive correlation with yield in pruning strategy C. The research shows that tree pruning alone will not result in adequate yield regulation in ‘Elstar’ apple.

  20. Optimal calibration of variable biofuel blend dual-injection engines using sparse Bayesian extreme learning machine and metaheuristic optimization

    International Nuclear Information System (INIS)

    Wong, Ka In; Wong, Pak Kin

    2017-01-01

    Highlights: • A new calibration method is proposed for dual-injection engines under biofuel blends. • Sparse Bayesian extreme learning machine and flower pollination algorithm are employed in the proposed method. • An SI engine is retrofitted for operating under dual-injection strategy. • The proposed method is verified experimentally under the two idle speed conditions. • Comparison with other machine learning methods and optimization algorithms is conducted. - Abstract: Although many combinations of biofuel blends are available in the market, it is more beneficial to vary the ratio of biofuel blends at different engine operating conditions for optimal engine performance. Dual-injection engines have the potential to implement such function. However, while optimal engine calibration is critical for achieving high performance, the use of two injection systems, together with other modern engine technologies, leads the calibration of the dual-injection engines to a very complicated task. Traditional trial-and-error-based calibration approach can no longer be adopted as it would be time-, fuel- and labor-consuming. Therefore, a new and fast calibration method based on sparse Bayesian extreme learning machine (SBELM) and metaheuristic optimization is proposed to optimize the dual-injection engines operating with biofuels. A dual-injection spark-ignition engine fueled with ethanol and gasoline is employed for demonstration purpose. The engine response for various parameters is firstly acquired, and an engine model is then constructed using SBELM. With the engine model, the optimal engine settings are determined based on recently proposed metaheuristic optimization methods. Experimental results validate the optimal settings obtained with the proposed methodology, indicating that the use of machine learning and metaheuristic optimization for dual-injection engine calibration is effective and promising.

  1. Learned Helplessness: A Model to Understand and Overcome a Child's Extreme Reaction to Failure.

    Science.gov (United States)

    Balk, David

    1983-01-01

    The author reviews literature on childrens' reactions to perceived failure and offers "learned helplessness" as a model to explain why a child who makes a mistake gives up. Suggestions for preventing these reactions are given. (Author/JMK)

  2. The prune belly syndrome in a female foetus with urorectal septum malformation sequence: a case report on a rare entity with an unusual association.

    Science.gov (United States)

    Goswami, Dibyajyoti; Kusre, Giriraj; Dutta, Hemonta Kumar; Sarma, Adity

    2013-08-01

    The prune belly syndrome is a rare congenital anomaly which is characterized by the triad of an absent or a deficient development of the abdominal muscle, bilateral cryptorchidism and an anomalous urinary tract. In its full form, this condition occurs only in males. However, a similar condition occurs in females in the absence of cryptorchidism. On the other hand, the urorectal septum malformation sequence is a lethal congenital malformation which is characterized by the development of a phallus like structure, a smooth perineum and the absence of urethral, vaginal and anal openings. We are reporting a case of a female foetus with the prune belly syndrome, which was associated with a urorectal septum malformation sequence. A dead foetus with a protruded abdomen and ambiguous genitalia, was born at 32 weeks of pregnancy. On autopsy, it was found to have female internal genital organs. The left kidney, the urinary bladder and the rectum were absent. The sigmoid colon, the ureters and the fallopian tubes opened into a common cloacal sac. The histopathological examination of the ovary showed the presence of Leydig's cells. The occurrence of the female counterpart of the prune belly syndrome is extremely rare and only few of such cases were found to be discussed in the details in the indexed English literature so far. Hence, we hope that this case report will contribute to the existing knowledge on the prune belly syndrome.

  3. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  4. Forecasting of flowrate under rolling motion flow instability condition based on on-line sequential extreme learning machine

    International Nuclear Information System (INIS)

    Chen Hanying; Gao Puzhen; Tan Sichao; Tang Jiguo; Hou Xiaofan; Xu Huiqiang; Wu Xiangcheng

    2015-01-01

    The coupling of multiple thermal-hydraulic parameters can result in complex flow instability in natural circulation system under rolling motion. A real-time thermal-hydraulic condition prediction is helpful to the operation of systems in such condition. A single hidden layer feedforward neural networks algorithm named extreme learning machine (ELM) is considered as suitable method for this application because of its extremely fast training time, good accuracy and simplicity. However, traditional ELM assumes that all the training data are ready before the training process, while the training data is received sequentially in practical forecasting of flowrate. Therefore, this paper proposes a forecasting method for flowrate under rolling motion based on on-line sequential ELM (OS-ELM), which can learn the data one by one or chunk-by-chunk. The experiment results show that the OS-ELM method can achieve a better forecasting performance than basic ELM method and still keep the advantage of fast training and simplicity. (author)

  5. Improving Simulations of Extreme Flows by Coupling a Physically-based Hydrologic Model with a Machine Learning Model

    Science.gov (United States)

    Mohammed, K.; Islam, A. S.; Khan, M. J. U.; Das, M. K.

    2017-12-01

    With the large number of hydrologic models presently available along with the global weather and geographic datasets, streamflows of almost any river in the world can be easily modeled. And if a reasonable amount of observed data from that river is available, then simulations of high accuracy can sometimes be performed after calibrating the model parameters against those observed data through inverse modeling. Although such calibrated models can succeed in simulating the general trend or mean of the observed flows very well, more often than not they fail to adequately simulate the extreme flows. This causes difficulty in tasks such as generating reliable projections of future changes in extreme flows due to climate change, which is obviously an important task due to floods and droughts being closely connected to people's lives and livelihoods. We propose an approach where the outputs of a physically-based hydrologic model are used as an input to a machine learning model to try and better simulate the extreme flows. To demonstrate this offline-coupling approach, the Soil and Water Assessment Tool (SWAT) was selected as the physically-based hydrologic model, the Artificial Neural Network (ANN) as the machine learning model and the Ganges-Brahmaputra-Meghna (GBM) river system as the study area. The GBM river system, located in South Asia, is the third largest in the world in terms of freshwater generated and forms the largest delta in the world. The flows of the GBM rivers were simulated separately in order to test the performance of this proposed approach in accurately simulating the extreme flows generated by different basins that vary in size, climate, hydrology and anthropogenic intervention on stream networks. Results show that by post-processing the simulated flows of the SWAT models with ANN models, simulations of extreme flows can be significantly improved. The mean absolute errors in simulating annual maximum/minimum daily flows were minimized from 4967

  6. Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics

    International Nuclear Information System (INIS)

    Chen, Zhicong; Wu, Lijun; Cheng, Shuying; Lin, Peijie; Wu, Yue; Lin, Wencheng

    2017-01-01

    Highlights: •An improved Simulink based modeling method is proposed for PV modules and arrays. •Key points of I-V curves and PV model parameters are used as the feature variables. •Kernel extreme learning machine (KELM) is explored for PV arrays fault diagnosis. •The parameters of KELM algorithm are optimized by the Nelder-Mead simplex method. •The optimized KELM fault diagnosis model achieves high accuracy and reliability. -- Abstract: Fault diagnosis of photovoltaic (PV) arrays is important for improving the reliability, efficiency and safety of PV power stations, because the PV arrays usually operate in harsh outdoor environment and tend to suffer various faults. Due to the nonlinear output characteristics and varying operating environment of PV arrays, many machine learning based fault diagnosis methods have been proposed. However, there still exist some issues: fault diagnosis performance is still limited due to insufficient monitored information; fault diagnosis models are not efficient to be trained and updated; labeled fault data samples are hard to obtain by field experiments. To address these issues, this paper makes contribution in the following three aspects: (1) based on the key points and model parameters extracted from monitored I-V characteristic curves and environment condition, an effective and efficient feature vector of seven dimensions is proposed as the input of the fault diagnosis model; (2) the emerging kernel based extreme learning machine (KELM), which features extremely fast learning speed and good generalization performance, is utilized to automatically establish the fault diagnosis model. Moreover, the Nelder-Mead Simplex (NMS) optimization method is employed to optimize the KELM parameters which affect the classification performance; (3) an improved accurate Simulink based PV modeling approach is proposed for a laboratory PV array to facilitate the fault simulation and data sample acquisition. Intensive fault experiments are

  7. Unusual presentation of prune belly syndrome: a case report.

    Science.gov (United States)

    Demisse, Abayneh Girma; Berhanu, Ashenafi; Tadesse, Temesgen

    2017-12-04

    Prune belly syndrome is a rare congenital malformation of unknown etiology, with the following triad of findings: abdominal muscle wall weakness, undescended testes, and urinary tract abnormalities. In most cases, detection of prune belly syndrome occurs during neonatal or infancy period. In this case report, we describe a 12-year-old boy from Ethiopia with the triad of findings of prune belly syndrome along with skeletal malformations. We are unaware of any previous report of prune belly syndrome in Ethiopia. A 12-year-old Amhara boy from the Northwest Gondar Amhara regional state presented to our referral hospital with a complaint of swelling over his left flank for the past 3 months. Maternal pregnancy course and medical history were noncontributory, and he had an attended birth at a health center. He has seven siblings, none of whom had similar symptoms. On examination he had a distended abdomen, asymmetric with bulging left flank, visible horizontal line, upward umbilical slit, and absent rectus abdominis muscles. His abdomen was soft with a tender cystic, bimanually palpable mass on the left flank measuring 13 × 11 cm. Both testes were undescended and he also has developmental dysplasia of the hips. An abdominal ultrasound revealed a large cystic mass in his left kidney area with echo debris and a hip X-ray showed bilateral developmental dysplasia of the hip. Intraoperative findings were cystic left kidney, both testes were intraperitoneal, tortuous left renal vein, enlarged bladder reaching above umbilicus, and left megaureter. bilateral orchidectomy and left nephrectomy were done. He was given intravenously administered antibiotics for treatment of pyelonephritis and discharged home with an appointment for follow up and possible abdominoplasty. In the current report delayed presentation contributed to testicular atrophy and decision for orchidectomy. Furthermore, he will be at potential risk for sex hormone abnormality. Therefore, diagnosis of prune

  8. Influence of coffee pruning on the severity of frost damage

    Directory of Open Access Journals (Sweden)

    Armando Androcioli Filho

    2000-01-01

    Full Text Available Frost damages in a field experiment of pruning types and systems for the cultivars of Coffea arabica Catuaí and Mundo Novo, were evaluated at Londrina (23º22’S, 52º10´W, State of Parana, southern Brazil, during the winter of 1990 and 1994. Pruning types evaluated were ‘esqueletamento’ (cutting off all plagiotropic branches at 20-30 cm from the orthotropic branch, ‘decote’ (cutting off the orthotropic branch at 1.5 m and 2.0 m above ground and ‘recepa’ (cutting off the orthotropic branch at 0.8 m above ground, performed on all rows and on alternate rows, and on different sections of the plant. Results indicated that frost damage could increase according to the type and height of pruning. The pruning type ‘esqueletamento’ and prunings at higher levels were more suitable for regions with frost risk. Under severe frost condition, pruning type did not affect the damage in anyone of the treatments evaluated.Foram avaliados os danos causados pelas geadas ocorridas em 1990 e 1994 em cafeeiros de duas cultivares de Coffea arabica L., Catuaí e Mundo Novo, conduzidos em Londrina-PR. Os tipos e sistemas de podas aplicados foram o esqueletamento a 20-30 cm do tronco, decote a 1,5 m e 2,0 m de altura e recepa a 0,80 m de altura. As podas foram feitas em área total e em linhas alternadas e em diferentes partes da planta. Os dados obtidos indicaram que os danos por geada podem ser intensificados em função do tipo e altura da poda. A poda do tipo esqueletamento e as podas altas são mais indicadas para o manejo das lavouras nas regiões mais sujeitas ao fenômeno de geada. No caso de geada severa, todos os cafeeiros foram afetados, independente do tipo de poda.

  9. Extremity War Injuries XII: Homeland Defense as a Translation of War Lessons Learned.

    Science.gov (United States)

    Stinner, Maj Daniel J; Schmidt, Andrew H

    2018-06-12

    The 12th Extremity War Injuries Symposium focused on issues related to the transitions in medical care that are occurring as the focus of the war on terror changes. The symposium highlighted the results of Department of Defense-funded research in musculoskeletal injury, the evolution of combat casualty care, and the readiness of the fighting force. Presentations and discussions focused on force readiness of both troops and their medical support as well as the maintenance of the combat care expertise that has been developed during the previous decade of conflict.

  10. Creating a Learning Organization for State, Local, and Tribal Law Enforcement to Combat Violent Extremism

    Science.gov (United States)

    2016-09-01

    investigate blind spots and signals of unexpected events. 2. Combine scenario thinking and explorations of organizational purpose. 3. Develop...communication, collaboration, strategic thinking , learning organizations, law enforcement partners 15. NUMBER OF PAGES 103 16. PRICE CODE 17... THINKING ............................................................................30 E. SCENARIO PLANNING

  11. Oral manifestations associated with systemic complications of prune belly syndrome.

    Science.gov (United States)

    Pessoa, Larissa; Galvão, Virgilio

    2013-01-01

    Prune belly syndrome (PBS) is a rare congenital malformation of unknown etiology characterized by congenital abnormalities including abdominal wall flaccidity, urinary tract alterations, and bilateral cryptorchidism. The incidence of the syndrome is between 1/35000 and 1/50000 live births and there is little information about the oral findings. The present case describes the oral manifestations of a 15-year-old boy diagnosed with PBS. The findings include enamel hypoplasia associated with generalized hypocalcemic dental lines. In the radiographic exam, pronounced demineralization of the trabecular bone of the jaws, loss of lamina dura in all the teeth, and discontinuity of the mandibular cortical bone were observed. Prune belly syndrome is a rare disease, whose clinical dental aspects are not pathognomonic of the syndrome. The comprehension of the systemic mechanism of PBS and its comorbidities enable an understanding of the systemic etiologic factors associated with oral manifestations. Copyright © 2013 Elsevier Inc. All rights reserved.

  12. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  13. Behavioral Repertoire Influences the Rate and Nature of Learning in Climbing: Implications for Individualized Learning Design in Preparation for Extreme Sports Participation

    Directory of Open Access Journals (Sweden)

    Dominic Orth

    2018-06-01

    Full Text Available Extreme climbing where participants perform while knowing that a simple mistake could result in death requires a skill set normally acquired in non-extreme environments. In the ecological dynamics approach to perception and action, skill acquisition involves a process where the existing repertoire of behavioral capabilities (or coordination repertoire of a learner are destabilized and re-organized through practice—this process can expand the individuals affordance boundaries allowing the individual to explore new environments. Change in coordination repertoire has been observed in bi-manual coordination and postural regulation tasks, where individuals begin practice using one mode of coordination before transitioning to another, more effective, coordination mode during practice. However, individuals may also improve through practice without qualitatively reorganizing movement system components—they do not find a new mode of coordination. To explain these individual differences during learning (i.e., whether or not a new action is discovered, a key candidate is the existing coordination repertoire present prior to practice. In this study, the learning dynamics of body configuration patterns organized with respect to an indoor climbing surface were observed and the existing repertoire of coordination evaluated prior to and after practice. Specifically, performance outcomes and movement patterns of eight beginners were observed across 42 trials of practice over a 7-week period. A pre- and post-test scanning procedure was used to determine existing patterns of movement coordination and the emergence of new movement patterns after the practice period. Data suggested the presence of different learning dynamics by examining trial-to-trial performance in terms of jerk (an indicator of climbing fluency, at the individual level of analysis. The different learning dynamics (identified qualitatively included: continuous improvement, sudden improvement

  14. Dynamic abdominoplasty for the treatment of prune belly syndrome.

    Science.gov (United States)

    Fearon, Jeffrey A; Varkarakis, George

    2012-09-01

    The deficient abdominal wall musculature associated with prune belly syndrome often results in numerous functional disabilities, including diminished cough, impaired bladder and bowel function, and poor posture and balance. Traditional abdominoplasties focus on static fascial excisions or plications. The authors sought to assess their preliminary experience with a new abdominoplasty technique that incorporates standard fascial tightening with bilateral pedicled rectus femoris muscle transfers. This case series review included all patients treated with prune belly syndrome at the authors' center. Physical presentation, operative procedures, hospitalization, complications, and postoperative functional status were assessed, and a systematic analysis of published surgical series was performed. Over a 16-year period, the authors treated 13 patients with prune belly syndrome. All underwent standard "vest over pants" fascial plications, with 11 of 13 undergoing additional rectus femoris muscle transpositions at a mean age of 4 years (range, 12 months to 13 years). Hospitalization averaged 9.3 days, and the average follow-up was over 1.5 years. The authors identified three minor complications (chylous leak, fungal urinary tract infection, and partial umbilical necrosis), yielding a complication rate similar to those identified in our systematic analysis of published standard abdominoplasties. Postoperatively, all transposed muscles were palpably functional, one patient was successfully weaned off a ventilator, and all demonstrated improvements with balance and ambulation. The authors' preliminary review suggests that this new procedure, which supplements the standard prune belly abdominoplasty with bilateral rectus femoris transposition flaps, is not associated with substantially higher complication rates yet does appear to have the potential to provide functional improvements.

  15. Familial prune belly syndrome in a Nigerian family

    Directory of Open Access Journals (Sweden)

    Michael Okoeguale Ibadin

    2012-01-01

    Full Text Available A case of Prune Belly Syndrome in an infant, the second in a middle class family with both parents in their late thirties, is presented because of its rarity. Constraints in the manage-ment are discussed and relevant literature reviewed. This is intended to awaken interest and sharpen indices of suspicion that would facilitate early diagnosis, enhance management, and mitigate prejudices.

  16. Stock Picking via Nonsymmetrically Pruned Binary Decision Trees

    OpenAIRE

    Anton Andriyashin

    2008-01-01

    Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity tradeoff and used in the majority of studies employing binary decision trees, this paper introduces a no...

  17. Radiation processing of fruits: application to strawberries and prunes

    International Nuclear Information System (INIS)

    Levillain, M.

    1986-10-01

    Extending the shelf-life of fresh fruit by means of low-dose irradiation (radurization) is not a new idea: experiments in that field started in the early sixties. These experiments have actually proved that, in some cases, irradiation can achieve shelf-life extension, either through a delay in ripening (bananas), or through rot inhibition (soft cherries, apricots, tomatoes, strawberries). Alas, they have also highlighted the intolerance showed by a number of fruits when radurized: irradiation is apt to have them ripen more rapidly (peaches, nectarines) or to soften them too much (pears, table grapes, oranges, apples, plums, grapefruit, melons, honeydew melons). Even in those cases where irradiation results in a benefit, this benefit varies depending on the variety of fruit involved, as can be seen from a deep survey of the irradiation of strawberries. Preservation of dehydrated fruit is a different matter. Prunes, for instance, would be contamined by molds, wasn't it for the addition of sorbic acid during the fabrication process. Ionization of prunes can allow producers to avoid the use of a chemical, and to keep the prunes at a greater degree of humidity [fr

  18. Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

    Science.gov (United States)

    Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

    2018-06-01

    Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

  19. Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks.

    Science.gov (United States)

    Zhang, Yang; Liu, Yun; Chao, Han-Chieh; Zhang, Zhenjiang; Zhang, Zhiyuan

    2018-03-30

    In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data.

  20. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM in advanced metering infrastructure of smart grid.

    Directory of Open Access Journals (Sweden)

    Yuancheng Li

    Full Text Available Advanced Metering Infrastructure (AMI realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  1. Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

    Science.gov (United States)

    Li, Yang; Li, Guoqing; Wang, Zhenhao

    2015-01-01

    In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

  2. Effect of Root Pruning and Irrigation Regimes on Yield and Physiology of Pear Trees

    DEFF Research Database (Denmark)

    Wang, Yufei

    Clara Frijs’ is the dominant pear (Pyrus communis L.) cultivar in Denmark. It is vigorous with long annual shoots, and therefore can be difficult to prune. Root pruning has been widely used to control the canopy size of fruit trees including pears. However, root pruned trees are more likely......, it was concluded that root pruning not only decreases water uptake but also nutrient uptake, and both have contributed to the reduced canopy growth. Supplemental irrigation partially improved the tree water status and nitrogen uptake without stimulating additional shoot growth in the root pruned trees....... A combination of root pruning and irrigation could be a promising practice to control tree size and secure a stable fruit yield in pear orchard....

  3. Genetic basis of prune belly syndrome: screening for HNF1β gene.

    Science.gov (United States)

    Granberg, Candace F; Harrison, Steven M; Dajusta, Daniel; Zhang, Shaohua; Hajarnis, Sachin; Igarashi, Peter; Baker, Linda A

    2012-01-01

    Although the cause of prune belly syndrome is unknown, familial evidence suggests a genetic component. Recently 2 nonfamilial cases of prune belly syndrome with chromosome 17q12 deletions encompassing the HNF1β gene have made this a candidate gene for prune belly syndrome. To date, there has been no large-scale screening of patients with prune belly syndrome for HNF1β mutations. We assessed the role of HNF1β in prune belly syndrome by screening for genomic mutations with functional characterization of any detected mutations. We studied patients with prune belly syndrome who were prospectively enrolled in our Pediatric Genitourinary DNA Repository since 2001. DNA from patient samples was amplified by polymerase chain reaction, sequenced for coding and splice regions of the HNF1β gene, and compared to control databases. We performed functional assay testing of the ability of mutant HNF1β to activate a luciferase construct with an HNF1β DNA binding site. From 32 prune belly syndrome probands (30 males, 2 females) HNF1β sequencing detected a missense mutation (V61G) in 1 child with prune belly syndrome. Absent in control databases, V61G was previously reported in 2 patients without prune belly syndrome who had congenital genitourinary anomalies. Functional testing showed similar luciferase activity compared to wild-type HNF1β, suggesting the V61G substitution does not disturb HNF1β function. One genomic HNF1β mutation was detected in 3% of patients with prune belly syndrome but found to be functionally normal. Thus, functionally significant HNF1β mutations are uncommon in prune belly syndrome, despite case reports of HNF1β deletions. Further genetic study is necessary, as identification of the genetic basis of prune belly syndrome may ultimately lead to prevention and improved treatments for this rare but severe syndrome. Copyright © 2012 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

  4. Multipl Konjenital Anomalilerin Eşlik Ettiği Bir Prune-Belly Sendromu Olgusu

    OpenAIRE

    YILDIZ, L.; AYDIN, O.; BORAN, Ç.; KANDEMİR, B.; ALPER, T.

    2010-01-01

    A Case of Prune-Belly Syndrome Associated with Multiple Congenital Anomalies Abdominal muscle deficiency, urinary tract abnormalities and cryptoorchidism are the three major features of the Prune-belly syndrome. Massive acites and intraabdominal urine accumulation had produced abdominal wall atrophy. A functional or anatomic urethral obstruction may detect on cases. As an addition classic triad of Prune-belly syndrome our case has polydactily, cleft lip and palate. Chromosomal analysis cou...

  5. Capture, learning, and classification of upper extremity movement primitives in healthy controls and stroke patients.

    Science.gov (United States)

    Guerra, Jorge; Uddin, Jasim; Nilsen, Dawn; Mclnerney, James; Fadoo, Ammarah; Omofuma, Isirame B; Hughes, Shatif; Agrawal, Sunil; Allen, Peter; Schambra, Heidi M

    2017-07-01

    There currently exist no practical tools to identify functional movements in the upper extremities (UEs). This absence has limited the precise therapeutic dosing of patients recovering from stroke. In this proof-of-principle study, we aimed to develop an accurate approach for classifying UE functional movement primitives, which comprise functional movements. Data were generated from inertial measurement units (IMUs) placed on upper body segments of older healthy individuals and chronic stroke patients. Subjects performed activities commonly trained during rehabilitation after stroke. Data processing involved the use of a sliding window to obtain statistical descriptors, and resulting features were processed by a Hidden Markov Model (HMM). The likelihoods of the states, resulting from the HMM, were segmented by a second sliding window and their averages were calculated. The final predictions were mapped to human functional movement primitives using a Logistic Regression algorithm. Algorithm performance was assessed with a leave-one-out analysis, which determined its sensitivity, specificity, and positive and negative predictive values for all classified primitives. In healthy control and stroke participants, our approach identified functional movement primitives embedded in training activities with, on average, 80% precision. This approach may support functional movement dosing in stroke rehabilitation.

  6. Source-Sink Relations in Fruits VII. Effects of Pruning in Sour Cherry and Plum

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo; Hansen, P.

    1993-01-01

    Sour cherries cv. 'Stevnsbær' and plums cv. 'Victoria' were heavily pruned in 1987. Fruit samples were collected during the growing season and concentrations of different quality components were determined. Pruning resulted in a small increase in fruit size, the effect being greater on the older...... trees (sour cherries) or at the lower crop load (plum). Additionally, pruning decreased the con­centrations of total and soluble dry matter and of anthocya­nins ('Stevnsbær'), while titratable acids showed an increas­ing tendency. The effects of pruning are discussed based on influences on root...

  7. PHENOLOGICAL CHARACTERISTICS OF GENOTYPES FROM CATTLEY GUAVA AND GUAVA TREES SUBMITTED TO FRUCTIFICATION PRUNING

    Directory of Open Access Journals (Sweden)

    CINTIA APARECIDA BREMENKAMP

    Full Text Available ABSTRACT Psidium cattleianum Sabine is a species from the Myrtaceae family that serves as an option for the native fruits cultivation, besides being considered a source of resistance to the Meloidogyne enterolobii nematode. Although cattley guava trees from this species produce flower buds in young branches, there are no reports of response to fructification pruning or phenological synchronism with the guava tree. The objective of this paper was the comparative evaluation of the genotype response of strawberry guava trees and guava cultivars to fructification pruning, thus, describing the phenology of both species under the same cultivation conditions. The experiment was conducted under an entirely randomized outline, in 7x2 factorial scheme, being evaluated seven genotypes (three from strawberry guava and four from guava trees, and with pruning performed in two seasons (May 2012 and March 2013, with three repetitions. Fructification pruning was executed by a lopping on all mature branches, from the last growth flow in the woody branch region. Were evaluated budding characteristics and fruit harvesting, as well as number of days from pruning to the observation of the phenological event. Cattley guava tree pruning stimulated fructification of all three genotypes after pruning done on May and two genotypes after the March’s pruning. There has been a sync between the guava cultivars’ flowering and both strawberry guava trees genotypes, when those were pruned on May.

  8. Spinal motor neuron involvement in a patient with homozygous PRUNE mutation.

    Science.gov (United States)

    Iacomino, Michele; Fiorillo, Chiara; Torella, Annalaura; Severino, Mariasavina; Broda, Paolo; Romano, Catia; Falsaperla, Raffaele; Pozzolini, Giulia; Minetti, Carlo; Striano, Pasquale; Nigro, Vincenzo; Zara, Federico

    2018-05-01

    In the last few years, whole exome sequencing (WES) allowed the identification of PRUNE mutations in patients featuring a complex neurological phenotype characterized by severe neurodevelopmental delay, microcephaly, epilepsy, optic atrophy, and brain or cerebellar atrophy. We describe an additional patient with homozygous PRUNE mutation who presented with spinal muscular atrophy phenotype, in addition to the already known brain developmental disorder. This novel feature expands the clinical consequences of PRUNE mutations and allow to converge PRUNE syndrome with previous descriptions of neurodevelopmental/neurodegenerative disorders linked to altered microtubule dynamics. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

  9. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

  10. An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

    Science.gov (United States)

    Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

    2017-12-07

    Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

  11. A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

    Science.gov (United States)

    Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

    2012-09-01

    The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Science.gov (United States)

    Luo, Guangchun; Qin, Ke; Wang, Nan; Niu, Weina

    2018-01-01

    Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy. PMID:29494543

  13. Online Sensor Drift Compensation for E-Nose Systems Using Domain Adaptation and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiyuan Ma

    2018-03-01

    Full Text Available Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

  14. Lower extremity compartment syndrome in the acute care surgery paradigm: safety lessons learned

    Directory of Open Access Journals (Sweden)

    Cothren Clay C

    2009-06-01

    Full Text Available Abstract Background Prompt diagnosis and decompression of acute lower extremity compartment syndrome (LECS in the multisystem injured patient is essential to avoid the devastating complications of progressive tissue necrosis and amputation. Despite collaborative trauma and orthopedic management of these difficult cases, significant delays in diagnosis and treatment occur. Periodic system review of our trauma and orthopedic data for complications of LECS led us to hypothesize that delayed diagnosis and limb loss were potentially preventable events in our trauma center. Setting Academic level 1 trauma center. Methods We performed a prospective review of our trauma registry for all cases of LECS over a 7 year period (2/98–10/2005. Variables reviewed included demographics, injury patterns, tissue necrosis, amputation and mortality. Results Eighty-three (10 female, 73 male cases were reviewed. Mean age = 33.3 years (range 1–78. Mean ISS = 19.4, GCS = 12.5. Five (6.0% had amputations; 7 (8.4% died. Fractures occurred in 68.7% (n = 57, and vascular injuries were present in 38.6% (n = 32. In 7 patients (8.4%, a delayed compartment release resulted in muscle necrosis requiring multiple debridements, subsequent wound closure problems, and long term disability. Of note, none of these patients had prior compartment pressure measurements. Furthermore, 6 patients (7% had superficial peroneal nerve transections as complications of their fasciotomy. Conclusion In the multisystem injured patient, LECS remains a major diagnostic and treatment challenge with significant risks of limb loss as well as complications from decompressive fasciotomy. These data underscore the importance of routine surveillance for LECS. In addition, a thorough knowledge of regional anatomy is essential to avoid technical morbidity.

  15. A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-05-01

    Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

  16. Effect of Shoot Pruning and Flower Thinning on Quality and Quantity of Semi-Determinate Tomato (Lycopersicon esculentum Mill.

    Directory of Open Access Journals (Sweden)

    Abdolali HESAMI

    2012-02-01

    Full Text Available There are many constraints of space, light and availability of fruits to harvest in tomatoes greenhouse. Therefore, two experiments were carried out to determine the effect of shoot pruning and flower thinning on quality and quantity of fruits of semi-determinate tomato in a greenhouse of the Faculty of Agriculture and Natural Resources, Persian Gulf University of Bushehr. Experimental design was randomized complete block designs in which the effect of shoot pruning (single branch pruning, double branch pruning, pyramidal pruning and control or flower thinning (Cluster with 4 and 5 remained flowers and control were studied separately. Results showed that, leaf area and plants yield were higher in treatments which were pruned than control. Yields from pyramidal pruning and cluster thinning with 5 remaining flowers were significantly higher than other treatments. On the other hand, qualitative study identified that pyramidal pruning increases vitamin C in fruits, but had no significant effect on total soluble solids.

  17. Hepatoblastoma and prune belly syndrome: a potential association.

    Science.gov (United States)

    Becknell, Brian; Pais, Priya; Onimoe, Grace; Rangarajan, Hemalatha; Schwaderer, Andrew L; McHugh, Kirk; Ranalli, Mark A; Hains, David S

    2011-08-01

    Prune belly syndrome (PBS) is a congenital anomaly characterized by the clinical triad of lax abdominal musculature, bilateral cryptorchidism, and abnormalities of the kidney and urinary tract. Previous reports of malignancy in patients with PBS have been limited to germ cell tumors. Hepatoblastoma (HBL) is the most common hepatic malignancy of childhood, affecting approximately 100 children each year in the USA. We describe a set of 4 pediatric patients with PBS and HBL. All individuals were born after 2002. These subjects lacked genetic, natal, or environmental factors known to confer risk of HBL. The occurrence of PBS and HBL in these patients constitutes a novel potential association.

  18. Pregnancy outcome in a woman with prune belly syndrome.

    Science.gov (United States)

    Hillman, R Tyler; Garabedian, Matthew James; Wallerstein, Robert J

    2012-11-30

    Prune belly syndrome is a rare congenital syndrome that primarily affects male fetuses. Affected men are universally infertile; however, there is a paucity of information published on the reproductive potential of affected women. Pregnancy outcomes in affected women have not been described in the literature. We describe the case of pregnancy in an affected woman. Her pregnancy progressed without complication. Her fetus had no stigmata of the syndrome. Her labour and delivery were, however, complicated by a prolonged second stage of labour and need for vacuum-assisted vaginal delivery.

  19. Prune belly syndrome with pouch colon and absent dermatome.

    Science.gov (United States)

    Baba, Aejaz A; Hussain, Syed A; Shera, Altaf H; Patnaik, Rekha

    2010-01-01

    Prune belly syndrome (PBS) is a rare congenital constellation of defects in pediatric surgical practice. Although anorectal anomalies have been reported in association with PBS, only few case of pouch colon with PBS has been reported. [1] In addition, our patient had deficient abdominal wall with absent dermatome in left upper quadrant, which has never been reported in the English literature. This association with abdominal wall deficiency and absent dermatome not only strengthens the theory of mesodermal arrest in the etiology of PBS but also points towards a defect in the ectodermal development.

  20. Prune belly syndrome with pouch colon and absent dermatome

    Directory of Open Access Journals (Sweden)

    Baba Aejaz

    2010-01-01

    Full Text Available Prune belly syndrome (PBS is a rare congenital constellation of defects in pediatric surgical practice. Although anorectal anomalies have been reported in association with PBS, only few case of pouch colon with PBS has been reported. [1] In addition, our patient had deficient abdominal wall with absent dermatome in left upper quadrant, which has never been reported in the English literature. This association with abdominal wall deficiency and absent dermatome not only strengthens the theory of mesodermal arrest in the etiology of PBS but also points towards a defect in the ectodermal development.

  1. Using Wearable Sensors and Machine Learning Models to Separate Functional Upper Extremity Use From Walking-Associated Arm Movements.

    Science.gov (United States)

    McLeod, Adam; Bochniewicz, Elaine M; Lum, Peter S; Holley, Rahsaan J; Emmer, Geoff; Dromerick, Alexander W

    2016-02-01

    To improve measurement of upper extremity (UE) use in the community by evaluating the feasibility of using body-worn sensor data and machine learning models to distinguish productive prehensile and bimanual UE activity use from extraneous movements associated with walking. Comparison of machine learning classification models with criterion standard of manually scored videos of performance in UE prosthesis users. Rehabilitation hospital training apartment. Convenience sample of UE prosthesis users (n=5) and controls (n=13) similar in age and hand dominance (N=18). Participants were filmed executing a series of functional activities; a trained observer annotated each frame to indicate either UE movement directed at functional activity or walking. Synchronized data from an inertial sensor attached to the dominant wrist were similarly classified as indicating either a functional use or walking. These data were used to train 3 classification models to predict the functional versus walking state given the associated sensor information. Models were trained over 4 trials: on UE amputees and controls and both within subject and across subject. Model performance was also examined with and without preprocessing (centering) in the across-subject trials. Percent correct classification. With the exception of the amputee/across-subject trial, at least 1 model classified >95% of test data correctly for all trial types. The top performer in the amputee/across-subject trial classified 85% of test examples correctly. We have demonstrated that computationally lightweight classification models can use inertial data collected from wrist-worn sensors to reliably distinguish prosthetic UE movements during functional use from walking-associated movement. This approach has promise in objectively measuring real-world UE use of prosthetic limbs and may be helpful in clinical trials and in measuring response to treatment of other UE pathologies. Copyright © 2016 American Congress of

  2. It's not All Doom and Gloom: Prune Belly Syndrome Associated with VACTERL.

    Science.gov (United States)

    Awad, Karim; Lall, Anupam

    2016-01-01

    Prune belly syndrome is a rare abnormality; its association with VACTERL is even rarer. This association has been reported in literature a few times since first reported in 1993 and so far the majority have either been stillbirths or died shortly after birth. We present a case of Prune belly syndrome associated with VACTERL who is now one year old.

  3. Suspected Urine Leak in a Pediatric Renal Transplant Patient With Prune Belly Syndrome.

    Science.gov (United States)

    Liu, Bin; Kaplan, Summer L; Zhuang, Hongming

    2016-03-01

    Patients with prune belly syndrome usually have tortuous ureters, which can cause difficulty in the interpretation of renal scan used to evaluate possible urine leak after renal transplant. We reported a renal scan finding in a pediatric renal transplant patient with prune belly syndrome. The radioactivity in the dilated ureter, which was lateral to the renal transplant, appears to be urine leak.

  4. Prune belly syndrome associated with bilateral multicystic dysplastic kidneys and urethral obstruction: A case report

    Directory of Open Access Journals (Sweden)

    Arzu Akdag

    2015-06-01

    Full Text Available Prune belly syndrome is a rare congenital disorder defined by a characteristic clinical triad: Abdominal muscle deficiency, severe urinary tract abnormalities, and bilateral cryptorchidism. We describe a preterm neonate of Prune Belly syndrome who had abdominal muscle deficiency, multicystic dysplastic kidney, urethral hypoplasia and pulmonary hypoplasia. We presented this rare case with the data gathered from the literatüre.

  5. Acoustic Log Prediction on the Basis of Kernel Extreme Learning Machine for Wells in GJH Survey, Erdos Basin

    Directory of Open Access Journals (Sweden)

    Jianhua Cao

    2017-01-01

    Full Text Available In petroleum exploration, the acoustic log (DT is popularly used as an estimator to calculate formation porosity, to carry out petrophysical studies, or to participate in geological analysis and research (e.g., to map abnormal pore-fluid pressure. But sometime it does not exist in those old wells drilled 20 years ago, either because of data loss or because of just being not recorded at that time. Thus synthesizing the DT log becomes the necessary task for the researchers. In this paper we propose using kernel extreme learning machine (KELM to predict missing sonic (DT logs when only common logs (e.g., natural gamma ray: GR, deep resistivity: REID, and bulk density: DEN are available. The common logs are set as predictors and the DT log is the target. By using KELM, a prediction model is firstly created based on the experimental data and then confirmed and validated by blind-testing the results in wells containing both the predictors and the target (DT values used in the supervised training. Finally the optimal model is set up as a predictor. A case study for wells in GJH survey from the Erdos Basin, about velocity inversion using the KELM-estimated DT values, is presented. The results are promising and encouraging.

  6. Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

    Science.gov (United States)

    Cho, Dongrae; Ham, Jinsil; Oh, Jooyoung; Park, Jeanho; Kim, Sayup; Lee, Nak-Kyu; Lee, Boreom

    2017-10-24

    Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

  7. Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2018-03-01

    Full Text Available In the production of cold-rolled strip, the strip surface may suffer from various defects which need to be detected and identified using an online inspection system. The system is equipped with high-speed and high-resolution cameras to acquire images from the moving strip surface. Features are then extracted from the images and are used as inputs of a pre-trained classifier to identify the type of defect. New types of defect often appear in production. At this point the pre-trained classifier needs to be quickly retrained and deployed in seconds to meet the requirement of the online identification of all defects in the environment of a continuous production line. Therefore, the method for extracting the image features and the training for the classification model should be automated and fast enough, normally within seconds. This paper presents our findings in investigating the computational and classification performance of various feature extraction methods and classification models for the strip surface defect identification. The methods include Scale Invariant Feature Transform (SIFT, Speeded Up Robust Features (SURF and Local Binary Patterns (LBP. The classifiers we have assessed include Back Propagation (BP neural network, Support Vector Machine (SVM and Extreme Learning Machine (ELM. By comparing various combinations of different feature extraction and classification methods, our experiments show that the hybrid method of LBP for feature extraction and ELM for defect classification results in less training and identification time with higher classification accuracy, which satisfied online real-time identification.

  8. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lili Chen

    2017-01-01

    Full Text Available Preterm birth (PTB is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT and extreme learning machine (ELM. For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs using empirical mode decomposition (EMD. Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  9. Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm

    Directory of Open Access Journals (Sweden)

    R. Ghiasi

    2017-09-01

    Full Text Available Utilizing surrogate models based on artificial intelligence methods for detecting structural damages has attracted the attention of many researchers in recent decades. In this study, a new kernel based on Littlewood-Paley Wavelet (LPW is proposed for Extreme Learning Machine (ELM algorithm to improve the accuracy of detecting multiple damages in structural systems.  ELM is used as metamodel (surrogate model of exact finite element analysis of structures in order to efficiently reduce the computational cost through updating process. In the proposed two-step method, first a damage index, based on Frequency Response Function (FRF of the structure, is used to identify the location of damages. In the second step, the severity of damages in identified elements is detected using ELM. In order to evaluate the efficacy of ELM, the results obtained from the proposed kernel were compared with other kernels proposed for ELM as well as Least Square Support Vector Machine algorithm. The solved numerical problems indicated that ELM algorithm accuracy in detecting structural damages is increased drastically in case of using LPW kernel.

  10. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  11. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  12. Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting

    International Nuclear Information System (INIS)

    Tang, Pingzhou; Chen, Di; Hou, Yushuo

    2016-01-01

    As the world’s energy problem becomes more severe day by day, photovoltaic power generation has opened a new door for us with no doubt. It will provide an effective solution for this severe energy problem and meet human’s needs for energy if we can apply photovoltaic power generation in real life, Similar to wind power generation, photovoltaic power generation is uncertain. Therefore, the forecast of photovoltaic power generation is very crucial. In this paper, entropy method and extreme learning machine (ELM) method were combined to forecast a short-term photovoltaic power generation. First, entropy method is used to process initial data, train the network through the data after unification, and then forecast electricity generation. Finally, the data results obtained through the entropy method with ELM were compared with that generated through generalized regression neural network (GRNN) and radial basis function neural network (RBF) method. We found that entropy method combining with ELM method possesses higher accuracy and the calculation is faster.

  13. Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

    2017-06-08

    Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

  14. Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Zhang, Yingchen

    2016-11-14

    This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

  15. Integrated analysis of CFD data with K-means clustering algorithm and extreme learning machine for localized HVAC control

    International Nuclear Information System (INIS)

    Zhou, Hongming; Soh, Yeng Chai; Wu, Xiaoying

    2015-01-01

    Maintaining a desired comfort level while minimizing the total energy consumed is an interesting optimization problem in Heating, ventilating and air conditioning (HVAC) system control. This paper proposes a localized control strategy that uses Computational Fluid Dynamics (CFD) simulation results and K-means clustering algorithm to optimally partition an air-conditioned room into different zones. The temperature and air velocity results from CFD simulation are combined in two ways: 1) based on the relationship indicated in predicted mean vote (PMV) formula; 2) based on the relationship extracted from ASHRAE RP-884 database using extreme learning machine (ELM). Localized control can then be effected in which each of the zones can be treated individually and an optimal control strategy can be developed based on the partitioning result. - Highlights: • The paper provides a visual guideline for thermal comfort analysis. • CFD, K-means, PMV and ELM are used to analyze thermal conditions within a room. • Localized control strategy could be developed based on our clustering results

  16. Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

    Directory of Open Access Journals (Sweden)

    Yang Li

    Full Text Available In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA methods, a new rule extraction method based on extreme learning machine (ELM and an improved Ant-miner (IAM algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

  17. [Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

    Science.gov (United States)

    Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

    2014-04-01

    In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

  18. Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

    2016-01-01

    Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

  19. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

    Science.gov (United States)

    Chen, Lili; Hao, Yaru

    2017-01-01

    Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  20. Síndrome de prune belly: presentación de caso Prune belly syndrome: a case report

    Directory of Open Access Journals (Sweden)

    María Elena Toledo Lamela

    2008-03-01

    Full Text Available El síndrome de prune belly es una rara enfermedad congénita de causa desconocida. Se presenta el caso de un recién nacido a término, del sexo masculino y de un día de nacido, que fue remitido al servicio de urología pediátrica por presentar ausencia de los músculos de la pared anterior del abdomen (rectos anteriores, criptorquidia bilateral y gran globo vesical. A partir de los hallazgos del examen físico se planteó el diagnóstico de síndrome de prune belly. Se encontraron anomalías asociadas como escoliosis y agenesia del pie derecho. En el estudio radiológico del tracto urinario se confirmaron malformaciones congénitas como valva de uretra posterior y megavejiga con uretero-hidronefrosis bilateral. Los análisis de laboratorio confirmaron la afectación de la función renal y una infección urinaria asociada. Se practicó una cistostomía a cielo abierto. El paciente falleció a los 10 días a causa de las complicaciones de la insuficiencia renalThe prune belly syndrome is a congenital rare disease of unknown origin. The case of a one-day-old full- term male newborn infant that was referred to the pediatric urology service for presenting absence of the muscles of the anterior abdomen wall (anterior rectus muscle, bilateral cryptochordism and big vesical globe, was presented. Starting from the findings of the physical examination, the prune belly syndrome was diagnosed. Associated abnormalities such as scoliosis and agenesis of the right leg were found. In the radiological study of the urinary tract, congenital malformations as posterior urethra valve and megabladder with bilateral ureterohydronephrosis were confirmed. The lab tests corroborated the affectation of the renal function and an associated urinary infection. The patient died at 10 days as a result of the complications of renal failure

  1. Chemical composition of biomass generated in the guava tree pruning

    Science.gov (United States)

    Camarena-Tello, Julio César; Rocha-Guzmán, Nuria Elizabeth; Gallegos-Infante, José Alberto; González-Laredo, Rubén Francisco; Pedraza-Bucio, Fabiola Eugenia; López-Albarrán, Pablo; Herrera-Bucio, Rafael; Rutiaga-Quiñones, José Guadalupe

    2015-01-01

    Psidium guajava L. (Myrtaceae) is a native plant of Central America and is now widely cultivated in many tropical regions of the world for the fruit production. In Mexico, in the guava orchards common practices to control fruit production are: water stress, defoliation and pruning. In this study, we report the chemical composition of the biomass (branches and leaves) generated in the pruning practices. The results ranged as follows: pH (4.98-5.88), soda solubility (39.01-70.49 %), ash (1.87-8.20 %); potassium and calcium were the major inorganic elements in ash. No heavy metals were detected in the studied samples; total solubility (15.21-46.60 %), Runkel lignin (17.77-35.26 %), holocellulose (26.56 -69.49 %), α-cellulose (15.53-35.36 %), hemicelluloses (11.02-34.12 %), tannins in aqueous extracts (3.81-9.06 %), and tannins in ethanolic extracts (3.42-15.24 %). PMID:26417359

  2. Unique features of prune belly syndrome in laparoscopic surgery.

    Science.gov (United States)

    Saxena, Amulya K; Brinkmann, Olaf A

    2007-08-01

    The aim of this study was to evaluate the laparoscopic abdominal access modifications in children with prune belly syndrome undergoing a first stage Fowler-Stephens procedure. Eleven consecutive boys underwent a transperitoneal laparoscopic bilateral first stage Fowler-Stephens procedure. Patient age ranged from 1.5 to 3 years (mean age 2.2 years). In these patients, the floppy abdominal wall required a modified approach with regard to access technique, insufflation pressures, and work port stabilization methods. Duration of the procedures and intraoperative technical challenges encountered were prospectively documented. Mean operative time was 40 minutes (range 30 to 75 minutes), and all procedures were completed without any complications. Forceful insertion of ports was not possible, and all ports were introduced under complete open access. Larger volumes of carbon dioxide were used in the initial part of our series, when the ports were not sutured to the abdominal wall. An abdominal pressure of 8 mmHg was maintained in all patients and was considered optimal for the procedures. Short laparoscopy instruments (240 mm) were unsuitable for the procedures and had to be replaced by longer instruments (310 mm or 430 mm). Technical modifications are required to the approach in laparoscopic abdominal access to overcome the challenges posed by the floppy abdominal wall in prune belly patients. Open access, suture fixation of the optic and work ports, use of threaded sleeve ports, and use of proper length of laparoscopy instruments are valuable modifications to overcome the technical hurdles posed by these patients.

  3. First steps in translating human cognitive processes of cane pruning grapevines into AI rules for automated robotic pruning

    Directory of Open Access Journals (Sweden)

    Saxton Valerie

    2014-01-01

    Full Text Available Cane pruning of grapevines is a skilled task for which, internationally, there is a dire shortage of human pruners. As part of a larger project developing an automated robotic pruner, we have used artificial intelligence (AI algorithms to create an expert system for selecting new canes and cutting off unwanted canes. A domain and ontology has been created for AI, which reflects the expertise of expert human pruners. The first step in the creation of an expert system was to generate virtual vines, which were then ‘pruned’ by human pruners and also by the expert system in its infancy. Here we examined the decisions of 12 human pruners, for consistency of decision, on 60 virtual vines. 96.7% of the 12 pruners agreed on at least one cane choice after which there was diminishing agreement on which further canes to select for laying. Our results indicate that techniques developed in computational intelligence can be used to co-ordinate and synthesise the expertise of human pruners into a best practice format. This paper describes first steps in this knowledge elicitation process, and discusses the fit between cane pruning expertise and the expertise that can be elicited using AI based expert system techniques.

  4. Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

    Science.gov (United States)

    Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

    2017-12-01

    Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was

  5. Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

    Science.gov (United States)

    An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

    2017-08-18

    Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

  6. Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Bai

    2015-04-01

    Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

  7. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

    Directory of Open Access Journals (Sweden)

    Chan-Uk Yeom

    2017-10-01

    Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

  8. 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.

  9. Effect of main stem pruning and fruit thinning on the postharvest conservation of melon

    Directory of Open Access Journals (Sweden)

    Rafaella M. de A. Ferreira

    Full Text Available ABSTRACT Main stem pruning and fruit thinning are cultivation practices that can influence the yield and quality of the fruit. This study aimed to evaluate the effects of main stem pruning and fruit thinning on the postharvest conservation of Charentais Banzai melons. In the field, the plants were subjected to main stem pruning and fruit thinning, with harvesting done 74 days after sowing (DAS. The fruits were transported to the laboratory where they were cleaned, characterized, and stored in a cold chamber (5 °C and 90 ± 2% RH. In the field, the experiment was designed as a split-plot using a 2 × 4 + 1 factorial design, with two levels of main stem pruning (pruned and unpruned, four levels of thinning times (42, 45, 48, and 51 DAS, and a control (unpruned and unthinned. The sub-plot consisted of storage times (0, 7, 14, 21, and 28 days, with four blocks. The preharvest treatments did not significantly influence the production characteristics of the Banzai hybrid. The treatment without pruning increased the titratable acidity of fruit, and the thinning at 51 days after sowing (DAS reduced soluble sugars. There was a decline in pulp firmness, titratable acidity, reducing sugars, and an increase in soluble solids, total soluble sugar, and non-reducing sugars during storage. Pruning the main melon stem reduced the weight loss of the fruit after 28 days of storage.

  10. Effects of pruning in Monterey pine plantations affected by Fusarium circinatum

    Energy Technology Data Exchange (ETDEWEB)

    Bezos, D.; Lomba, J. M.; Martinez-Alvarez, P.; Fernandez, M.; Diez, J. J.

    2012-07-01

    Fusarium circinatum Nirenberg and O'Donnell (1998) is the causal agent of Pitch Canker Disease (PCD) in Pinus species, producing damage to the main trunk and lateral branches as well as causing branch dieback. The disease has been detected recently in northern Spain in Pinus spp. seedlings at nurseries and in Pinus radiata D. Don adult trees in plantations. Fusarium circinatum seems to require a wound to enter the tree, not only that as caused by insects but also that resulting from damage by humans, i.e. mechanical wounds. However, the effects of pruning on the infection process have yet to be studied. The aim of the present study was to know how the presence of mechanical damage caused by pruning affects PCD occurrence and severity in P. radiata plantations. Fifty P. radiata plots (pruned and unpruned) distributed throughout 16 sites affected by F. circinatum in the Cantabria region (northern Spain) were studied. Symptoms of PCD presence, such as dieback, oozing cankers and trunk deformation were evaluated in 25 trees per plot and related to pruning effect. A significant relationship between pruning and the number of cankers per tree was observed, concluding that wounds caused by pruning increase the chance of pathogen infection. Other trunk symptoms, such as the presence of resin outside the cankers, were also higher in pruned plots. These results should be taken into account for future management of Monterey Pine plantations. (Author) 36 refs.

  11. Structural study of gubernaculum testis in fetuses with prune belly syndrome.

    Science.gov (United States)

    Costa, Suelen F; Costa, Waldemar S; Sampaio, Francisco J B; Favorito, Luciano A

    2015-05-01

    We compared and contrasted the structure of the gubernaculum testis in fetuses with prune belly syndrome and normal controls. We studied a total of 6 gubernacula from 3 male fetuses with prune belly syndrome and a total of 14 from 7 male fetuses without an anomaly. Gubernacular specimens were cut into 5 μm sections and stained with Masson trichrome to quantify connective tissue and smooth muscle cells, with Weigert stain to observe elastic fibers and with picrosirius red with polarization to observe collagen. Immunohistochemical analysis was done with tubulin to observe the nerves. Images were captured with a BX51 microscope and DP70 camera (Olympus®). Stereological analysis was done with Image-Pro and ImageJ (MediaCybernetics®) using a grid to determine volumetric density. Means were statistically compared with the Mann-Whitney test. All tests were 2-sided with p Prune belly syndrome fetuses were at 17 to 31 weeks of gestation and control fetuses were at 12 to 35 weeks of gestation. Quantitative analysis showed no difference in the volumetric density of smooth muscle cells in prune belly syndrome vs control gubernacula (mean 15.70% vs 19%, p = 0.2321). Collagen fiber analysis revealed a predominance of green areas in prune belly syndrome gubernacula, suggesting collagen type III, and a predominance of red areas in control gubernacula, suggesting collagen type I. Elastic fibers were significantly smaller in prune belly syndrome gubernacula than in control gubernacula (mean 14.06% vs 24.6%, p = 0.0190). Quantitative analysis demonstrated no difference in the volumetric density of nerves in prune belly syndrome or control gubernacula (mean 5.200% vs 3.158%, p = 0.2302). The gubernaculum in fetuses with prune belly syndrome had altered concentrations of collagen and elastic fibers. These structural alterations could be one of the factors involved in cryptorchidism in prune belly syndrome. Copyright © 2015. Published by Elsevier Inc.

  12. Prune belly syndrome--report of 47 cases.

    Science.gov (United States)

    Woodhouse, C R; Ransley, P G; Innes-Williams, D

    1982-01-01

    Forty-seven cases of prune belly syndrome in children born between 1948 and 1977 are described. They have been classified into three groups according to the state of the urinary tract in the neonatal period. The results achieved in these cases form the basis of our present management. In group I, the most severely affected, early death is inevitable. In group 2 the children are ill as neonates; high diversion is often required and later reconstruction may be possible. Group 3 patients are healthy as neonates and little reconstructive surgery is required. The prognosis in groups 2 and 3 is good. Half the group 2 children and three-quarters of the group 3 children grew up normally with satisfactory renal function and health. It is important to establish free drainage of the urinary tract and avoid infection. Images Figure PMID:6128960

  13. Associated rare anomalies in prune belly syndrome: A case report

    Directory of Open Access Journals (Sweden)

    Andreas Fette

    2015-02-01

    Full Text Available The triad of deficient abdominal wall musculature, undescended testes and urinary tract anomalies characterizes the Prune Belly Syndrome (PBS. PBS can be associated with other comorbid urological and non urological conditions. But the full pathogenesis and best treatment is still a matter of debate. A term newborn with a classical PBS (Woodhouse Group 2, Smith and Woodard Group 2 plus lung hypoplasia and funnel chest deformity, a megapenis with a tight phimosis and an obturated anterior urethra is presented. Unfortunately, the baby died in urosepsis and renal failure in his 3rd week of life, despite urine drainage surgery and peritoneal dialysis undertaken. According to the best of our knowledge, this is an unique combination of rare anomalies in PBS patients.

  14. Pruning high-value Douglas-fir can reduce dwarf mistletoe severity and increase longevity in central Oregon

    Science.gov (United States)

    Maffei, Helen M; Filip, Gregory M; Gruelke, Nancy E; Oblinger, Brent W; Margolis, Ellis; Chadwick, Kristen L

    2016-01-01

    Mid- to very large-sized Douglas-fir (Pseudotsuga menzieseii var. menziesii) that were lightly- to moderately-infected by dwarf mistletoe (Arceuthobium douglasii) were analyzed over a 14-year period to evaluate whether mechanical pruning could eradicate mistletoe (or at least delay the onset of severe infection) without significantly affecting tree vitality and by inference, longevity. Immediate and longterm pruning effects on mistletoe infection severity were assessed by comparing pruned trees (n = 173) to unpruned trees (n = 55) with respect to: (1) percentage of trees with no visible infections 14 years post-pruning, (2) Broom Volume Rating (BVR), and (3) rate of BVR increase 14 years postpruning. Vitality/longevity (compared with unpruned trees) was assessed using six indicators: (1) tree survival, (2) the development of severe infections, (3) the development of dead tops, (4) tree-ring width indices, (5) Normalized Difference Vegetation Index (NDVI) from high-resolution multi-spectral imagery, and (6) live-crown ratio (LCR) and increment. Twenty-four percent of the pruned trees remained free of mistletoe 14 years post-pruning. Pruning is most likely to successfully eradicate mistletoe in lightly infected trees (BVR 1 or 2) without infected neighbors. Pruning significantly decreased mean BVR in the pruned versus the unpruned trees. However, the subsequent average rate of intensification (1.3–1.5 BVR per decade) was not affected, implying that a single pruning provides ~14 years respite in the progression of infection levels. Post-pruning infection intensification was slower on dominant and co-dominants than on intermediate or suppressed trees. The success of mistletoe eradication via pruning and need for follow-up pruning should be evaluated no sooner than 14 years after pruning to allow for the development of detectable brooms. Based on six indicators, foliage from witches brooms contribute little to long-term tree vitality since removal appears to have

  15. Síndrome de Prune Belly- Relato de caso

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    Anna Catarina Rocha Ferreira

    2018-03-01

    Full Text Available A Síndrome de Prune Belly (SPB é caracterizada por sintomas que se organizam em uma tríade baseada pela ausência, deficiência ou hipoplasia congênita de toda musculatura abdominal além de alterações do trato urinário acompanhada de criptorquidia bilateral.  O objetivo deste estudo foi relatar um caso clínico desta grave síndrome e apresentar um breve protocolo de atendimento Fisioterapêutico. O caso relatado é de um paciente de 03 anos, sexo masculino com criptorquidia bilateral, mal- formação da musculatura abdominal e dificuldade respiratória fazendo uso de traqueostomia e sonda nasoenteral, foi investigado na Faculdade de Ciências e Educação Sena Aires em Valparaiso de Goiás, onde foi submetido a um protocolo fisioterapêutico. Por ser uma síndrome grave é necessário um diagnóstico eficaz para uma rápida intervenção fisioterapêutica, pois assim será possível conhecer as limitações desses pacientes e prestar um atendimento eficaz com o objetivo de melhorar a qualidade de vida e sua inserção na sociedade promovendo o bem-estar da criança, além de servir como norteador de novas pesquisas na área, sobretudo aquelas com poucas evidências. Descritores: Síndrome de Prune Belly; Anormalidades congênitas; Parede abdominal; Criptorquidismo.

  16. Síndrome de Prune Belly: Presentación de un caso y revisión de la literatura Prune Belly Syndrome: Case report and review

    Directory of Open Access Journals (Sweden)

    Albert Franz Guerrero

    2010-04-01

    Full Text Available Introducción: El síndrome de Prune Belly (SPB, también conocido como el síndrome de Eagle Barrett, se caracteriza por una triada de anomalías que incluye grados variables de hipoplasia de la musculatura abdominal, anomalías del tracto urinario y criptorquidia bilateral. Objetivo: Se describe el caso de un paciente masculino con Síndrome de Prune Belly y se realiza una revisión de la literatura sobre esta rara enfermedad. Conclusión: La característica arrugada del abdomen similar a una ciruela pasa, le da el nombre al síndrome. Además, puede estar asociado a alteraciones cardiovasculares, respiratorias, ortopédicas y gastrointestinales. Salud UIS 2010; 42: 78-85Introduction: Prune-belly syndrome, also known as Eagle-Barrett syndrome is characterized by a triad of anomalies that include varying degrees of abdominal musculature hypoplasia, urinary tract anomalies, and bilateral cryptorchidism. Objective: We describe the case of a male patient with Prune Belly Syndrome and we review the literature on this rare disease. Conclusions: The characteristic wrinkled, prune-like abdomen, gives the name to the syndrome. Can also be associated with cardiovascular, respiratory, orthopedic and gastrointestinal anomalies. Salud UIS 2010; 42: 78-85.

  17. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

    Directory of Open Access Journals (Sweden)

    Pak Kin Wong

    2014-01-01

    Full Text Available Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP, which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM. While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications.

  18. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects

    KAUST Repository

    Johansen, Kasper; Raharjo, Tri; McCabe, Matthew

    2018-01-01

    Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect

  19. 7 CFR 944.350 - Safeguard procedures for avocados, grapefruit, kiwifruit, olives, oranges, prune variety plums...

    Science.gov (United States)

    2010-01-01

    ... plums (fresh prunes), and table grapes for processing; (3) Olives for processing into oil; (4) Grapefruit for animal feed; or (5) Avocados for seed shall obtain an “Importer's Exempt Commodity Form” (FV-6...

  20. Factors affecting branch wound occlusion and associated decay following pruning – a case study with wild cherry (Prunus avium L.

    Directory of Open Access Journals (Sweden)

    Jonathan Sheppard

    2016-11-01

    Full Text Available Pruning wild cherry (Prunus avium L. is a common silvicultural practice carried out to produce valuable timber at a veneer wood quality. Sub-optimal pruning treatments can permit un-occluded pruning wounds to develop devaluing decay. The aim of this study is to determine relevant branch, tree and pruning characteristics affecting the occlusion process of pruning wounds. Important factors influencing occlusion time for an optimised pruning treatment for valuable timber production utilising wild cherry are derived. 85 artificially pruned branches originating from ten wild cherry trees were retrospectively analysed. Branch stub length, branch diameter and radial stem increment during occlusion were found to be significant predictors for occlusion time. From the results it could be concluded that for the long term success of artificial pruning of wild cherry it is crucial to (i keep branch stubs short (while avoiding damage to the branch collar, (ii to enable the tree to maintain significant radial growth after pruning, (iii to avoid large pruning wounds (>2.5 cm by removing steeply angled and fast growing branches at an early stage.

  1. Regulating mineralization rates of Tithonia diversifolia and Lantana camara prunings to improve phosphorus availability in calcareous soils

    Directory of Open Access Journals (Sweden)

    Y. Nuraini

    2014-01-01

    Full Text Available The effect of mixing of Tithonia diversifolia and Lantana camara prunings to improve synchronization between P released from the prunings with crop demand for P was studied in a laboratory and in a glasshouse. Tithonia diversifolia prunings (Td, Lantana camara prunings (Lc, and farmyard manure (Pk were thoroughly mixed with the proportion (% of dry weight of; 25Td +75 Lc ; 50Td +50 Lc ; 75Td +25 Lc ; 90Lc +10 Pk ; 45Td +45 +10 Lc Pk ; 100Td and 100Lc, and then mixed with 100 g of air-dried soil with a rate equivalent to 100 kg P / ha. Results of the study showed that the pruning mixtures decomposed and mineralized faster than that of Lantana camara pruning only, but slower than that of Tithonia diversifolia pruning only. The amount of P released from the pruning mixtures increased with increasing proportion of Tithonia diversifolia pruning in the mixtures. Increasing proportion of Tithonia diversifolia pruning in the mixture applied to the soil increased the amount of P taken up by maize.

  2. Congenital megalourethra in 2 weeks old boy associated with Prune-Belly syndrome

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    Lawal Barau Abdullahi

    2015-02-01

    Full Text Available The megalourethra is a rare congenital anomaly of the penile urethra. It is characterized by the congenital absence of the corpus spongiosum and/or corpus cavernosum. It is especially common associated with Prune-Belly syndrome, and with upper tract abnormalities. We present a 2 weeks old boy with congenital megalourethra because of its association with the Prune-Belly syndrome.

  3. Loss of mTOR-Dependent Macroautophagy Causes Autistic-like Synaptic Pruning Deficits

    OpenAIRE

    Tang, Guomei; Gudsnuk, Kathryn; Kuo, Sheng-Han; Cotrina, Marisa L.; Rosoklija, Gorazd; Sosunov, Alexander; Sonders, Mark S.; Kanter, Ellen; Castagna, Candace; Yamamoto, Ai; Yue, Zhenyu; Arancio, Ottavio; Peterson, Bradley S.; Champagne, Frances; Dwork, Andrew J.

    2014-01-01

    Developmental alterations of excitatory synapses are implicated in autism spectrum disorders (ASDs). Here, we report increased dendritic spine density with reduced developmental spine pruning in layer V pyramidal neurons in postmortem ASD temporal lobe. These spine deficits correlate with hyperactivated mTOR and impaired autophagy. In Tsc2+/- ASD mice where mTOR is constitutively overactive, we observed postnatal spine pruning defects, blockade of autophagy, and ASD-like social...

  4. Unified commutation-pruning technique for efficient computation of composite DFTs

    Science.gov (United States)

    Castro-Palazuelos, David E.; Medina-Melendrez, Modesto Gpe.; Torres-Roman, Deni L.; Shkvarko, Yuriy V.

    2015-12-01

    An efficient computation of a composite length discrete Fourier transform (DFT), as well as a fast Fourier transform (FFT) of both time and space data sequences in uncertain (non-sparse or sparse) computational scenarios, requires specific processing algorithms. Traditional algorithms typically employ some pruning methods without any commutations, which prevents them from attaining the potential computational efficiency. In this paper, we propose an alternative unified approach with automatic commutations between three computational modalities aimed at efficient computations of the pruned DFTs adapted for variable composite lengths of the non-sparse input-output data. The first modality is an implementation of the direct computation of a composite length DFT, the second one employs the second-order recursive filtering method, and the third one performs the new pruned decomposed transform. The pruned decomposed transform algorithm performs the decimation in time or space (DIT) data acquisition domain and, then, decimation in frequency (DIF). The unified combination of these three algorithms is addressed as the DFTCOMM technique. Based on the treatment of the combinational-type hypotheses testing optimization problem of preferable allocations between all feasible commuting-pruning modalities, we have found the global optimal solution to the pruning problem that always requires a fewer or, at most, the same number of arithmetic operations than other feasible modalities. The DFTCOMM method outperforms the existing competing pruning techniques in the sense of attainable savings in the number of required arithmetic operations. It requires fewer or at most the same number of arithmetic operations for its execution than any other of the competing pruning methods reported in the literature. Finally, we provide the comparison of the DFTCOMM with the recently developed sparse fast Fourier transform (SFFT) algorithmic family. We feature that, in the sensing scenarios with

  5. Evaluation of soil carbon pools after the addition of prunings in subtropical orchards placed in terraces

    Science.gov (United States)

    Márquez San Emeterio, Layla; Martín Reyes, Marino Pedro; Ortiz Bernad, Irene; Fernández Ondoño, Emilia; Sierra Aragón, Manuel

    2017-04-01

    The amount of carbon that can be stored in a soil depends on many factors, such as the type of soil, the chemical composition of plant rests and the climate, and is also highly affected by land use and soil management. Agricultural ecosystems are proved to absorb a large amount of CO2 from the atmosphere through several sustainable management practices. In addition, organic materials such as leaves, grass, prunings, etc., comprise a significant type of agricultural practices as a result of waste recycling. The aim of this research was to evaluate the effects of the addition of different organic prunings on the potential for carbon sequestration in agricultural soils placed in terraces. Three subtropical orchards were sampled in Almuñécar (Granada, S Spain): mango (Mangifera indica L.), avocado (Persea americana Mill.) and cherimoya (Annonacherimola Mill.). The predominant climate is Subtropical Mediterranean and the soil is an Eutric Anthrosol. The experimental design consisted in the application of prunings from avocado, cherimoya and mango trees, placed on the surface soil underneath their correspondent trees, as well as garden prunings from the green areas surrounding the town center on the surface soils under the three orchard trees. Control experiences without the addition of prunings were also evaluated. These experiences were followed for three years. Soil samples were taken at4 cm depth. They were dried for 3-4 days and then sieved (<2 mm).Total soil organic C, water-soluble soil organic C, mineral-associated organic C and non-oxidable C were analyzed and expressed as carbon pools (Mg C ha-1for total soil organic C, or Kg C ha-1for the others). The results showed an increase of all organic carbon pools in all pruning treatments compared to the control experiences. Differences in total organic carbon pool were statistically significant between soils under avocado prunings and their control soil, and between soils under garden prunings with cherimoya and

  6. Beyond Traditional Extreme Value Theory Through a Metastatistical Approach: Lessons Learned from Precipitation, Hurricanes, and Storm Surges

    Science.gov (United States)

    Marani, M.; Zorzetto, E.; Hosseini, S. R.; Miniussi, A.; Scaioni, M.

    2017-12-01

    The Generalized Extreme Value (GEV) distribution is widely adopted irrespective of the properties of the stochastic process generating the extreme events. However, GEV presents several limitations, both theoretical (asymptotic validity for a large number of events/year or hypothesis of Poisson occurrences of Generalized Pareto events), and practical (fitting uses just yearly maxima or a few values above a high threshold). Here we describe the Metastatistical Extreme Value Distribution (MEVD, Marani & Ignaccolo, 2015), which relaxes asymptotic or Poisson/GPD assumptions and makes use of all available observations. We then illustrate the flexibility of the MEVD by applying it to daily precipitation, hurricane intensity, and storm surge magnitude. Application to daily rainfall from a global raingauge network shows that MEVD estimates are 50% more accurate than those from GEV when the recurrence interval of interest is much greater than the observational period. This makes MEVD suited for application to satellite rainfall observations ( 20 yrs length). Use of MEVD on TRMM data yields extreme event patterns that are in better agreement with surface observations than corresponding GEV estimates.Applied to the HURDAT2 Atlantic hurricane intensity dataset, MEVD significantly outperforms GEV estimates of extreme hurricanes. Interestingly, the Generalized Pareto distribution used for "ordinary" hurricane intensity points to the existence of a maximum limit wind speed that is significantly smaller than corresponding physically-based estimates. Finally, we applied the MEVD approach to water levels generated by tidal fluctuations and storm surges at a set of coastal sites spanning different storm-surge regimes. MEVD yields accurate estimates of large quantiles and inferences on tail thickness (fat vs. thin) of the underlying distribution of "ordinary" surges. In summary, the MEVD approach presents a number of theoretical and practical advantages, and outperforms traditional

  7. A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.

    Science.gov (United States)

    He, Yan-Lin; Geng, Zhi-Qiang; Xu, Yuan; Zhu, Qun-Xiong

    2015-09-01

    In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Thinning effect on plant growth of pruned eucalypt clone

    Directory of Open Access Journals (Sweden)

    Diego Correa Ramos

    2014-06-01

    Full Text Available A pruned stand of eucalypt clone underwent five thinning treatments with the removal of different proportion of the planted trees, at different ages: a 0% - unthinned, b 35% at 55 months, c 35% at 81 months, d 70% at 81 months, removing sprouts in the thinned plant stumps and, e 70% at 81 months, without coppice sprouts removal. By the age of 141 months, the Weibull distribution showed higher number of trees in the smallest diameter classes for the unthinned treatment. The 70% thinning, with thinned coppice sprouts removal, presented higher number of individuals in the largest diameter classes. Height and yield were the smallest with the removal of 70% of the trees at 81 months, maintaining coppice sprouts. The afterthinning periodic annual increment was greater by thinning 35% of the trees at 55 months resulting in greater number of trees in the largest diameter classes as compared to the other treatments. Yield was higher for the unthinned treatment. The results of this study indicated that thinning 70% of the trees at the age of 81 months, with coppice sprout removal, could be recommended to obtain trees of larger diameter for multiproduct.

  9. Prune-belly syndrome: an autopsy case report.

    Science.gov (United States)

    Vasconcelos, Marcela Arruda Pereira Silva; de Lima, Patricia Picciarelli

    2014-01-01

    Prune-belly syndrome (PBS) is a rare congenital anomaly characterized by a spectrum of mild-to-severe presentations of urinary tract malformations, deficient abdominal wall musculature, and cryptorchidism in male newborns or genital abnormalities in the female newborns. Currently, antenatal diagnosis is feasible with ultrasound examination, and treatment is based on case report experience. More recently, intrauterine management has been undertaken with encouraging results. The authors report a case of PBS diagnosed at the seventeenth gestation week, when ultrasonographic examination revealed the presence of ascites, distended bladder, thickened bladder wall and posterior urethral valve. The fetus was submitted to an intrauterine intervention at the nineteenth gestational week. Delivery occurred at 34 weeks of gestation and the newborn examination was consistent with PBS. On the second day of life, the newborn was submitted to abdominoplasty, colostomy, and orchiopexy. However, the outcome was unfavorable with respiratory failure and death on the fifteenth day of life. The autopsy confirmed the diagnosis of PBS, but the immediate cause of death was attributed to aspiration pneumonia.

  10. Prune-belly syndrome: an autopsy case report

    Directory of Open Access Journals (Sweden)

    Marcela Arruda Pereira Silva Vasconcelos

    2014-12-01

    Full Text Available Prune-belly syndrome (PBS is a rare congenital anomaly characterized by a spectrum of mild-to-severe presentations of urinary tract malformations, deficient abdominal wall musculature, and cryptorchidism in male newborns or genital abnormalities in the female newborns. Currently, antenatal diagnosis is feasible with ultrasound examination, and treatment is based on case report experience. More recently, intrauterine management has been undertaken with encouraging results. The authors report a case of PBS diagnosed at the seventeenth gestation week, when ultrasonographic examination revealed the presence of ascites, distended bladder, thickened bladder wall and posterior urethral valve. The fetus was submitted to an intrauterine intervention at the nineteenth gestational week. Delivery occurred at 34 weeks of gestation and the newborn examination was consistent with PBS. On the second day of life, the newborn was submitted to abdominoplasty, colostomy, and orchiopexy. However, the outcome was unfavorable with respiratory failure and death on the fifteenth day of life. The autopsy confirmed the diagnosis of PBS, but the immediate cause of death was attributed to aspiration pneumonia.

  11. Study of Testicular Structure in Fetuses with Prune Belly Syndrome.

    Science.gov (United States)

    Favorito, Luciano A; Costa, Suelen F; Costa, Waldemar S; Vieiralves, Rodrigo; Bernardo, Fabio O; Sampaio, Francisco J B

    2017-01-01

    To compare the structure of the testis in fetuses with prune belly syndrome (PBS) to normal controls. We studied 6 testes obtained from 3 fetuses with PBS and 14 testes from 7 male fetuses. The testicular specimens were cut into 5- μ m thick sections and stained with hematoxylin and eosin (HE), to observe the seminiferous tubules; Weigert's solution to observe elastic fibers; and picrosirius red to observe collagen. The images were captured with an Olympus BX51 microscope and Olympus DP70 camera. The stereological analysis was done with the Image Pro and Image J programs. Means were statistically compared using the Mann-Whitney U test ( p < 0.005). Quantitative analysis documented no differences ( p = 0.4) in number of seminiferous tubules (ST) in PBS testes (mean = 8.87%, SD = 1.59), when compared to the control (mean = 11.4%, SD = 2.99) and no differences ( p = 0.8) in diameter of ST in PBS testes (mean = 52.85  μ m, SD = 1.58) when compared to the control group (mean = 53.17  μ m, SD = 1.55), but we did observe a lower number ( p = 0.0002) of Leydig cells in the PBS testes (mean = 67.03% and SD = 3.697) when compared to the control group (mean = 90.1% and SD = 2.986). Our study showed a lower concentration of Leydig cells in the triad syndrome fetuses.

  12. Prune belly syndrome, splenic torsion, and malrotation: a case report.

    Science.gov (United States)

    Tran, Sifrance; Grossman, Eric; Barsness, Katherine A

    2013-02-01

    An 18 year old male with a history of prune belly syndrome (PBS) presented with acute abdominal pain and palpable left upper quadrant mass. Computed tomography (CT) of the abdomen revealed a medialized spleen with a "whirl sign" in the splenic vessels, consistent with splenic torsion. Coincidentally, the small bowel was also noted to be on the right side of the abdomen, while the colon was located on the left, indicative of malrotation. Emergent diagnostic laparoscopy confirmed splenic torsion and intestinal malrotation. Successful laparoscopic reduction of the splenic torsion was achieved, however, conversion to an open procedure by a vertical midline incision was necessary owing to the patient's unique anatomy. Open splenopexy with a mesh sling and Ladd's procedure were subsequently performed. Malrotation and wandering spleen are known, rare associated anomalies in PBS; however, both have not been reported concurrently in a patient with PBS in the literature. In patients with PBS, acute abdominal pain, and an abdominal mass, high clinical suspicion for gastrointestinal malformations and prompt attention can result in spleen preservation and appropriate malrotation management. We present a case of a teenager who presented with a history of PBS, acute abdominal pain, and a palpable abdominal mass. The patient was found to have splenic torsion and intestinal malrotation. The clinical findings, diagnostic imaging, and surgical treatment options of splenic torsion are reviewed. Copyright © 2013 Elsevier Inc. All rights reserved.

  13. Unwinding the hairball graph: Pruning algorithms for weighted complex networks

    Science.gov (United States)

    Dianati, Navid

    2016-01-01

    Empirical networks of weighted dyadic relations often contain "noisy" edges that alter the global characteristics of the network and obfuscate the most important structures therein. Graph pruning is the process of identifying the most significant edges according to a generative null model and extracting the subgraph consisting of those edges. Here, we focus on integer-weighted graphs commonly arising when weights count the occurrences of an "event" relating the nodes. We introduce a simple and intuitive null model related to the configuration model of network generation and derive two significance filters from it: the marginal likelihood filter (MLF) and the global likelihood filter (GLF). The former is a fast algorithm assigning a significance score to each edge based on the marginal distribution of edge weights, whereas the latter is an ensemble approach which takes into account the correlations among edges. We apply these filters to the network of air traffic volume between US airports and recover a geographically faithful representation of the graph. Furthermore, compared with thresholding based on edge weight, we show that our filters extract a larger and significantly sparser giant component.

  14. Dictionary Pruning with Visual Word Significance for Medical Image Retrieval.

    Science.gov (United States)

    Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

    2016-02-12

    Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency.

  15. Yield and crop cycle time of peaches cultivated in subtropical climates and subjected to different pruning times

    Directory of Open Access Journals (Sweden)

    Rafael Augusto Ferraz

    2015-12-01

    Full Text Available The cultivation of peaches in regions of subtropical and tropical climate is currently achieved through a set of practices such as using less demanding cultivars in cold conditions, applying plant growth regulators to break dormancy, and performing specific pruning, like production and renewal pruning. Research on the climate adaptation of cultivars is of great importance in establishing a crop in a given region. Therefore, the objective of this study was to evaluate the agronomic performance of three cultivars subjected to different production pruning times in Botucatu/SP, where 2-year old peach trees were evaluated, grown at a spacing of 6.0 x 4.0 meters. The experimental design was a split plot design with four blocks, using the cultivars Douradão, BRS Kampai and BRS Rubimel, and the subplots corresponded to pruning times in May, June, July and August. Ten plants were used per plot, with the four central plants considered useful and the remaining considered as margins. Pruning in June and July showed the best results in terms of percentage of fruit set and production. The cultivar BRS Rubimel showed the best percentage of fruit set when pruned in June (44.96%, and best fruit production when pruned in July (18.7 kg plant-1. Pruning in May anticipated the harvest of cultivar BRS Rubimel by 13 days whereas pruning carried out in July and August provided late harvests for cultivars Douradão and BRS Kampai.

  16. EVALUATION OF TEMPORALVARIATIONS IN MOISTURE AND CALORIFIC VALUE OF VINE AND OLIVE PRUNING

    Directory of Open Access Journals (Sweden)

    Pier Riccardo Porceddu

    2012-06-01

    Full Text Available In Italy arboreal crops, in particular vine and olive, cover a surface area of around 19.6×109 m2 from which about 4.6×109 kg of pruning are cut. These by-products are currently ploughed into the soil or else harvested and burned in open fields. On the other hand such materials would be more useful as an energy source. If these materials are to be used as fuel, it is important to know their calorific value. The calorific value is significantly influenced by the moisture content of wood. This work has evaluated the changes in moisture content and calorific value with time for different harvesting and storage systems of vine and olive pruning. The observed decrease in the moisture content of the vine and olive pruning depended on the storage system utilized, in particular on the product compression ratio and air circulation. Some differences were observed between the results obtained for vine and olive pruning. The time required for these materials to obtain their best energetic performance was identified at 32 weeks from their harvesting. Harvesting with balers and forwarding costs are about 6.21×10-2 €/kg for vine pruning and 4.64×10-2 €/kg for olive pruning. They are very similar to the price currently offered for energy biomass in Italy (5.00×10-2 €/kg. While the cost actually paid to plough pruning into the soil amounts to about 2.50×10-2 €/kg. Therefore the energy chain encourages a cost-and-benefit analysis.

  17. Cover crops and pruning in Bobal and Tempranillo vineyards have little influence on grapevine nutrition

    Directory of Open Access Journals (Sweden)

    Pedro Pérez-Bermúdez

    2016-06-01

    Full Text Available ABSTRACT Cover crops may improve vineyard soil properties, grapevine nutrient status and berry composition, however, factors such as cover crop type, annual rainfall, climate and irrigation may change their effects on vineyards. From 2008 to 2011, the effects of a non-permanent cover crop and two pruning techniques on soil as well as vine nutrients and grapevine performance of two vineyards (cv. Tempranillo and cv. Bobal were evaluated. For that purpose, two legumes were sown in inter-rows of hand-pruned vines in February and were tilled at flowering. Soil tillage, or cover cropping, was combined with either light pruning or severe pruning to study foliar nutrient variations. Soil N, P, K and total organic carbon (TOC were determined in samples taken from the Ap1 horizon in January prior to vine pruning. Foliar N, P, K contents were measured in leaves sampled upon grape veraison. The differences between vineyards with cover cropping and bare soils suggest that legumes positively affected soil N (1.55 vs. 1.68 g kg−1 and 1.49 vs. 1.76 g kg−1 in Bobal and Tempranillo vineyards, respectively and soil organic matter (SOM (12.5 vs. 15.5 g kg−1 and 12.9 vs. 17.2 g kg−1 in Bobal and Tempranillo vineyards, respectively. The use of cover crops did not affect grapevine yields nor quality of Bobal and Tempranillo berry . Cover crops, or light pruning, did not alter the foliar N, P, K contents of both cultivars since their concentrations were similar to those found in the leaves from vineyards with soil tillage or severe pruning.

  18. Mandelbrot's Extremism

    NARCIS (Netherlands)

    Beirlant, J.; Schoutens, W.; Segers, J.J.J.

    2004-01-01

    In the sixties Mandelbrot already showed that extreme price swings are more likely than some of us think or incorporate in our models.A modern toolbox for analyzing such rare events can be found in the field of extreme value theory.At the core of extreme value theory lies the modelling of maxima

  19. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

    Science.gov (United States)

    Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

    2017-04-19

    As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

  20. Density based pruning for identification of differentially expressed genes from microarray data

    Directory of Open Access Journals (Sweden)

    Xu Jia

    2010-11-01

    Full Text Available Abstract Motivation Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune

  1. Pruning management of Chardonnay grapevines at high altitude in Brazilian southeast

    Directory of Open Access Journals (Sweden)

    Tania dos Reis Mendonça

    2016-03-01

    Full Text Available ABSTRACT The agronomical responses of Chardonnay, a variety indicated for sparkling wine production, is influenced by the vineyard management and the edaphoclimatic conditions of the region. The objective of this study was to evaluate the effects of two pruning types (Royat and double Guyot on vegetative and reproductive development of Chardonnay vine growing at high altitude in the Brazilian southeastern region. The experiment was carried out in a commercial vineyard located at 1,280 m of altitude in Divinolândia, São Paulo State, Brazil. The Chardonnay vines (clone 96, grafted onto 1103 Paulsen rootstock and trained in a vertical shoot positioning trellis system, were assessed. Vegetative vigor, bud fruitfulness, production and physicochemical composition of grapes were evaluated during 2014 and 2015 growing seasons. The Royat pruning induced higher vegetative vigor and increased the bud fruitfulness, the cluster number and the productivity of Chardonnay vine when compared to Guyot pruning. Even though the increase on yield was observed, there was no effect of pruning type on grape final quality. Therefore, the choice of pruning method in function of variety genetic characteristics and their interaction with environment can optimize the vineyard profitability. In the Brazilian southeast, the Royat system is the most suitable one to grow Chardonnay for sparkling wines production.

  2. Study of Testicular Structure in Fetuses with Prune Belly Syndrome

    Directory of Open Access Journals (Sweden)

    Luciano A. Favorito

    2017-01-01

    Full Text Available Purpose. To compare the structure of the testis in fetuses with prune belly syndrome (PBS to normal controls. Materials and Methods. We studied 6 testes obtained from 3 fetuses with PBS and 14 testes from 7 male fetuses. The testicular specimens were cut into 5-μm thick sections and stained with hematoxylin and eosin (HE, to observe the seminiferous tubules; Weigert’s solution to observe elastic fibers; and picrosirius red to observe collagen. The images were captured with an Olympus BX51 microscope and Olympus DP70 camera. The stereological analysis was done with the Image Pro and Image J programs. Means were statistically compared using the Mann–Whitney U test (p<0.005. Results. Quantitative analysis documented no differences (p=0.4 in number of seminiferous tubules (ST in PBS testes (mean = 8.87%, SD=1.59, when compared to the control (mean = 11.4%, SD=2.99 and no differences (p=0.8 in diameter of ST in PBS testes (mean = 52.85 μm, SD=1.58 when compared to the control group (mean = 53.17 μm, SD=1.55, but we did observe a lower number (p=0.0002 of Leydig cells in the PBS testes (mean = 67.03% and SD=3.697 when compared to the control group (mean = 90.1% and SD=2.986. Conclusions. Our study showed a lower concentration of Leydig cells in the triad syndrome fetuses.

  3. Prune belly syndrome: early management outcome of nine consecutive cases.

    Science.gov (United States)

    Ekwunife, O H; Ugwu, J O; Modekwe, V

    2014-01-01

    Prune belly syndrome (PBS) is a rare congenital malformation of unclear etiology. The disease progress and outcome in developing countries are not clear as most reports are isolated case reports. A review of 9 patients managed for PBS in 5 years. There were 7 males and 2 females, aged 30 min-11 days (median = 5 days) at the time of presentation (a child presented as neonate, defaulted from follow-up and represented at 10 years of life). Their weights on admission were 2.5-4.2 kg (median = 3 kg). Maternal age range was 26-37 years (median = 32 years), with five mothers being above 30 years. Seven mothers had febrile illness in the first trimester and took antimalarial drugs or antibiotics. Intestinal malrotation was the most common associated anomaly. The degree of the anterior abdominal wall and the urinary tract morphology varies from patient to patient. Urinary tract anomalies were initially managed conservatively. Two infants however later had cutaneous ureterostomy due to worsening renal function and recalcitrant urinary tract infection (UTI). Four infants had abdominoplasty at the 2 nd week, 6 th week, 3 rd year and 10 th year of life. Seven orchiopexies were done. Four were done by Fowler-Stephen's method while the rest were via the inguinal route. Of the former, 3 testicles have normal volume 6 months after, whereas one atrophied. Post abdominoplasty, there was a significant reduction in the frequency of respiratory tract infection (RTI), UTI and post void urine volume in three infants. In addition, there was improved peer interaction and academic performance in the 10-year-old child. One infant died of pulmonary hypoplasia and two others from worsening urosepsis and progressive renal failure. PBS presents with a spectrum of features. Initial conservative management of the urinary tract was beneficial. Abdominoplasty and orchiopexy have both physiological and improved quality of life benefits. Early Parental education helped in reducing defaults from follow-up.

  4. Rare copy number variants identified in prune belly syndrome.

    Science.gov (United States)

    Boghossian, Nansi S; Sicko, Robert J; Giannakou, Andreas; Dimopoulos, Aggeliki; Caggana, Michele; Tsai, Michael Y; Yeung, Edwina H; Pankratz, Nathan; Cole, Benjamin R; Romitti, Paul A; Browne, Marilyn L; Fan, Ruzong; Liu, Aiyi; Kay, Denise M; Mills, James L

    2018-03-01

    Prune belly syndrome (PBS), also known as Eagle-Barrett syndrome, is a rare congenital disorder characterized by absence or hypoplasia of the abdominal wall musculature, urinary tract anomalies, and cryptorchidism in males. The etiology of PBS is largely unresolved, but genetic factors are implicated given its recurrence in families. We examined cases of PBS to identify novel pathogenic copy number variants (CNVs). A total of 34 cases (30 males and 4 females) with PBS identified from all live births in New York State (1998-2005) were genotyped using Illumina HumanOmni2.5 microarrays. CNVs were prioritized if they were absent from in-house controls, encompassed ≥10 consecutive probes, were ≥20 Kb in size, had ≤20% overlap with common variants in population reference controls, and had ≤20% overlap with any variant previously detected in other birth defect phenotypes screened in our laboratory. We identified 17 candidate autosomal CNVs; 10 cases each had one CNV and four cases each had two CNVs. The CNVs included a 158 Kb duplication at 4q22 that overlaps the BMPR1B gene; duplications of different sizes carried by two cases in the intron of STIM1 gene; a 67 Kb duplication 202 Kb downstream of the NOG gene, and a 1.34 Mb deletion including the MYOCD gene. The identified rare CNVs spanned genes involved in mesodermal, muscle, and urinary tract development and differentiation, which might help in elucidating the genetic contribution to PBS. We did not have parental DNA and cannot identify whether these CNVs were de novo or inherited. Further research on these CNVs, particularly BMP signaling is warranted to elucidate the pathogenesis of PBS. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

  5. Composting sewage sludge with green waste from tree pruning

    Directory of Open Access Journals (Sweden)

    Sarah Mello Leite Moretti

    2015-10-01

    Full Text Available Sewage sludge (SS has been widely used as organic fertilizer. However, its continuous use can cause imbalances in soil fertility as well as soil-water-plant system contamination. The study aimed to evaluate possible improvements in the chemical and microbiological characteristics of domestic SS, with low heavy metal contents and pathogens, through the composting process. Two composting piles were set up, based on an initial C/N ratio of 30:1, with successive layers of tree pruning waste and SS. The aeration of piles was performed by mechanical turnover when the temperature rose above 65 ºC. The piles were irrigated when the water content was less than 50 %. Composting was conducted for 120 days. Temperature, moisture content, pH, electrical conductivity (EC, carbon and nitrogen contents, and fecal coliforms were monitored during the composting. A reduction of 58 % in the EC of the compost (SSC compared with SS was observed and the pH reduced from 7.8 to 6.6. There was an increase in the value of cation exchange capacity/carbon content (CEC/C and carbon content. Total nitrogen remained constant and N-NO3- + N-NH4+ were immobilised in organic forms. The C/N ratio decreased from 25:1 to 12:1. Temperatures above 55 ºC were observed for 20 days. After 60 days of composting, fecal coliforms were reduced from 107 Most Probable Number per gram of total solids (MPN g−1 to 104 MPN g−1. I one pile the 103 MPN g−1 reached after 90 days in one pile; in another, there was recontamination from 105 to 106 MPN g−1. In SSC, helminth eggs were eliminated, making application sustainable for agriculture purposes.

  6. An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine

    Directory of Open Access Journals (Sweden)

    Ali Hashemi

    2014-06-01

    Full Text Available Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.

  7. Effect of root pruning and irrigation regimes on leaf water relations and xylem ABA and ionic concentrations in pear trees

    DEFF Research Database (Denmark)

    Wang, Yufei; Bertelsen, Marianne G.; Petersen, Karen Koefoed

    2014-01-01

    relation characteristics, stomatal conductance and xylem sap abscisic acid (ABA) and ionic concentrations. Results showed that leaf water potential, leaf turgor and stomatal conductance of root pruning (RP) treatment was significantly lower than those of non-root pruning (NP) treatment indicating that root...

  8. Longleaf Pine Root System Development and Seedling Quality in Response to Copper Root Pruning and Cavity Size

    Science.gov (United States)

    Mary Anne Sword Sayer; Shi-Jean Susana Sung; James D. Haywood

    2011-01-01

    Cultural practices that modify root system structure in the plug of container-grown seedlings have the potential to improve root system function after planting. Our objective was to assess how copper root pruning affects the quality and root system development of longleaf pine seedlings grown in three cavity sizes in a greenhouse. Copper root pruning increased seedling...

  9. Field performance of Quercus bicolor established as repeatedly air-root-pruned container and bareroot planting stock

    Science.gov (United States)

    J.W." Jerry" Van Sambeek; Larry D. Godsey; William D. Walter; Harold E. Garrett; John P. Dwyer

    2016-01-01

    Benefits of repeated air-root-pruning of seedlings when stepping up to progressively larger containers include excellent lateral root distribution immediately below the root collar and an exceptionally fibrous root ball. To evaluate long-term field performance of repeatedly air-root-pruned container stock, three plantings of swamp white oak (Quercus bicolor...

  10. Pruning for crop regulation in high density guava (Psidium guajava L.) plantation

    Energy Technology Data Exchange (ETDEWEB)

    Thakre, M.; Lal, S.; Uniyal, S.; Goswami, A.K. Prakash. P.

    2016-11-01

    High density management and crop regulation are two important aspects in guava (Psidium guajava L.) production. Therefore, to find out the economic way of managing high density planting and crop regulation, the present work was carried out on 6-year-old guava trees of cv. Pant Prabhat under double-hedge row system of planting during 2009-10 and 2010-11. Seven different forms of pruning [FBT: flower bud thinning by hand, FBTT: flower bud thinning by hand followed by removal of terminal one leaf pair, RLFO: removal of leaves and flower buds by hand, retaining one leaf pair at the top, RLF: removal of all leaves and flowers by hand, OLPS: one leaf pair shoot pruning, FSP: full shoot pruning, OLPF: one leaf pair pruning of fruited shoots only] were studied along with control (C).Minimum annual increase in tree volume (6.764 m3) was recorded with the treatment OLPF, which was 2.31 times less than the control (15.682 m3). Highest yield during winter season (55.30 kg/tree) and total yield (59.87 kg/tree) was obtained from treatment OLPF. One leaf pair pruning of fruited shoots only (OLPF) was also found profitable among other treatments by recording cost:benefit ratio of 1:2.96. This treatment also recorded the highest return distributed in rainy as well as in winter season. On the basis of findings it can be concluded that one leaf pair pruning of fruited shoots only is suitable for profitable high density management as well as crop regulation of guava in farmer friendly manner. (Author)

  11. Pruning for crop regulation in high density guava (Psidium guajava L. plantation

    Directory of Open Access Journals (Sweden)

    Madhubala Thakre

    2016-06-01

    Full Text Available High density management and crop regulation are two important aspects in guava (Psidium guajava L. production. Therefore, to find out the economic way of managing high density planting and crop regulation, the present work was carried out on 6-year-old guava trees of cv. Pant Prabhat under double-hedge row system of planting during 2009-10 and 2010-11. Seven different forms of pruning [FBT: flower bud thinning by hand, FBTT: flower bud thinning by hand followed by removal of terminal one leaf pair, RLFO: removal of leaves and flower buds by hand, retaining one leaf pair at the top, RLF: removal of all leaves and flowers by hand, OLPS: one leaf pair shoot pruning, FSP: full shoot pruning, OLPF: one leaf pair pruning of fruited shoots only] were studied along with control (C.Minimum annual increase in tree volume (6.764 m3 was recorded with the treatment OLPF, which was 2.31 times less than the control (15.682 m3. Highest yield during winter season (55.30 kg/tree and total yield (59.87 kg/tree was obtained from treatment OLPF. One leaf pair pruning of fruited shoots only (OLPF was also found profitable among other treatments by recording cost:benefit ratio of 1:2.96. This treatment also recorded the highest return distributed in rainy as well as in winter season. On the basis of findings it can be concluded that one leaf pair pruning of fruited shoots only is suitable for profitable high density management as well as crop regulation of guava in farmer friendly manner.

  12. Evaluation of biocontrol agents for grapevine pruning wound protection against trunk pathogen infection.

    Directory of Open Access Journals (Sweden)

    Charl KOTZE

    2011-12-01

    Full Text Available Trunk diseases of grapevine are caused by numerous pathogens, including Eutypa lata, Phaeomoniella chlamydospora, and species of Botryosphaeriaceae (incl. Botryosphaeria and aggregate genera, Phomopsis and Phaeoacremonium. Since infections occur mainly through pruning wounds, that have been shown by previous research to stay susceptible for up to 16 weeks after pruning, long-term pruning wound protection is required for prevention of infection. This study evaluated several biocontrol agents against a range of trunk disease pathogens in dual plate laboratory trials to determine macroscopic and microscopic interactions. The biocontrol agents had a substantial effect on all the pathogens, with a wide range of macroscopic and microscopic interactions observed. The best performing biocontrol agents were tested in two field trials. Fresh pruning wounds were treated with benomyl, Trichoderma products (Biotricho®, Vinevax® and ECO 77® and isolates (USPP-T1 and -T2, identified as T. atroviride and Bacillus subtilis. Seven days after treatment the pruning wounds were inoculated by spraying with spore suspensions of Neofusicoccum australe, N. parvum, Diplodia seriata, Lasiodiplodia theobromae, Eutypa lata, Phaeomoniella chlamydospora or Phomopsis viticola. Eight months after inoculation, the treatments were evaluated by isolation onto potato dextrose agar. The efficacy of the biocontrol agents was in most cases similar or superior to that observed for benomyl. Isolate USPP-T1, in particular, was very effective, reducing incidence of Ph. viticola, E. lata, Pa. chlamydospora, N. australe, N. parvum, D. seriata and L. theobromae by 69, 76, 77, 78, 80, 85 and 92%, respectively. This is the first report of biological protection of grapevine pruning wounds against this group of grapevine trunk disease pathogens.

  13. Corset Usage for Gastrointestinal and Respiratory Problems in a Newborn with Prune Belly Syndrome.

    Science.gov (United States)

    Satar, Mehmet; Özlü, Ferda; Yapıcıoğlu, Hacer; İskit, Serdar

    2016-07-01

    Prune Belly syndrome (PBS), comprises a triad of anomalies that include abdominal wall flaccidity, urologic anomalies and bilateral cryptorchidism in males. The abdominal musculature hypoplasia predisposes to respiratory problems, respiratory infections secondary to impaired cough mechanism, and cause chronic constipation secondary to ineffective valsalva ability. Here, the authors present a newborn baby with Prune Belly syndrome who had respiratory and gastrointestinal problems which resolved after corset use. To the authors knowledge, this is the first case of corset usage in the treatment of PBS in a newborn infant.

  14. Urachal catheter provides new choice for long-term urinary diversion in prune belly syndrome.

    Science.gov (United States)

    Chen, I-Lun; Huang, Hsin-Chun; Lee, Shin-Yi; Liu, Chieh-An; Tain, You-Lin; Ou-Yang, Mei-Chen; Chao, Pei-Hsin

    2011-02-01

    Prune belly syndrome has been identified as a clinical triad of abdominal muscle deficiency, bilateral cryptorchidism, and urologic abnormalities. We present the case of a discordant monozygotic twin with prune belly syndrome and voiding dysfunction that was relieved by long-term urinary catheterization by way of the urachus. To the best of our knowledge, this alternative method has not been previously reported. We suggest that for newborn infants with long-term voiding dysfunction, if the urachus retains patency, urinary catheterization through the urachus could be a choice for urine drainage instead of cystostomy, providing a better cosmetic appearance and quality of life. Copyright © 2011 Elsevier Inc. All rights reserved.

  15. Cane pruning on Chardonnay grapevine in the high-altitude regions of Southern Brazil

    Directory of Open Access Journals (Sweden)

    Filho José Luiz Marcon

    2016-01-01

    Full Text Available High-altitude regions of southern Brazil, located above 900 m above sea level, the cordon training with spur pruning is widely used because of easier application. In these regions, Chardonnay wine grape shows potential to produce quality wines, however, in commercial vineyards, the training system used has not provided productivities that makes economically viable the cultivation of this variety. Given this, the present study aimed to evaluate the effect of different cane-pruning systems on the vegetative, productive and enological potential of Chardonnay grapevines grown in the high-altitude region of Southern Brazil. The experiment was conducted in a commercial Chardonnay vineyard, located in São Joaquim – Santa Catarina State (28o17 ′39”S and 49∘ 55′56” W, to 1230 m a.s.l during 2015 and 2016 vintages. Chardonnay vines (grafted on 1103 Paulsen were planted in 2010, with a 3.0 m (row × 1.0 m (vine spacing. The treatments consisted of different cane-pruning systems: Cordon spur-pruning (control; Sylvoz; Cazenave; Capovolto; single Guyot and double Guyot. Pruning was performed in August of each year when the buds were in the green tip developmental stage. Data was analyzed by Scott Knott test (p < 0.05 following a randomized block design with four replicates, each consisting of 12 vines per plot. We observed higher yield in the Cazenave and double Guyot training system with three and two more tons of grapes than spur-pruning respectively. The bud fertility was higher in plants trained in double Guyot. Vines spur-pruned showed higher relation of leaf area: production, with values above 100 cm2 g−1 grape at 2016 vintage. Commercial maturity of grapes (soluble solids, acidity and polyphenols did not differ among training systems studied. The results suggest that cane-pruning systems could be an alternative to increase production efficiency of Chardonnay in high-altitude region of southern Brazil.

  16. The effect of motor learning and fatigue on pre-activation of the lower extremity muscles during different jumps.

    Science.gov (United States)

    Kamelska, Anna M; Kot, Bartosz

    2017-09-22

    The first step in identifying risk factors for injuries is to characterize the myoelectric activity of different muscles after ground contact, especially when fatigue is a limiting factor. This study aimed at: (a) recording the myoelectric activity of calf muscles after ground contact during different types of jumps and (b) investigating the effect of motor learning and fatigue on muscle pre-activation. Twenty four male students aged 24.3 ± 1.2 years old performed three different motor activities: (a) Jump from a box with counter landing (JCL) on 30x30 cm plate (b) Drop jump with bounce drop jump (BDJ) and (c) BDJ followed by a jump on 51-cm step. The surface EMG was used to examine the following muscles: m. tibialis anterior (TA), m. gastrocnemius medialis (GM), m. gastrocnemius lateralis (GL), and m. soleus (S). The measurements were taken during different jumps before and after motor learning and fatigue stimulus. There were significant differences in pre-activation for TA between JCL and BDJ followed by a jump under the influence of fatigue (p<0.05). The differences were observed also during BDJ between non-fatigued and fatigued conditions. There was a statistically significant difference for GL between BDJ pre- and post-movement motor learning and BDJ pre- and post-fatigue influence. Current results indicate that myoelectric activity of muscles during motor activities is different, and the effect of motor learning and fatigue was shown. Thus, it could be important in the injury prevention in sport.

  17. Extreme cosmos

    CERN Document Server

    Gaensler, Bryan

    2011-01-01

    The universe is all about extremes. Space has a temperature 270°C below freezing. Stars die in catastrophic supernova explosions a billion times brighter than the Sun. A black hole can generate 10 million trillion volts of electricity. And hypergiants are stars 2 billion kilometres across, larger than the orbit of Jupiter. Extreme Cosmos provides a stunning new view of the way the Universe works, seen through the lens of extremes: the fastest, hottest, heaviest, brightest, oldest, densest and even the loudest. This is an astronomy book that not only offers amazing facts and figures but also re

  18. Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

    International Nuclear Information System (INIS)

    Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

    2014-01-01

    A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its ''black box'' aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where ''all'' configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA

  19. Evaluating the Bulk Lorentz Factors of Outflow Material: Lessons Learned from the Extremely Energetic Outburst GRB 160625B

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yuan-Zhu; Wang, Hao; Zhang, Shuai; Liang, Yun-Feng; Jin, Zhi-Ping; He, Hao-Ning; Liao, Neng-Hui; Fan, Yi-Zhong; Wei, Da-Ming, E-mail: liangyf@pmo.ac.cn, E-mail: jin@pmo.ac.cn, E-mail: dmwei@pmo.ac.cn [Key Laboratory of Dark Matter and Space Astronomy, Purple Mountain Observatory, Chinese Academy of Science, Nanjing, 210008 (China)

    2017-02-10

    GRB 160625B is an extremely bright outburst with well-monitored afterglow emission. The geometry-corrected energy is high, up to ∼5.2 × 10{sup 52} erg or even ∼8 × 10{sup 52} erg, rendering it the most energetic GRB prompt emission recorded so far. We analyzed the time-resolved spectra of the prompt emission and found that in some intervals there were likely thermal-radiation components and the high energy emission was characterized by significant cutoff. The bulk Lorentz factors of the outflow material are estimated accordingly. We found out that the Lorentz factors derived in the thermal-radiation model are consistent with the luminosity-Lorentz factor correlation found in other bursts, as well as in GRB 090902B for the time-resolved thermal-radiation components, while the spectral cutoff model yields much lower Lorentz factors that are in tension with the constraints set by the electron pair Compton scattering process. We then suggest that these spectral cutoffs are more likely related to the particle acceleration process and that one should be careful in estimating the Lorentz factors if the spectrum cuts at a rather low energy (e.g., ∼tens of MeV). The nature of the central engine has also been discussed, and a stellar-mass black hole is favored.

  20. Can early thinning and pruning lessen the impact of pine plantations ...

    African Journals Online (AJOL)

    dwelling insects found in pine tree plantations in Patagonia. We compared the abundance, species richness and composition of the beetle and ant assemblages within 16-year-old pine stands (n = 10) subjected to early pruning and thinning (i.e. ...

  1. Dynamical pruning of static localized basis sets in time-dependent quantum dynamics

    NARCIS (Netherlands)

    McCormack, D.A.

    2006-01-01

    We investigate the viability of dynamical pruning of localized basis sets in time-dependent quantum wave packet methods. Basis functions that have a very small population at any given time are removed from the active set. The basis functions themselves are time independent, but the set of active

  2. Pruning affects the vegetative balance of the wine grape (Vitis vinifera L.

    Directory of Open Access Journals (Sweden)

    Pedro José Almanza-Merchán

    2014-08-01

    Full Text Available Grape cultivation for wine production at altitudes between 2,200 and 2,600 m a.s.l. started in the department of Boyaca in 1982. Quality wines are produced by the AinKarim Vineyard in Ricaurte High. Wine grapes have to possess suitable organoleptic compounds at harvest in order to guarantee quality grape must that can be converted into wine. Therefore, it is necessary to maintain a suitable ratio the sources and the sinks and to guarantee production, quality and vegetative sustainability over time, conserving the equilibrium and benefiting the productive potential of the vineyard. The aim of this study was to evaluate the productive and vegetative balance effect in the wine grape varieties Cabernet Sauvignon and Sauvignon Blanc in Sutamarchan-Boyaca, considering different pruning types (short, long, and mixed. A bifactorial, completely random statistical design was used. At the time of harvest, the fruit production and pruned wood were evaluated. The long-pruned vines showed the best behavior and the most balanced source/sink relationship,, while Sauvignon Blanc demonstrated a better productive yield. Meanwhile, the short and mixed prunings had the better values for the Ravaz index (balance between fruit production and vegetative growth, indicating that they are more suitable for the conditions of the region, allowing for sustainability during the productive cycles of the wine grapes.

  3. An Adaptive Pruning Algorithm for the Discrete L-Curve Criterion

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Toke Koldborg; Rodriguez, Giuseppe

    2004-01-01

    SVD or regularizing CG iterations). Our algorithm needs no pre-defined parameters, and in order to capture the global features of the curve in an adaptive fashion, we use a sequence of pruned L-curves that correspond to considering the curves at different scales. We compare our new algorithm...

  4. Encapsulating peritoneal sclerosis in a peritoneal dialysis patient with prune-belly syndrome: a case report.

    Science.gov (United States)

    Geurts, N; Hubens, G; Wojciechowski, M; Vaneerdeweg, W

    2010-01-01

    This case describes a prune-belly syndrome patient who had a kidney transplantation and was diagnosed with Encapsulating Peritoneal Sclerosis (EPS), a rare but potentially fatal condition, mostly associated with Peritoneal Dialysis (PD). The definition of EPS is based on the clinical findings linked to bowel obstruction and on the demonstration of peritoneal thickening. Surgical treatment is the only established basic treatment for the condition. Prune-belly syndrome is characterized by the triad of deficient abdominal musculature, urinary tract abnormality and cryptorchidism. Because it is often associated with end-stage renal disease, PD is essential in the treatment of patients with prune-belly syndrome. The aetiology of EPS follows a 'two-hit theory': the first 'hit' is peritoneal deterioration, caused by long-time exposure to PD. This causes peritoneal disruption which predisposes the patient to a second hit. In our patient, PD discontinuation and renal transplantation are possible 'second hits' that triggered the development of EPS. This case of prune-belly syndrome has all the necessary elements for the development of EPS, and we felt we should report it as the peroperative diagnosis was unexpected.

  5. The association between prune belly syndrome and dental anomalies: a case report.

    Science.gov (United States)

    Basso, Maria Daniela; Favretto, Carla Oliveira; Cunha, Robson Frederico

    2012-12-18

    Prune belly syndrome is a rare condition produced by an early mesodermal defect that causes abdominal abnormalities. However, the literature indicates that disturbances related to ectodermal development may also be present. This is the first case report in the literature to suggest that dental abnormalities are part of the broad spectrum of clinical features of prune belly syndrome. Because the syndrome causes many serious medical problems, early diagnosis of abnormalities involving the primary and permanent dentitions are encouraged. The authors report the clinical case of a 4-year-old Caucasian boy with prune belly syndrome. In addition to the triad of abdominal muscle deficiency, abnormalities of the gastrointestinal and urinary tracts, and cryptorchidism, a geminated mandibular right central incisor, agenesis of a mandibular permanent left incisor, and congenitally missing primary teeth (namely, the mandibular right and left lateral incisors) were noted. This original case report about prune belly syndrome highlights the possibility that dental abnormalities are a part of the broad spectrum of clinical features of the syndrome. Therefore, an accurate intra-oral clinical examination and radiographic evaluation are required for patients with this syndrome in order to provide an early diagnosis of abnormalities involving the primary and permanent dentitions.

  6. The association between prune belly syndrome and dental anomalies: a case report

    Directory of Open Access Journals (Sweden)

    Basso Maria Daniela

    2012-12-01

    Full Text Available Abstract Background Prune belly syndrome is a rare condition produced by an early mesodermal defect that causes abdominal abnormalities. However, the literature indicates that disturbances related to ectodermal development may also be present. This is the first case report in the literature to suggest that dental abnormalities are part of the broad spectrum of clinical features of prune belly syndrome. Because the syndrome causes many serious medical problems, early diagnosis of abnormalities involving the primary and permanent dentitions are encouraged. Case presentation The authors report the clinical case of a 4-year-old Caucasian boy with prune belly syndrome. In addition to the triad of abdominal muscle deficiency, abnormalities of the gastrointestinal and urinary tracts, and cryptorchidism, a geminated mandibular right central incisor, agenesis of a mandibular permanent left incisor, and congenitally missing primary teeth (namely, the mandibular right and left lateral incisors were noted. Conclusion This original case report about prune belly syndrome highlights the possibility that dental abnormalities are a part of the broad spectrum of clinical features of the syndrome. Therefore, an accurate intra-oral clinical examination and radiographic evaluation are required for patients with this syndrome in order to provide an early diagnosis of abnormalities involving the primary and permanent dentitions.

  7. Prune belly syndrome in an Egyptian infant with Down syndrome: A case report

    Directory of Open Access Journals (Sweden)

    Metwalley Kotb A

    2008-10-01

    Full Text Available Abstract Introduction Prune belly syndrome is a rare congenital anomaly of uncertain aetiology almost exclusive to males. The association between prune belly syndrome and Down syndrome is very rare. Case presentation A 4-month-old Egyptian boy was admitted to our institute for management of acute bronchiolitis. He was born at full term by normal vaginal delivery. His mother, a 42-year-Egyptian villager with six other children, had no antenatal or prenatal care. On examination, the boy was found to be hypotonic. In addition to features of Down syndrome, karyotyping confirmed the diagnosis of trisomy 21. Ultrasound examination of the abdomen showed bilateral gross hydronephrosis with megaureter. Micturating cystourethrography showed grade V vesicoureteric reflux bilaterally with no urethral obstruction. Serum creatinine concentration was 90 μmol/litre, serum sodium was 132 mmol/litre and serum potassium was 5.9 mmol/litre. Conclusion We report an Egyptian infant with Down syndrome and prune belly syndrome. The incidence of this association is unknown. Routine antenatal ultrasonography will help in discovering renal anomalies which can be followed postnatally. Postnatal detection of prune belly syndrome necessitates full radiological investigation to detect any renal anomalies. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  8. Surgical tactic of high bilateral abdominal testicular retention in patient with a prune belly syndrome

    Directory of Open Access Journals (Sweden)

    I. A. Panchenko

    2014-11-01

    Full Text Available Bilateral abdominal cryptorchism in combination with other defects of urogenital system and a prune belly syndrome keep within a syndrome of connective tissue dysplasia. In our medical center developed the method of surgical correction of a high bilateral abdominal testicular retention with preservation of vessels.

  9. Surgical tactic of high bilateral abdominal testicular retention in patient with a prune belly syndrome

    Directory of Open Access Journals (Sweden)

    I. A. Panchenko

    2013-01-01

    Full Text Available Bilateral abdominal cryptorchism in combination with other defects of urogenital system and a prune belly syndrome keep within a syndrome of connective tissue dysplasia. In our medical center developed the method of surgical correction of a high bilateral abdominal testicular retention with preservation of vessels.

  10. Prune belly syndrome in an Egyptian infant with Down syndrome: a case report.

    Science.gov (United States)

    Metwalley, Kotb A; Farghalley, Hekma S; Abd-Elsayed, Alaa A

    2008-10-02

    Prune belly syndrome is a rare congenital anomaly of uncertain aetiology almost exclusive to males. The association between prune belly syndrome and Down syndrome is very rare. A 4-month-old Egyptian boy was admitted to our institute for management of acute bronchiolitis. He was born at full term by normal vaginal delivery. His mother, a 42-year-Egyptian villager with six other children, had no antenatal or prenatal care. On examination, the boy was found to be hypotonic. In addition to features of Down syndrome, karyotyping confirmed the diagnosis of trisomy 21. Ultrasound examination of the abdomen showed bilateral gross hydronephrosis with megaureter. Micturating cystourethrography showed grade V vesicoureteric reflux bilaterally with no urethral obstruction. Serum creatinine concentration was 90 mumol/litre, serum sodium was 132 mmol/litre and serum potassium was 5.9 mmol/litre. We report an Egyptian infant with Down syndrome and prune belly syndrome. The incidence of this association is unknown. Routine antenatal ultrasonography will help in discovering renal anomalies which can be followed postnatally. Postnatal detection of prune belly syndrome necessitates full radiological investigation to detect any renal anomalies. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  11. Type V Pouch Colon, Prune Belly Syndrome, and Congenital Anterior Urethrocutaneous Fistula.

    Science.gov (United States)

    Raj, Prince; Birua, Hirendra

    2017-01-01

    Congenital pouch colon (CPC) or short colon syndrome is a rare type of anorectal malformation(ARM). Type V is the rarest form of CPC. We present a 1-day-old male child with type V CPC with prune belly syndrome and congenital anterior urethrocutaneous fistula (CAUF).

  12. Outcomes of renal replacement therapy in boys with prune belly syndrome

    DEFF Research Database (Denmark)

    Yalcinkaya, Fatos; Bonthuis, Marjolein; Erdogan, Beyza Doganay

    2018-01-01

    BACKGROUND: As outcome data for prune belly syndrome (PBS) complicated by end-stage renal disease are scarce, we analyzed characteristics and outcomes of children with PBS using the European Society for Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association...

  13. Impact of Early Pruning and Thinning on Lumber Grade Yield From Loblolly Pine

    Science.gov (United States)

    Alexander Clark; Mike Strub; Larry R. Anderson; H. Gwynne Lloyd; Richard F. Daniels; James H. Scarborough

    2004-01-01

    The Sudden Sawlog Study was established in 1954 near Crossett, AR, in a 9-year-old loblolly pine plantation to test the hypothesis that loblolly plantations can produce sawtimber in 30 years. To stimulate diameter and height growth and clear wood production, study plots were heavily thinned, trees pruned to 33 feet by age 24 years, under-story mowed, and growth of...

  14. On failure of the pruning technique in "error repair in shift-reduce parsers"

    NARCIS (Netherlands)

    Bertsch, E; Nederhof, MJ

    A previous article presented a technique to compute the least-cost error repair by incrementally generating configurations that result from inserting and deleting tokens in a syntactically incorrect input. An additional mechanism to improve the run-time efficiency of this algorithm by pruning some

  15. Air lateral root pruning affects longleaf pine seedling root system morphology

    Science.gov (United States)

    Shi-Jean Susana Sung; Dave Haywood

    2016-01-01

    Longleaf pine (Pinus palustris) seedlings were cultured with air lateral root pruning (side-vented containers, VT) or without (solid-walled containers, SW). Seedling root system morphology and growth were assessed before planting and 8 and 14 months after planting. Although VT seedlings had greater root collar diameter than the SW before planting,...

  16. Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits.

    Science.gov (United States)

    Tang, Guomei; Gudsnuk, Kathryn; Kuo, Sheng-Han; Cotrina, Marisa L; Rosoklija, Gorazd; Sosunov, Alexander; Sonders, Mark S; Kanter, Ellen; Castagna, Candace; Yamamoto, Ai; Yue, Zhenyu; Arancio, Ottavio; Peterson, Bradley S; Champagne, Frances; Dwork, Andrew J; Goldman, James; Sulzer, David

    2014-09-03

    Developmental alterations of excitatory synapses are implicated in autism spectrum disorders (ASDs). Here, we report increased dendritic spine density with reduced developmental spine pruning in layer V pyramidal neurons in postmortem ASD temporal lobe. These spine deficits correlate with hyperactivated mTOR and impaired autophagy. In Tsc2 ± ASD mice where mTOR is constitutively overactive, we observed postnatal spine pruning defects, blockade of autophagy, and ASD-like social behaviors. The mTOR inhibitor rapamycin corrected ASD-like behaviors and spine pruning defects in Tsc2 ± mice, but not in Atg7(CKO) neuronal autophagy-deficient mice or Tsc2 ± :Atg7(CKO) double mutants. Neuronal autophagy furthermore enabled spine elimination with no effects on spine formation. Our findings suggest that mTOR-regulated autophagy is required for developmental spine pruning, and activation of neuronal autophagy corrects synaptic pathology and social behavior deficits in ASD models with hyperactivated mTOR. Copyright © 2014 Elsevier Inc. All rights reserved.

  17. Prune belly syndrome in uniovular twin: a new finding or a case of ...

    African Journals Online (AJOL)

    A case of Prune Belly Syndrome associated with High Anorectal Anomaly and Unilateral Lung Cyst occurring in a uniovular twin is hereby presented. The twin brother has no clinical evidence of congenital anomaly in any system. We therefore ask; is this a new finding or a case of improper documentation of data. Keywords: ...

  18. Effects of root pruning in sour cherry (Prunus cersus) "Stevnsbaer"

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben; Jensen, Nauja Lisa; Dencker, Ivar Blücher

    2007-01-01

    with trees of middle age on rootstock Colt were selected. Three trees with intact root systems were pulled up in each of the orchards. Based on the architecture of the root systems, a pruning distance of 30-35 cm from the trunk was used, removing approximately 30 percent of the roots. The one-sided root...

  19. Nitrogen fixation in Leucaena leucocephala and effects of pruning s on cereal yields

    International Nuclear Information System (INIS)

    Bekunda, M.

    1998-01-01

    Leucaena leucocephala was interplanted with reference tree species, Cassia siamea and Cassia spectabilis, and estimates of percent N derived from N 2 fixation (%Ndfa) were made, by the isotope-dilution method, at 4, 6, 14, 20 and 30 months after transplanting. The %Ndfa values were low and variable throughout the growth period, except after thinning at 14 months when there was a five-fold increase. The two non-fixing reference species outperformed the N 2 -fixing Leucaena in above-ground vegetative production, and provided different fixed-N estimates. Prunings from the L. leucocephala and C. Siamea trees were applied separately to soil as green manure. Maize was planted to test the effects of the Leucaena green manure on soil fertility, and millet was the test crop for the Cassia. Whether surface-applied or incorporated, the prunings significantly improved yields, which were generally similar among rates and methods of application. The proportions of cereal N obtained from prunings ranged from 8 to 33%, with no cereal-yield correlation. The data indicate that multipurpose tree prunings are of potential use to farmers as organic sources of nutrients, even at relatively low application rates, without need for incorporation into the soil. (author)

  20. Scab severity in relation to hedge pruning pecan trees in the Southeastern USA

    Science.gov (United States)

    Scab is the most damaging disease of pecan in the Southeastern USA. Pecan trees are tall (up to 30+ m), and managing disease in the upper canopy is problematic. Hedge pruning trees to ~12 m is being explored to facilitate efficacy of ground-based fungicide sprays, but resulting vigorous shoot growth...

  1. Prune belly syndrome in a set of twins, a family tragedy: Case report ...

    African Journals Online (AJOL)

    We report prune belly syndrome, a rare congenital malformation, in a set of twins delivered to a young couple with a history of three previous first trimester spontaneous abortions, discordant HIV seropositivity and antenatal ultrasound report that indicated renal abnormalities in only one of the twins. The challenges of ...

  2. Sensitivity of directed networks to the addition and pruning of edges and vertices

    Science.gov (United States)

    Goltsev, A. V.; Timár, G.; Mendes, J. F. F.

    2017-08-01

    Directed networks have various topologically different extensive components, in contrast to a single giant component in undirected networks. We study the sensitivity (response) of the sizes of these extensive components in directed complex networks to the addition and pruning of edges and vertices. We introduce the susceptibility, which quantifies this sensitivity. We show that topologically different parts of a directed network have different sensitivity to the addition and pruning of edges and vertices and, therefore, they are characterized by different susceptibilities. These susceptibilities diverge at the critical point of the directed percolation transition, signaling the appearance (or disappearance) of the giant strongly connected component in the infinite size limit. We demonstrate this behavior in randomly damaged real and synthetic directed complex networks, such as the World Wide Web, Twitter, the Caenorhabditis elegans neural network, directed Erdős-Rényi graphs, and others. We reveal a nonmonotonic dependence of the sensitivity to random pruning of edges or vertices in the case of C. elegans and Twitter that manifests specific structural peculiarities of these networks. We propose the measurements of the susceptibilities during the addition or pruning of edges and vertices as a new method for studying structural peculiarities of directed networks.

  3. Planting and care of fine hardwood seedlings: Fertilizing, pruning, and thinning

    Science.gov (United States)

    James McKenna; Keith Woeste

    2004-01-01

    This publication outlines the cultural operations of pruning, fertilizing, and thinning that can greatly enhance the timber value of a plantation. This summary is based on the findings of many individuals. While there are alternatives to many of the recommended practices and differences of opinion existing among professionals, our purpose is to give the landowner a...

  4. Selective pruning in pineapple plants as means to reduce heterogeneity in fruit quality

    NARCIS (Netherlands)

    Fassinou Hotegni, V.N.; Lommen, W.J.M.; Struik, P.C.; Agbossou, E.K.

    2015-01-01

    Heterogeneity in fruit quality (size and taste) is a major problem in pineapple production chains. The possibilities were investigated of reducing the heterogeneity in pineapple in the field by pruning slips on selected plants, in order to promote the fruit growth on these plants. Slips are side

  5. Development of epicormic sprouts in Sitka spruce following thinning and pruning in south-east Alaska.

    Science.gov (United States)

    Robert L. Deal; R. James Barbour; Michael H. McClellan; Dean L. Parry

    2003-01-01

    The frequency and size of epicormic sprouts in Sitka spruce (Picea sitchensis (Bong.) Carr.) were assessed in five 23-29 year-old mixed Sitka spruce-western hemlock (Tsuga heterophylla (Raf.) Sarg.) stands that were uniformly thinned and pruned to 2.4, 3.7 and 5.2 m lift heights. Six to nine years after treatment sprouts were...

  6. MG Travessia: a coffee arabica cultivar productive and responsive to pruning

    OpenAIRE

    Carvalho, Gladyston Rodrigues; Bartholo, Gabriel Ferreira; Pereira, Antônio Alves; Rezende, Juliana Costa de; Botelho, Cesar Elias; Oliveira, Antônio Carlos Baião de; Silva, Felipe Lopes da

    2017-01-01

    Abstract This paper presents the results of progeny 1190-1170-2, which was recorded as ‘MGS Travessia’ and selected based on its performance in the state of Minas Gerais. The cultivar has short size, cylindrical canopy, high yield capacity, high vegetative vigor, very satisfactory husk/bean ratio, grain quality compatible to traditional cultivars, and is very responsive to skeleton pruning.

  7. 75 FR 67607 - Dried Prunes Produced in California; Increased Assessment Rate

    Science.gov (United States)

    2010-11-03

    ... Order 12988, Civil Justice Reform. Under the marketing order now in effect, California dried prune... DEPARTMENT OF AGRICULTURE Agricultural Marketing Service 7 CFR Part 993 [Doc. No. AMS-FV-10-0057... Marketing Service, USDA. ACTION: Final rule. SUMMARY: This rule increases the assessment rate established...

  8. 76 FR 53813 - Dried Prunes Produced in California; Decreased Assessment Rate

    Science.gov (United States)

    2011-08-30

    ... Reform. Under the marketing order now in effect, California dried prune handlers are subject to... DEPARTMENT OF AGRICULTURE Agricultural Marketing Service 7 CFR Part 993 [Doc. No. AMS-FV-11-0068... Marketing Service, USDA. ACTION: Interim rule with request for comments. SUMMARY: This rule decreases the...

  9. 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.

  10. 78 FR 63128 - Dried Prunes Produced in California; Increased Assessment Rate

    Science.gov (United States)

    2013-10-23

    ...; Increased Assessment Rate AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: This... the Regulatory Flexibility Act (RFA) (5 U.S.C. 601-612), the Agricultural Marketing Service (AMS) has... prunes. Dated: October 17, 2013. Rex A. Barnes, Associate Administrator, Agricultural Marketing Service...

  11. Deletion of hepatocyte nuclear factor-1-beta in an infant with prune belly syndrome.

    Science.gov (United States)

    Haeri, Sina; Devers, Patricia L; Kaiser-Rogers, Kathleen A; Moylan, Vincent J; Torchia, Beth S; Horton, Amanda L; Wolfe, Honor M; Aylsworth, Arthur S

    2010-08-01

    Prune belly syndrome is a rare congenital disorder characterized by deficiency of abdominal wall muscles, cryptorchidism, and urinary tract anomalies. We have had the opportunity to study a baby with prune belly syndrome associated with an apparently de novo 1.3-megabase interstitial 17q12 microdeletion that includes the hepatocyte nuclear factor-1-beta gene at 17q12. One previous patient, an adult, has been reported with prune belly syndrome and a hepatocyte nuclear factor-1-beta microdeletion. Hepatocyte nuclear factor-1-beta is a widely expressed transcription factor that regulates tissue-specific gene expression and is expressed in numerous tissues including mesonephric duct derivatives, the renal tubule of the metanephros, and the developing prostate of the mouse. Mutations in hepatocyte nuclear factor-1-beta cause the "renal cysts and diabetes syndrome," isolated renal cystic dysplasia, and a variety of other malformations. Based on its expression pattern and the observation of two affected cases, we propose that haploinsufficiency of hepatocyte nuclear factor-1-beta may be causally related to the production of the prune belly syndrome phenotype through a mechanism of prostatic and ureteral hypoplasia that results in severe obstructive uropathy with urinary tract and abdominal distension. Copyright Thieme Medical Publishers.

  12. Effect of Staking and Pruning on the Growth and Yield of Cucumber ...

    African Journals Online (AJOL)

    A field trial was conducted to evaluate the effect of pruning and staking on the vegetative growth and yield of cucumber ( Cucumis sativus L.). The experiment was a 3 x 2 factorial laid out in Randomized Complete Block Design (RCBD) with five replications. The results showed that vine length, number of flowers, total ...

  13. Pruning dwarf mistletoe brooms reduces stress on Jeffrey pines, Cleveland National Forest, California

    Science.gov (United States)

    Robert F. Scharpf; Richard S. Smith; Detlev Vogler

    1987-01-01

    Western dwarf mistletoe (Arceuthobium campylopodum) is a damaging parasite of Jeffrey pines (Pinus jeffreyi) in southern California. Infected branches that develop into brooms are believed to reduce tlee vigor and increase mortality. Brooms were pruned from Jeffrey pines with varying levels of dwarf mistletoe infection and live...

  14. 7 CFR 993.159 - Payments for services performed with respect to reserve tonnage prunes.

    Science.gov (United States)

    2010-01-01

    ..., incidental to acquisition or storage; (ii) Direct labor costs, which include those for weighing, receiving... overhead costs, which include those for supervision, indirect labor, fuel, power and water, taxes and... the costs for necessary services rendered by handlers in connection with reserve prunes. (2) Such...

  15. Outcomes of renal replacement therapy in boys with prune belly syndrome

    DEFF Research Database (Denmark)

    Yalcinkaya, Fatos; Bonthuis, Marjolein; Erdogan, Beyza Doganay

    2018-01-01

    BACKGROUND: As outcome data for prune belly syndrome (PBS) complicated by end-stage renal disease are scarce, we analyzed characteristics and outcomes of children with PBS using the European Society for Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (...

  16. Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters.

    Science.gov (United States)

    Liu, Zhijian; Li, Hao; Tang, Xindong; Zhang, Xinyu; Lin, Fan; Cheng, Kewei

    2016-01-01

    Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower. To address this problem, we previously used artificial neural networks and support vector machine to develop precise knowledge-based models for predicting the heat collection rates and heat loss coefficients of water-in-glass evacuated tube solar water heaters, setting the properties measured by "portable test instruments" as the independent variables. A robust software for determination was also developed. However, in previous results, the prediction accuracy of heat loss coefficients can still be improved compared to those of heat collection rates. Also, in practical applications, even a small reduction in root mean square errors (RMSEs) can sometimes significantly improve the evaluation and business processes. As a further study, in this short report, we show that using a novel and fast machine learning algorithm-extreme learning machine can generate better predicted results for heat loss coefficient, which reduces the average RMSEs to 0.67 in testing.

  17. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  18. Impact and frequency of extra-genitourinary manifestations of prune belly syndrome.

    Science.gov (United States)

    Grimsby, G M; Harrison, S M; Granberg, C F; Bernstein, I H; Baker, L A

    2015-10-01

    Prune belly syndrome (PBS) extra-genitourinary (extra-GU) manifestations are serious comorbidities beyond the genitourinary (GU) anomalies of this disease. We hypothesized an underestimation of the reported frequency and understated impact on quality of life (QOL) of extra-GU comorbidities in PBS survivors beyond the newborn period. To assess this, the frequencies of extra-GU manifestations of PBS in a contemporary cohort of living patients were compared to compiled frequencies from published literature. Second, the impact of extra-GU PBS manifestations on patient/family QOL was assessed via a non-validated open-ended survey. From 2010 to 2013, PBS survivors were prospectively recruited locally or at three PBS Network National Conventions. The family/subject was asked to complete a detailed PBS questionnaire, non-validated QOL survey, and provide medical records for review. Clinical data were extracted from medical records for local patients. The frequencies of extra-GU manifestations were compared between the contemporary, living cohort and a published literature cohort derived from PubMed. Seven of 706 published studies met criteria for frequencies tabulation of extra-GU PBS manifestations. This largest reported living PBS patient cohort (n = 65) was 99% male with mean age 10 years (1 month-45 years). The living PBS cohort had a statistically significantly higher incidence of gastrointestinal (63%), orthopedic (65%), and cardiopulmonary (49%) diagnoses compared to the compiled published cohort (n = 204). Eleven PBS males and 32 family members completed the QOL survey. Of these, 47% listed at least one non-GU problem (i.e. lung disease, skeletal problems, constipation) as negatively affecting their QOL; 42% listed at least one GU problem (i.e. self-catheterization, recurrent UTIs) as negatively affecting their QOL; 56% reported musculoskeletal surgery and 21% reported gastrointestinal surgery/medication as positively impacting their QOL. In this large contemporary

  19. The potential of legume tree prunings as organic matters for improving phosphorus availability in an acid soil

    Directory of Open Access Journals (Sweden)

    I Wahyudi

    2015-01-01

    Full Text Available A study that was aimed to elucidate roles of Gliricidia sepium and Tithonia diversifolia prunings and their extracted humic and fulvic acids on improving phosphorus availability and decreasing aluminum concentration in an Ultisol was conducted in a glasshouse. Thirteen treatments consisting of two prunings, six rates of pruning application (5, 7.5, 10, 20, 40 and 80 t/ha and one control (no added prunings were arranged in a randomized block design with four replicates. Each mixture of prunings and soil was placed in a pot containing 8 kg of soil and maize of Srikandi cultivar was grown on it for 45 days. At harvest, soil pH, P content and aluminium concentration were measured. Results of the glasshouse experiment showed that application of Gliricidia and Tithonia prunings significantly increased soil pH, reduced Alo concentration, increased Alp content, increased P availability, and increased P taken up by maize grown for 45 days. The optimum rate of both Gliricidia and Tithonia pruning should be 40 t/ha. However, at the same rate, optimum production gained by Tithonia would be higher than that of Gliricidia.

  20. Spindle-F Is the Central Mediator of Ik2 Kinase-Dependent Dendrite Pruning in Drosophila Sensory Neurons.

    Directory of Open Access Journals (Sweden)

    Tzu Lin

    2015-11-01

    Full Text Available During development, certain Drosophila sensory neurons undergo dendrite pruning that selectively eliminates their dendrites but leaves the axons intact. How these neurons regulate pruning activity in the dendrites remains unknown. Here, we identify a coiled-coil protein Spindle-F (Spn-F that is required for dendrite pruning in Drosophila sensory neurons. Spn-F acts downstream of IKK-related kinase Ik2 in the same pathway for dendrite pruning. Spn-F exhibits a punctate pattern in larval neurons, whereas these Spn-F puncta become redistributed in pupal neurons, a step that is essential for dendrite pruning. The redistribution of Spn-F from puncta in pupal neurons requires the phosphorylation of Spn-F by Ik2 kinase to decrease Spn-F self-association, and depends on the function of microtubule motor dynein complex. Spn-F is a key component to link Ik2 kinase to dynein motor complex, and the formation of Ik2/Spn-F/dynein complex is critical for Spn-F redistribution and for dendrite pruning. Our findings reveal a novel regulatory mechanism for dendrite pruning achieved by temporal activation of Ik2 kinase and dynein-mediated redistribution of Ik2/Spn-F complex in neurons.

  1. Effect of pruning height on the architecture of plants of camu camu (Myrciaria dubia HBK Mc Vaugh in the experimental station of IIAP, Ucayali, Peru.

    Directory of Open Access Journals (Sweden)

    Carlos Abanto

    2011-06-01

    Full Text Available In order to evaluate the response camu camu’s plants in plantations initial undergoing training pruning, an experiment was installed in EE - IIAP-Ucayali, under a design randomized complete block, with 3 replications, making use of 20 plants per experimental unit. The pruning treatments were performing at different heights from the base of the stem, it was considered T0 [witness without pruning], T1 [pruning to 10 cm from the base], T2 [pruning to 20 cm from the base] and T3 [pruning to 40 cm from the base] in plants of known provenance. The investigation was directed to evaluate the number of shoots, shoot growth, plant height, basal diameter, crown diameter and number of branches. After of 9 months of evaluation was found significant differences between the variables except in top diameter. In the variable Height, treatment T0 [witness without pruning] outstanding because they were not pruned, among the remaining treatments has similarly behaved, surpassing the control treatment by 267 %. In addition to this, the basal diameter was found that T1 [pruning to 10 cm from the base] has a better performed with an average value of 1.96 cm compared with the control (1.7cm. The pruning had a positive influence in the number of branches, with an average of 13.4 branches compared with T0 that get 3.1 branches on average, its shows that the pruning can increased up the production of branches to 432.3%.

  2. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects

    Directory of Open Access Journals (Sweden)

    Kasper Johansen

    2018-06-01

    Full Text Available Unmanned aerial vehicles (UAV provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e., tree crown perimeter, width, height, area and Plant Projective Cover (PPC, was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m2, and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV-based approach for larger orchards. The different flying heights (i.e., 30, 50 and 70 m produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.

  3. Using Multi-Spectral UAV Imagery to Extract Tree Crop Structural Properties and Assess Pruning Effects

    KAUST Repository

    Johansen, Kasper

    2018-04-18

    Unmanned aerial vehicles (UAV) provide an unprecedented capacity to monitor the development and dynamics of tree growth and structure through time. It is generally thought that the pruning of tree crops encourages new growth, has a positive effect on fruiting, makes fruit-picking easier, and may increase yield, as it increases light interception and tree crown surface area. To establish the response of pruning in an orchard of lychee trees, an assessment of changes in tree structure, i.e. tree crown perimeter, width, height, area and Plant Projective Cover (PPC), was undertaken using multi-spectral UAV imagery collected before and after a pruning event. While tree crown perimeter, width and area could be derived directly from the delineated tree crowns, height was estimated from a produced canopy height model and PPC was most accurately predicted based on the NIR band. Pre- and post-pruning results showed significant differences in all measured tree structural parameters, including an average decrease in tree crown perimeter of 1.94 m, tree crown width of 0.57 m, tree crown height of 0.62 m, tree crown area of 3.5 m2, and PPC of 14.8%. In order to provide guidance on data collection protocols for orchard management, the impact of flying height variations was also examined, offering some insight into the influence of scale and the scalability of this UAV based approach for larger orchards. The different flying heights (i.e. 30, 50 and 70 m) produced similar measurements of tree crown width and PPC, while tree crown perimeter, area and height measurements decreased with increasing flying height. Overall, these results illustrate that routine collection of multi-spectral UAV imagery can provide a means of assessing pruning effects on changes in tree structure in commercial orchards, and highlight the importance of collecting imagery with consistent flight configurations, as varying flying heights may cause changes to tree structural measurements.

  4. Nephrogenic adenoma of the bladder in a prune belly syndrome patient: case report and review of the literature.

    Science.gov (United States)

    Broecker, Justine S; Steelman, Charlotte K; Broecker, Bruce H; Shehata, Bahig M

    2011-01-01

    Nephrogenic adenoma (NA) is a rare lesion of the urinary tract widely considered to be a metaplastic response to urothelial injury. Herein, we present the case of an 8-year-old male with prune belly syndrome who presented with gross hematuria. Investigation revealed a bladder mass; however, upon cystoscopic examination, multiple polypoid lesions were identified. Microscopic examination revealed NA of the bladder. To our knowledge, this is the second reported case of NA of the bladder in association with prune belly syndrome.

  5. Prune belly anomaly on prenatal ultrasound as a presenting feature of ectrodactyly-ectodermal dysplasia-clefting syndrome (EEC).

    Science.gov (United States)

    Janssens, S; Defoort, P; Vandenbroecke, C; Scheffer, H; Mortier, G

    2008-01-01

    We report on a fetus with prune belly anomaly presenting at 16 weeks gestation. Clinical evaluation after birth revealed other malformations reminiscent of the EEC syndrome. This diagnosis was also suspected in the mother and finally confirmed in both relatives by identification of a heterozygous mutation (p.R204W) in the p63 gene. With this paper we confirm the previously reported occurrence of prune belly anomaly in the EEC syndrome, however here in this family proven by genetic analysis.

  6. Effects of long-term pruning, meristem origin, and branch order on the rooting of Douglas-fir stem cuttings.

    Science.gov (United States)

    D.L. Copes

    1992-01-01

    The rooting percentages of 14 Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) clones were examined annually from 1974 to 1988. The trees were 10 and 13 years old in 1974 and were pruned to 2.0 m in 1978 and 1979 and then recut annually to 0.5, 1.0, or 1.5 m, starting in 1983. The pruned trees showed no evidence of decreased rooting percentage...

  7. Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-11-01

    Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.

  8. Sistem Pendukung Keputusan Peramalan Jumlah Kunjungan Pasien Menggunakan Metode Extreme Learning Machine (Studi Kasus : Poli Gigi Rsu Dr. Wahidin Sudiro Husodo Mojokerto

    Directory of Open Access Journals (Sweden)

    Delia Putri Fardani

    2015-04-01

    Full Text Available In this research, a decision support system to predict the number of patients visit RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto was designed and developed using Extreme Learning Machine (ELM method which aims to assist director in making decision for the hospital, managing human and financial resource, as well as distributing material resource properly especially in the Department of Dentistry. The design of this decision support system to predict the number of patients visit with ELM method is divided into several stages. The first stage is to identify the input data collection needed in the calculation method of ELM. The next stage is processing the data; the data is divided into training data and testing data and then normalized, in which training data is 80% (452 data and testing 579 data 20% (116 data. The third stage is problem solving using ELM. The last stage is the design and development of systems using sysflow and desktop-based system that includes the implementation and evaluation of the system. The result of this research is an application of decision supporting system to predict number of patients. By using 116 testing data based on the binary sigmoid activation function using 7 units of hidden layer and 500 Epoch then Optimal MSE value that was obtained is 0.027.

  9. A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

    Science.gov (United States)

    Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier

    2017-02-15

    The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine.

    Science.gov (United States)

    Silitonga, Arridina Susan; Hassan, Masjuki Haji; Ong, Hwai Chyuan; Kusumo, Fitranto

    2017-11-01

    The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.

  11. Quadratus lumborum block for post-operative pain relief in patient with Prune belly syndrome.

    Science.gov (United States)

    Garg, Chitra; Khanna, Sangeeta; Mehta, Yatin

    2017-10-01

    Abdominal field blocks are commonly used as part of multimodal analgesia for post-operative pain relief in patients undergoing abdominal surgery. Conventionally, transversus abdominis plane block is used, but has the disadvantage of limited spread only to T10-T12 segments, providing only partial pain relief. The new quadratus lumborum (QL) block has the advantage of providing wider sensory block from T6 to L1 and thus has an evolving role in opioid-free anaesthesia. Opioid-induced cough depression, urinary retention, and drowsiness can be problematic in patients with Prune belly syndrome, who have deficient abdominal muscles and myriad of genitourinary problems. We report a case of a young male with Prune belly syndrome, who had a pain-free post-operative period after high inguinal orchidectomy with unilateral QL block.

  12. Prune-belly syndrome in two children and review of the literature.

    Science.gov (United States)

    Bogart, Megan M; Arnold, Holly E; Greer, Kenneth E

    2006-01-01

    Prune-belly syndrome is a congenital disorder characterized by abdominal wall musculature deficiency, urinary tract anomalies, and bilateral cryptorchidism. Because of the defect in the musculature, the abdominal skin has a peculiar wrinkled appearance. The syndrome is commonly associated with pulmonary, skeletal, cardiac, and gastrointestinal defects. Developmental delays and growth retardation have also been reported. The incidence of prune belly syndrome is approximately 1:40,000 live births. Over 95% of patients are men. Urinary tract disease is the major prognostic factor, with the complications of pulmonary hypoplasia and end stage renal disease resulting in a mortality rate of 60%. Treatment involves surgical correction of the abdominal wall defect and urinary tract abnormalities, early orchiopexy, and supportive management of associated defects.

  13. Quadratus lumborum block for post-operative pain relief in patient with Prune belly syndrome

    Directory of Open Access Journals (Sweden)

    Chitra Garg

    2017-01-01

    Full Text Available Abdominal field blocks are commonly used as part of multimodal analgesia for post-operative pain relief in patients undergoing abdominal surgery. Conventionally, transversus abdominis plane block is used, but has the disadvantage of limited spread only to T10–T12 segments, providing only partial pain relief. The new quadratus lumborum (QL block has the advantage of providing wider sensory block from T6 to L1 and thus has an evolving role in opioid-free anaesthesia. Opioid-induced cough depression, urinary retention, and drowsiness can be problematic in patients with Prune belly syndrome, who have deficient abdominal muscles and myriad of genitourinary problems. We report a case of a young male with Prune belly syndrome, who had a pain-free post-operative period after high inguinal orchidectomy with unilateral QL block.

  14. Combining soft decision algorithms and scale-sequential hypotheses pruning for object recognition

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, V.P.; Manolakos, E.S. [Northeastern Univ., Boston, MA (United States)

    1996-12-31

    This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.

  15. The improved Apriori algorithm based on matrix pruning and weight analysis

    Science.gov (United States)

    Lang, Zhenhong

    2018-04-01

    This paper uses the matrix compression algorithm and weight analysis algorithm for reference and proposes an improved matrix pruning and weight analysis Apriori algorithm. After the transactional database is scanned for only once, the algorithm will construct the boolean transaction matrix. Through the calculation of one figure in the rows and columns of the matrix, the infrequent item set is pruned, and a new candidate item set is formed. Then, the item's weight and the transaction's weight as well as the weight support for items are calculated, thus the frequent item sets are gained. The experimental result shows that the improved Apriori algorithm not only reduces the number of repeated scans of the database, but also improves the efficiency of data correlation mining.

  16. Degree Associated Edge Reconstruction Number of Graphs with Regular Pruned Graph

    Directory of Open Access Journals (Sweden)

    P. Anusha Devi

    2015-10-01

    Full Text Available An ecard of a graph $G$ is a subgraph formed by deleting an edge. A da-ecard specifies the degree of the deleted edge along with the ecard. The degree associated edge reconstruction number of a graph $G,~dern(G,$ is the minimum number of da-ecards that uniquely determines $G.$  The adversary degree associated edge reconstruction number of a graph $G, adern(G,$ is the minimum number $k$ such that every collection of $k$ da-ecards of $G$ uniquely determines $G.$ The maximal subgraph without end vertices of a graph $G$ which is not a tree is the pruned graph of $G.$ It is shown that $dern$ of complete multipartite graphs and some connected graphs with regular pruned graph is $1$ or $2.$ We also determine $dern$ and $adern$ of corona product of standard graphs.

  17. Mycotoxin risks and toxigenic fungi in date, prune and dried apricot among Mediterranean crops

    Directory of Open Access Journals (Sweden)

    Hayrettin OZER

    2012-05-01

    Full Text Available Dried fruit is fruit that is preserved by removing the original water content naturally, through sun drying or artificially, by the use of specialized dryers or dehydrators. Dried fruit has a long tradition of use dating back to the fourth millennium BC in Mesopotamia and is prized because of its sweet taste, nutritive value and long shelf life. Traditional dried fruits such as raisins, figs, dates, apricots and prunes have been a staple of Mediterranean diets for millennia. The Mediterranean region is very favourable for production of dried fruits, not only with its climatic conditions, but also its exceptional fertile lands. Additionally, proximity to trade routes historically has allowed Mediterranean countries more access to dried fruits than landlocked countries. Today, dried fruit consumption is widespread. Nearly half of the dried fruits sold throughout the world are raisins, followed by dates, prunes (dried plums, figs, apricots, peaches, apples and pears. Dates, prunes, apricots, figs and raisins are the major dried fruits produced in the Mediterranean area. Dried fruits are not perishable but can support mold growth, some of which can produce mycotoxins. Occurence of toxigenic molds and mycotoxins on these dried fruits can be a problem in the Mediterranean basin, as in the other parts of the world, being a health hazard to the population as well as a trade issue for the export of local products. Although the most important mycotoxins occuring in Mediterranean crops are aflatoxins (B1, B2, G1 and G2 and ochratoxin A, the type and level of mycotoxins and toxigenic molds vary by crop and also by country and in some cases geographic location within a country. In this review mycotoxin risks and toxigenic fungi in date, prune and dried apricot among Mediterranean crops are reported and discussed.

  18. Two- and three-dimensional prenatal sonographic diagnosis of prune-belly syndrome.

    Science.gov (United States)

    Chen, Lizhu; Cai, Ailu; Wang, Xiaoguang; Wang, Bing; Li, Jingyu

    2010-06-01

    We report the prenatal diagnosis of 6 cases of Prune-belly syndrome in the 2(nd) trimester. The sonographic diagnosis was based on the findings of oligohydramnios, renal anomalies, and a lower abdominal cystic mass representing the abnormal dilatation of the bladder on conventional 2-dimensional sonographic examination. We discuss the role of Doppler imaging and 3-dimensional sonography as complementary methods to conventional sonography. Four of our 6 cases were confirmed with associated defects.

  19. Congenital mydriasis and prune belly syndrome in a child with an ACTA2 mutation.

    Science.gov (United States)

    Brodsky, Michael C; Turan, Kadriye Erkan; Khanna, Cheryl L; Patton, Alice; Kirmani, Salman

    2014-08-01

    We report the association of congenital mydriasis with prune belly syndrome and cerebrovascular anomalies in a 9-year-old boy who was found to have an ACTA2 mutation. This case illustrates the spectrum of systemic malformations that are attributable to mutations in ACTA2 and expands the spectrum of cerebrovascular anomalies that are now known to accompany congenital mydriasis. Copyright © 2014 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

  20. Prune Belly Syndrome Associated with Full Spectrum of VACTERL in a New Born.

    Science.gov (United States)

    Younous, Said; Zarrouki, Youssef; Boutbaoucht, Mustapha; Mouaffak, Youssef; El Idrissi, Kawtar Ennour; Aboussair, Nissrine; Saiad, Mohammed O

    2012-01-01

    Prune belly syndrome (PBS) is a rare congenital anomaly of uncertain etiology. Many associations of PBS with other malformations were previously reported, but only few cases of the association with VACTERL have been described. We report a rare case of a Moroccan new born with PBS and complete VACTERL association. The cause of this association is still unknown, but a common etiology is possible, especially when for the two syndromes, a defect in mesodermal differentiation, in early first trimester, has been suggested.

  1. Megacystis-microcolon-intestinal hypoperistalsis syndrome associated with prune belly syndrome: a case report.

    Science.gov (United States)

    Akhtar, Tanveer; Alladi, Anand; Siddappa, O S

    2012-01-01

    Megacystis Microcolon Intestinal Hypoperistalsis Syndrome is a quite rare congenital anomaly that presents with a functional obstruction of the gastrointestinal tract which is usually fatal. It is three to four times more prevalent in females. We present a case of a rare association of a male neonate with Megacystis Microcolon Intestinal Hypoperistalsis Syndrome who in addition had the classical triad of Prune Belly Syndrome and thus suggest a possibility of different spectrums with a common pathogenesis.

  2. The prune belly anomaly: heterogeneity and superficial X-linkage mimicry.

    Science.gov (United States)

    Riccardi, V M; Grum, C M

    1977-01-01

    The genetic, clinical, and necropsy findings of 2 brothers with the prune belly anomaly are presented and the literature reviewed. The combined data emphasise the clinical and genetic heterogeneity of the disorder and show that in at least some instances a heritable component may be the primary insult. The most likely heritable explanation involves a two-step autosomal dominant mutation with sex-limited expression that partially mimics X-linkage. PMID:144797

  3. PRUNE BELLY SYNDROME: CASE REPORT OF A FAILED MANAGEMENT IN A LOWINCOME COUNTRY.

    Directory of Open Access Journals (Sweden)

    Marcella Schiavone

    2016-04-01

    Full Text Available Prune Belly Syndrome (PBS is a rare congenital syndrome characterized by three main features: abdominal wall flaccidity, bilateral intra-abdominal cryptorchidism, and urologic abnormalities. In this study we describe the case of a 2,600 gr baby, born at the Central Hospital of Beira, Mozambique. Our study confirms that in a low-income country only conservative management can be delivered, and therefore prognosis is worse and less effective than high-income countries.

  4. Growth following pruning of young loblolly pine trees: some early results

    Science.gov (United States)

    Ralph L. Amateis; Harold E. Burkhart

    2006-01-01

    In the spring of 2000, a designed experiment was established to study the effects of pruning on juvenile loblolly pine (Pinus taeda L.) tree growth and the subsequent formation of mature wood. Trees were planted at a 3 m x 3 m square spacing in plots of 6 rows with 6 trees per row, with the inner 16 trees constituting the measurement plot. Among the...

  5. MG Travessia: a coffee arabica cultivar productive and responsive to pruning

    Directory of Open Access Journals (Sweden)

    Gladyston Rodrigues Carvalho

    2017-06-01

    Full Text Available This paper presents the results of progeny 1190-1170-2, which was recorded as ‘MGS Travessia’ and selected based on its performance in the state of Minas Gerais. The cultivar has short size, cylindrical canopy, high yield capacity, high vegetative vigor, very satisfactory husk/bean ratio, grain quality compatible to traditional cultivars, and is very responsive to skeleton pruning.

  6. Pruning cycles and nitrogen fertilization of coffee fields conducted in the “safra zero” system

    OpenAIRE

    Japiassú, Leonardo Bíscaro; Fundação Procafé; Garcia, André Luiz Alvarenga; Fundação Procafé; Guimarães, Rubens José; Universidade Federal de Lavras - UFLA; Padilha, Lílian; Embrapa Café; Carvalho, Carlos Henrique Siqueira; Embrapa Café

    2010-01-01

    Modern, competitive and cost effective coffee production requires plants with high productivity that are more adapted to mechanical and manual harvesting. “Safra Zero” is a cultivation system designed to limit plant height and eliminate the need for expensive harvesting during years of low productivity, which usually follow years of high productivity. This system is based on pruning cycles, nitrogen fertilization and different management methods. To evaluate the “Safra Zero&...

  7. Canopy and leaf composition drive patterns of nutrient release from pruning residues in a coffee agroforest.

    Science.gov (United States)

    Tully, Katherine L; Lawrence, Deborah

    2012-06-01

    In a coffee agroforest, the crop is cultivated under the shade of fruit-bearing and nitrogen (N)-fixing trees. These trees are periodically pruned to promote flowering and fruiting as well as to make nutrients stored in tree biomass available to plants. We investigated the effect of canopy composition and substrate quality on decomposition rates and patterns of nutrient release from pruning residues in a coffee agroforest located in Costa Rica's Central Valley. Initial phosphorus (P) release was enhanced under a canopy composed solely of N-fixing, Erythrina poeppigiana compared to a mixed canopy of Erythrina and Musa acuminata (banana). Both initial and final N release were similar under the two canopy types. However, after five months of decomposition, a higher proportion of initial N had been released under the single canopy. Although patterns of decomposition and nutrient release were not predicted by initial substrate quality, mass loss in leaf mixtures rates were well predicted by mean mass loss of their component species. This study identifies specific pruning regimes that may regulate N and P release during crucial growth periods, and it suggests that strategic pruning can enhance nutrient availability. For example, during the onset of rapid fruit growth, a two-species mixture may release more P than a three-species mixture. However, by the time of the harvest, the two- and three-species mixtures have released roughly the same amount of N and P. These nutrients do not always follow the same pattern, as N release can be maximized in single-species substrates, while P release is often facilitated in species mixtures. Our study indicates the importance of management practices in mediating patterns of nutrient release. Future research should investigate how canopy composition and farm management can also mediate on-farm nutrient losses.

  8. DNA hypomethylation, transient neonatal diabetes, and prune belly sequence in one of two identical twins.

    Science.gov (United States)

    Laborie, Lene Bjerke; Mackay, Deborah J G; Temple, I Karen; Molven, Anders; Søvik, Oddmund; Njølstad, Pål Rasmus

    2010-02-01

    One known genetic mechanism for transient neonatal diabetes is loss of methylation at 6q24. The etiology of prune belly sequence is unknown but a genetic defect, affecting the mesoderm from which the triad abdominal muscle hypoplasia, urinary tract abnormalities, and cryptorchidism develop, has been suggested. We investigated a family, including one twin, with transient neonatal diabetes and prune belly sequence. Autoantibody tests excluded type 1 diabetes. Microsatellite marker analysis confirmed the twins being monozygotic. We identified no mutations in ZFP57, KCNJ11, ABCC8, GCK, HNF1A, HNF1B, HNF3B, IPF1, PAX4, or ZIC3. The proband had loss of methylation at the 6q24 locus TNDM and also at the loci IGF2R, DIRAS3, and PEG1, while the other family members, including the healthy monozygotic twin, had normal findings. The loss of methylation on chromosome 6q24 and elsewhere may indicate a generalized maternal hypomethylation syndrome, which accounts for both transient neonatal diabetes and prune belly sequence.

  9. Traceability System for Improved Utilization of Solid Biofuel from Agricultural Prunings

    Directory of Open Access Journals (Sweden)

    Techane Bosona

    2018-01-01

    Full Text Available Biomass production and supply for renewable energy generation should be managed well and carried out in a sustainable manner. An effective traceability system (TS is required to provide sufficient information and assure the quality of the biomass. The objective of this study is to define and develop a TS to assure the pruning biomass quality for the production of solid biofuels and to provide guarantee to the final user that the biomass is in good condition according to recommended quality criteria. It is designed for an agricultural pruning supply chain in which farmers, biomass traders, transporters, and end users are major actors. It is based on the biofuel quality requirements required by final users and other standards such as the new European standards EN 14961-1, EN15234:1-2011, and EN14961-1:2010, which describe solid fuel quality parameters. Traceable quality parameters include origin and source of product, traded form, bale dimension, chips size distribution, moisture content, ash content, and density of biomass. In this TS, a unique product label is introduced and integrated into a smart logistics system (SLS. The TS uses information captured at different stages of the product supply chain. It enables the management of the whole pruning biomass supply chain with the support of a centralized web-based information platform, an integral part of the SLS.

  10. Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, M. [Silesian Technical University, Gliwice (Poland)

    2011-06-15

    The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard state in coal mines. Due to imbalanced distribution of examples describing states 'hazardous' and 'safe', the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters' values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier's quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.

  11. Carbon footprint associated with four disposal scenarios for urban pruning waste.

    Science.gov (United States)

    Araújo, Yuri Rommel Vieira; de Góis, Monijany Lins; Junior, Luiz Moreira Coelho; Carvalho, Monica

    2018-01-01

    The inadequate disposal of urban pruning residues can cause significant environmental impacts. The objective of the study presented herein was to quantify the carbon footprint and analyze four disposal scenarios for the urban pruning waste of the city of Joao Pessoa (Northeast Brazil). Software SimaPro was utilized for the quantification of the carbon footprint, with the IPCC 2013 GWP 100y impact evaluation method. The end-of-life treatments considered were sanitary landfilling (with and without collection of methane), simple municipal incineration, and reutilization of wood (transformation into briquettes). The results indicated that simple disposal in sanitary landfill generated 136.34 kg CO 2 /t urban pruning waste collected (highest carbon footprint), sanitary landfill with methane collection emitted 113.43 kg CO 2 /t waste, municipal incineration generated 71.31 kg CO 2 /t waste, and reutilization of woody residues was the scenario with the lowest carbon footprint, with 27.82 kg CO 2 /t waste. This study demonstrated that reutilization of biomass, besides being environmentally viable, presents the potential to contribute to the city's environmental quality, including the possibility of being used to obtain carbon credits.

  12. Nitrogen fixation and effects of pruning on Gliricidia sepium and Leucaena leucocephala

    International Nuclear Information System (INIS)

    Liyanage, M.S. de

    1998-01-01

    This 7-year study examined genetic variability in N 2 fixation by Gliricidia sepium and the N 2 -fixing capacity in G. sepium and Leucaena leucocephala as influenced by frequency of pruning, age, and shade from coconut. The 15 N-dilution method was used with the non-nodulating tree legume Senna siamea as the non-fixing reference. There were significant differences in total dry matter, N yield and N 2 -fixation capacity among four G. sepium provenances. Gliricidia had higher values than Leucaena for dry matter, N yield, and amount of N fixed; %Ndfa was comparable in both species (47-55%). A substantial amount (18%) of fixed N 2 was present in the roots of both species. In a long-term study aimed at comparing the effect of pruning practices and age of trees, G. sepium grown under coconut outperformed L. leucocephala in terms of dry matter, N yield and amounts of N 2 fixation. Coconut saplings supplied with G. sepium and L. leucocephala prunings as green manure grew better than those supplied with S. siamea; the fraction of coconut-sapling N obtained from Gliricidia and Leucaena was 40 and 36%, respectively. These results suggest that G. sepium, which demonstrated a high potential for biomass production and N 2 fixation, is appropriate for interplanting with coconut palms. Also, S. Siamea was found to be a suitable reference species. (author)

  13. Combustion of a Pb(II)-loaded olive tree pruning used as biosorbent.

    Science.gov (United States)

    Ronda, A; Della Zassa, M; Martín-Lara, M A; Calero, M; Canu, P

    2016-05-05

    The olive tree pruning is a specific agroindustrial waste that can be successfully used as adsorbent, to remove Pb(II) from contaminated wastewater. Its final incineration has been studied in a thermobalance and in a laboratory flow reactor. The study aims at evaluating the fate of Pb during combustion, at two different scales of investigation. The flow reactor can treat samples approximately 10(2) larger than the conventional TGA. A detailed characterization of the raw and Pb(II)-loaded waste, before and after combustion is presented, including analysis of gas and solids products. The Pb(II)-loaded olive tree pruning has been prepared by a previous biosorption step in a lead solution, reaching a concentration of lead of 2.3 wt%. Several characterizations of the ashes and the mass balances proved that after the combustion, all the lead presents in the waste remained in ashes. Combustion in a flow reactor produced results consistent with those obtained in the thermobalance. It is thus confirmed that the combustion of Pb(II)-loaded olive tree pruning is a viable option to use it after the biosorption process. The Pb contained in the solid remained in the ashes, preventing possible environmental hazards. Copyright © 2016 Elsevier B.V. All rights reserved.

  14. Evaluation of fungicides to protect pruning wounds from Botryosphaeriaceae species infections on almond trees

    Directory of Open Access Journals (Sweden)

    Diego OLMO

    2017-05-01

    Full Text Available In vitro efficacy of ten fungicides was evaluated against four Botryosphaeriaceae spp. (Diplodia seriata, Neofusicoccum luteum, N. mediterraneum and N. parvum associated with branch cankers on almond trees. Cyproconazole, pyraclostrobin, tebuconazole, and thiophanate-methyl were effective for the inhibition of mycelial growth of most of these fungi. An experiment on 3-year-old almond trees evaluated boscalid, mancozeb, thiophanate-methyl, pyraclostrobin and tebuconazole for preventative ability against infections caused by the four pathogens. Five months after pruning and fungicide application, lesion length measurements and isolation percentages showed no significant differences among the four pathogens after they were inoculated onto the trees, and also between the two inoculation times tested (1 or 7 d after fungicide application. Thiophanate-methyl was the most effective fungicide, resulting in the shortest lesion lengths and the lowest isolation percentages from artificially inoculated pruning wounds. This chemical is therefore a candidate for inclusion in integrated disease management, to protect pruning wounds from infections caused by species of Botryosphaeriaceae. This study represents the first approach to development of chemical control strategies for the management of canker diseases caused by Botryosphaeriaceae fungi on almond trees. 

  15. Jatropha curcas, L. Pruning Residues for Energy: Characteristics of an Untapped By-Product

    Directory of Open Access Journals (Sweden)

    Luigi Pari

    2018-06-01

    Full Text Available Jatropha (Jatropha curcas, L. is an energy crop mainly cultivated for the oil-seed, and the oil is usually used as bio-fuel. However, few studies have reported information about the utilization of the wood as a fuel for boiler heating systems. With 2500 jatropha trees per hectare, it is possible to produce about 3 t·ha−1·y−1 of woody biomass from pruning. In addition, jatropha trees are commonly cut down to a height of 45 cm once every 10 years, with a production of 80 t·ha−1 of dry matter of woody biomass. The use of this biomass has not yet been investigated. During the European project JatroMed, woody biomass from jatropha pruning was collected in Morocco. Chemical and physical characteristics of the wood were conducted according to UNI EN ISO standards. The following jatropha wood characteristics have been analyzed: Moisture and ash contents, the ash melting point, heating value, and concentrations of C, H, N, and S. This research focused on the evaluation of the potential use of jatropha pruning for energy production, and the results represent critical data that is useful for future studies and business potential.

  16. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial.

    Science.gov (United States)

    McCabe, Jessica; Monkiewicz, Michelle; Holcomb, John; Pundik, Svetlana; Daly, Janis J

    2015-06-01

    To compare response to upper-limb treatment using robotics plus motor learning (ML) versus functional electrical stimulation (FES) plus ML versus ML alone, according to a measure of complex functional everyday tasks for chronic, severely impaired stroke survivors. Single-blind, randomized trial. Medical center. Enrolled subjects (N=39) were >1 year postsingle stroke (attrition rate=10%; 35 completed the study). All groups received treatment 5d/wk for 5h/d (60 sessions), with unique treatment as follows: ML alone (n=11) (5h/d partial- and whole-task practice of complex functional tasks), robotics plus ML (n=12) (3.5h/d of ML and 1.5h/d of shoulder/elbow robotics), and FES plus ML (n=12) (3.5h/d of ML and 1.5h/d of FES wrist/hand coordination training). Primary measure: Arm Motor Ability Test (AMAT), with 13 complex functional tasks; secondary measure: upper-limb Fugl-Meyer coordination scale (FM). There was no significant difference found in treatment response across groups (AMAT: P≥.584; FM coordination: P≥.590). All 3 treatment groups demonstrated clinically and statistically significant improvement in response to treatment (AMAT and FM coordination: P≤.009). A group treatment paradigm of 1:3 (therapist/patient) ratio proved feasible for provision of the intensive treatment. No adverse effects. Severely impaired stroke survivors with persistent (>1y) upper-extremity dysfunction can make clinically and statistically significant gains in coordination and functional task performance in response to robotics plus ML, FES plus ML, and ML alone in an intensive and long-duration intervention; no group differences were found. Additional studies are warranted to determine the effectiveness of these methods in the clinical setting. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  17. Future Projection with an Extreme-Learning Machine and Support Vector Regression of Reference Evapotranspiration in a Mountainous Inland Watershed in North-West China

    Directory of Open Access Journals (Sweden)

    Zhenliang Yin

    2017-11-01

    Full Text Available This study aims to project future variability of reference evapotranspiration (ET0 using artificial intelligence methods, constructed with an extreme-learning machine (ELM and support vector regression (SVR in a mountainous inland watershed in north-west China. Eight global climate model (GCM outputs retrieved from the Coupled Model Inter-comparison Project Phase 5 (CMIP5 were employed to downscale monthly ET0 for the historical period 1960–2005 as a validation approach and for the future period 2010–2099 as a projection of ET0 under the Representative Concentration Pathway (RCP 4.5 and 8.5 scenarios. The following conclusions can be drawn: the ELM and SVR methods demonstrate a very good performance in estimating Food and Agriculture Organization (FAO-56 Penman–Monteith ET0. Variation in future ET0 mainly occurs in the spring and autumn seasons, while the summer and winter ET0 changes are moderately small. Annually, the ET0 values were shown to increase at a rate of approximately 7.5 mm, 7.5 mm, 0.0 mm (8.2 mm, 15.0 mm, 15.0 mm decade−1, respectively, for the near-term projection (2010–2039, mid-term projection (2040–2069, and long-term projection (2070–2099 under the RCP4.5 (RCP8.5 scenario. Compared to the historical period, the relative changes in ET0 were found to be approximately 2%, 5% and 6% (2%, 7% and 13%, during the near, mid- and long-term periods, respectively, under the RCP4.5 (RCP8.5 warming scenarios. In accordance with the analyses, we aver that the opportunity to downscale monthly ET0 with artificial intelligence is useful in practice for water-management policies.

  18. Energy and carbon emissions analysis and prediction of complex petrochemical systems based on an improved extreme learning machine integrated interpretative structural model

    International Nuclear Information System (INIS)

    Han, Yongming; Zhu, Qunxiong; Geng, Zhiqiang; Xu, Yuan

    2017-01-01

    Highlights: • The ELM integrated ISM (ISM-ELM) method is proposed. • The proposed method is more efficient and accurate than the ELM through the UCI data set. • Energy and carbon emissions analysis and prediction of petrochemical industries based ISM-ELM is obtained. • The proposed method is valid in improving energy efficiency and reducing carbon emissions of ethylene plants. - Abstract: Energy saving and carbon emissions reduction of the petrochemical industry are affected by many factors. Thus, it is difficult to analyze and optimize the energy of complex petrochemical systems accurately. This paper proposes an energy and carbon emissions analysis and prediction approach based on an improved extreme learning machine (ELM) integrated interpretative structural model (ISM) (ISM-ELM). ISM based the partial correlation coefficient is utilized to analyze key parameters that affect the energy and carbon emissions of the complex petrochemical system, and can denoise and reduce dimensions of data to decrease the training time and errors of the ELM prediction model. Meanwhile, in terms of the model accuracy and the training time, the robustness and effectiveness of the ISM-ELM model are better than the ELM through standard data sets from the University of California Irvine (UCI) repository. Moreover, a multi-inputs and single-output (MISO) model of energy and carbon emissions of complex ethylene systems is established based on the ISM-ELM. Finally, detailed analyses and simulations using the real ethylene plant data demonstrate the effectiveness of the ISM-ELM and can guide the improvement direction of energy saving and carbon emissions reduction in complex petrochemical systems.

  19. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    2017-09-01

    Full Text Available Multimodal features of structural and functional magnetic resonance imaging (MRI of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P < 0.0001 accuracy which preserves high discriminatory power through the weight of the individual feature type. The classification was performed by an extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis.

  20. The effects of pruning and nodal adventitious roots on polychlorinated biphenyl uptake by Cucurbita pepo grown in field conditions

    Energy Technology Data Exchange (ETDEWEB)

    Low, Jennifer E.; Whitfield Aslund, Melissa L. [Department of Chemistry and Chemical Engineering, Royal Military College of Canada, PO Box 17000 Station Forces, Kingston, ON, K7K 7B4 (Canada); Rutter, Allison [School of Environmental Studies, Rm 0626 Biosciences Complex, Queen' s University, 116 Barrie St., Kingston, ON, K7L 3N6 (Canada); Zeeb, Barbara A., E-mail: zeeb-b@rmc.ca [Department of Chemistry and Chemical Engineering, Royal Military College of Canada, PO Box 17000 Station Forces, Kingston, ON, K7K 7B4 (Canada)

    2011-03-15

    Two cultivation techniques (i-pruning and ii-nodal adventitious root encouragement) were investigated for their ability to increase PCB phytoextraction by Cucurbita pepo ssp pepo cv. Howden (pumpkin) plants in situ at a contaminated industrial site in Ontario (Aroclor 1248, mean soil [PCB] = 5.6 {mu}g g{sup -1}). Pruning was implemented to increase plant biomass close to the root where PCB concentration is known to be highest. This treatment was found to have no effect on final shoot biomass or PCB concentration. However, material pruned from the plant is not included in the final shoot biomass. The encouragement of nodal adventitious roots at stem nodes did significantly increase the PCB concentration in the primary stem, while not affecting shoot biomass. Both techniques are easily applied cultivation practices that may be implemented to decrease phytoextraction treatment time. - Research highlights: > Presence of nodal adventitious roots do increase phytoextraction efficiency. > Pruning may increase the biomass of pumpkin plants during phytoextraction. > [Aroclor 1248] decreases in plant tissue with increasing distance from the root. - The application of cultivation practices (pruning and nodal adventitious root encouragement) increases phytoextraction of PCBs in C. pepo.

  1. The effects of pruning and nodal adventitious roots on polychlorinated biphenyl uptake by Cucurbita pepo grown in field conditions

    International Nuclear Information System (INIS)

    Low, Jennifer E.; Whitfield Aslund, Melissa L.; Rutter, Allison; Zeeb, Barbara A.

    2011-01-01

    Two cultivation techniques (i-pruning and ii-nodal adventitious root encouragement) were investigated for their ability to increase PCB phytoextraction by Cucurbita pepo ssp pepo cv. Howden (pumpkin) plants in situ at a contaminated industrial site in Ontario (Aroclor 1248, mean soil [PCB] = 5.6 μg g -1 ). Pruning was implemented to increase plant biomass close to the root where PCB concentration is known to be highest. This treatment was found to have no effect on final shoot biomass or PCB concentration. However, material pruned from the plant is not included in the final shoot biomass. The encouragement of nodal adventitious roots at stem nodes did significantly increase the PCB concentration in the primary stem, while not affecting shoot biomass. Both techniques are easily applied cultivation practices that may be implemented to decrease phytoextraction treatment time. - Research highlights: → Presence of nodal adventitious roots do increase phytoextraction efficiency. → Pruning may increase the biomass of pumpkin plants during phytoextraction. → [Aroclor 1248] decreases in plant tissue with increasing distance from the root. - The application of cultivation practices (pruning and nodal adventitious root encouragement) increases phytoextraction of PCBs in C. pepo.

  2. Using a pruned, nondirect product basis in conjunction with the multi-configuration time-dependent Hartree (MCTDH) method

    Energy Technology Data Exchange (ETDEWEB)

    Wodraszka, Robert, E-mail: Robert.Wodraszka@chem.queensu.ca; Carrington, Tucker, E-mail: Tucker.Carrington@queensu.ca [Department of Chemistry, Queen’s University, Kingston, Ontario K7L 3N6 (Canada)

    2016-07-28

    In this paper, we propose a pruned, nondirect product multi-configuration time dependent Hartree (MCTDH) method for solving the Schrödinger equation. MCTDH uses optimized 1D basis functions, called single particle functions, but the size of the standard direct product MCTDH basis scales exponentially with D, the number of coordinates. We compare the pruned approach to standard MCTDH calculations for basis sizes small enough that the latter are possible and demonstrate that pruning the basis reduces the CPU cost of computing vibrational energy levels of acetonitrile (D = 12) by more than two orders of magnitude. Using the pruned method, it is possible to do calculations with larger bases, for which the cost of standard MCTDH calculations is prohibitive. Pruning the basis complicates the evaluation of matrix-vector products. In this paper, they are done term by term for a sum-of-products Hamiltonian. When no attempt is made to exploit the fact that matrices representing some of the factors of a term are identity matrices, one needs only to carefully constrain indices. In this paper, we develop new ideas that make it possible to further reduce the CPU time by exploiting identity matrices.

  3. Bonsai trees in your head: how the pavlovian system sculpts goal-directed choices by pruning decision trees.

    Directory of Open Access Journals (Sweden)

    Quentin J M Huys

    Full Text Available When planning a series of actions, it is usually infeasible to consider all potential future sequences; instead, one must prune the decision tree. Provably optimal pruning is, however, still computationally ruinous and the specific approximations humans employ remain unknown. We designed a new sequential reinforcement-based task and showed that human subjects adopted a simple pruning strategy: during mental evaluation of a sequence of choices, they curtailed any further evaluation of a sequence as soon as they encountered a large loss. This pruning strategy was Pavlovian: it was reflexively evoked by large losses and persisted even when overwhelmingly counterproductive. It was also evident above and beyond loss aversion. We found that the tendency towards Pavlovian pruning was selectively predicted by the degree to which subjects exhibited sub-clinical mood disturbance, in accordance with theories that ascribe Pavlovian behavioural inhibition, via serotonin, a role in mood disorders. We conclude that Pavlovian behavioural inhibition shapes highly flexible, goal-directed choices in a manner that may be important for theories of decision-making in mood disorders.

  4. Axon Termination, Pruning, and Synaptogenesis in the Giant Fiber System of Drosophila melanogaster Is Promoted by Highwire.

    Science.gov (United States)

    Borgen, Melissa; Rowland, Kimberly; Boerner, Jana; Lloyd, Brandon; Khan, Aruna; Murphey, Rodney

    2017-03-01

    The ubiquitin ligase Highwire has a conserved role in synapse formation. Here, we show that Highwire coordinates several facets of central synapse formation in the Drosophila melanogaster giant fiber system, including axon termination, axon pruning, and synaptic function. Despite the similarities to the fly neuromuscular junction, the role of Highwire and the underlying signaling pathways are distinct in the fly's giant fiber system. During development, branching of the giant fiber presynaptic terminal occurs and, normally, the transient branches are pruned away. However, in highwire mutants these ectopic branches persist, indicating that Highwire promotes axon pruning. highwire mutants also exhibit defects in synaptic function. Highwire promotes axon pruning and synaptic function cell-autonomously by attenuating a mitogen-activated protein kinase pathway including Wallenda, c-Jun N-terminal kinase/Basket, and the transcription factor Jun. We also show a novel role for Highwire in non-cell autonomous promotion of synaptic function from the midline glia. Highwire also regulates axon termination in the giant fibers, as highwire mutant axons exhibit severe overgrowth beyond the pruning defect. This excessive axon growth is increased by manipulating Fos expression in the cells surrounding the giant fiber terminal, suggesting that Fos regulates a trans -synaptic signal that promotes giant fiber axon growth. Copyright © 2017 by the Genetics Society of America.

  5. Pseudo Prune Belly Syndrome: Diagnosis Revealed by Imaging – A Case Report and Brief Review

    Science.gov (United States)

    Grover, Hemal; Sethi, Sanjay; Garg, Jatin; Ahluwalia, Amrit Pal

    2017-01-01

    Summary Background Prune Belly Syndrome (PBS) is a rare entity, usually found in male neonates. It comprises complex urinary tract anomalies, bilateral undescended testis and absence of anterior abdominal wall muscles. Patients with unilateral abdominal wall deficiency, unilateral undescended testis and female neonates with abdominal wall laxity are classified as Pseudo Prune Belly syndrome (PPBS). Reports on PPBS do not highlight the radiological and imaging characteristics of this syndrome and the current literature on the role of newer imaging modalities, such as Magnetic Resonance Imaging (MRI), remains relatively sparse. We describe a new case of PPBS and emphasize the role of imaging, especially ultrasound and MRI in the process of diagnosis and briefly review the subject. Case Report A male infant of four months of age was referred for evaluation of left-sided cryptorchidism. Clinical examination revealed laxity of the left abdominal wall. Ultrasound examination of the abdomen, pelvis and scrotum was performed together with routine laboratory tests. Ultrasound examination was followed by intravenous urography, voiding cysto-urethrography and MRI of the abdomen. On ultrasound, the left testis was located in the inguinal canal, the right kidney was slightly enlarged and the left kidney could not be localized. Ultrasound appearances suggested chronic obstruction in the urinary bladder. Intravenous urography, voiding cysto-urethrography and MRI confirmed the ultrasound diagnosis and also revealed a left dysplastic kidney with a dilated, tortuous ureter. Clinical and imaging features were consistent with pseudo prune belly syndrome (PPBS). Conclusions We report a new occurrence of PPBS, a rare entity. The imaging approach for a comprehensive evaluation of the renal system in PPBS, especially with MRI, is emphasized. PMID:28580040

  6. Pseudo Prune Belly Syndrome: Diagnosis Revealed by Imaging - A Case Report and Brief Review.

    Science.gov (United States)

    Grover, Hemal; Sethi, Sanjay; Garg, Jatin; Ahluwalia, Amrit Pal

    2017-01-01

    Prune Belly Syndrome (PBS) is a rare entity, usually found in male neonates. It comprises complex urinary tract anomalies, bilateral undescended testis and absence of anterior abdominal wall muscles. Patients with unilateral abdominal wall deficiency, unilateral undescended testis and female neonates with abdominal wall laxity are classified as Pseudo Prune Belly syndrome (PPBS). Reports on PPBS do not highlight the radiological and imaging characteristics of this syndrome and the current literature on the role of newer imaging modalities, such as Magnetic Resonance Imaging (MRI), remains relatively sparse. We describe a new case of PPBS and emphasize the role of imaging, especially ultrasound and MRI in the process of diagnosis and briefly review the subject. A male infant of four months of age was referred for evaluation of left-sided cryptorchidism. Clinical examination revealed laxity of the left abdominal wall. Ultrasound examination of the abdomen, pelvis and scrotum was performed together with routine laboratory tests. Ultrasound examination was followed by intravenous urography, voiding cysto-urethrography and MRI of the abdomen. On ultrasound, the left testis was located in the inguinal canal, the right kidney was slightly enlarged and the left kidney could not be localized. Ultrasound appearances suggested chronic obstruction in the urinary bladder. Intravenous urography, voiding cysto-urethrography and MRI confirmed the ultrasound diagnosis and also revealed a left dysplastic kidney with a dilated, tortuous ureter. Clinical and imaging features were consistent with pseudo prune belly syndrome (PPBS). We report a new occurrence of PPBS, a rare entity. The imaging approach for a comprehensive evaluation of the renal system in PPBS, especially with MRI, is emphasized.

  7. Combustion of a Pb(II)-loaded olive tree pruning used as biosorbent

    Energy Technology Data Exchange (ETDEWEB)

    Ronda, A., E-mail: alirg@ugr.es [Department of Chemical Engineering, University of Granada, 18071 Granada (Spain); Della Zassa, M. [Department of Industrial Engineering, University of Padua, 35131 Padova (Italy); Martín-Lara, M.A.; Calero, M. [Department of Chemical Engineering, University of Granada, 18071 Granada (Spain); Canu, P. [Department of Industrial Engineering, University of Padua, 35131 Padova (Italy)

    2016-05-05

    Highlights: • The fate of Pb during combustion at two scales of investigation was studied. • Results from combustion in a flow reactor and in the thermobalance were consistent. • The Pb contained in the solid remained in the ashes. • The Pb does not interfere in the use of OTP as fuel. • The combustion of Pb(II)-loaded OTP does not cause environmental hazards. - Abstract: The olive tree pruning is a specific agroindustrial waste that can be successfully used as adsorbent, to remove Pb(II) from contaminated wastewater. Its final incineration has been studied in a thermobalance and in a laboratory flow reactor. The study aims at evaluating the fate of Pb during combustion, at two different scales of investigation. The flow reactor can treat samples approximately 10{sup 2} larger than the conventional TGA. A detailed characterization of the raw and Pb(II)-loaded waste, before and after combustion is presented, including analysis of gas and solids products. The Pb(II)-loaded olive tree pruning has been prepared by a previous biosorption step in a lead solution, reaching a concentration of lead of 2.3 wt%. Several characterizations of the ashes and the mass balances proved that after the combustion, all the lead presents in the waste remained in ashes. Combustion in a flow reactor produced results consistent with those obtained in the thermobalance. It is thus confirmed that the combustion of Pb(II)-loaded olive tree pruning is a viable option to use it after the biosorption process. The Pb contained in the solid remained in the ashes, preventing possible environmental hazards.

  8. Colonic volvulus detected by CT scan in a case with mental retardation and prune belly syndrome.

    Science.gov (United States)

    Oka, Yoichiro; Masumoto, Kouji; Nakamura, Masatoshi; Iwasaki, Akinori

    2011-10-01

    Colonic volvulus is a rare disease in children. Delayed diagnosis of the condition can often be fatal, especially in pediatric patients with mental retardation. We herein present the case of a female pediatric patient with colonic volvulus, prune belly syndrome, and mental retardation. Preoperative CT scans showed the characteristic signs of this disease. The volvulus occurred in the proximal colon of the colostomy. The release of the colonic volvulus and reconstruction of the colostomy were performed without the resection of the ischemic colon. The postoperative clinical course was uneventful. Copyright © 2012. Published by Elsevier B.V.

  9. Colonic volvulus detected by CT scan in a case with mental retardation and prune belly syndrome

    Directory of Open Access Journals (Sweden)

    Yoichiro Oka

    2011-10-01

    Full Text Available Colonic volvulus is a rare disease in children. Delayed diagnosis of the condition can often be fatal, especially in pediatric patients with mental retardation. We herein present the case of a female pediatric patient with colonic volvulus, prune belly syndrome, and mental retardation. Preoperative CT scans showed the characteristic signs of this disease. The volvulus occurred in the proximal colon of the colostomy. The release of the colonic volvulus and reconstruction of the colostomy were performed without the resection of the ischemic colon. The postoperative clinical course was uneventful.

  10. Combustion behaviour of Olive pruning/animal manure blends in a fluidized bed combustor

    Directory of Open Access Journals (Sweden)

    Despina Vamvuka

    2017-09-01

    Both fuels burned mostly within the bed. The maximum temperature of animal manure was 50 °C lower than that of olive pruning, however efficiency was nearly 99%. CO emissions were low, SO2 emissions were negligible, whereas NOx emissions of blends exceeded legislation limits, when excess air ratio was over 1.4. Decreasing excess air from 50 to 30%, or reducing reactor loading, resulted in improved burnout. The optimum performance for the blends was achieved when the feed rate was 0.6 kg/h and excess air was 30%.

  11. Survival of FUngi Associated with Grapevine Decline in Pruned Wood after Composting

    Directory of Open Access Journals (Sweden)

    P. Lecomte

    2006-04-01

    Full Text Available Recycling vine wood pruned in winter in the vineyard, after grinding and composting, might pose a risk of recontamination with fungi associated with grapevine decline. The survival of four ascomycete fungi (Botryosphaeria obtusa, Phaeomoniella chlamydospora, Phaeoacremonium aleophilum and Eutypa lata in composted material was investigated in a 3-year study conducted in the Bordeaux area. Naturally and artificially infested material was examined before and after composting using classical isolation procedures. Results clearly showed that a composting process can eradicate the four target fungi efficiently.

  12. Repair of pectus excavatum in a toddler with Prune Belly syndrome and left bronchus compression

    Directory of Open Access Journals (Sweden)

    Shawn T. Liechty

    2017-01-01

    Full Text Available A 2-year-old boy with prune-belly syndrome and severe pectus excavatum experienced recurrent pulmonary infections. A CT scan of the chest demonstrated compression of the left mainstem bronchus and leftward shift of the heart. The bronchial compression resulted in left upper lobe collapse and left lower lobe air-trapping requiring two hospitalizations for respiratory distress and pneumonia. The child underwent minimally invasive repair of his pectus excavatum and has not experienced any further pulmonary events. The pectus bar was removed 3 years post-operatively and at seven years following surgery he has a sustained repair.

  13. Application of the pessimistic pruning to increase the accuracy of C4.5 algorithm in diagnosing chronic kidney disease

    Science.gov (United States)

    Muslim, M. A.; Herowati, A. J.; Sugiharti, E.; Prasetiyo, B.

    2018-03-01

    A technique to dig valuable information buried or hidden in data collection which is so big to be found an interesting patterns that was previously unknown is called data mining. Data mining has been applied in the healthcare industry. One technique used data mining is classification. The decision tree included in the classification of data mining and algorithm developed by decision tree is C4.5 algorithm. A classifier is designed using applying pessimistic pruning in C4.5 algorithm in diagnosing chronic kidney disease. Pessimistic pruning use to identify and remove branches that are not needed, this is done to avoid overfitting the decision tree generated by the C4.5 algorithm. In this paper, the result obtained using these classifiers are presented and discussed. Using pessimistic pruning shows increase accuracy of C4.5 algorithm of 1.5% from 95% to 96.5% in diagnosing of chronic kidney disease.

  14. Symbiotic nitrogen fixation and yield of Pachyrhizus Erosus (L) urban cultivars and Pachyrhizus Ahipa (WEDD) parodi landraces as affected by flower pruning

    DEFF Research Database (Denmark)

    Castellanos, J.Z.; Zapata, F.; Badillo, V.

    1997-01-01

    biomass without N fertiliser application. In some climatic regions P. erosus is reproductively pruned in order to obtain economic yields, but little is known about how the pruning influences the capacity of these tuber legumes to fix nitrogen. Two experiments were carried out to investigate the effect...

  15. Effects of root pruning on the growth and rhizosphere soil characteristics of short-rotation closed-canopy poplar

    Energy Technology Data Exchange (ETDEWEB)

    Du, Z. Y.; Xing, S. J.; Ma, B. Y.; Liu, F. C.; Ma, H. L.; Wang, Q. H.

    2012-11-01

    When poplar trees planted at a high density are canopy-closed in plantation after 4-5 years of growth, the roots of adjacent trees will inevitably intermingle together, which possibly restricts the nutrient uptake by root system. Root pruning might stimulate the emergence of fine roots and benefit the tree growth of short-rotation poplar at the stage of canopy closing. The aim of this study is to evaluate the effects of root pruning on DBH (diameter at breast height, 1.3 m), tree height, nutrients (N, P and K) and hormones (indoleacetic acid and cytokinin) in poplar leaves, gas exchange variables (photosynthetic rate and stomatal conductance), and rhizosphere soil characteristics. Field experiment was carried out with four-yearold poplar (Populus × euramericana cv. ‘Neva’) planted in a fluvo-aquic loam soil in Shandong province, China in early April, 2008. Three root pruning treatments (severe, moderate and light degree) were conducted at the distances of 6, 8 and 10 times DBH on both inter-row sides of the trees to the depth of 30 cm, respectively. The results showed that the growth performance was obtained in the following order of treatments: moderate > light = control > severe. In the rhizophere soil, moderate and light pruning increased the microbial populations, enzymatic activities, and the concentrations of available N, P, K and organic matter. Generally, root pruning to improve tree growth and rhizosphere soil fertility can be recommended in canopy-closed poplar plantation. The appropriate selection of root pruning intensity is a pivotal factor for the effectiveness of this technique. (Author) 35 refs.

  16. Effect of pruning history on growth and dry mass partitioning of jatropha on a plantation site in Madagascar

    International Nuclear Information System (INIS)

    Rajaona, Arisoa M.; Brueck, Holger; Asch, Folkard

    2011-01-01

    While technical aspects of oil processing of seeds of jatropha are under intensive investigation, comparably little is known about the performance of jatropha in the field. We investigated the effects of water availability (rainfed versus irrigated) and pruning-induced differences in plant stature on growth, biomass partitioning, and canopy size at a plantation site in Madagascar in 2010. Plants of different pruning types differed in trunk height (43 versus 29 cm) and primary branches total length (171 versus 310 cm). The two pruning types had effects on dry mass formation and leaf area projection (LAP) during the vegetation period. Trees which had a shorter trunk and longer lateral branches produced more biomass and had a higher LAP. Total dry mass formation varied from 489 to 912 g m −2 and LAP from 3.26 to 7.37. Total aboveground biomass increased from 2.3 ± 0.5 to 4.89 ± 1.4 kg tree −1 and from 4.6 ± 1.8 to 8.9 ± 1.0 kg tree −1 for the pruning types with shorter and longer lateral branches, respectively. Growth of twigs and leaves was positively correlated with total length of branches. Relative dry mass allocation to branches, twigs and leaves, length of twigs per cm of branches and specific leaf area (13.57 ± 0.72 m 2 kg −1 ) were not affected by pruning and water supply. Trees with shorter branches had higher LAD. Results indicate that pruning type should be considered as a management tool to optimize biomass production. Detailed studies on effects of canopy size and shape on radiation interception and growth are required to improve the productivity of jatropha. -- Highlights: ▶ Correlation between branch length and newly formed twigs and leaves. ▶ Comparison of LAI and leaf area density in the field. ▶ Twigs per unit length of branches.

  17. A favorable outcome following 32 vesicocentesis and amnioinfusion procedures in a fetus with severe prune belly syndrome.

    Science.gov (United States)

    Galati, Vincenzo; Beeson, James H; Confer, Stephen D; Frimberger, Dominic; Campbell, Jeffrey B; Ramji, Faridali G; Kropp, Bradley P

    2008-04-01

    Patients with severe prune belly syndrome rarely survive beyond the first days of life. We present a case of prune belly syndrome that initially presented with severe oligohydramnios, megacystis and associated poor urine biochemistries. Due to an anteriorly located placenta the patient was referred to three major centers, but was turned down because of the unfavorable prognostic findings. Therefore, fetal intervention was performed with 32 vesicocentesis and amnioinfusion procedures. Despite the unfavorable prenatal findings, and having undergone numerous fetal interventions, the birth resulted in a viable infant.

  18. Recurrent nephrogenic adenoma in a 10-year-old boy with prune belly syndrome : a case presentation.

    Science.gov (United States)

    Vemulakonda, Vijaya M; Kopp, Ryan P; Sorensen, Mathew D; Grady, Richard W

    2008-05-01

    Nephrogenic adenoma is a rare benign lesion of the urinary tract that is associated with a history of irritation or injury of the urothelium. Predisposing factors include infection, calculi, surgery, trauma, and renal transplantation. Nephrogenic adenoma commonly presents with lower urinary tract symptoms or hematuria. We present the case of recurrent nephrogenic adenoma in a 10-year-old boy with a history of prune belly syndrome and discuss management of this disease in the pediatric population. To our knowledge this represents the first reported case of recurrent nephrogenic adenoma associated with prune belly syndrome.

  19. Involvements of PCD and changes in gene expression profile during self-pruning of spring shoots in sweet orange (Citrus sinensis).

    Science.gov (United States)

    Zhang, Jin-Zhi; Zhao, Kun; Ai, Xiao-Yan; Hu, Chun-Gen

    2014-10-13

    Citrus shoot tips abscise at an anatomically distinct abscission zone (AZ) that separates the top part of the shoots into basal and apical portions (citrus self-pruning). Cell separation occurs only at the AZ, which suggests its cells have distinctive molecular regulation. Although several studies have looked into the morphological aspects of self-pruning process, the underlying molecular mechanisms remain unknown. In this study, the hallmarks of programmed cell death (PCD) were identified by TUNEL experiments, transmission electron microscopy (TEM) and histochemical staining for reactive oxygen species (ROS) during self-pruning of the spring shoots in sweet orange. Our results indicated that PCD occurred systematically and progressively and may play an important role in the control of self-pruning of citrus. Microarray analysis was used to examine transcriptome changes at three stages of self-pruning, and 1,378 differentially expressed genes were identified. Some genes were related to PCD, while others were associated with cell wall biosynthesis or metabolism. These results strongly suggest that abscission layers activate both catabolic and anabolic wall modification pathways during the self-pruning process. In addition, a strong correlation was observed between self-pruning and the expression of hormone-related genes. Self-pruning plays an important role in citrus floral bud initiation. Therefore, several key flowering homologs of Arabidopsis and tomato shoot apical meristem (SAM) activity genes were investigated in sweet orange by real-time PCR and in situ hybridization, and the results indicated that these genes were preferentially expressed in SAM as well as axillary meristem. Based on these findings, a model for sweet orange spring shoot self-pruning is proposed, which will enable us to better understand the mechanism of self-pruning and abscission.

  20. The Prune Belly syndrome: urological aspects and long-term outcomes of a rare disease.

    Science.gov (United States)

    Zugor, Vahudin; Schott, Günter E; Labanaris, Apostolos P

    2012-04-02

    Prune-Belly syndrome is a disorder characterized by the following triad of symptoms: deficiency of the abdominal muscles, malformations of the urinary tract and bilateral cryptorchidism. This study included a total of 16 patients. The findings included clinical characteristics, diagnostics, therapy and long-term clinical outcomes. All patients were asked to complete a questionnaire and, in some cases, were given further examination. All patients were diagnosed with congenital aplasia of the abdominal wall and a variety of urogenital malformations. Cryptorchidism was present in 11 patients (68.8%), malformations of the prostate in 3 (18.8%), urethral malformations in 8 (50%) and mega-ureter in 14 patients (87.5%). A mega-bladder was observed in 13 patients (81.3%). Distinctive renal malformations, such as renal dysplasia, in 3 patients (18.8%) and hydronephrosis in 9 patients (56.3%), respectively. Abdominoplasty was performed on 4 patients (25%). Urethral surgery was performed in 10 patients (62.5%). Seven patients (43.8%) required ureter surgery, most of which involved re-implantation of the ureter and, in some cases, additional ureter modeling. Renal surgery was performed on 5 patients. Four patients with non-functioning kidneys with hydronephrosis underwent a nephrectomy and one patient pyeloplasty. We demonstrate that successful treatment is possible even in cases of serious and complex malformations, such as those of the Prune-Belly syndrome. Treatment must be tailored to the individual patient. The severity of the renal dysplasia is the main prognostic factor.

  1. Concordant posterior urethral valves in male monochorionic twins with secondary prune belly syndrome.

    Science.gov (United States)

    Nouaili, Emira Ben Hamida; Chaouachi, Sihem; Nouira, Faouzi; Benmassoud, Ines; Laabidi, Kamel; Chaouachi, Beji; Marrakchi, Zahra

    2008-12-01

    Posterior urethral valves (PUVs), the most common congenital cause of lower urinary tract obstruction, have been described to occur in identical and nonidentical twins. Until now, reports have been published on 15 cases of PUVs. We report a new case of concordant PUVs in one set of male monochorionic twins with secondary Prune Belly Syndrome. The twins were born by elective cesarean section at 38 weeks of gestation to a 36-year-old mother, gravida 6, para 6. On ultrasound perfomed at 18 weeks's gestation, both fetuses showed signs of PUVs. At birth, physical examination of both revealed a secondary Prune Belly Syndrome (PBS). Postnatal renal ultrasound confirmed the diagnosis of PUV. The two infants underwent transurethral resection of the valves after a cystoscopic evaluation of the urethra. Since this procedure, their voiding has been unremarkable with stable renal function and sterile urine until their discharge. We have documented a rare association between VUP and PBS in two monochiorionic twins. More studies are needed to throw light on the significance of the present associated anomalies.

  2. Production of fuel ethanol from steam-explosion pretreated olive tree pruning

    Energy Technology Data Exchange (ETDEWEB)

    Cristobal Cara; Encarnacion Ruiz; Mercedes Ballesteros; Paloma Manzanares; Ma Jose Negro; Eulogio Castro [University of Jaen, Jaen (Spain). Department of Chemical, Environmental and Materials Engineering

    2008-05-15

    This work deals with the production of fuel ethanol from olive tree pruning. This raw material is a renewable, low cost, largely available, and lacking of economic alternatives agricultural residue. Olive tree pruning was submitted to steam explosion pre-treatment in the temperature range 190-240{sup o}C, with or without previous impregnation by water or sulphuric acid solutions. The influence of both pre-treatment temperature and impregnation conditions on sugar and ethanol yields was investigated by enzymatic hydrolysis and simultaneous saccharification and fermentation on the pretreated solids. Results show that the maximum ethanol yield (7.2 g ethanol/100 g raw material) is obtained from water impregnated, steam pretreated residue at 240{sup o}C. Nevertheless if all sugars solubilized during pre-treatment are taken into account, up to 15.9 g ethanol/100 g raw material may be obtained (pre-treatment conditions: 230{sup o}C and impregnation with 1% w/w sulphuric acid concentration), assuming theoretical conversion of these sugars to ethanol. 29 refs., 2 figs., 5 tabs.

  3. EVALUATION OF THE WORK CONDITIONS OF ACTIVITIES OF URBAN TREE PRUNING

    Directory of Open Access Journals (Sweden)

    Nilton César Fiedler

    2007-03-01

    Full Text Available this work analyzed the work environment in the trees pruning activities in the urban arborization, comparison with the values of the legislation and the practical application of results to provide a better comfort, security, health, welfare to workers, and also a better efficiency and quality of the work. The weather conditions, the noise levels, the light conditions and vibration were analyzed using suitable ergonomic methods. The weather conditions in the work environment were according the permissible values in the legislation (NR15 for index of humid bulb and globe thermometer (IBUTG of 25°C for the activities of pruning, with exception of the schedule to twelve hours (26,2°C, the hours of working should be of 30 minutes of work and 30 minutes of rest. The noise levels found in the activities of cut were 105,7 dB (A and bucking were 103.9 dB (A, above the level permited by legislation (NR15. The minimum light conditions values were acceptable for legislation (NBR 5413/92, but the global indices were too high being able to cause problems to the worker health. The vibration conditions were acceptable.

  4. Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

    Science.gov (United States)

    Zhou, Zhongmei; Wang, Weiping

    2014-01-01

    The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy. PMID:24511304

  5. Classification based on pruning and double covered rule sets for the internet of things applications.

    Science.gov (United States)

    Li, Shasha; Zhou, Zhongmei; Wang, Weiping

    2014-01-01

    The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.

  6. Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

    Science.gov (United States)

    Deo, Ravinesh C; Downs, Nathan; Parisi, Alfio V; Adamowski, Jan F; Quilty, John M

    2017-05-01

    Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model

  7. From Neurons to Brainpower: Cognitive Neuroscience and Brain-Based Learning

    Science.gov (United States)

    Phillips, Janet M.

    2005-01-01

    We have learned more about the brain in the past five years than the previous 100. Neuroimaging, lesion studies, and animal studies have revealed the intricate inner workings of the brain and learning. Synaptogenesis, pruning, sensitive periods, and plasticity have all become accepted concepts of cognitive neuroscience that are now being applied…

  8. Pruning high-value Douglas-fir can reduce dwarf mistletoe severity and increase longevity in Central Oregon

    Science.gov (United States)

    Helen M. Maffei; Gregory M. Filip; Nancy E. Grulke; Brent W. Oblinger; Ellis Q. Margolis; Kristen L. Chadwick

    2016-01-01

    Mid- to very large-sized Douglas-fir (Pseudotsuga menzieseii var. menziesii) that were lightly- to moderately-infected by dwarf mistletoe (Arceuthobium douglasii) were analyzed over a 14-year period to evaluate whether mechanical pruning could eradicate mistletoe (or at least delay the onset of severe infection) without...

  9. 75 FR 43039 - Fresh Prunes Grown in Designated Counties in Washington and in Umatilla County, OR; Suspension of...

    Science.gov (United States)

    2010-07-23

    ... marketing of fresh prunes over the past four years. Based on its analysis, the Committee has determined that... DEPARTMENT OF AGRICULTURE Agricultural Marketing Service 7 CFR Part 924 [Doc. No. AMS-FV-10-0054...; Suspension of Reporting and Assessment Requirements AGENCY: Agricultural Marketing Service, USDA. ACTION...

  10. Effect of irradiation, pruning and removal of in vitro formed roots on ex vitro growth in micropropagated grape

    International Nuclear Information System (INIS)

    Charbaji, T.; Ayyoubi, Z.

    2002-06-01

    In vitro rootstock (Ru 140) and Helwani variety were cultured on DSD1 media, were irradiated at low doses of gamma irradiation before acclimatization. Ru 140 were exposed to 0-5 Gy, while Helwani was exposed to 0-7 Gy. Then, the plants were divided into three different groups: in the first group the plant roots were pruned, in the second the plant roots were completely removed and in the third group the plant roots were kept intact (control). The ex vitro plants were observed after 45 days of planting. Shoots groeth, leaf number and dry weight of Ru 140 were significantly higher than those of the control when roots were pruned and 5 Gy was applied. Those parameters were negatively affected by root removal. Gamma irradiation had a positive effect on the control comparing to unirradiated plants. Root pruning had positive effects on shoot growth, leaf number and dry weight of helwani veriety, while root removal had a contrary effect on this variety. Gamma irradiation positively affected shoot growth and dry weight of control comparing to unirradiated plants, similar effect was observed on leaf number of control and pruned plant of Helwani. (author)

  11. Prune belly anomaly on prenatal ultrasound as a presenting feature of ectrodactyly-ectodermal dysplasia-clefting syndrome (EEC).

    NARCIS (Netherlands)

    Janssens, S.; Defoort, P.; Vandenbroecke, C.; Scheffer, H.; Mortier, G.

    2008-01-01

    We report on a fetus with prune belly anomaly presenting at 16 weeks gestation. Clinical evaluation after birth revealed other malformations reminiscent of the EEC syndrome. This diagnosis was also suspected in the mother and finally confirmed in both relatives by identification of a heterozygous

  12. Feasibility and early outcomes of robotic-assisted laparoscopic Mitrofanoff appendicovesicostomy in patients with prune belly syndrome.

    Science.gov (United States)

    Wille, Mark A; Jayram, Gautam; Gundeti, Mohan S

    2012-01-01

    To evaluate the feasibility and report our initial experience with Robotic-Assisted Laparoscopic Mitrofanoff Appendicovesicostomy (RALMA) in patients with prune belly syndrome. The Mitrofanoff appendicovesicostomy procedure uses the appendix to create an easily accessible continent, catheterizable channel into the urinary bladder. Historically, the procedure is performed by an open surgical approach in prune belly patients. We describe our initial experience herein. Between October 2008 and February 2010 three patients with prune belly syndrome underwent RALMA. The appendicovesicostomy anastomosis was performed on the anterior bladder wall and the stoma was brought to the umbilical site or right lower quadrant. At least 4 cm of detrusor backing was ensured. The appendicovesicostomy stent was left in place for 4 weeks postoperatively before initiation of catheterization. Mean age at surgery was 9.7 years (range 5-14 years). Blood loss volume was 20 mL in each case. Overall mean operative time was 352 min (range 319-402 min). There were no intraoperative complications and no open conversions. There was one postoperative complication in the form of wound infection. All patients are catheterizing their stomas and are continent at an average follow-up of 14.7 months (range 5-21 months). In our initial experience, RALMA is a feasible option with encouraging early experience for creating a continent catheterizable channel into the urinary bladder in patients with prune belly syndrome. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

  13. Outcomes of renal replacement therapy in boys with prune belly syndrome: findings from the ESPN/ERA-EDTA Registry

    NARCIS (Netherlands)

    Yalcinkaya, Fatos; Bonthuis, Marjolein; Erdogan, Beyza Doganay; van Stralen, Karlijn J.; Baiko, Sergey; Chehade, Hassib; Maxwell, Heather; Montini, Giovanni; Rönnholm, Kai; Sørensen, Søren Schwartz; Ulinski, Tim; Verrina, Enrico; Weber, Stefanie; Harambat, Jérôme; Schaefer, Franz; Jager, Kitty J.; Groothoff, Jaap W.

    2018-01-01

    As outcome data for prune belly syndrome (PBS) complicated by end-stage renal disease are scarce, we analyzed characteristics and outcomes of children with PBS using the European Society for Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (ESPN/ERA-EDTA)

  14. 76 FR 21618 - Fresh Prunes Grown in Designated Counties in Washington and in Umatilla County, OR; Termination...

    Science.gov (United States)

    2011-04-18

    ... marketing order is no longer an effective marketing tool for the fresh prune industry, and that termination... operation of the marketing order. DATES: Effective Date: April 19, 2011. FOR FURTHER INFORMATION CONTACT....64 of Marketing Agreement and Order No. 924, both as amended (7 CFR part 924), effective under the...

  15. Thyroid hormone is required for pruning, functioning and long-term maintenance of afferent inner hair cell synapses.

    Science.gov (United States)

    Sundaresan, Srividya; Kong, Jee-Hyun; Fang, Qing; Salles, Felipe T; Wangsawihardja, Felix; Ricci, Anthony J; Mustapha, Mirna

    2016-01-01

    Functional maturation of afferent synaptic connections to inner hair cells (IHCs) involves pruning of excess synapses formed during development, as well as the strengthening and survival of the retained synapses. These events take place during the thyroid hormone (TH)-critical period of cochlear development, which is in the perinatal period for mice and in the third trimester for humans. Here, we used the hypothyroid Snell dwarf mouse (Pit1(dw)) as a model to study the role of TH in afferent type I synaptic refinement and functional maturation. We observed defects in afferent synaptic pruning and delays in calcium channel clustering in the IHCs of Pit1(dw) mice. Nevertheless, calcium currents and capacitance reached near normal levels in Pit1(dw) IHCs by the age of onset of hearing, despite the excess number of retained synapses. We restored normal synaptic pruning in Pit1(dw) IHCs by supplementing with TH from postnatal day (P)3 to P8, establishing this window as being critical for TH action on this process. Afferent terminals of older Pit1(dw) IHCs showed evidence of excitotoxic damage accompanied by a concomitant reduction in the levels of the glial glutamate transporter, GLAST. Our results indicate that a lack of TH during a critical period of inner ear development causes defects in pruning and long-term homeostatic maintenance of afferent synapses. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

  16. Severity of scab and its effects on fruit weight in mechanically hedge-pruned and topped pecan trees

    Science.gov (United States)

    Scab is the most damaging disease of pecan in the southeastern USA. Pecan trees can attain 44 m in height, so managing disease in the upper canopy is a problem. Fungicide is ordinarily applied using ground-based air-blast sprayers. Although mechanical hedge-pruning and topping of pecan is done for s...

  17. Effect of irradiation, pruning and removal of in vitro formed roots on ex vitro growth in micro propagated grape

    International Nuclear Information System (INIS)

    Charbaji, T.; Ayyoubi, Z.

    2003-01-01

    In vitro rootstock (Ru 140) and Helwani variety were cultured on DSD1 media, were irradiated at low doses of gamma irradiation before acclimatization. Ru 140 were exposed to 0-5 Gy, while Helwani was exposed to 0-7 Gy. Then, the plants were divided into three different groups: in the first group the plant roots were pruned, in the second the plant roots were completely removed and in the third group the plant roots were kept intact (control). The ex vitro plants were observed after 45 days of planting. Shoots growth, leaf number and dry weight of Ru 140 were significantly higher than those of the control when roots were pruned and 5 Gy was applied. Those parameters were negatively affected by root removal. Gamma irradiation had a positive effect on the control comparing to unirradiated plants. Root pruning had positive effects on shoot growth, leaf number and dry weight of Helwani variety, while root removal had a contrary effect on this variety. Gamma irradiation positively affected shoot growth and dry weight of control comparing to unirradiated plants, similar effect was observed on leaf number of control and pruned plant of Helwani. (author)

  18. Cavity size and copper root pruning affect production and establishment of container-grown longleaf pine seedlings

    Science.gov (United States)

    Marry Anne Sword Sayer; James D. Haywood; Shi-Jean Susana Sung

    2009-01-01

    With six container types, we tested the effects of cavity size (i.e., 60, 93, and 170 ml) and copper root pruning on the root system development of longleaf pine (Pinus palustris Mill.) seedlings grown in a greenhouse. We then evaluated root egress during a root growth potential test and assessed seedling morphology and root system development 1 year after planting in...

  19. Effect of Irradiation, Pruning and Removal of In Vitro Formed Roots on Ex Vitro Growth in Micropropagated Grape

    International Nuclear Information System (INIS)

    Charabaji, T.; Ayyoubi, Z.; Karajoly, I

    2007-01-01

    In vitro rootstock (Ru 140) and Helwani variety were cultured on DSD1 media, were irradiated at low doses of gamma irradiation before acclimatization. Ru 140 was exposed to 0-5 Gy, while Helwani was exposed to 0-7 Gy. Then, the plants were divided into three different procedures, 1)- the plant roots were pruned, 2)- the plant roots were completely removed, 3)- the plant roots were kept intact (control). The ex vitro plants were observed after 45 days of planting. Shoots growth, leaf number and dry weight of Ru 140 were significantly higher than those of the control when roots were pruned and 5 Gy was applied. Those parameters were negatively affected by root removal. Gamma irradiation had a positive effect on the control comparing to unirradiated plants. Root pruning had positive effects on shoot growth, leaf number and dry weight of Helwani variety, while root removal had a contrary effect on this variety. Gamma irradiation positively affected shoot growth and dry weight of control comparing to unirradiated plants, similar effect was observed on leaf number of control and pruned plant of Helwani.

  20. Influence of Pre- and Postharvest Summer Pruning on the Growth, Yield, Fruit Quality, and Carbohydrate Content of Early Season Peach Cultivars

    Science.gov (United States)

    Ikinci, Ali

    2014-01-01

    Winter and summer pruning are widely applied processes in all fruit trees, including in peach orchard management. This study was conducted to determine the effects of summer prunings (SP), as compared to winter pruning (WP), on shoot length, shoot diameter, trunk cross sectional area (TCSA) increment, fruit yield, fruit quality, and carbohydrate content of two early ripening peach cultivars (“Early Red” and “Maycrest”) of six years of age, grown in semiarid climate conditions, in 2008 to 2010. The trees were grafted on GF 677 rootstocks, trained with a central leader system, and spaced 5 × 5 m apart. The SP carried out after harvesting in July and August decreased the shoot length significantly; however, it increased its diameter. Compared to 2009, this effect was more marked in year 2010. In general, control and winter pruned trees of both cultivars had the highest TCSA increment and yield efficiency. The SP increased the average fruit weight and soluble solids contents (SSC) more than both control and WP. The titratable acidity showed no consistent response to pruning time. The carbohydrate accumulation in shoot was higher in WP and in control than in SP trees. SP significantly affected carbohydrate accumulation; postharvest pruning showed higher carbohydrate content than preharvest pruning. PMID:24737954

  1. Small-Scale Effect of Pine Stand Pruning on Snowpack Distribution in the Pyrenees Observed with a Terrestrial Laser Scanner

    Directory of Open Access Journals (Sweden)

    Jesús Revuelto

    2016-07-01

    Full Text Available Forests in snow-dominated areas have substantial effects on the snowpack and its evolution over time. Such interactions have significant consequences for the hydrological response of mountain rivers. Thus, the impact of forest management actions on the snow distribution, and hence the storage of water in the form of snow during winter and spring, is a major concern. The results of this study provide the first detailed comparison of the small-scale effect of forest characteristics on the snowpack distribution, assessed prior to and following major modification of the structure of the canopy by pruning of the lower branches of the trees to 3 m above the ground. This is a common management practice aimed at reducing the spread of forest fires. The snowpack distribution was determined using terrestrial laser scanning (LiDAR technology at a high spatial resolution (0.25 m over a 1000 m2 study area during 23 survey dates over three snow seasons in a small study area in the central Pyrenees. The pruning was conducted during summer following the snow season in the second year of the study (i.e., the study duration encompassed two seasons prior to canopy pruning and one following. Principal component analysis (PCA was used to identify recurring spatial patterns of snow distribution. The results showed that pruning reduced the average radius of the canopy of trees by 1.2 m, and increased the clearance around the trunks, as all the branches that formerly contacted the ground were removed. However, the impact on the snowpack was moderate. The PCA revealed that the spatial configuration of the snowpack did not change significantly, as the principal components included survey days from different periods of the snow season, and did not discriminate days surveyed prior to and following pruning. Nevertheless, removal of the lower branches reduced the area beneath the canopy by 36%, and led to an average increase in total snow depth of approximately 14%.

  2. Qualidade de frutos da tangerina 'Ponkan' após poda de recuperação Quality of 'Ponkan' tangerine tree after recovering pruning

    Directory of Open Access Journals (Sweden)

    Vander Mendonça

    2006-04-01

    Full Text Available Objetivou-se com esta pesquisa avaliar a qualidade de frutos de tangerineira 'Ponkan' em três safras subseqüentes aos tratamentos: poda de topo no rebaixamento da copa e poda da parte baixa da planta (saia. O experimento foi conduzido na Fazenda Vito Crincoli, localizada no município de Perdões - MG. O delineamento experimental utilizado foi em blocos ao acaso, em esquema fatorial 4 x 2, sendo poda do topo (sem poda, poda a 3,0; 2,5 e 2,0 m e poda da saia (sem e com a poda com quatro repetições e três plantas úteis por parcela. Os diferentes tipos de poda não prejudicaram a qualidade de frutos de tangerineira 'Ponkan' nas três safras subseqüente as podas. Após o terceiro ano as plantas que sofreram podas mais severas produziram frutos com peso superiores, demonstrando a viabilidade da poda na recuperação da qualidade dos frutos.This research aimed to test top pruning effect on lowering the top canopy and pruning the lower part of the plant on the recovering of 12 years old 'Ponkan' tangerine tree. Plants were four meters height, 6x4 spacing, grafted on 'Cravo' lemon tree. This experiment was carried out at Vito Crincoli' s Farm in Perdões, MG, Brazil . It was used a randomized block experimental design in a factorial scheme of 4x2, top pruning (without pruning, pruning at 3.0; 2.5 and 2.0m from soil level and circumference pruning (with and without pruning with four replications. The useful plot was constituted of three tangerine plants. After third year of treatment plants that had been under more severe pruning gave higher fruit weight. Indicating the used of pruning to recover fruit quality.

  3. Extremely Preterm Birth

    Science.gov (United States)

    ... Events Advocacy For Patients About ACOG Extremely Preterm Birth Home For Patients Search FAQs Extremely Preterm Birth ... Spanish FAQ173, June 2016 PDF Format Extremely Preterm Birth Pregnancy When is a baby considered “preterm” or “ ...

  4. Prune belly syndrome associated with cloacal anomaly, patent urachal remnant, and omphalocele in a female infant.

    Science.gov (United States)

    Giuliani, Stefano; Vendryes, Christopher; Malhotra, Ajay; Shaul, Donald B; Anselmo, Dean M

    2010-11-01

    Prune belly syndrome (PBS), megacystis-microcolon-intestinal hypoperistalsis (MMIH), and omphalocele-exstrophy of the bladder-imperforate anus-spine abnormalities complex (OEIS) are rare congenital malformations of the newborn that lead to incomplete formation of the gastrointestinal and genitourinary tract systems. To date, incomplete mesodermal development is identified as the cause for all these complex genetic syndromes even if the etiology is still unknown. We present an original case sharing characteristics common to PBS, MMIH, and OEIS complex, without a clear inclination toward any particular one. This case hints toward a common pathway in the creation of the 3 syndromes. We hypothesize that they are a spectrum of malformations based on the time frame when the mesoderm fails to create a normal interaction between infraumbilical mesoderm, urorectal septum, lumbosacral somites in the formation of the abdominal wall and the genitourinary and lower gastrointestinal tracts. Published by Elsevier Inc.

  5. Fuzzy Pruning Based LS-SVM Modeling Development for a Fermentation Process

    Directory of Open Access Journals (Sweden)

    Weili Xiong

    2014-01-01

    Full Text Available Due to the complexity and uncertainty of microbial fermentation processes, data coming from the plants often contain some outliers. However, these data may be treated as the normal support vectors, which always deteriorate the performance of soft sensor modeling. Since the outliers also contaminate the correlation structure of the least square support vector machine (LS-SVM, the fuzzy pruning method is provided to deal with the problem. Furthermore, by assigning different fuzzy membership scores to data samples, the sensitivity of the model to the outliers can be reduced greatly. The effectiveness and efficiency of the proposed approach are demonstrated through two numerical examples as well as a simulator case of penicillin fermentation process.

  6. LHCb: Optimising query execution time in LHCb Bookkeeping System using partition pruning and partition wise joins

    CERN Multimedia

    Mathe, Z

    2013-01-01

    The LHCb experiment produces a huge amount of data which has associated metadata such as run number, data taking condition (detector status when the data was taken), simulation condition, etc. The data are stored in files, replicated on the Computing Grid around the world. The LHCb Bookkeeping System provides methods for retrieving datasets based on their metadata. The metadata is stored in a hybrid database model, which is a mixture of Relational and Hierarchical database models and is based on the Oracle Relational Database Management System (RDBMS). The database access has to be reliable and fast. In order to achieve a high timing performance, the tables are partitioned and the queries are executed in parallel. When we store large amounts of data the partition pruning is essential for database performance, because it reduces the amount of data retrieved from the disk and optimises the resource utilisation. This research presented here is focusing on the extended composite partitioning strategy such as rang...

  7. Closing oil palm yield gaps among Indonesian smallholders through industry schemes, pruning, weeding and improved seeds.

    Science.gov (United States)

    Soliman, T; Lim, F K S; Lee, J S H; Carrasco, L R

    2016-08-01

    Oil palm production has led to large losses of valuable habitats for tropical biodiversity. Sparing of land for nature could in theory be attained if oil palm yields increased. The efficiency of oil palm smallholders is below its potential capacity, but the factors determining efficiency are poorly understood. We employed a two-stage data envelopment analysis approach to assess the influence of agronomic, supply chain and management factors on oil palm production efficiency in 190 smallholders in six villages in Indonesia. The results show that, on average, yield increases of 65% were possible and that fertilizer and herbicide use was excessive and inefficient. Adopting industry-supported scheme management practices, use of high-quality seeds and higher pruning and weeding rates were found to improve efficiency. Smallholder oil palm production intensification in Indonesia has the capacity to increase production by 26%, an equivalent of 1.75 million hectares of land.

  8. Marjane Satrapi’s Poulet aux Prunes: fetish, desire, and illusions

    Directory of Open Access Journals (Sweden)

    Stefania Rimini

    2013-07-01

    Full Text Available Il saggio si propone di individuare e descrivere le sfumature erotiche e sentimentali del film Poulet aux prunes di Marjane Satrapi, felice adattamento dell’omonima graphic novel. Il racconto procede per scarti, ellissi, anticipazioni e flasback, dando luogo a una narrazione labirintica dalla singolare trama arabescante. Le sottili dinamiche seduttive messe in campo dal complesso stile di regia di Satrapi-Paronnaud vengono analizzate alla luce delle categorie feticistiche individuate da Massimo Fusillo, che offrono interessanti modelli di interazione fra desiderio e creatività. L’esito di tale indagine consente di ampliare il dibattito relativo al rapporto di scambio e interferenza fra oggetti, pulsioni e sguardi nel cinema contemporaneo.

  9. The Prune Belly syndrome: urological aspects and long-term outcomes of a rare disease

    Directory of Open Access Journals (Sweden)

    Vahudin Zugor

    2012-06-01

    Full Text Available Prune-Belly syndrome is a disorder characterized by the following triad of symptoms: deficiency of the abdominal muscles, malformations of the urinary tract and bilateral cryptorchidism. This study included a total of 16 patients. The findings included clinical characteristics, diagnostics, therapy and long-term clinical outcomes. All patients were asked to complete a questionnaire and, in some cases, were given further examination. All patients were diagnosed with congenital aplasia of the abdominal wall and a variety of urogenital malformations. Cryptorchidism was present in 11 patients (68.8%, malformations of the prostate in 3 (18.8%, urethral malformations in 8 (50% and mega-ureter in 14 patients (87.5%. A mega-bladder was observed in 13 patients (81.3%. Distinctive renal malformations, such as renal dysplasia, in 3 patients (18.8% and hydronephrosis in 9 patients (56.3%, respectively. Abdominoplasty was performed on 4 patients (25%. Urethral surgery was performed in 10 patients (62.5%. Seven patients (43.8% required ureter surgery, most of which involved re-implantation of the ureter and, in some cases, additional ureter modeling. Renal surgery was performed on 5 patients. Four patients with non-functioning kidneys with hydronephrosis underwent a nephrectomy and one patient pyeloplasty. We demonstrate that successful treatment is possible even in cases of serious and complex malformations, such as those of the Prune-Belly syndrome. Treatment must be tailored to the individual patient. The severity of the renal dysplasia is the main prognostic factor.

  10. Extreme Events in Nature and Society

    CERN Document Server

    Albeverio, Sergio; Kantz, Holger

    2006-01-01

    Significant, and usually unwelcome, surprises, such as floods, financial crisis, epileptic seizures, or material rupture, are the topics of Extreme Events in Nature and Society. The book, authored by foremost experts in these fields, reveals unifying and distinguishing features of extreme events, including problems of understanding and modelling their origin, spatial and temporal extension, and potential impact. The chapters converge towards the difficult problem of anticipation: forecasting the event and proposing measures to moderate or prevent it. Extreme Events in Nature and Society will interest not only specialists, but also the general reader eager to learn how the multifaceted field of extreme events can be viewed as a coherent whole.

  11. Legacies from extreme drought increase ecosystem sensitivity to future extremes

    Science.gov (United States)

    Smith, M. D.; Knapp, A.; Hoover, D. L.; Avolio, M. L.; Felton, A. J.; Wilcox, K. R.

    2016-12-01

    Climate extremes, such as drought, are increasing in frequency and intensity, and the ecological consequences of these extreme events can be substantial and widespread. Although there is still much to be learned about how ecosystems will respond to an intensification of drought, even less is known about the factors that determine post-drought recovery of ecosystem function. Such knowledge is particularly important because post-drought recovery periods can be protracted depending on the extent to which key plant populations, community structure and biogeochemical processes are affected. These drought legacies may alter ecosystem function for many years post-drought and may impact future sensitivity to climate extremes. We experimentally imposed two extreme growing season droughts in a central US grassland to assess the impacts of repeated droughts on ecosystem resistance (response) and resilience (recovery). We found that this grassland was not resistant to the first extreme drought due to reduced productivity and differential sensitivity of the co-dominant C4 grass (Andropogon gerardii) and C3 forb (Solidago canadensis) species. This differential sensitivity led to a reordering of species abundances within the plant community. Yet, despite this large shift in plant community composition, which persisted post-drought, the grassland was highly resilient post-drought, due to increased abundance of the dominant C4 grass. Because of this shift to increased C4 grass dominance, we expected that previously-droughted grassland would be more resistant to a second extreme drought. However, contrary to these expectations, previously droughted grassland was more sensitive to drought than grassland that had not experienced drought. Thus, our result suggest that legacies of drought (shift in community composition) may increase ecosystem sensitivity to future extreme events.

  12. Prune-belly syndrome detected by ultrasound in the first trimester and the usefulness of vesicocentesis as a modality of treatment.

    Science.gov (United States)

    Byon, Mina; Kim, Gwang Jun

    2013-07-01

    Prune-belly syndrome may be related to lower urinary tract obstruction (LUTO). LUTO in the early gestational age exacerbates fetal renal function and may require intrauterine intervention. If early developed LUTO causes bladder distension and abdominal musculature deficiency, it will result in prune belly syndrome. Therefore, early detection of the disease and proper treatment before the renal impairment is important. However, there are few literatures concerning the treatment of prune belly syndrome in the first trimester. We report a case of prune belly syndrome diagnosed at 11+6 weeks of gestation and the value of vesicocentesis as a modality of treatment. Ultrasound showed dilated fetal bladder and vesicocentesis was successful in reducing the volume of the bladder. However, the pregnancy was terminated upon request.

  13. Exploring the Extreme: High Performance Learning Activities in Mathematics, Science and Technology. An Educator's Guide. EG-2002-10-001-DFRC

    Science.gov (United States)

    Dana, Judi; Kock, Meri; Lewis, Mike; Peterson, Bruce; Stowe, Steve

    2010-01-01

    The many activities contained in this teaching guide emphasize hands-on involvement, prediction, data collection and interpretation, teamwork, and problem solving. The guide also contains background information about aeronautical research that can help students learn how airplanes fly. Following the background sections are a series of activities…

  14. The Effect of Extremely Low Frequency Electromagnetic Fields on Visual Learning & Memory and Anatomical Structures of the Brain in Male Rhesus Monkeys

    Directory of Open Access Journals (Sweden)

    Elahe Tekieh

    2018-04-01

    Full Text Available Background: Humans in modern societies expose to substantially elevated levels of electromagnetic field (EMF emissions with different frequencies.The neurobiological effects of EMF have been the subject of debate and intensive research over the past few decades. Therefore, we evaluated the effects of EMF on visual learning and anatomical dimensions of the hippocampus and the prefrontal area (PFA in male Rhesus monkeys. Materials and Methods:In this study, four rhesus monkeys were irradiated by 0.7 microtesla ELF-EMF either at 5 or 30 Hz, 4 h a day, for 30 days. Alterations in visual learning and memory were assessed before and after irradiation phase by using a box designed that cchallenging animals for gaining rewards Also, the monkeys’ brains were scanned by using MRI technique one week before and one week after irradiation. The monkeys were anesthetized by intramuscular injection of ketamine hydrochloride (10–20 mg/kg and xylazine (0.2–0.4 mg/kg, and scanned with a 3-Tesla Magnetom, in axial, sagittal, and coronal planes using T2 weight­ed protocol with a slice thickness of 3 mm. The anatomical changes of hippocampus and the prefrontal area (PFA was measured by volumetric study. Results: Electromagnetic field exposure at a frequency of 30 Hz reduced the number of correct responses in the learning process and delayed memory formation in the two tested monkeys. While, ELF-EMF at 5 Hz had no effect on the visual learning and memory changes. No anatomical changes were found in the prefrontal area and the hippocampus at both frequencies. Conclusion: ELF-EMF irradiation at 30 Hz adversely affected visual learning and memory, pprobably through these changes apply through effects on other factors except changes in brain structure and anatomy.

  15. Produção da tangerineira 'ponkan' após poda de recuperação Production of 'ponkan' tangerine tree after pruning recovery

    Directory of Open Access Journals (Sweden)

    Vander Mendonça

    2008-02-01

    Full Text Available Objetivou esta pesquisa testar o efeito da poda de topo no rebaixamento da copa e poda da saia, na recuperação da tangerineira 'Ponkan' com 12 anos de idade, altura de 4 metros, espaçadas de 6 x 4 m e enxertadas sobre limoeiro 'Cravo'. O experimento foi conduzido na Fazenda Vito Crincoli localizada no município de Perdões, MG. O delineamento experimental utilizado foi em blocos ao acaso, em esquema fatorial 4 x 2, sendo poda do topo (sem poda, poda a 3,0; 2,5 e 2,0m e poda da saia (sem e com a poda com quatro repetições. A parcela útil foi constituída de três plantas. As podas drásticas de topo prejudicaram a primeira produção, contudo a partir do segundo ano após a poda, as plantas apresentaram boa recuperação. Esse comportamento foi confirmado na terceira colheita, quando os diferentes tipos de podas do topo não se diferenciaram na produtividade, sendo que o tratamento com poda da saia foi superior ao sem poda.This research aimed to test top pruning effect on the lowering of plant canopy, pruning the lower canopy on the recover of 12 years old 'Ponkan' tangerine tree, 4 meters height, 6 x 4 spaced and grafted on 'Cravo' lemon rootstock. This experiment was carried out at Vito Crincoli's Farm in Perdões, MG. It was carried out under randomized plots in a factorial scheme of 4x2, top pruning (without pruning, pruning at 3.0; 2.5 and 2.0 m and skirt pruning (with and without pruning with 4 replications. Plot size was composed by three plants. Heavy pruning of the top canopy lowered the first tree yield, however, and in the begining of the second year, the plants showed a very good recover. The same behavior was seem for the third harvest when several kinds of top pruning did not differ in the yield. Skirt pruning treatment was superior to that without any pruning.

  16. Bioavailability of Compounds Susceptible to Enzymatic Oxidation Enhances Growth of Shiitake Medicinal Mushroom (Lentinus edodes) in Solid-State Fermentation with Vineyard Prunings.

    Science.gov (United States)

    Cabrera, Rosina; López-Peña, Damian; Asaff, Ali; Esqueda, Martín; Valenzuela-Soto, Elisa M

    2018-01-01

    Grapes are widely produced in northwestern Mexico, generating many wood trimmings (vineyard prunings) that have no further local use. This makes vineyard prunings a very attractive alternative for the cultivation of white-rot medicinal mushrooms such as Lentinus edodes. This type of wood can also offer a model for the evaluation of oxidative enzyme production during the fermentation process. We tested the effect of wood from vineyard prunings on the vegetative growth of and production of ligninolytic enzymes in L. edodes in solid-state fermentation and with wheat straw as the control substrate. The specific growth rate of the fungus was 2-fold higher on vineyard pruning culture (μM = 0.95 day-1) than on wheat straw culture (μM = 0.47 day-1). Laccase-specific production was 4 times higher in the vineyard prunings culture than on wheat straw (0.34 and 0.08 mU · mg protein-1 · ppm CO2-1, respectively), and manganese peroxidase production was 3.7 times higher on wheat straw culture than on vineyard prunings (2.21 and 0.60 mU · mg protein-1 · ppm CO2-1, respectively). To explain accurately these differences in growth and ligninolytic enzyme activity, methanol extracts were obtained from each substrate and characterized. Resveratrol and catechins were the main compounds identified in vineyard prunings, whereas epigallocatechin was the only one detected in wheat straw. Compounds susceptible to enzymatic oxidation are more bioavailable in vineyard prunings than in wheat straw, and thus the highest L. edodes growth rate is associated with the presence of these compounds.

  17. Biofiltration of composting gases using different municipal solid waste-pruning residue composts: monitoring by using an electronic nose.

    Science.gov (United States)

    López, R; Cabeza, I O; Giráldez, I; Díaz, M J

    2011-09-01

    The concentration of volatile organic compounds (VOCs) during the composting of kitchen waste and pruning residues, and the abatement of VOCs by different compost biofilters was studied. VOCs removal efficiencies greater than 90% were obtained using composts of municipal solid waste (MSW) or MSW-pruning residue as biofilter material. An electronic nose identified qualitative differences among the biofilter output gases at very low concentrations of VOCs. These differences were related to compost constituents, compost particle size (2-7 or 7-20mm), and a combination of both factors. The total concentration of VOCs determined by a photoionization analyser and inferred from electronic nose data sets were correlated over an ample range of concentrations of VOCs, showing that these techniques could be specially adapted for the monitoring of these processes. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Megacystis-microlon-intestinal hypoperistalsis syndrome in a newborn girl whose brother had Prune Belly syndrome: Common Pathogenesis

    International Nuclear Information System (INIS)

    Oliveira, G.; Boechat, M.I.; Fereira, M.A.

    1983-01-01

    A case of Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (MMIHS) is presented. There were important findings: a urachal remnant and a brother with Prune Belly Syndrome (PBS). After a review of the literature, many common characteristics of MMIHS and PBS are described: flaccid abdomen, dilatation of the urinary tract, intestinal malrotation, cryptorchidism, urachal remnants and familial incidence. MMIHS and PBS may be manifestations of the same underlying process. (orig.)

  19. Prune belly syndrome with urethral hypoplasia and vesico-cutaneous fistula: A case report and review of literature

    Directory of Open Access Journals (Sweden)

    Osama M Sarhan

    2013-01-01

    Full Text Available Association between Prune belly syndrome (PBS and urethral hypoplasia is an unusual condition. It is usually fatal unless there is a communication between the fetal bladder and the amniotic sac. We report a case of PBS with urethral hypoplasia and congenital vesico-cutaneous fistula in a male neonate. Patient underwent cutaneous vesicostomy and was discharged for close follow up of his renal function and for future reconstruction.

  20. Prune belly syndrome with urethral hypoplasia and vesico-cutaneous fistula: A case report and review of literature.

    Science.gov (United States)

    Sarhan, Osama M; Al-Ghanbar, Mustafa S; Nakshabandi, Ziad M

    2013-10-01

    Association between Prune belly syndrome (PBS) and urethral hypoplasia is an unusual condition. It is usually fatal unless there is a communication between the fetal bladder and the amniotic sac. We report a case of PBS with urethral hypoplasia and congenital vesico-cutaneous fistula in a male neonate. Patient underwent cutaneous vesicostomy and was discharged for close follow up of his renal function and for future reconstruction.

  1. Megacystis-microlon-intestinal hypoperistalsis syndrome in a newborn girl whose brother had Prune Belly syndrome: Common Pathogenesis

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, G.; Boechat, M.I.; Fereira, M.A.

    1983-07-01

    A case of Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (MMIHS) is presented. There were important findings: a urachal remnant and a brother with Prune Belly Syndrome (PBS). After a review of the literature, many common characteristics of MMIHS and PBS are described: flaccid abdomen, dilatation of the urinary tract, intestinal malrotation, cryptorchidism, urachal remnants and familial incidence. MMIHS and PBS may be manifestations of the same underlying process.

  2. Highly erodible terrain in agriculture land against chipped pruned branches. Or how to stop the soil erosion with low investment

    Science.gov (United States)

    Cerdà, A.

    2009-04-01

    The session on "Soil erosion and sediment control with vegetation and bioengineering on severely eroded terrain" pays special attention to the severe soil erosion suffered on steep slopes and erodible parent materials and soils. Within the last 20 years, in the Mediterranean lands, the citrus orchards were reallocated on steep slopes due to the urban development and better climatic and management conditions of the new plantations. The lack of vegetation cover on the new slope plantations of citrus resulted in high erosion rates. Those non-sustainable soil losses were measured by means of rainfall simulation experiments, Gerlach collectors, geomorphological transect and topographical measurements. The October 2007 and October 2008 rainy periods resulted in sheet, rill and gully erosion. Some recently planted orchards (2005) had the first pruning season in 2008. The pruned chipped branches reduced the soil losses to 50 % of the expected, although the litter (pruned branches) covered 4.67 % of the soil. This is why a research was developed by means of simulated rainfall experiments to determine the vegetation cover (litter, mainly leaves) to protect the soil to reach a sustainable erosion rate. Rainfall simulation experiments at 43 mm h-1 where performed on 1 m2 plots covered with 0, 3, 7, 15, 30, 45, 60, 80 and 100 % litter cover (pruned chipped branches) to determine the sustainable litter cover to avoid the soil losses. The results show that more that 45 % litter cover almost reduces the soil losses to negligible rates. The results confirm that 4 % of vegetation cover reduces the soil losses to 50 %. Key words: Agriculture land, erodible terrain, land management, citrus, erosion, Spain, Valencia, herbicides. Acknowledgements, We thanks the financial support of the Ministerio de Ciencia e Innovación by means of the project CGL2008-02879/BTE, "PERDIDA DE SUELO EN NUEVAS EXPLOTACIONES CITRICOLAS EN PENDIENTE. ESTRATEGIAS PARA EL CONTROL DE LA EROSION HIDRICA"

  3. The Effect of Green Pruning on the Yield and Fruit Quality of the Crawling Grape Vines Cultivar Keshmeshy in the Climatic Conditions of Shirvan

    Directory of Open Access Journals (Sweden)

    F. Sadeghian

    2016-02-01

    Full Text Available Introduction: Green pruning or summer pruning completes winter pruning, and it is conducted during the growing season. The purpose of green pruning is to maximize yield of high quality grapes. Green pruning in fruit trees improves light penetration and increases the quality of fruits. The objectives of this research were to determine the influence of green pruning on fruit quantitative and qualitative attributes in grapevine cultivar 'keshmeshi'. Materials and Methods: The present research was undertaken in Shirvan, Northern Khorasan province. The vines were highly uniform, 17 years old and all had equal vegetative growth strength.The planting distances were 3 × 3 m. The present study was conducted on cultivar 'Keshmeshi' that is considered to be one of the best grapevine cultivars mostly used for raisin production as well as table fresh fruits. In order to evaluate the effect of green pruning on different quantitative and qualitative attributes of fruits in cultivar "keshmeshi", the experiment was carried out in two way randomized complete block design with 12 treatments and three replications. Vines were pruned three times including full bloom, two weeks after full bloom and veraison (eight weeks after full bloom. Pruning was carried out in three levels including tipping after two, four or six nodes above the final cluster. The characteristics studied were cluster weight, berry weight, cluster length and width, berry length and width, number of cluster and berry, vine yield, leaf area, soluble solid, total acidity, pH, berry color, berry sunscald and number of shot berry. The SAS software (SAS, version 9.1 was used for statistical analysis of the recorded data. The mean comparison was performed based on Duncan's multiple range tests at %5 and %1 levels. Results and Discussion: The results of this study indicate that the severity of green pruning has no significant effect on the number and width of berries. This treatment also had no

  4. 27 years of experience with the comprehensive surgical treatment of prune belly syndrome.

    Science.gov (United States)

    Lopes, R I; Tavares, A; Srougi, M; Dénes, F T

    2015-10-01

    Prune belly syndrome (PBS) presents with three main features: abdominal wall flaccidity, urological abnormalities and cryptorchidism. As a result, urologists must consider the eventual repair of the abdominal wall flaccidity and urinary tract abnormalities, and the mandatory correction of cryptorchidism, as well as decide whether to perform the procedures in a single comprehensive approach or in multiple steps. To report experiences with comprehensive surgical management of prune belly syndrome. From 1987 to 2014, 46 children with PBS were submitted for comprehensive surgical treatment. According to individual needs, treatment aimed to correct the abdominal flaccidity, reconstruct the urinary tract, and perform bilateral orchiopexy and circumcision, which were performed in one procedure. Urinary tract reconstruction was indicated whenever pyelo-ureteral dilatation with evidence of significant stasis and/or vesicoureteral reflux was associated with recurrent urinary tract infections (UTI). Treatment for this cohort included: 44 abdominoplasties, 40 upper urinary tract reconstructions, 44 cystoplasties associated with three appendico-vesicostomies, 46 bilateral orchiopexies and 36 circumcisions. The median age at surgery was 16 months and children were followed for a median of 143 months. Abdominal appearance and tonus were improved in 90% of the children after the primary surgery and 100% after reoperation. Upper urinary tract reconstruction was performed in most children and long-term follow-up showed functional stabilization of the urinary tract in about 90% of the children, with progression to renal failure in 10%. Lower urinary tract reconstruction was performed in most children (95.6%); on late follow-up, continence was observed in 81% of them, while incontinence was present in 19% and usually associated with polyuria. Adequate bladder emptying was possible in most boys (82.6%), while the remaining required clean intermittent catheterization. Pre-operative UTI

  5. Growth of Gypsophila paniculata According to the Pruning Time and Ridge Position in Sub-alpine Area

    International Nuclear Information System (INIS)

    Cheong, D.C.; Lim, H.C.; Song, Y.J.; Park, H.B.

    2008-01-01

    This study was carried out to investigate the growth of Gypsophila paniculata affected by pruning time (July 10, July 18, and July 25) and ridge position (middle or window side) under south-north oriented plastic house in sub-alpine area. The average night temperature was similar between the two ridges, but the average day temperature and soil temperature were higher at the middle ridge; particularly, there was distinct difference after late October. Also the accumulative solar radiation was higher at the middle ridge than the window side ridge owing to the shading by neighboring plastic house and the structure of plastic house. The root activity, photosynthetic rate and transpiration rate of plants surveyed in late October were inclined to be more increased at the middle than the window side ridge. The flowering traits at the pruning time of July 10 and July 18 were similar between the two ridges, but the flower malformation rate was higher at the middle ridge. On the other hand, in case of the pruning time of July 25, the blooming was advanced by 13 days, and the flowering traits such as flower stalk length and branch number were better; also, the flower malformation and rosette formation rate decreased at the middle ridge, because of its higher air and soil temperature and the accumulative solar radiation

  6. Thyroid hormone is required for the pruning of afferent type II spiral ganglion neurons in the mouse cochlea

    Science.gov (United States)

    Sundaresan, Srividya; Balasubbu, Suganthalakshmi; Mustapha, Mirna

    2015-01-01

    Afferent connections to the sensory inner and outer hair cells in the cochlea refine and functionally mature during the thyroid hormone (TH)- critical period of inner ear development that occurs perinatally in rodents. In this study, we investigated the effects of hypothyroidism on afferent type II innervation to outer hair cells (OHCs) using the Snell dwarf mouse (Pit1dw). Using a transgenic approach to specifically label type II spiral ganglion neurons, we found that a lack of TH causes persistence of excess type II SGN connections to the OHCs, as well as continued expression of the hair cell functional marker, otoferlin, in the OHCs beyond the maturation period. We also observed a concurrent delay in efferent attachment to the OHCs. Supplementing with TH during the early postnatal period from postnatal day (P) 3 to P4 reversed the defect in type II SGN pruning but did not alter otoferlin expression. Our results show that hypothyroidism causes a defect in the large-scale pruning of afferent type II spiral ganglion neurons in the cochlea, and a delay in efferent attachment and the maturation of otoferlin expression. Our data suggest that the state of maturation of hair cells, as determined by otoferlin expression, may not regulate the pruning of their afferent innervation. PMID:26592716

  7. [Peritoneal dialysis in adult patients with prune belly syndrome: an impossible challenge?].

    Science.gov (United States)

    Musone, Dario; Nicosia, Valentina; D'Alessandro, Riccardo; Treglia, Antonio; Saltarelli, Giuseppe; Montella, Maurizio; Sparagna, Alessandro; Amoroso, Francesco

    2013-01-01

    Prune belly syndrome (PBS) is a rare congenital syndrome characterized by hypoplasia of the abdominal muscles, urinary tract malformations, and cryptorchidism in males. The estimated incidence is 1 in 35,000 to 50,000 live births. Chronic renal failure and end-stage renal disease (ESRD), due both to different degrees of renal hypoplasia or dysplasia and infectious complications, develops in 20-30% of patients who survive the neonatal period. No data are available on progression time to ESRD, owing to the variability of the phenotypic features of nephropathy. Nevertheless, PBS is primarily a pathology of pediatric interest as demonstrated, for example, by the reported average age at transplantation which usually does not exceed fifteen years of age. Therefore the need for renal replacement therapy (RRT) in adult patients with PBS is unusual. It is reasonable to suppose that the abdominal muscular defects may represent a limit for peritoneal dialysis (PD) utilization in PBS adult patients in many Kidney Units where, conversely, treatment with hemodialysis would be probably the easier choice. Here we describe the case of a 44 -year- old man with PBS who, at the age of 41, required RRT and was faced with the challenge of accepting PD. After more than three years of nocturnal automated peritoneal dialysis treatment we can safely say, as the following case illustrates, that PD is a feasible option in PBS adult patients.

  8. Three cases of prune belly syndrome at the Lagos State University Teaching Hospital, Ikeja.

    Science.gov (United States)

    Solarin, Adaobi U; Disu, Elizabeth A; Gbelee, Henry O; Animasahaun, Adeola B; Aremu, Oluwatosin E; Ogbuokiri, Eucharia; Ogunnaike, Gbemisola O; Oladimeji, Alaba

    2018-01-01

    Prune belly syndrome (PBS) is a rare congenital disorder affecting 2.5 to 3.8/100,000 live births worldwide. Our objective of this report is to describe clinical manifestation, laboratory, and radiological characteristics of PBS in our patients, to highlight the limitations to offering appropriate patient care due to parents demanding discharge against medical advice and the need to increase the awareness regarding this rare disease. We report three cases; all referred after birth with lax abdominal wall, congenital anomalies of kidney, and urinary tract. One of the patients had an absent right foot. They all had cryptorchidism, and in one, there was deranged renal function. The reported cases had both medical and radiological interventions to varying degrees. They all had an abdominal ultrasound which revealed varying degrees of hydronephrosis, hydroureters, and bladder changes. Voiding cystourethrogram showed vesicoureteric reflux in one of the reported cases. Urinary tract infections were appropriately treated with antibiotics based on sensitivity. PBS management in our setting remains a challenge because of strong cultural beliefs, and high rate of discharge against medical advice. Focus should be on parent education, early diagnosis, and multidisciplinary management approach.

  9. Three cases of prune belly syndrome at the Lagos State University Teaching Hospital, Ikeja

    Directory of Open Access Journals (Sweden)

    Adaobi U Solarin

    2018-01-01

    Full Text Available Prune belly syndrome (PBS is a rare congenital disorder affecting 2.5 to 3.8/100,000 live births worldwide. Our objective of this report is to describe clinical manifestation, laboratory, and radiological characteristics of PBS in our patients, to highlight the limitations to offering appropriate patient care due to parents demanding discharge against medical advice and the need to increase the awareness regarding this rare disease. We report three cases; all referred after birth with lax abdominal wall, congenital anomalies of kidney, and urinary tract. One of the patients had an absent right foot. They all had cryptorchidism, and in one, there was deranged renal function. The reported cases had both medical and radiological interventions to varying degrees. They all had an abdominal ultrasound which revealed varying degrees of hydronephrosis, hydroureters, and bladder changes. Voiding cystourethrogram showed vesicoureteric reflux in one of the reported cases. Urinary tract infections were appropriately treated with antibiotics based on sensitivity. PBS management in our setting remains a challenge because of strong cultural beliefs, and high rate of discharge against medical advice. Focus should be on parent education, early diagnosis, and multidisciplinary management approach.

  10. Elective appendicovesicostomy in association with monfort abdominoplasty in the treatment of prune belly syndrome

    Directory of Open Access Journals (Sweden)

    Riberto Liguori

    2006-12-01

    Full Text Available OBJECTIVE: To evaluate the role of elective appendicovesicostomy in association with Monfort abdominoplasty to avoid urinary tract infection (UTI and renal damage in the post-operative follow-up of patients with prune belly syndrome. MATERIALS AND METHODS: We followed 4 patients operated in our institution (UNIFESP (Monfort, orchidopexy and Mitrofanoff and compared them to 2 patients treated similarly, but without an appendicovesicostomy, in a second institution (UFBA. We evaluated postoperative clinical complications, UTI and preservation of renal parenchyma. Patients were followed as outpatients with urinalysis, ultrasonography (US and occasionally with renal scintigraphy. RESULTS: Mean follow-up was 23.5 months. Immediate post-operative course was uneventful. We observed that only one patient with the Mitrofanoff channel persisted with UTI, while the 2 patients used as controls persisted with recurrent pyelonephritis (> 2 UTI year. CONCLUSION: Our data suggest that no morbidity was added by the appendicovesicostomy to immediate postoperative surgical recovery and that this procedure may have a beneficial effect in reducing postoperative UTI events and their consequences by reducing the postvoid residuals in the early abdominoplasty follow-up. However, we recognize that the series is small and only a longer follow-up with a larger number of patients will allow us to confirm our suppositions. We could not make any statistically significant assumptions regarding differences in renal preservation due to the same limitations.

  11. Urethral hydrodistension for management of urethral hypoplasia in prune belly syndrome: long-term results.

    Science.gov (United States)

    Kajbafzadeh, Abdol-Mohammad; Rasouli, Mohammad Reza; Dianat, SeyedSaeid; Nezami, Behtash G; Mahboubi, Amir Hassan; Sina, Alireza

    2010-11-01

    The aim of the study was to evaluate the efficacy and safety of urethral hydrodistension for management of urethral hypoplasia in prune belly syndrome (PBS). During a 10-year period, 7 infants with PBS and urethral hypoplasia presented either with open urachus or surgically created urinary diversion referred to our hospital. Five milliliters of normal saline was pushed via a 22-gauge plastic angiocatheter into the urethra with simultaneous finger pressure on the perineum to occlude the proximal urethra that was repeated with higher volumes of the solution (up to 20 mL). The procedure was continued until a 6F or 8F feeding tube catheter confirmed the urethral patency. Hydrodistension was repeated in 3-month intervals till complete patency was confirmed by imaging. Median age of the infants was 6 (1-8) months. All urethral hydrodistension were successful after 1 to 3 sessions. Follow-up imaging studies showed significant improvement in all patients except one. Natural and surgically created urinary diversions were closed in 6 infants. The hydrodistension create an equal and constant pressure into the urethral wall without any urethral damage. This technique can be considered along with the other available methods for management of urethral hypoplasia in selected cases of PBS. Copyright © 2010 Elsevier Inc. All rights reserved.

  12. Penerapan Algoritma Alphabeta Pruning Sebagai Kecerdasan Buatan pada Game Pawn Battle

    Directory of Open Access Journals (Sweden)

    Ridho Rahman Hariadi

    2017-05-01

    Full Text Available Catur merupakan game strategi. Catur dimainkan oleh dua orang. Ada dua jenis warna bidak pada permainan catur, yaitu: bidak hitam dan bidak putih. Agar dapat memenangkan sebuah permainan catur, pemain harus menguasai strategi-strategi dalam bermain catur. Ada banyak startegi dalam bermain catur yang hanya dapat dipahami dengan banyak bermain dan berlatih. Modul-modul cara bermain catur pada umumnya hanya menjelaskan kejadian yang biasa terjadi dalam permainan catur. Sehingga berlatih merupakan satu-satu nya cara yang dapat digunakan untuk meningkatkan kemampuan dalam bermain catur. Penelitian ini merupakan penelitian implementasi yang menggunakan algoritma Alpha Beta Prunnning sebagai kecerdasan buatan dalam permainan catur. Algoritma yang biasanya digunakan dalam permainan catur adalah algoritma Min-Max. Algoritma Min-Max merupakan algoritma yang digunakan untuk menemukan langkah terbaik dalam permainan catur. Sedangkan Algoritma Alpha Beta Pruning adalah algoritma yang digunakan untuk mencegah perluasan cabang/node untuk mendapatkan hasil pencarian langkah yang lebih baik dari sebelumnya. Penelitian ini diharapkan dapat membantu memberikan gambaran penerapan algoritma Alpha Beta Prunning yang digunakan dalam  membangun sebuah kecerdasan buatan pada permainan catur.

  13. Usability-driven pruning of large ontologies: the case of SNOMED CT.

    Science.gov (United States)

    López-García, Pablo; Boeker, Martin; Illarramendi, Arantza; Schulz, Stefan

    2012-06-01

    To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.

  14. Multi-pruning of decision trees for knowledge representation and classification

    KAUST Repository

    Azad, Mohammad

    2016-06-09

    We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

  15. Ethanol production from olive prunings by autohydrolysis and fermentation with Candida tropicalis

    Energy Technology Data Exchange (ETDEWEB)

    Garcia Martin, Juan Francisco; Bravo, Vicente [Department of Chemical Engineering, University of Granada, Campus Universitario de Fuentenueva, 18071 Granada (Spain); Cuevas, Manuel; Sanchez, Sebastian [Department of Chemical, Environmental and Materials Engineering, University of Jaen, Campus Las Lagunillas, 23071 Jaen (Spain)

    2010-07-15

    Hydrolysates from olive prunings (a renewable, low-cost, easily available, agricultural residue) were fermented with the unconventional yeast Candida tropicalis NBRC 0618 to produce not only ethanol fuel but also xylitol as a by-product, which adds value to the economic viability of the bioprocess. Autohydrolysis took place at 200 C in a stirred stainless-steel tank reactor. The influence of the solid/liquid ratio in the reactor was studied. Fermentation experiments were conducted in a batch-culture reactor at a temperature of 30 C, a stirring rate of 500 rpm and pH values of between 5.0 and 6.5. Under the operating conditions tested the highest yields of ethanol and xylitol were obtained with the hydrolysate fermented at pH 5.0 and solely the airflow that entered via the stirring vortex. Under these conditions, the instantaneous ethanol yield was 0.44 g g{sup -1} and the overall xylitol yield 0.13 g g{sup -1}. (author)

  16. Evaluation of genetic divergence among clones of conilon coffee after scheduled cycle pruning.

    Science.gov (United States)

    Dalcomo, J M; Vieira, H D; Ferreira, A; Lima, W L; Ferrão, R G; Fonseca, A F A; Ferrão, M A G; Partelli, F L

    2015-11-30

    Coffea canephora genotypes from the breeding program of Instituto Capixaba de Pesquisa e Extensão Rural were evaluated, and genetic diversity was estimated with the aim of future improvement strategies. From an initial group of 55 genotypes, 18 from the region of Castelo, ES, were selected, and three clones of the cultivars "Vitória" and "robusta tropical." Upon completion of the scheduled cycle pruning, 17 morphoagronomic traits were measured in the 22 genotypes selected. The principal components method was used to evaluate the contributions relative to the traits. The genetic dissimilarity matrix was obtained through Mahalanobis generalized distance, and genotypes were grouped using the hierarchical method based on the mean of the distances. The most promising clones of Avaliação Castelo were AC02, AC03, AC12, AC13, AC22, AC24, AC26, AC27, AC28, AC29, AC30, AC35, AC36, AC37, AC39, AC40, AC43, and AC46. These methods detected high genetic variability, grouping, by similarity, the genotypes in five groups. The trait that contributed the least to genetic divergence was the number of leaves in plagiotropic branches; however, this was not eliminated, because discarding it altered the groups. There are superior genotypes with potential for use in the next stages of the breeding program, aimed at both the composition of clonal variety and hybridizations.

  17. Multi-pruning of decision trees for knowledge representation and classification

    KAUST Repository

    Azad, Mohammad; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail

    2016-01-01

    We consider two important questions related to decision trees: first how to construct a decision tree with reasonable number of nodes and reasonable number of misclassification, and second how to improve the prediction accuracy of decision trees when they are used as classifiers. We have created a dynamic programming based approach for bi-criteria optimization of decision trees relative to the number of nodes and the number of misclassification. This approach allows us to construct the set of all Pareto optimal points and to derive, for each such point, decision trees with parameters corresponding to that point. Experiments on datasets from UCI ML Repository show that, very often, we can find a suitable Pareto optimal point and derive a decision tree with small number of nodes at the expense of small increment in number of misclassification. Based on the created approach we have proposed a multi-pruning procedure which constructs decision trees that, as classifiers, often outperform decision trees constructed by CART. © 2015 IEEE.

  18. Extreme environment electronics

    CERN Document Server

    Cressler, John D

    2012-01-01

    Unfriendly to conventional electronic devices, circuits, and systems, extreme environments represent a serious challenge to designers and mission architects. The first truly comprehensive guide to this specialized field, Extreme Environment Electronics explains the essential aspects of designing and using devices, circuits, and electronic systems intended to operate in extreme environments, including across wide temperature ranges and in radiation-intense scenarios such as space. The Definitive Guide to Extreme Environment Electronics Featuring contributions by some of the world's foremost exp

  19. Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

    KAUST Repository

    Siddiqui, Shahzeb; Alzayer, Fatemah; Feki, Saber

    2015-01-01

    on a given system. A historic learning based methodology is suggested to prune the parameter search space for a more efficient auto-tuning process. This approach is applied to tune the OpenACC gang and vector clauses for a better mapping of the compute

  20. Extreme value distributions

    CERN Document Server

    Ahsanullah, Mohammad

    2016-01-01

    The aim of the book is to give a through account of the basic theory of extreme value distributions. The book cover a wide range of materials available to date. The central ideas and results of extreme value distributions are presented. The book rwill be useful o applied statisticians as well statisticians interrested to work in the area of extreme value distributions.vmonograph presents the central ideas and results of extreme value distributions.The monograph gives self-contained of theory and applications of extreme value distributions.

  1. Moving in extreme environments: what's extreme and who decides?

    Science.gov (United States)

    Cotter, James David; Tipton, Michael J

    2014-01-01

    Humans work, rest and play in immensely varied extreme environments. The term 'extreme' typically refers to insufficiency or excess of one or more stressors, such as thermal energy or gravity. Individuals' behavioural and physiological capacity to endure and enjoy such environments varies immensely. Adverse effects of acute exposure to these environments are readily identifiable (e.g. heat stroke or bone fracture), whereas adverse effects of chronic exposure (e.g. stress fractures or osteoporosis) may be as important but much less discernable. Modern societies have increasingly sought to protect people from such stressors and, in that way, minimise their adverse effects. Regulations are thus established, and advice is provided on what is 'acceptable' exposure. Examples include work/rest cycles in the heat, hydration regimes, rates of ascent to and duration of stay at altitude and diving depth. While usually valuable and well intentioned, it is important to realise the breadth and importance of limitations associated with such guidelines. Regulations and advisories leave less room for self-determination, learning and perhaps adaptation. Regulations based on stress (e.g. work/rest cycles relative to WBGT) are more practical but less direct than those based on strain (e.g. core temperature), but even the latter can be substantively limited (e.g. by lack of criterion validation and allowance for behavioural regulation in the research on which they are based). Extreme Physiology & Medicine is publishing a series of reviews aimed at critically examining the issues involved with self- versus regulation-controlled human movement acutely and chronically in extreme environments. These papers, arising from a research symposium in 2013, are about the impact of people engaging in such environments and the effect of rules and guidelines on their safety, enjoyment, autonomy and productivity. The reviews will cover occupational heat stress, sporting heat stress, hydration, diving

  2. Trying to Learn Lessons for Response to Extreme Events: Paradigm Shifts Affecting Civil Defense in the Trinational Region of Southwestern Amazonia

    Science.gov (United States)

    Santos, G. L. P.

    2015-12-01

    The last ten years have seen several extreme climate events in southwestern Amazonia with historic impacts. The City of Rio Branco, Capital of Acre, Brazil´s westernmost State, suffered its seventh consecutive annual flooding and its worst in March 2015. The city of Tarauacá, also in Acre, registered 12 flooding events between November 2014 and April 2015. The most recent flood of the trinational Acre River in 2015 set historic records for flood stage and number of displaced persons in Cobija, the Capital of Pando, Bolivia. From February to April 2014, floods of the Madeira River disrupted the one highway between Acre and southern Brazil. Puerto Maldonado, the capital in Madre de Dios Region of Peru had its worst flood in 50 years during 2014. In 2005 and 2010, prolonged droughts combined with ignition sources resulted in tens to hundreds of thousands of hectares of fire-damaged rainforests in the Madre de Dios, Acre and Pando (MAP) Region. The Civil Defenses in these three contiguous political units faced several abrupt paradigm shifts that affected their responses: 1) The drought of 2005 showed dramatically that regional rainforests do burn; 2) The recent flooding history, particularly in 2012 and 2015, demolished the cultural icon of a nine-year recurrence interval; 3) What happens outside your territory can be devastating. The Madeira River flood impeded an estimated 200 million dollars from circulating in Acre; 4) The past can be a terrible guide. For Cobija and Rio Branco, the 2015 flood was on the order of a meter higher than any other. Many home dwellers did not evacuate in time because they used past floods as a guide; 5) A collapse in communication - cell phones, land lines, and Internet - can get worse. In 2012, such a collapse occurred in two border towns for 5 days, yet in 2015 it lasted more than 11 days. Research is needed to address how institutions linked to Civil Defense can shift paradigms in time to be more effective.

  3. Invited commentary on comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial.

    Science.gov (United States)

    Kwakkel, Gert; van Wegen, Erwin E; Meskers, Carel M

    2015-06-01

    In this issue of Archives of Physical Medicine and Rehabilitation, Jessica McCabe and colleagues report findings from their methodologically sound, dose-matched clinical trial in 39 patients beyond 6 months poststroke. In this phase II trial, the effects of 60 treatment sessions, each involving 3.5 hours of intensive practice plus either 1.5 hours of functional electrical stimulation (FES) or a shoulder-arm robotic therapy, were compared with 5 hours of intensive daily practice alone. Although no significant between-group differences were found on the primary outcome measure of Arm Motor Ability Test and the secondary outcome measure of Fugl-Meyer Arm motor score, 10% to 15% within-group therapeutic gains were on the Arm Motor Ability Test and Fugl-Meyer Arm. These gains are clinically meaningful for patients with stroke. However, the underlying mechanisms that drive these improvements remain poorly understood. The approximately $1000 cost reduction per patient calculated for the use of motor learning (ML) methods alone or combined with FES, compared with the combination of ML and shoulder-arm robotics, further emphasizes the need for cost considerations when making clinical decisions about selecting the most appropriate therapy for the upper paretic limb in patients with chronic stroke. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  4. Method Extreme Learning Machine for Forecasting Number of Patients’ Visits in Dental Poli (A Case Study: Community Health Centers Kamal Madura Indonesia)

    Science.gov (United States)

    Sari Rochman, E. M.; Rachmad, A.; Syakur, M. A.; Suzanti, I. O.

    2018-01-01

    Community Health Centers (Puskesmas) are health service institutions that provide individual health services for outpatient, inpatient and emergency care services. In the outpatient service, there are several polyclinics, including the polyclinic of Ear, Nose, and Throat (ENT), Eyes, Dentistry, Children, and internal disease. Dental Poli is a form of dental and oral health services which is directed to the community. At this moment, the management team in dental poli often has difficulties when they do the preparation and planning to serve a number of patients. It is because the dental poli does not have the appropriate workers with the right qualification. The purpose of this study is to make the system of forecasting the patient’s visit to predict how many patients will come; so that the resources that have been provided will be in accordance with the needs of the Puskesmas. In the ELM method, input and bias weights are initially determined randomly to obtain final weights using Generalized Invers. The matrix used in the final weights is a matrix whose outputs are from each input to a hidden layer. So ELM has a fast learning speed. The result of the experiment of ELM method in this research is able to generate a prediction of a number of patient visit with the RMSE value which is equal to 0.0426.

  5. The importance of pruning to the quality of wine grape fruits (Vitis vinifera L. cultivated under high-altitude tropical conditions

    Directory of Open Access Journals (Sweden)

    Pedro José Almanza-Merchán

    2014-12-01

    Full Text Available Since 1998, the Ain-Karim Vineyard has been growing different grape varieties for the production of high-altitude tropical wines in the municipality of Sutamarchan, located in the Alto Ricaurte region of Boyaca (Colombia. Pruning is used to limit the number and length of branches, generating a suitable balance between plant vigor and production; thereby, regulating fruit quantity and quality and ensuring reserves for the subsequent production. This study aimed to evaluate the effect of three pruning types (short = two buds on two spurs; long = five buds on three spurs and mixed = combination of short and long pruning types on the fruit quality of V. vinifera, Cabernet Sauvignon and Sauvignon Blanc varieties. To accomplish this, a completely randomized two-factor design was used. Physicochemical variables of fruit quality (fresh cluster weight, water content, total soluble solids (TSS, total titratable acidity (TTA, technical maturity index (TMI, and pH were determined at harvest. The long pruning type presented the highest values for the fresh cluster weight and TSS of the fruits from both varieties and a higher TMI in the Cabernet Sauvignon variety. These results indicate that, under the conditions of the vineyard, long pruning is the most suitable.

  6. Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix.

    Directory of Open Access Journals (Sweden)

    Jakob H Havgaard

    2007-10-01

    Full Text Available It has become clear that noncoding RNAs (ncRNA play important roles in cells, and emerging studies indicate that there might be a large number of unknown ncRNAs in mammalian genomes. There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at http://foldalign.ku.dk.

  7. Health-related Quality of Life in Children With Prune-belly Syndrome and Their Caregivers.

    Science.gov (United States)

    Arlen, Angela M; Kirsch, Susan S; Seidel, Natan E; Garcia-Roig, Michael; Smith, Edwin A; Kirsch, Andrew J

    2016-01-01

    To compare health-related quality of life (HRQoL) in children with prune-belly syndrome (PBS) and their caregivers to healthy controls, as children and adolescents with PBS face numerous potential physical and psychosocial challenges. Study participants completed the Pediatric Quality of Life Inventory Generic Core Scales (PedsQL) 4.0 generic core scales (children) or Quality of Life Enjoyment and Satisfaction Questionnaire Short Form (Q-LES-Q-SF) (caregivers) in an online, anonymous format. The PedsQL 4.0 is a 23-item, age-adjusted, validated questionnaire that assesses physical, emotional, social, and school functioning in pediatric patients. The Q-LES-Q-SF is a validated, self-report measure that assesses various areas of daily functioning in adults. PedsQL 4.0 was completed by 32 children with PBS. Individual physical (66.3 ± 20 vs 84.4 ± 17.3; P < .0001), emotional (68.4 ± 23.4 vs 80.9 ± 19.6; P < .01), social (63.1 ± 21.3 vs 87.4 ± 17.2; P  <  .0001), and school (53 ± 21.7 vs 78.6 ± 20.5; P < .0001) functioning scales were all significantly lower than in healthy children. Nineteen caregivers completed the Q-LES-Q-SF. Caregivers had a mean raw score of 54.8 ± 9.6, which was significantly lower (P  =  .02) than the comparative healthy adult cohort (59.8 ± 11.3). PBS profoundly impacts HRQoL in children, negatively affecting physical, emotional, social, and school functioning. Caregivers of PBS patients also report an overall lower quality of life, highlighting the challenges that families with chronically ill children often face. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Structural study of the bladder in fetuses with prune belly syndrome.

    Science.gov (United States)

    Julio Junior, Helce R; Costa, Suelen F; Costa, Waldemar S; Barcellos Sampaio, Francisco J; Favorito, Luciano A

    2018-01-01

    To study the bladder structure of fetuses with prune belly syndrome (PBS). We studied three bladders obtained from three male fetuses with PBS and seven bladders from seven male fetuses without anomalies. Each bladder was dissected and embedded in paraffin, from which 5 μm thick sections were obtained and stained with Masson's trichrome (to quantify connective tissue and smooth muscle) and picrosirius red with polarization (to observe collagen). Immunohistochemistry with tubulin (Tubulin, beta III, Mouse Monoclonal Antibody) was applied to observe the bladder nerves. The images were captured with an Olympus BX51 microscope and Olympus DP70 camera. The stereological analysis was done with the Image Pro and Image J programs, using a grid to determine volumetric densities (Vv). Means were statistically compared using the Mann-Whitney test (P < 0.05). Quantitative analysis documented that smooth muscle fibers were significantly smaller (P = 0.04) in PBS fetuses (9.67% to 17.75%, mean = 13.2%) compared to control group (13.33% to 26.56%, mean = 17.43%). The analysis of collagen fibers showed predominance of green in the control group, suggesting collagen type III presence, and predominance of red in the in PBS fetal bladders, suggesting collagen type I presence in this group. The qualitative analysis of the nerves with immunohistochemistry with tubulin showed predominance of nerves in the control group. The bladder in PBS had lower concentrations of smooth muscle fibers, collagen type III, and nerves. These structural alterations can be one of the factors involved in urinary tract abnormality such as distended bladder in patients with PBS. © 2017 Wiley Periodicals, Inc.

  9. Contemporary epidemiology and characterization of newborn males with prune belly syndrome.

    Science.gov (United States)

    Routh, Jonathan C; Huang, Lin; Retik, Alan B; Nelson, Caleb P

    2010-07-01

    Prune belly syndrome (PBS) is a rare condition characterized by the congenital absence or deficiency of the abdominal wall musculature, with associated abnormalities of the genitourinary tract, including hydronephrosis and cryptorchidism. Few population-based epidemiology or mortality data are available. We retrospectively reviewed the Kids' Inpatient Database to evaluate PBS among newborn infants during their initial hospitalization in 2000, 2003, and 2006. The International Classification of Diseases, Ninth Revision, Clinical Modification codes were used to identify patients and to determine the comorbidity status. The PBS incidence, demographics, comorbid conditions, and disposition were assessed. A total of 133 newborn male infants diagnosed with PBS were identified of 1,420,991 live male births, for a weighted incidence estimate of 3.8 cases/100,000 live births. Of the newborns with PBS, 50% were white, 31% black, and 10% were Hispanic. In-hospital mortality was high (39 of 133, 29%). Of the 133 patients with PBS, 55 (41%) were discharged home and 39 (29%) required inpatient transfer or home nursing care. Fifty-seven patients (43%) were born premature; 56% of the PBS deaths occurred in premature infants. Mechanical ventilation was required in 64 newborns (48%), and 33 (24%) had coexisting congenital cardiovascular anomalies. Renal failure was uncommon, occurring in only 5 newborns (4%); none required dialysis. Only 13 patients (10%) underwent urinary diversion (vesicostomy or ureterostomy). The incidence of PBS was 3.8 cases/100,000 live births. Despite advances in care for children with PBS, this condition continues to be associated with high perinatal mortality, likely related to the associated prematurity and pulmonary complications. Renal failure was rare, as was immediate urinary diversion. Copyright 2010 Elsevier Inc. All rights reserved.

  10. Immunoexpression of adrenergic receptors in detrusor from patients with prune belly syndrome: a digital quantification.

    Science.gov (United States)

    Schneider-Monteiro, Edison D; Dénes, Francisco T; Hampel, Christian; Leite, Katia R M; Thüroff, Joachim W; Srougi, Miguel

    2010-06-01

    Prune belly syndrome (PBS) presents with large-capacity bladders, high compliance and post-void residual volumes. Operative and conservative treatments are controversial. When histologically compared to normal bladder, bladder outlet obstruction results in an up- or down-regulation of adrenoceptors. Our goal was to study the immunoexpression of adrenoceptors in detrusor from patients with PBS. Bladder domes from PBS patients (n=14) were studied (PBG). For normal controls, bladder specimens were obtained at adult surgery (n=13) (CG1) and at child autopsy (n=5) (CG2). Staining was performed using antibodies to alpha1a, alpha1b, alpha1d and beta3 adrenoceptors. Five to 10 images were captured on an optic microscope with a digital camera and analysed with Photoshop. The immunocyhistochemical index with arbitrary units was calculated and compared. Mean age was 1.28, 64 and 1.41 years for PBG, CG1 and CG2, respectively. The immunohistochemical index with arbitrary units of alpha1a receptors was 0.06 in PBG, 0.16 in CG1 and 0.14 in CG2 (p=0.008); of alpha1b 0.06, 0.06 and 0.07 (p=0.781); and of alpha1d 0.04, 0.04 and 0.05 (p=0.618). Regarding beta3 the respective values were 0.07, 0.14 and 0.10 (p=0.378). Our results show a decrease in alpha1a-adrenoceptor immunostaining intensity in detrusor from children with PBS. Further in vitro studies are needed to determine whether these observations are physiologically significant. Copyright (c) 2010 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

  11. Laparoscopic orchidopexy in boys with prune belly syndrome--outcome and technical considerations.

    Science.gov (United States)

    Philip, Joe; Mullassery, Dhanya; Craigie, Ross J; Manikandan, Ramaswamy; Kenny, Simon E

    2011-07-01

    Cryptorchidism is an ubiquitous feature in prune belly syndrome (PBS). Laparoscopic orchidopexy allows dissection of the spermatic cord with minimal morbidity. We discuss the technical difficulties and outcome of three boys with PBS who underwent two-stage laparoscopic Fowler-Stephens orchidopexy (F-SO). Three boys, ages 1, 2, and 4, underwent laparoscopic F-SO. All boys had viable testes that were found within 3 cm of the deep inguinal ring. The testicular vessels were either ligated bilaterally with 4/0 polyglactin or monopolar diathermy was used and the vessels divided. Bilateral second-stage F-SO was performed within 6 months in two boys and limited to one side in the third boy. One boy awaits the contralateral second stage. All three boys have adequately sized gonads. Based on our experience, the port incisions should be smaller than routine practice to prevent air leak in PBS. Although the intra-abdominal pressure of 12 mm Hg did not vary from our normal practice, a high flow rate is necessary after initial insufflation (6 L/min) to compensate for inevitable gas leaks because the abdominal wall is so thin. Risk of diathermy injury to the thin abdominal wall and the vessels is significant. Laparoscopy enables easy visualization of the ureter, testes, and testicular vessels and permits complete dissection of testicular vessels. It is easier to maintain integrity of spermatic vessels. Use of radially expanding trocars, small incisions, and high gas flow rates permit this procedure to be performed safely with good outcome and cosmetic results in this challenging group of boys.

  12. From "rest" to language task: Task activation selects and prunes from broader resting-state network.

    Science.gov (United States)

    Doucet, Gaelle E; He, Xiaosong; Sperling, Michael R; Sharan, Ashwini; Tracy, Joseph I

    2017-05-01

    Resting-state networks (RSNs) show spatial patterns generally consistent with networks revealed during cognitive tasks. However, the exact degree of overlap between these networks has not been clearly quantified. Such an investigation shows promise for decoding altered functional connectivity (FC) related to abnormal language functioning in clinical populations such as temporal lobe epilepsy (TLE). In this context, we investigated the network configurations during a language task and during resting state using FC. Twenty-four healthy controls, 24 right and 24 left TLE patients completed a verb generation (VG) task and a resting-state fMRI scan. We compared the language network revealed by the VG task with three FC-based networks (seeding the left inferior frontal cortex (IFC)/Broca): two from the task (ON, OFF blocks) and one from the resting state. We found that, for both left TLE patients and controls, the RSN recruited regions bilaterally, whereas both VG-on and VG-off conditions produced more left-lateralized FC networks, matching more closely with the activated language network. TLE brings with it variability in both task-dependent and task-independent networks, reflective of atypical language organization. Overall, our findings suggest that our RSN captured bilateral activity, reflecting a set of prepotent language regions. We propose that this relationship can be best understood by the notion of pruning or winnowing down of the larger language-ready RSN to carry out specific task demands. Our data suggest that multiple types of network analyses may be needed to decode the association between language deficits and the underlying functional mechanisms altered by disease. Hum Brain Mapp 38:2540-2552, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  13. A case of ultrasound-guided prenatal diagnosis of prune belly syndrome in Papua New Guinea--implications for management.

    Science.gov (United States)

    Ome, Maria; Wangnapi, Regina; Hamura, Nancy; Umbers, Alexandra J; Siba, Peter; Laman, Moses; Bolnga, John; Rogerson, Sheryle; Unger, Holger W

    2013-05-07

    Prune belly syndrome is a rare congenital malformation of unknown aetiology and is characterised by abnormalities of the urinary tract, a deficiency of abdominal musculature and bilateral cryptorchidism in males. We report a case of prune belly syndrome from Papua New Guinea, which was suspected on pregnancy ultrasound scan and confirmed upon delivery. A 26-year-old married woman, Gravida 3 Para 2, presented to antenatal clinic in Madang, Papua New Guinea, at 21(+5) weeks' gestation by dates. She was well with no past medical or family history of note. She gave consent to participate in a clinical trial on prevention of malaria in pregnancy and underwent repeated ultrasound examinations which revealed a live fetus with persistent megacystis and anhydramnios. Both mother and clinicians agreed on conservative management of the congenital abnormality. The mother spontaneously delivered a male fetus weighing 2010 grams at 34 weeks' gestation with grossly abnormal genitalia including cryptorchidism, penile aplasia and an absent urethral meatus, absent abdominal muscles and hypoplastic lungs. The infant passed away two hours after delivery. This report discusses the implications of prenatal detection of severe congenital abnormalities in PNG. This first, formally reported, case of prune belly syndrome from a resource-limited setting in the Oceania region highlights the importance of identifying and documenting congenital abnormalities. Women undergoing antenatal ultrasound examinations must be carefully counseled on the purpose and the limitations of the scan. The increasing use of obstetric ultrasound in PNG will inevitably result in a rise in prenatal detection of congenital abnormalities. This will need to be met with adequate training, referral mechanisms and better knowledge of women's attitudes and beliefs on birth defects and ultrasound. National medicolegal guidance regarding induced abortion and resuscitation of a fetus with severe congenital abnormalities may

  14. A case of ultrasound-guided prenatal diagnosis of prune belly syndrome in Papua New Guinea – implications for management

    Science.gov (United States)

    2013-01-01

    Background Prune belly syndrome is a rare congenital malformation of unknown aetiology and is characterised by abnormalities of the urinary tract, a deficiency of abdominal musculature and bilateral cryptorchidism in males. We report a case of prune belly syndrome from Papua New Guinea, which was suspected on pregnancy ultrasound scan and confirmed upon delivery. Case presentation A 26-year-old married woman, Gravida 3 Para 2, presented to antenatal clinic in Madang, Papua New Guinea, at 21+5 weeks’ gestation by dates. She was well with no past medical or family history of note. She gave consent to participate in a clinical trial on prevention of malaria in pregnancy and underwent repeated ultrasound examinations which revealed a live fetus with persistent megacystis and anhydramnios. Both mother and clinicians agreed on conservative management of the congenital abnormality. The mother spontaneously delivered a male fetus weighing 2010 grams at 34 weeks’ gestation with grossly abnormal genitalia including cryptorchidism, penile aplasia and an absent urethral meatus, absent abdominal muscles and hypoplastic lungs. The infant passed away two hours after delivery. This report discusses the implications of prenatal detection of severe congenital abnormalities in PNG. Conclusion This first, formally reported, case of prune belly syndrome from a resource-limited setting in the Oceania region highlights the importance of identifying and documenting congenital abnormalities. Women undergoing antenatal ultrasound examinations must be carefully counseled on the purpose and the limitations of the scan. The increasing use of obstetric ultrasound in PNG will inevitably result in a rise in prenatal detection of congenital abnormalities. This will need to be met with adequate training, referral mechanisms and better knowledge of women’s attitudes and beliefs on birth defects and ultrasound. National medicolegal guidance regarding induced abortion and resuscitation of a

  15. Effects of tree species and wood particle size on the properties of cement-bonded particleboard manufacturing from tree prunings.

    Science.gov (United States)

    Nasser, Ramadan A; Al-Mefarrej, H A; Abdel-Aal, M A; Alshahrani, T S

    2014-09-01

    This study investigated the possibility of using the prunings of six locally grown tree species in Saudi Arabia for cement-bonded particleboard (CBP) production. Panels were made using four different wood particle sizes and a constant wood/cement ratio (1/3 by weight) and target density (1200 kg/m3). The mechanical properties and dimensional stability of the produced panels were determined. The interfacial area and distribution of the wood particles in cement matrix were also investigated by scanning electron microscopy. The results revealed that the panels produced from these pruning materials at a target density of 1200 kg m(-3) meet the strength and dimensional stability requirements of the commercial CBP panels. The mean moduli of rupture and elasticity (MOR and MOE) ranged from 9.68 to 11.78 N mm2 and from 3952 to 5667 N mm2, respectively. The mean percent water absorption for twenty four hours (WA24) ranged from 12.93% to 23.39%. Thickness swelling values ranged from 0.62% to 1.53%. For CBP panels with high mechanical properties and good dimensional stability, mixed-size or coarse particles should be used. Using the tree prunings for CBPs production may help to solve the problem of getting rid of these residues by reducing their negative effects on environment, which are caused by poor disposal of such materials through direct combustion process and appearance of black cloud and then the impact on human health or the random accumulation and its indirect effects on the environment.

  16. How extreme is extreme hourly precipitation?

    Science.gov (United States)

    Papalexiou, Simon Michael; Dialynas, Yannis G.; Pappas, Christoforos

    2016-04-01

    The importance of accurate representation of precipitation at fine time scales (e.g., hourly), directly associated with flash flood events, is crucial in hydrological design and prediction. The upper part of a probability distribution, known as the distribution tail, determines the behavior of extreme events. In general, and loosely speaking, tails can be categorized in two families: the subexponential and the hyperexponential family, with the first generating more intense and more frequent extremes compared to the latter. In past studies, the focus has been mainly on daily precipitation, with the Gamma distribution being the most popular model. Here, we investigate the behaviour of tails of hourly precipitation by comparing the upper part of empirical distributions of thousands of records with three general types of tails corresponding to the Pareto, Lognormal, and Weibull distributions. Specifically, we use thousands of hourly rainfall records from all over the USA. The analysis indicates that heavier-tailed distributions describe better the observed hourly rainfall extremes in comparison to lighter tails. Traditional representations of the marginal distribution of hourly rainfall may significantly deviate from observed behaviours of extremes, with direct implications on hydroclimatic variables modelling and engineering design.

  17. Optimization with Extremal Dynamics

    International Nuclear Information System (INIS)

    Boettcher, Stefan; Percus, Allon G.

    2001-01-01

    We explore a new general-purpose heuristic for finding high-quality solutions to hard discrete optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively updates extremely undesirable variables of a single suboptimal solution, assigning them new, random values. Large fluctuations ensue, efficiently exploring many local optima. We use extremal optimization to elucidate the phase transition in the 3-coloring problem, and we provide independent confirmation of previously reported extrapolations for the ground-state energy of ±J spin glasses in d=3 and 4

  18. Further evidence of the etiology of prune belly syndrome provided by a transient massive intraabdominal cyst in a female.

    Science.gov (United States)

    Wijesinghe, Uddaka S; Muthucumaru, Mathievathaniy; Beasley, Spencer W

    2016-08-01

    We present a female neonate born with prune belly syndrome (PBS) in whom a large intraabdominal cyst was diagnosed at 12weeks of gestation. Rapid and exponential growth of the cyst caused pressure effects on the intraabdominal organs and stretching of the anterior abdominal wall by 19weeks of gestation. This led to drainage of the massive cyst at 20weeks of gestation to prevent fetal demise. This case provides further clues to the likely etiology of PBS: transient stretching and attenuation of the fetal abdominal wall secondary to gross fetal abdominal distension - from any cause. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Prune belly syndrome with overlapping presentation of partial urorectal septum malformation sequence in a female newborn with absent perineal openings.

    Science.gov (United States)

    Farooqui, Azhar; AlAqeel, Alaa; Habib, Zakaria

    2014-01-01

    Prune belly syndrome (PBS) is a rare congenital anomaly characterized in males by a triad of anomalous genitourinary tract, deficient development of abdominal wall muscles, and bilateral cryptorchidism. Although similar anomalies have been reported in females, by definition they do not full fill the classical triad. Urorectal septum malformation sequence (URSM) is a lethal condition characterized by presence of ambiguous genitalia, absent perineal openings (urogenital and anal), and lumbosacral abnormalities. In this original case report, the authors discuss the presentation and management of what would be analogous to a Woodhouse category 1 PBS in a female newborn associated with an overlapping presentation of URSM.

  20. Prune Belly Syndrome with Overlapping Presentation of Partial Urorectal Septum Malformation Sequence in a Female Newborn with Absent Perineal Openings

    Directory of Open Access Journals (Sweden)

    Azhar Farooqui

    2014-01-01

    Full Text Available Prune belly syndrome (PBS is a rare congenital anomaly characterized in males by a triad of anomalous genitourinary tract, deficient development of abdominal wall muscles, and bilateral cryptorchidism. Although similar anomalies have been reported in females, by definition they do not full fill the classical triad. Urorectal septum malformation sequence (URSM is a lethal condition characterized by presence of ambiguous genitalia, absent perineal openings (urogenital and anal, and lumbosacral abnormalities. In this original case report, the authors discuss the presentation and management of what would be analogous to a Woodhouse category 1 PBS in a female newborn associated with an overlapping presentation of URSM.