Multilayer perceptron for nonlinear programming
A new method for solving nonlinear programming problems within the framework of a multilayer neural network perceptron is proposed. The method employs the Penalty Function method to transform a constrained optimization problem into a sequence of unconstrained optimization problems and then solves the sequence of unconstrained optimizations of the transformed problem by training a series of multilayer perceptrons. The neural network formulation is represented in such a way that the multilayer perceptron prediction error to be minimized mimics the objective function of the unconstrained problem, and therefore, the minimization of the objective function for each unconstrained optimization is attained by training a single perceptron. The multilayer perceptron allows for the transformation of problems with two-sided bounding constraints on the decision variables x, e.g., a≤xn≤b, into equivalent optimization problems in which these constraints do not explicitly appear. Hence, when these are the only constraints in the problem, the transformed problem is constraint free (i.e., the transformed objective function contains no penalty terms) and is solved by training a multilayer perceptron only once. In addition, we present a new Penalty Function method for solving nonlinear programming problems that is parameter free and guarantees that feasible solutions are obtained when the optimal solution is on the boundary of the feasible region. Simulation results, including an example from operations research, illustrate the proposed methods.
KLASIFIKASI WEBSITE MENGGUNAKAN ALGORITMA MULTILAYER PERCEPTRON
Nyoman Purnama
2014-12-01
Full Text Available Sistem klasifikasi merupakan proses temu balik informasi yang sangat bergantung dari elemen-elemen penyusunnya.Sistem ini banyak digunakan untuk mengatasi permasalahan segmentasi data. Klasifikasi dapat digunakan pada website sebagaimetode untuk mengelompokkan website. Website merupakan salah satu data yang memiliki informasi yang beraneka-ragam,sehingga pengelompokan data ini penting untuk diteliti. Sistem klasifikasi dimulai dengan melakukan proses pengumpulaninformasi dari halaman website (parsing dan untuk setiap hasil parsing dilakukan proses penghapusan kata henti, stemming,feature selection dengan tf-idf. Hasil dari proses ini berupa fitur yang menjadi inputan algoritma Multilayer Perceptron. Dalamalgoritma ini terjadi proses pembelajaran terhadap pola input masukan dan pembuatan bobot pelatihan. Bobot ini akandigunakan pada proses klasifikasi. Hasil dari penelitian menunjukkan bahwa algoritma Multilayer Perceptron dapatmenghasilkan klasifikasi website dengan akurasi yang bagus. Hal ini dibuktikan dengan beberapa tahapan penelitian yangberbeda dan didapatkan nilai akurasi rata-rata diatas 70%.
Quaternionic Multilayer Perceptron with Local Analyticity
Nobuyuki Matsui; Haruhiko Nishimura; Teijiro Isokawa
2012-01-01
A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights...
Channel Equalization Using Multilayer Perceptron Networks
Saba Baloch; Javed Ali Baloch; Mukhtiar Ali Unar
2012-01-01
In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference) to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks). The simulated network is a multilayer feedforward Perceptron ANN,...
Channel Equalization Using Multilayer Perceptron Networks
Baloch, Saba; Baloch, Javed Ali; Unar, Mukhtiar Ali
2016-01-01
In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference) to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks). The simulated network is a multilayer feedforward Perceptron ANN, which has b...
Quaternionic Multilayer Perceptron with Local Analyticity
Nobuyuki Matsui
2012-11-01
Full Text Available A multi-layered perceptron type neural network is presented and analyzed in this paper. All neuronal parameters such as input, output, action potential and connection weight are encoded by quaternions, which are a class of hypercomplex number system. Local analytic condition is imposed on the activation function in updating neurons’ states in order to construct learning algorithm for this network. An error back-propagation algorithm is introduced for modifying the connection weights of the network.
Multilayer Perceptrons to Approximate Quaternion Valued Functions.
Xibilia, M G.; Muscato, G; Fortuna, L; Arena, P
1997-03-01
In this paper a new type of multilayer feedforward neural network is introduced. Such a structure, called hypercomplex multilayer perceptron (HMLP), is developed in quaternion algebra and allows quaternionic input and output signals to be dealt with, requiring a lower number of neurons than the real MLP, thus providing a reduced computational complexity. The structure introduced represents a generalization of the multilayer perceptron in the complex space (CMLP) reported in the literature. The fundamental result reported in the paper is a new density theorem which makes HMLPs universal interpolators of quaternion valued continuous functions. Moreover the proof of the density theorem can be restricted in order to formulate a density theorem in the complex space. Due to the identity between the quaternion and the four-dimensional real space, such a structure is also useful to approximate multidimensional real valued functions with a lower number of real parameters, decreasing the probability of being trapped in local minima during the learning phase. A numerical example is also reported in order to show the efficiency of the proposed structure. Copyright 1997 Elsevier Science Ltd. All Rights Reserved. PMID:12662531
Channel Equalization Using Multilayer Perceptron Networks
Saba Baloch
2012-07-01
Full Text Available In most digital communication systems, bandwidth limited channel along with multipath propagation causes ISI (Inter Symbol Interference to occur. This phenomenon causes distortion of the given transmitted symbol due to other transmitted symbols. With the help of equalization ISI can be reduced. This paper presents a solution to the ISI problem by performing blind equalization using ANN (Artificial Neural Networks. The simulated network is a multilayer feedforward Perceptron ANN, which has been trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network. This paper presents a very effective method for blind channel equalization, being more efficient than the pre-existing algorithms. The obtained results show a visible reduction in the noise content.
Extreme Learning Machine for Multilayer Perceptron.
Tang, Jiexiong; Deng, Chenwei; Huang, Guang-Bin
2016-04-01
Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme. PMID:25966483
Wind speed estimation using multilayer perceptron
Highlights: • We present a method for determining the average wind speed using neural networks. • We use data from that site in the short term and data from other nearby stations. • The inputs used in the ANN were wind speed and direction data from a station. • The method allows knowing the wind speed without topographical data. - Abstract: Wind speed knowledge is prerequisite in the siting of wind turbines. In consequence the wind energy use requires meticulous and specified knowledge of the wind characteristics at a location. This paper presents a method for determining the annual average wind speed at a complex terrain site by using neural networks, when only short term data are available for that site. This information is useful for preliminary calculations of the wind resource at a remote area having only a short time period of wind measurements measurement in a site. Artificial neural networks are useful for implementing non-linear process variables over time, and therefore are a useful tool for estimating the wind speed. The neural network used is multilayer perceptron with three layers and the supervised learning algorithm used is backpropagation. The inputs used in the neural network were wind speed and direction data from a single station, and the training patterns used correspond to sixty days data. The results obtained by simulating the annual average wind speed at the selected site based on data from nearby stations with correlation coefficients above 0.5 were satisfactory, compared with actual values. Reliable estimations were obtained, with errors below 6%
Approximation by fully complex multilayer perceptrons.
Kim, Taehwan; Adali, Tülay
2003-07-01
We investigate the approximation ability of a multilayer perceptron (MLP) network when it is extended to the complex domain. The main challenge for processing complex data with neural networks has been the lack of bounded and analytic complex nonlinear activation functions in the complex domain, as stated by Liouville's theorem. To avoid the conflict between the boundedness and the analyticity of a nonlinear complex function in the complex domain, a number of ad hoc MLPs that include using two real-valued MLPs, one processing the real part and the other processing the imaginary part, have been traditionally employed. However, since nonanalytic functions do not meet the Cauchy-Riemann conditions, they render themselves into degenerative backpropagation algorithms that compromise the efficiency of nonlinear approximation and learning in the complex vector field. A number of elementary transcendental functions (ETFs) derivable from the entire exponential function e(z) that are analytic are defined as fully complex activation functions and are shown to provide a parsimonious structure for processing data in the complex domain and address most of the shortcomings of the traditional approach. The introduction of ETFs, however, raises a new question in the approximation capability of this fully complex MLP. In this letter, three proofs of the approximation capability of the fully complex MLP are provided based on the characteristics of singularity among ETFs. First, the fully complex MLPs with continuous ETFs over a compact set in the complex vector field are shown to be the universal approximator of any continuous complex mappings. The complex universal approximation theorem extends to bounded measurable ETFs possessing a removable singularity. Finally, it is shown that the output of complex MLPs using ETFs with isolated and essential singularities uniformly converges to any nonlinear mapping in the deleted annulus of singularity nearest to the origin. PMID:12816570
Prediction of Parametric Roll Resonance by Multilayer Perceptron Neural Network
Míguez González, M; López Peña, F.; Díaz Casás, V.; Galeazzi, Roberto; Blanke, Mogens
acknowledged in the last few years. This work proposes a prediction system based on a multilayer perceptron (MP) neural network. The training and testing of the MP network is accomplished by feeding it with simulated data of a three degrees-of-freedom nonlinear model of a fishing vessel. The neural network is...
Newton's Method Backpropagation for Complex-Valued Holomorphic Multilayer Perceptrons
La Corte, Diana Thomson; Zou, Yi ming
2014-01-01
The study of Newton's method in complex-valued neural networks faces many difficulties. In this paper, we derive Newton's method backpropagation algorithms for complex-valued holomorphic multilayer perceptrons, and investigate the convergence of the one-step Newton steplength algorithm for the minimization of real-valued complex functions via Newton's method. To provide experimental support for the use of holomorphic activation functions, we perform a comparison of using sigmoidal functions v...
Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics
F. Cadini
2008-01-01
Full Text Available Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated.
Validation of Infinite Impulse Response Multilayer Perceptron for Modelling Nuclear Dynamics
Artificial neural networks are powerful algorithms for constructing nonlinear empirical models from operational data. Their use is becoming increasingly popular in the complex modeling tasks required by diagnostic, safety, and control applications in complex technologies such as those employed in the nuclear industry. In this paper, the nonlinear modeling capabilities of an infinite impulse response multilayer perceptron (IIR-MLP) for nuclear dynamics are considered in comparison to static modeling by a finite impulse response multilayer perceptron (FIR-MLP) and a conventional static MLP. The comparison is made with respect to the nonlinear dynamics of a nuclear reactor as investigated by IIR-MLP in a previous paper. The superior performance of the locally recurrent scheme is demonstrated
Implementing Semantic Deduction of Propositional Knowledge in an Extension Multi-layer Perceptron
HUANGTian-min; PEIZheng
2003-01-01
The paper presents an extension multi-layer perceptron model that is capable of representing and reasoning propositional knowledge base. An extended version of propositional calculus is developed,and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of propositional knowledge base can be imple-ment by the extension multi-layer perceptron, and by learning, an unknown formula set can be found.
A Parallel Framework for Multilayer Perceptron for Human Face Recognition
Bhowmik, M K; Nasipuri, M; Basu, D K; Kundu, M
2010-01-01
Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and ...
Efficient training of multilayer perceptrons using principal component analysis
A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior
Learning of Multilayer Perceptrons with Piecewise-Linear Activation Functions
Kozub, P.; Holeňa, Martin
Praha: Matfyzpress, 2008 - (Obdržálek, D.; Štanclová, J.; Plátek, M.), s. 27-46 ISBN 978-80-7378-076-0. [MIS 2008. Malý informatický seminář /25./. Josefův důl (CZ), 12.01.2008-19.01.2008] R&D Projects: GA ČR GA201/08/0802; GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : artificial neural networks * multilayer perceptrons * activation functions * function approximation * constrained optimization Subject RIV: IN - Informatics, Computer Science
A Choice of Input Variables for a Multilayer Perceptron
In the paper some aspects of multilayer perceptron (MLP) application to the problem of classifying the events presented by empirical samples of a finite volume are considered. The results of the MLP learning for various forms of the input data are analyzed and the reasons leading to the effect of an instantaneous learning of the MLP and rise of the neural network are investigated for the case when the input data are presented in a form of variational series. The problem of hidden layer neuron reduction without raising the recognition error is discussed. (author). 13 refs., 6 figs., 1 tab
Detection and classification of undersea objects using multilayer perceptrons
Shazeer, Dov J.; Bello, Martin G.
1991-08-01
A large number of underwater missions, such as obstacle avoidance, surveying, object recovery, and detection, classification, and recognition of hazards, are simply too dangerous or costly for manned vehicles. Remotely operated vehicles are subject to different limitations, such as communication bandwidth, operator fatigue, and a restricted radius of operation. These considerations make autonomous underwater vehicles (AUV) an increasingly attractive alternative. To be truly autonomous, an underwater vehicle requires scene recognition capabilities. Advances in pattern recognition and the use of increasingly high-resolution underwater sensors hold the promise that such capabilities will be developed in the near future. This paper reports the training and testing of multilayer perceptrons designed to classify specific manmade underwater objects under various environmental conditions, from arbitrary viewing aspects, and in highly cluttered environments. The trained classifiers have been tested against difficult side-scan sonar imagery and appear to work as well as a trained human analyst. Feature sets that account for the sensor response to range and that adapt to environmental variations improve performance and make the design robust. Receiver Operating Curves (ROC) show up to a 96 detection rate for a 2 false alarm rate. The set of multilayer perceptron networks have been demonstrated on special-purpose hardware and run in real time.
Dynamics of learning in multilayer perceptrons near singularities.
Cousseau, Florent; Ozeki, Tomoko; Amari, Shun-Ichi
2008-08-01
The dynamical behavior of learning is known to be very slow for the multilayer perceptron, being often trapped in the "plateau." It has been recently understood that this is due to the singularity in the parameter space of perceptrons, in which trajectories of learning are drawn. The space is Riemannian from the point of view of information geometry and contains singular regions where the Riemannian metric or the Fisher information matrix degenerates. This paper analyzes the dynamics of learning in a neighborhood of the singular regions when the true teacher machine lies at the singularity. We give explicit asymptotic analytical solutions (trajectories) both for the standard gradient (SGD) and natural gradient (NGD) methods. It is clearly shown, in the case of the SGD method, that the plateau phenomenon appears in a neighborhood of the critical regions, where the dynamical behavior is extremely slow. The analysis of the NGD method is much more difficult, because the inverse of the Fisher information matrix diverges. We conquer the difficulty by introducing the "blow-down" technique used in algebraic geometry. The NGD method works efficiently, and the state converges directly to the true parameters very quickly while it staggers in the case of the SGD method. The analytical results are compared with computer simulations, showing good agreement. The effects of singularities on learning are thus qualitatively clarified for both standard and NGD methods. PMID:18701364
A Parallel Framework for Multilayer Perceptron for Human Face Recognition
Mita Nasipuri
2010-01-01
Full Text Available Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP have been demonstrated. The first architecture is All-Class-in-One-Network (ACON where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Experimental results show that the proposed OCON structure performs better than the conventional ACON in terms of network training convergence speed and which can be easily exercised in a parallel environment.
Power grid higher-order harmonics estimation with multilayer perceptrons
Nguyen, Thien Minh; Wira, Patrice
2015-12-01
This work proposes a new neural approach based on the structure of a Multi-Layer Perceptron (MLP) for identifying current harmonics in power systems. The learning approach is based on several MLP, adopts the Fourier decomposition of a signal and a training set generated from harmonic waveforms is used to calculate the weights. After training, each MLP is able to identify two coefficients for each harmonic term of the input signal. The effectiveness of the new approach is evaluated by experiments. Results show that the proposed MLPs of the new approach enable to identify effectively the amplitudes of harmonic terms from the signals under noisy condition. Results are compared to other and recent MLP approaches. The new approach can be applied in harmonic compensation strategies by being implement in an active power filter to ensure the power quality in electrical power systems.
Classification of fused face images using multilayer perceptron neural network
Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas
2010-01-01
This paper presents a concept of image pixel fusion of visual and thermal faces, which can significantly improve the overall performance of a face recognition system. Several factors affect face recognition performance including pose variations, facial expression changes, occlusions, and most importantly illumination changes. So, image pixel fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Fused images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 95.07%. The main objective of employing fusion is to produce a fused image that provides the most detailed and reliable information. Fusion of multip...
Optical proximity correction using a multilayer perceptron neural network
Optical proximity correction (OPC) is one of the resolution enhancement techniques (RETs) in optical lithography, where the mask pattern is modified to improve the output pattern fidelity. Algorithms are needed to generate the modified mask pattern automatically and efficiently. In this paper, a multilayer perceptron (MLP) neural network (NN) is used to synthesize the mask pattern. We employ the pixel-based approach in this work. The MLP takes the pixel values of the desired output wafer pattern as input, and outputs the optimal mask pixel values. The MLP is trained with the backpropagation algorithm, with a training set retrieved from the desired output pattern, and the optimal mask pattern obtained by the model-based method. After training, the MLP is able to generate the optimal mask pattern non-iteratively with good pattern fidelity. (paper)
Online learning dynamics of multilayer perceptrons with unidentifiable parameters
In the over-realizable learning scenario of multilayer perceptrons, in which the student network has a larger number of hidden units than the true or optimal network, some of the weight parameters are unidentifiable. In this case, the teacher network consists of a union of optimal subspaces included in the parameter space. The optimal subspaces, which lead to singularities, are known to affect the estimation performance of neural networks. Using statistical mechanics, we investigate the online learning dynamics of two-layer neural networks in the over-realizable scenario with unidentifiable parameters. We show that the convergence speed strongly depends on the initial parameter conditions. We also show that there is a quasi-plateau around the optimal subspace, which differs from the well-known plateaus caused by permutation symmetry. In addition, we discuss the property of the final learning state, relating this to the singular structures
Optical proximity correction using a multilayer perceptron neural network
Luo, Rui
2013-07-01
Optical proximity correction (OPC) is one of the resolution enhancement techniques (RETs) in optical lithography, where the mask pattern is modified to improve the output pattern fidelity. Algorithms are needed to generate the modified mask pattern automatically and efficiently. In this paper, a multilayer perceptron (MLP) neural network (NN) is used to synthesize the mask pattern. We employ the pixel-based approach in this work. The MLP takes the pixel values of the desired output wafer pattern as input, and outputs the optimal mask pixel values. The MLP is trained with the backpropagation algorithm, with a training set retrieved from the desired output pattern, and the optimal mask pattern obtained by the model-based method. After training, the MLP is able to generate the optimal mask pattern non-iteratively with good pattern fidelity.
Forecasting PM10 in Algiers: efficacy of multilayer perceptron networks.
Abderrahim, Hamza; Chellali, Mohammed Reda; Hamou, Ahmed
2016-01-01
Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached. PMID:26381787
Alireza Taravat; Simon Proud; Simone Peronaci; Fabio Del Frate; Natascha Oppelt
2014-01-01
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons l...
Asymptotic law of likelihood ratio for multilayer perceptron models
Rynkiewicz, Joseph
2010-01-01
We consider regression models involving multilayer perceptrons (MLP) with one hidden layer and a Gaussian noise. The data are assumed to be generated by a true MLP model and the estimation of the parameters of the MLP is done by maximizing the likelihood of the model. When the number of hidden units of the true model is known, the asymptotic distribution of the maximum likelihood estimator (MLE) and the likelihood ratio (LR) statistic is easy to compute and converge to a $\\chi^2$ law. However, if the number of hidden unit is over-estimated the Fischer information matrix of the model is singular and the asymptotic behavior of the MLE is unknown. This paper deals with this case, and gives the exact asymptotic law of the LR statistics. Namely, if the parameters of the MLP lie in a suitable compact set, we show that the LR statistics is the supremum of the square of a Gaussian process indexed by a class of limit score functions.
Zhang, Haowei; Gao, Yanni; Yuan, Chengmei; Liu, Ying; Ding, Yuqing
2015-06-01
Multi-layer perceptron (MLP) neural network belongs to multi-layer feedforward neural network, and has the ability and characteristics of high intelligence. It can realize the complex nonlinear mapping by its own learning through the network. Bipolar disorder is a serious mental illness with high recurrence rate, high self-harm rate and high suicide rate. Most of the onset of the bipolar disorder starts with depressive episode, which can be easily misdiagnosed as unipolar depression and lead to a delayed treatment so as to influence the prognosis. The early identifica- tion of bipolar disorder is of great importance for patients with bipolar disorder. Due to the fact that the process of early identification of bipolar disorder is nonlinear, we in this paper discuss the MLP neural network application in early identification of bipolar disorder. This study covered 250 cases, including 143 cases with recurrent depression and 107 cases with bipolar disorder, and clinical features were statistically analyzed between the two groups. A total of 42 variables with significant differences were screened as the input variables of the neural network. Part of the samples were randomly selected as the learning sample, and the other as the test sample. By choosing different neu- ral network structures, all results of the identification of bipolar disorder were relatively good, which showed that MLP neural network could be used in the early identification of bipolar disorder. PMID:26485974
Proud, Simon Richard
2015-01-01
A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden...... and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery....
In the thesis the results of verification of multilayer perceptron (MLP) {20–41–1} application with sigmoid activation function for prediction of lateral radionuclide migration are presented. The calculated values of Cs 137 and Sr 90 volumetric activity are close to experimental measurement limits, indicating the possibility of MLP application for the solving problem. (authors)
Evolutionary Learning of Multi-Layer Perceptron Neural Networks
Neruda, Roman; Slušný, Stanislav
Košice : Prírodovedecká fakulta, Univerzita P. J. Šafárika, 2006 - (Vojtáš, P.), s. 125-130 ISBN 80-969184-4-3. [ITAT 2006. Workshop on Theory and Practice of Information Theory. Bystrá dolina (SK), 26.09.2006-01.10.2006] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : perceptron networks * learning * evolutionary algorithms Subject RIV: IN - Informatics, Computer Science
Flávio Clésio Silva de Souza
2014-06-01
Full Text Available The purpose of the present research is to apply a Multilayer Perceptron (MLP neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP architecture of Artificial Neural Network (ANN, classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.
Second-Order Learning Methods for a Multilayer Perceptron
First- and second-order learning methods for feed-forward multilayer neural networks are studied. Newton-type and quasi-Newton algorithms are considered and compared with commonly used back-propagation algorithm. It is shown that, although second-order algorithms require enhanced computer facilities, they provide better convergence and simplicity in usage. 13 refs., 2 figs., 2 tabs
de Albuquerque, Victor Hugo C.; Auzuir Ripardo de Alexandria; Paulo César Cortez; João Manuel R. S. Tavares
2009-01-01
Artificial neuronal networks have been used intensively in many domains to accomplish different computational tasks. One of these tasks is the segmentation of objects in images, like to segment microstructures from metallographic images, and for that goal several network topologies were proposed. This paper presents a comparative analysis between multilayer perceptron and selforganizing map topologies applied to segment microstructures from metallographic images. The multilayer perceptron neu...
Vanzella, E.; Cristiani, S.; Fontana, A.; M. Nonino(INAF/OAT); Arnouts, S.; Giallongo, E.; Grazian, A.; Fasano, G.; Popesso, P.; Saracco, P.; Zaggia, S.
2003-01-01
We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models of the spectral ene...
Marwala, Tshilidzi; Chakraverty, Snehashish
2007-01-01
Gaussian mixture models (GMM) and support vector machines (SVM) are introduced to classify faults in a population of cylindrical shells. The proposed procedures are tested on a population of 20 cylindrical shells and their performance is compared to the procedure, which uses multi-layer perceptrons (MLP). The modal properties extracted from vibration data are used to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM produces 94% classification accuracy while the MLP produces 88% classification rates.
Siamese Multi-layer Perceptrons for Dimensionality Reduction and Face Identification
Zheng, Lilei; Duffner, Stefan; Idrissi, Khalid; Garcia, Christophe; Baskurt, Atilla
2015-01-01
This paper presents a framework using siamese Multi-layer Percep-trons (MLP) for supervised dimensionality reduction and face identification. Compared with the classical MLP that trains on fully labeled data, the siamese MLP learns on side information only, i.e., how similar of data examples are to each other. In this study, we compare it with the classical MLP on the problem of face identification. Experimental results on the Extended Yale B database demonstrate that the siamese MLP training...
This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm.
Highly Accurate Multi-layer Perceptron Neural Network for Air Data System
H. S. Krishna
2009-01-01
The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam's Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05) accuracy, hith...
Prediction of mortality in stroke patients using multilayer perceptron neural networks
Süt, Necdet; ÇELİK, Yahya
2012-01-01
We aim ed to predict mortality in stroke patients by using multilayer perceptron (MLP) neural networks. Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. The effect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fibrillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 different MLP algorithms [quick propagation (QP), Levenberg...
Belief Propagation for Error Correcting Codes and Lossy Compression Using Multilayer Perceptrons
Mimura, Kazushi; Cousseau, Florent; Okada, Masato
2011-01-01
The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong limitation, while the theoretical results seems a bit promising. Instead, it reveals it might have a ri...
Experiments with Evolutionary and Hybrid Learning of Multi-layer Perceptron Neural Networks
Neruda, Roman; Slušný, Stanislav
Ostrava : VŠB Technická univerzita, 2007 - (Mikulecký, P.; Dvorský, J.; Krátký, M.), s. 75-84 ISBN 978-80-248-1279-3. [Znalosti 2007. Ostrava (CZ), 21.02.2007-23.02.2007] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : multilayer perceptron * evolutionary learning * hybrid algorithms Subject RIV: IN - Informatics, Computer Science
Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.
1992-01-01
In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.
Madyastha, Raghavendra K.; Aazhang, Behnaam; Henson, Troy F.; Huxhold, Wendy L.
1992-01-01
This paper addresses the issue of applying a globally convergent optimization algorithm to the training of multilayer perceptrons, a class of Artificial Neural Networks. The multilayer perceptrons are trained towards the solution of two highly nonlinear problems: (1) signal detection in a multi-user communication network, and (2) solving the inverse kinematics for a robotic manipulator. The research is motivated by the fact that a multilayer perceptron is theoretically capable of approximating any nonlinear function to within a specified accuracy. The algorithm that has been employed in this study combines the merits of two well known optimization algorithms, the Conjugate Gradients and the Trust Regions Algorithms. The performance is compared to a widely used algorithm, the Backpropagation Algorithm, that is basically a gradient-based algorithm, and hence, slow in converging. The performances of the two algorithms are compared with the convergence rate. Furthermore, in the case of the signal detection problem, performances are also benchmarked by the decision boundaries drawn as well as the probability of error obtained in either case.
Khuat Thanh Tung
2016-11-01
Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.
A nonlinear multivariable empirical model is developed for a U-tube steam generator using the recurrent multilayer perceptron network as the underlying model structure. The recurrent multilayer perceptron is a dynamic neural network, very effective in the input-output modeling of complex process systems. A dynamic gradient descent learning algorithm is used to train the recurrent multilayer perceptron, resulting in an order of magnitude improvement in convergence speed over static learning algorithms. In developing the U-tube steam generator empirical model, the effects of actuator, process,and sensor noise on the training and testing sets are investigated. Learning and prediction both appear very effective, despite the presence of training and testing set noise, respectively. The recurrent multilayer perceptron appears to learn the deterministic part of a stochastic training set, and it predicts approximately a moving average response. Extensive model validation studies indicate that the empirical model can substantially generalize (extrapolate), though online learning becomes necessary for tracking transients significantly different than the ones included in the training set and slowly varying U-tube steam generator dynamics. In view of the satisfactory modeling accuracy and the associated short development time, neural networks based empirical models in some cases appear to provide a serious alternative to first principles models. Caution, however, must be exercised because extensive on-line validation of these models is still warranted
Alireza Taravat
2015-02-01
Full Text Available A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.
Multilayered perceptron neural networks to compute energy losses in magnetic cores
This paper presents a new approach based on multilayered perceptrons (MLPs) to compute the specific energy losses of toroidal wound cores built from 3% SiFe 0.27 mm thick M4, 0.1 and 0.08 mm thin gauge electrical steel strips. The MLP has been trained by a back-propagation and extended delta-bar-delta learning algorithm. The results obtained by using the MLP model were compared with a commonly used conventional method. The comparison has shown that the proposed model improved loss estimation with respect to the conventional method
Functional Multi-Layer Perceptron: a Nonlinear Tool for Functional Data Analysis
Rossi, Fabrice
2005-01-01
In this paper, we study a natural extension of Multi-Layer Perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.
Apply Multi-Layer Perceptrons Neural Network for Off-Line Signature Verification and Recognition
Suhail Odeh
2011-11-01
Full Text Available This paper discusses the applying of Multi-layer perceptrons for signature verification and recognition using a new approach enables the user to recognize whether a signature is original or a fraud. The approach starts by scanning images into the computer, then modifying their quality through image enhancement and noise reduction, followed by feature extraction and neural network training, and finally verifies the authenticity of the signature. The paper discusses the different stages of the process including: image pre-processing, feature extraction and pattern recognition through neural networks.
Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification
Martin, Arnaud
2008-01-01
The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.
Belief Propagation for Error Correcting Codes and Lossy Compression Using Multilayer Perceptrons
Mimura, Kazushi; Cousseau, Florent; Okada, Masato
2011-03-01
The belief propagation (BP) based algorithm is investigated as a potential decoder for both of error correcting codes and lossy compression, which are based on non-monotonic tree-like multilayer perceptron encoders. We discuss that whether the BP can give practical algorithms or not in these schemes. The BP implementations in those kind of fully connected networks unfortunately shows strong limitation, while the theoretical results seems a bit promising. Instead, it reveals it might have a rich and complex structure of the solution space via the BP-based algorithms.
Umar Draz
2016-01-01
Full Text Available SMEs (Small and Medium Sized Enterprises sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that ?firm registered with OEM (Original Equipment Manufacturer and ?size of firm? are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies
SMEs (Small and Medium Sized Enterprises) sector is facing problems relating to implementation of international quality standards. These SMEs need to identify factors affecting business success abroad for intelligent allocation of resources to the process of internationalization. In this paper, MLP NN (Multi-Layer Perceptron Neural Network) has been used for identifying relative importance of key variables related to firm basics, manufacturing, quality inspection labs and level of education in determining the exporting status of Pakistani SMEs. A survey has been conducted for scoring out the pertinent variables in SMEs and coded in MLP NNs. It is found that firm registered with OEM (Original Equipment Manufacturer) and size of firm are the most important in determining exporting status of SMEs followed by other variables. For internationalization, the results aid policy makers in formulating strategies. (author)
An Analog Multilayer Perceptron Neural Network for a Portable Electronic Nose
Chih-Heng Pan
2012-12-01
Full Text Available This study examines an analog circuit comprising a multilayer perceptron neural network (MLPNN. This study proposes a low-power and small-area analog MLP circuit to implement in an E-nose as a classifier, such that the E-nose would be relatively small, power-efficient, and portable. The analog MLP circuit had only four input neurons, four hidden neurons, and one output neuron. The circuit was designed and fabricated using a 0.18 μm standard CMOS process with a 1.8 V supply. The power consumption was 0.553 mW, and the area was approximately 1.36 × 1.36 mm2. The chip measurements showed that this MLPNN successfully identified the fruit odors of bananas, lemons, and lychees with 91.7% accuracy.
Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition
Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas
2010-01-01
This paper presents a novel approach to handle the challenges of face recognition. In this work thermal face images are considered, which minimizes the affect of illumination changes and occlusion due to moustache, beards, adornments etc. The proposed approach registers the training and testing thermal face images in polar coordinate, which is capable to handle complicacies introduced by scaling and rotation. Polar images are projected into eigenspace and finally classified using a multi-layer perceptron. In the experiments we have used Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal face images. Experimental results show that the proposed approach significantly improves the verification and identification performance and the success rate is 97.05%.
Digital modulation classification using multi-layer perceptron and time-frequency features
Yuan Ye; Mei Wenbo
2007-01-01
Considering that real communication signals corrupted by noise are generally nonstationary, and time-frequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals.The extracted time-frequency features have good classification information, and they are insensitive to signal to noise ratio (SNR) variation.According to good classification by the correct rate of a neural network classifier, a multilayer perceptron (MLP) classifier with better generalization, as well as, addition of time-frequency features set for classifying six different modulation types has been proposed.Computer simulations show that the MLP classifier outperforms the decision-theoretic classifier at low SNRs, and the classification experiments for real MPSK signals verify engineering significance of the MLP classifier.
Analysis of 7Be behaviour in the air by using a multilayer perceptron neural network
A multilayer perceptron artificial neural network (ANN) model for the prediction of the 7Be behaviour in the air as the function of meteorological parameters was developed. The model was optimized and tested using 7Be activity concentrations obtained by standard gamma-ray spectrometric analysis of air samples collected in Belgrade (Serbia) during 2009–2011 and meteorological data for the same period. Good correlation (r = 0.91) between experimental values of 7Be activity concentrations and those predicted by ANN was obtained. The good performance of the model in prediction of 7Be activity concentrations could provide basis for construction of models which would forecast behaviour of other airborne radionuclides. - Highlights: • Neural network analysis was used to predict airborne 7Be activity using meteorological parameters as inputs. • Strong correlation between calculated and measured activities was found. • Obtained results can help in construction of a general model of 7Be activity variation in air
Highly Accurate Multi-layer Perceptron Neural Network for Air Data System
H. S. Krishna
2009-11-01
Full Text Available The error backpropagation multi-layer perceptron algorithm is revisited. This algorithm is used to train and validate two models of three-layer neural networks that can be used to calibrate a 5-hole pressure probe. This paper addresses Occam's Razor problem as it describes the adhoc training methodology applied to improve accuracy and sensitivity. The trained outputs from 5-4-3 feed-forward network architecture with jump connection are comparable to second decimal digit (~0.05 accuracy, hitherto unreported in literature.Defence Science Journal, 2009, 59(6, pp.670-674, DOI:http://dx.doi.org/10.14429/dsj.59.1574
An equalized error backpropagation algorithm for the on-line training of multilayer perceptrons.
Martens, J P; Weymaere, N
2002-01-01
The error backpropagation (EBP) training of a multilayer perceptron (MLP) may require a very large number of training epochs. Although the training time can usually be reduced considerably by adopting an on-line training paradigm, it can still be excessive when large networks have to be trained on lots of data. In this paper, a new on-line training algorithm is presented. It is called equalized EBP (EEBP), and it offers improved accuracy, speed, and robustness against badly scaled inputs. A major characteristic of EEBP is its utilization of weight specific learning rates whose relative magnitudes are derived from a priori computable properties of the network and the training data. PMID:18244454
Zhang, Yudong; Sun, Yi; Phillips, Preetha; Liu, Ge; Zhou, Xingxing; Wang, Shuihua
2016-07-01
This work aims at developing a novel pathological brain detection system (PBDS) to assist neuroradiologists to interpret magnetic resonance (MR) brain images. We simplify this problem as recognizing pathological brains from healthy brains. First, 12 fractional Fourier entropy (FRFE) features were extracted from each brain image. Next, we submit those features to a multi-layer perceptron (MLP) classifier. Two improvements were proposed for MLP. One improvement is the pruning technique that determines the optimal hidden neuron number. We compared three pruning techniques: dynamic pruning (DP), Bayesian detection boundaries (BDB), and Kappa coefficient (KC). The other improvement is to use the adaptive real-coded biogeography-based optimization (ARCBBO) to train the biases and weights of MLP. The experiments showed that the proposed FRFE + KC-MLP + ARCBBO achieved an average accuracy of 99.53 % based on 10 repetitions of K-fold cross validation, which was better than 11 recent PBDS methods. PMID:27250502
Recognition of Epileptiform Patterns in the Human Electroencephalogram Using Multi-Layer Perceptron
V. Mokran
1995-06-01
Full Text Available Automatic detection of epileptiform patterns is highly desirable during continuous monitoring of patients with epilepsy. This paper describes an unconvential system for automatic off-line recognition of epileptic sharp transients in the human electroencephalogram (EEG, based on a standard neural network architecture - multi-layer perceptron (MLP, and implemented on a Silicon Graphics Indigo workstation. The system makes comprehensive use of wide spatial contextual information available on 12 channels of EEG and takes advantage of discrete dyadic wavelet transform (DDWT for efficient parameterisation of EEG data. The EEG database consists of 12 patients, 7 of which are used in the process of training of MLP. The resulting MLP is presented with the testing data set consisting of all data vectors from all 12 patients, and is shown to be capable to recognise a wide variety of epileptic signals.
A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron
Ayan Mukhopadhyay
2012-07-01
Full Text Available The question of financial health and sustenance of a firm is so intriguing that it has spanned numerous studies. For investors,stakeholders and lenders, assessing the risk associated with an enterprise is vital. Several tools have been formulated to deal with predicting the solvency of a firm. This paper attempts to combine Data Envelopment Analysis and Multi-Layer Perceptron (MLP to suggest a new method for prediction of bankruptcy that not only focusses on historical financial data of firms that filed for bankruptcy like other past studies but also takes into account the data of those firms that were likely to do so. This method thus identifies firms that have a high chance of facing bankruptcy along with those that have filed for bankruptcy. The performance of this procedure is compared with MLP. The suggested method outperforms MLP in prediction of bankruptcy.
Mohammad Fathian
2012-04-01
Full Text Available In this paper, the problem of predicting the exchange rate time series in the foreign exchange rate market is going to be solved using a time-delayed multilayer perceptron neural network with gold price as external factor. The input for the learning phase of the artificial neural network are the exchange rate data of the last five days plus the gold price in two different currencies of the exchange rate as the external factor for helping the artificial neural network improving its forecast accuracy. The five-day delay has been chosen because of the weekly cyclic behavior of the exchange rate time series with the consideration of two holidays in a week. The result of forecasts are then compared with using the multilayer peceptron neural network without gold price external factor by two most important evaluation techniques in the literature of exchange rate prediction. For the experimental analysis phase, the data of three important exchange rates of EUR/USD, GBP/USD, and USD/JPY are used.
Kucuk, Nil; Manohara, S.R.; Hanagodimath, S.M.; Gerward, L.
2013-01-01
In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15MeV, and...
Geomagnetic Dst index forecast using a multilayer perceptrons artificial neural network
Complete text of publication follows. The best known manifestations of the impact of solar wind on the magnetosphere are the geomagnetic storms. The prediction of geomagnetic field behavior allows the alert of geomagnetic storms occurrence, as those phenomena can cause many damages in the planet. The Artificial Intelligence tools have been applied in many multidisciplinary studies, covering several areas of knowledge, as a choice of approach to the solution of problems with characteristics like non-linearity, imprecision, and other features that can not be easily solved with conventional computational models. Techniques such as Artificial Neural Networks, Expert Systems and Decision Trees have been used in the Space Weather studies to perform tasks such as forecasting geomagnetic storms and the investigation of rules and parameters related on its occurrence. The main focus of this work is on forecasting the geomagnetic field behavior, represented this time by the Dst index, using for that task, mainly, the interplanetary magnetic field components and solar wind data. The tool chosen here to solve the non-linear problem was a Multi-layer Perceptrons Artificial Neural Network, trained with the backpropagation algorithm. Unlike what was done in other studies, we chose to predict calm and disturbed periods like, for example, a full month of data, for application in a real time forecasting system. It was possible to predict the geomagnetic Dst index one or two hours before with great percentage efficiency.
Vanzella, E; Fontana, A; Nonino, M; Arnouts, S; Giallongo, E; Grazian, A; Fasano, G; Popesso, P; Saracco, P; Zaggia, S R
2003-01-01
We present a technique for the estimation of photometric redshifts based on feed-forward neural networks. The Multilayer Perceptron (MLP) Artificial Neural Network is used to predict photometric redshifts in the HDF-S from an ultra deep multicolor catalog. Various possible approaches for the training of the neural network are explored, including the deepest and most complete spectroscopic redshift catalog currently available (the Hubble Deep Field North dataset) and models of the spectral energy distribution of galaxies available in the literature. The MLP can be trained on observed data, theoretical data and mixed samples. The prediction of the method is tested on the spectroscopic sample in the HDF-S (44 galaxies). Over the entire redshift range, $0.1
H. Hashemi
2008-11-01
Full Text Available Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA. In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP and support vector classifier (SVC are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.
无
2007-01-01
Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor (ATFBR) with a mesoporous granular activated carbon (GAC) as a support material. The experimental protocol was defined to examine the effect of the maximum organic loading rate (OLR), hydraulic retention time (HRT), the efficiency of the reactor and to report on its steady-state performance. The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m3·d). The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m3/(m3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m3·d), the COD removal efficiency decreases. Also an artificial neural network (ANN) model using multilayer perceptron (MLP) has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data, the experimental values obtained have been used. The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data. The mean square error (MSE) was found to be only 0.0146.
Landslide Occurrence Prediction Using Trainable Cascade Forward Network and Multilayer Perceptron
Mohammad Subhi Al-batah
2015-01-01
Full Text Available Landslides are one of the dangerous natural phenomena that hinder the development in Penang Island, Malaysia. Therefore, finding the reliable method to predict the occurrence of landslides is still the research of interest. In this paper, two models of artificial neural network, namely, Multilayer Perceptron (MLP and Cascade Forward Neural Network (CFNN, are introduced to predict the landslide hazard map of Penang Island. These two models were tested and compared using eleven machine learning algorithms, that is, Levenberg Marquardt, Broyden Fletcher Goldfarb, Resilient Back Propagation, Scaled Conjugate Gradient, Conjugate Gradient with Beale, Conjugate Gradient with Fletcher Reeves updates, Conjugate Gradient with Polakribiere updates, One Step Secant, Gradient Descent, Gradient Descent with Momentum and Adaptive Learning Rate, and Gradient Descent with Momentum algorithm. Often, the performance of the landslide prediction depends on the input factors beside the prediction method. In this research work, 14 input factors were used. The prediction accuracies of networks were verified using the Area under the Curve method for the Receiver Operating Characteristics. The results indicated that the best prediction accuracy of 82.89% was achieved using the CFNN network with the Levenberg Marquardt learning algorithm for the training data set and 81.62% for the testing data set.
Kamal Ahmed; Shamsuddin Shahid; Sobri Bin Haroon; Wang Xiao-Jun
2015-08-01
Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961–1990 and 1991–2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.
Ahmed, Kamal; Shahid, Shamsuddin; Haroon, Sobri Bin; Xiao-jun, Wang
2015-08-01
Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.
Using multilayer perceptron and a satellite image for the estimation of soil salinity
Applying the model of the Perceptron multilayer with momentum of an artificial neural network particularly and a multispectral image of high resolution spatial and radiometric, for the first time estimated the salinity of the soil cultivated with sugar cane. The study area is the UBPC 'Lazaro Romero' of the sugar company 'Hector Molina' of the locality San Nicolas de Bari, Havana province, located at 22° 44' North latitude and 81 ° 56' longitude West. The experiments were made in the framework of the El-479 project funded by the Inter universities Council of Flanders, Belgium. 36 samples geo referenced of soils were taken at 3 depths in each of the 4 sugar cane selected blocks, which determined the electrical conductivity of the saturation extract; half of that amount of data was used for the training of the network and the other half for control in a computer program of the artificial neural network created to that effect, together with the reflectance of vegetation indexes for the image, which were maps of electrical conductivity of each block and bands. They were compared with those obtained by simple linear regression between the normalized difference vegetation index and electrical conductivity, Ndv with the approach of the neuronal network, the correlation coefficient was 0.78 to 0.83, while the linear regression was between 0.65 to 0.75
An application of the multilayer perceptron: Solar radiation maps in Spain
Hontoria, L.; Aguilera, J. [Grupo Investigacion y Desarrollo en Energia Solar y Automatica, Dpto. de Ingenieria Electronica, de Telecomunicaciones y Automatica, Escuela Politecnica Superior de Jaen, Campus de las Lagunillas, Universidad de Jaen, 23071 Jaen (Spain); Zufiria, P. [Grupo de Redes Neuronales, Dpto. de Matematica Aplicada a las Tecnologias de la Informacion, ETSI Telecomunicaciones, UPM Ciudad Universitaria s/n, 28040 Madrid (Spain)
2005-11-01
In this work an application of a methodology to obtain solar radiation maps is presented. This methodology is based on a neural network system [Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE ASSP Magazine, 4-22] called Multi-Layer Perceptron (MLP) [Haykin, S., 1994. Neural Networks. A Comprehensive Foundation. Macmillan Publishing Company; Hornik, K., Stinchcombe, M., White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366]. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the zone of the map where it is going to be drawn. For most of the locations all over the world the records of these data (solar radiation in whatever scale, daily or hourly values) are non-existent. Only very few locations have the privilege of having good meteorological stations where records of solar radiation have being registered. But even in those locations with historical records of solar data, the quality of these solar series is not as good as it should be for most purposes. In addition, to draw solar radiation maps the number of points on the maps (real sites) that it is necessary to work with makes this problem difficult to solve. Nevertheless, with the application of the methodology proposed in this paper, this problem has been solved and solar radiation maps have been obtained for a small region of Spain: Jaen province, a southern province of Spain between parallels 38{sup o}25' N and 37{sup o}25' N, and meridians 4{sup o}10' W and 2{sup o}10' W, and for a larger region: Andalucia, the most southern region of Spain situated between parallels 38{sup o}40' N and 36{sup o}00' N, and meridians 7{sup o}30' W and 1{sup o}40' W. (author)
Amit, Yali; Walker, Jacob
2012-01-01
We describe an attractor network of binary perceptrons receiving inputs from a retinotopic visual feature layer. Each class is represented by a random subpopulation of the attractor layer, which is turned on in a supervised manner during learning of the feed forward connections. These are discrete three state synapses and are updated based on a simple field dependent Hebbian rule. For testing, the attractor layer is initialized by the feedforward inputs and then undergoes asynchronous random updating until convergence to a stable state. Classification is indicated by the sub-population that is persistently activated. The contribution of this paper is two-fold. This is the first example of competitive classification rates of real data being achieved through recurrent dynamics in the attractor layer, which is only stable if recurrent inhibition is introduced. Second, we demonstrate that employing three state synapses with feedforward inhibition is essential for achieving the competitive classification rates due to the ability to effectively employ both positive and negative informative features. PMID:22737121
黄天民; 裴峥
2003-01-01
The paper presents an extension multi-layer perceptron model that is capable of representing and reasoning propositional knowledge base. An extended version of propositional calculus is developed, and its some properties is discussed. Formulas of the extended calculus can be expressed in the extension multi-layer perceptron. Naturally, semantic deduction of propositional knowledge base can be implement by the extension multi-layer perceptron, and by learning, an unknown formula set can be found.
Chaudhuri, Sutapa; Das, Debanjana; Sarkar, Ishita; Goswami, Sayantika
2015-10-01
The reduction in the visibility during fog significantly influences surface as well as air transport operations. The prediction of fog remains difficult despite improvements in numerical weather prediction models. The present study aims at identifying a suitable neural network model with proper architecture to provide precise nowcast of the horizontal visibility during fog over the airports of three significantly affected metropolises of India, namely: Kolkata (22°32'N; 88°20'E), Delhi (28°38'N; 77°12'E) and Bengaluru (12°95'N; 77°72'E). The investigation shows that the multilayer perceptron (MLP) model provides considerably less error in nowcasting the visibility during fog over the said metropolises than radial basis function network, generalized regression neural network or linear neural network. The MLP models of different architectures are trained with the data and records from 2000 to 2010. The model results are validated with observations from 2011 to 2014. Our results reveal that MLP models with different configurations (1) four input layers, three hidden layers with three hidden nodes in each layer and a single output; (2) four input layers with two hidden layers having one hidden node in the first hidden layer and two hidden nodes in the second hidden layer, and a single output layer; and (3) four input layers with two hidden layers having two hidden nodes in each hidden layer and a single output layer] provide minimum error in nowcasting the visibility during fog over the airports of Kolkata, Delhi and Bengaluru, respectively. The results show that the MLP model is well suited for nowcasting visibility during fog with 6 h lead time, however, the study reveals that the MLP model sensitive to dissimilar station altitudes in nowcasting visibility, as the minimum prediction error for the three metropolises having dissimilar mean sea level altitudes is observed through different configurations of the model.
Cross Validation Evaluation for Breast Cancer Prediction Using Multilayer Perceptron Neural Networks
Shirin A. Mojarad
2011-01-01
Full Text Available Problem statement: The presence of metastasis in the regional lymph nodes is the most important factor in predicting prognosis in breast cancer. Many biomarkers have been identified that appear to relate to the aggressive behaviour of cancer. However, the nonlinear relation of these markers to nodal status and also the existence of complex interaction between markers have prohibited an accurate prognosis. Approach: The aim of this study is to investigate the effectiveness of a Multilayer Perceptron (MLP for predicting breast cancer progression using a set of four biomarkers of breast tumors. The biomarkers include DNA ploidy, cell cycle distribution (G0G1/G2M, steroid receptors (ER/PR and S-Phase Fraction (SPF. A further objective of the study is to explore the predictive potential of these markers in defining the state of nodal involvement in breast cancer. Two methods of outcome evaluation viz. stratified and simple k-fold Cross Validation (CV are studied in order to assess their accuracy and reliability for neural network validation. Criteria such as output accuracy, sensitivity and specificity are used for selecting the best validation technique besides evaluating the network outcome for different combinations of markers. Results: The results show that stratified 2-fold CV is more accurate and reliable compared to simple k-fold CV as it obtains a higher accuracy and specificity and also provides a more stable network validation in terms of sensitivity. Best prediction results are obtained by using an individual marker-SPF which obtains an accuracy of 65%. Conclusion/Recommendations: Our findings suggest that MLP-based analysis provides an accurate and reliable platform for breast cancer prediction given that an appropriate design and validation method is employed.
Multilayer Perceptron applied to Data Assimilation for the Global FSU Atmospheric Model
Cocke, S.; Cintra, R. S.; Campos Velho, H. F.
2015-12-01
The better quality of forecasts is given the more accurate of the initial conditions. Data assimilation (DA) is the process by which short-forecast and observations are combined to obtain an accurate representation of the state of the modeled system, e.g. is a technique to generate an initial condition to a weather forecasts. This paper shows the results of a DA technique using artificial neural networks (NN) to obtain the analysis to the atmospheric model for the Florida State University. The Local Ensemble Transform Kalman filter (LETKF) is implemented with Florida State University Global Spectral Model (FSUGSM). The ANN data assimilation is made to emulate the initial condition from LETKF to run the FSUGSM. LETKF is a version of Kalman filter with Monte-Carlo ensembles of short-term forecasts to solve the data assimilation problem. The model FSUGSM is a multilevel spectral primitive equation model with vertical sigma coordinates, at resolution T63L27. The data assimilation experiments are based in simulated observations data and FSUGSM 6-hours forecasts. For the NN data assimilation, we use Multilayer Perceptron (MLP) with supervised training algorithm where NN receives input vectors with their corresponding response from LETKF data assimilation. The surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity results are presented. A self-configuration method finds the optimal NN and configures a set of 52 MLPs to DA experiment, referred as MLP-DA. A methodology developed with self-configuration using a meta-heuristic called the Multiple Particle Collision Algorithm to compute the optimal topology for NN. The MLP presents four input nodes, two nodes coordinates vector, one for the 6-hours forecast vector and one node for observation vector; one output node for the analysis vector results. The vector represents the values for one grid model point. The ANNs were trained with data from each month of 2001, 2002, and 2003. The
Fast accurate MEG source localization using a multilayer perceptron trained with real brain noise
Iterative gradient methods such as Levenberg-Marquardt (LM) are in widespread use for source localization from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Unfortunately, LM depends sensitively on the initial guess, necessitating repeated runs. This, combined with LM's high per-step cost, makes its computational burden quite high. To reduce this burden, we trained a multilayer perceptron (MLP) as a real-time localizer. We used an analytical model of quasistatic electromagnetic propagation through a spherical head to map randomly chosen dipoles to sensor activities according to the sensor geometry of a 4D Neuroimaging Neuromag-122 MEG system, and trained a MLP to invert this mapping in the absence of noise or in the presence of various sorts of noise such as white Gaussian noise, correlated noise, or real brain noise. A MLP structure was chosen to trade off computation and accuracy. This MLP was trained four times, with each type of noise. We measured the effects of initial guesses on LM performance, which motivated a hybrid MLP-start-LM method, in which the trained MLP initializes LM. We also compared the localization performance of LM, MLPs, and hybrid MLP-start-LMs for realistic brain signals. Trained MLPs are much faster than other methods, while the hybrid MLP-start-LMs are faster and more accurate than fixed-4-start-LM. In particular, the hybrid MLP-start-LM initialized by a MLP trained with the real brain noise dataset is 60 times faster and is comparable in accuracy to random-20-start-LM, and this hybrid system (localization error: 0.28 cm, computation time: 36 ms) shows almost as good performance as optimal-1-start-LM (localization error: 0.23 cm, computation time: 22 ms), which initializes LM with the correct dipole location. MLPs trained with noise perform better than the MLP trained without noise, and the MLP trained with real brain noise is almost as good an initial guesser for LM as the correct dipole location. (author) )
Heremans, Stien; Suykens, Johan A. K.; Van Orshoven, Jos
2016-02-01
To be physically interpretable, sub-pixel land cover fractions or abundances should fulfill two constraints, the Abundance Non-negativity Constraint (ANC) and the Abundance Sum-to-one Constraint (ASC). This paper focuses on the effect of imposing these constraints onto the MultiLayer Perceptron (MLP) for a multi-class sub-pixel land cover classification of a time series of low resolution MODIS-images covering the northern part of Belgium. Two constraining modes were compared, (i) an in-training approach that uses 'softmax' as the transfer function in the MLP's output layer and (ii) a post-training approach that linearly rescales the outputs of the unconstrained MLP. Our results demonstrate that the pixel-level prediction accuracy is markedly increased by the explicit enforcement, both in-training and post-training, of the ANC and the ASC. For aggregations of pixels (municipalities), the constrained perceptrons perform at least as well as their unconstrained counterparts. Although the difference in performance between the in-training and post-training approach is small, we recommend the former for integrating the fractional abundance constraints into MLPs meant for sub-pixel land cover estimation, regardless of the targeted level of spatial aggregation.
Cebrian, Manuel
2007-01-01
The random initialization of weights of a multilayer perceptron makes it possible to model its training process as a Las Vegas algorithm, i.e. a randomized algorithm which stops when some required training error is obtained, and whose execution time is a random variable. This modelling is used to perform a case study on a well-known pattern recognition benchmark: the UCI Thyroid Disease Database. Empirical evidence is presented of the training time probability distribution exhibiting a heavy tail behavior, meaning a big probability mass of long executions. This fact is exploited to reduce the training time cost by applying two simple restart strategies. The first assumes full knowledge of the distribution yielding a 40% cut down in expected time with respect to the training without restarts. The second, assumes null knowledge, yielding a reduction ranging from 9% to 23%.
Érica Signori Romagnoli
2016-04-01
Full Text Available Samples of automotive ethanol, marketed in the northern and eastern regions of the state of Paraná, Brazil, underwent physical and chemical tests. Rates were assessed by Multilayer Perceptron (MLP neural network for classification. For network training, two hundred epochs, a 0.05 learning rate and a random subdivision of samples in three groups with 70 for training, 15 for test and 15% for validation were employed. Sixty networks were trained from three different initializations. Three networks, one at each start-up, were highlighted and the one with the best performance presented 8 neurons in the hidden layer, with 95 accuracy training, 96 in the test and 96% in validation. The most important variables in classifications, identified by the network, occurred in the following order: alcohol content, density, pH and electrical conductivity. Application of MLP segmented ethanol samples and identified the commercialization regions.
Ouadfeul, S.-A.; Aliouane, L.
2013-06-01
In this paper, a combination of supervised and unsupervised leanings is used for lithofacies classification from well log data. The main idea consists of enhancing the multilayer perceptron (MLP) learning by the output of the self-organizing map (SOM) neural network. Application to real data of two wells located the Algerian Sahara clearly shows that the lithofacies model built by the neural combination is able to give better results than a self-organizing map.
Alireza Taravat
2014-12-01
Full Text Available Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR, as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM and MultiLayer Perceptron (MLP neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN model generates poor accuracies.
Mawloud GUERMOUI
2016-07-01
Full Text Available Accurate estimation of Daily Global Solar Radiation (DGSR has been a major goal for solar energy application. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly of the search for relationships between weather variables, such as temperature, humidity, sunshine duration, etc. In this respect, the present study focuses on the development of artificial neural network (ANN model for estimation of daily global solar radiation on horizontal surface in Ghardaia city (South Algeria. In this analysis back-propagation algorithm is applied. Daily mean air temperature, relative humidity and sunshine duration was used as climatic inputs parameters, while the daily global solar radiation (DGSR was the only output of the ANN. We have evaluated Multi-Layer Perceptron (MLP models to estimate DGSR using three year of measurement (2005-2008. It was found that MLP-model based on sunshine duration and mean air temperature give accurate results in term of Mean Absolute Bias Error, Root Mean Square Error, Relative Square Error and Correlation Coefficient. The obtained values of these indicators are 0.67 MJ/m², 1.28 MJ/m², 6.12%and 98.18%, respectively which shows that MLP is highly qualified for DGSR estimation in semi-arid climates.
Highlights: • Multilayer perceptrons are used to simulate the I–V curve of thin-film PV modules. • APE from the spectral irradiance was added as an input variable to the network. • A self-organised map is used to select the curves used for training the network. • Curve error and maximum power error decrease when using this technique. • This method could provide accurate estimation of the output of a PV plant. - Abstract: In this paper, we propose the use of a methodology to characterise the electrical parameters of several thin-film photovoltaic module technologies. This methodology allows us to use not only solar irradiance and module temperature as classical models do, but also spectral distribution of solar radiation. The methodology is based on the use of neural network models. From all measured I–V curves of a module, a previous selection of them has been used in order to train the neural network model. This selection is performed using a Kohonen self-organising map fed with spectral data. This spectral information has been added as an input to the neural network itself. The results show that the incorporation of spectral measurements to simulate thin-film modules improves significantly both the fitting of the predicted I–V curve to the measured one and the peak power point estimation
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method
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
Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported
LI Chang-ping; ZHI Xin-yue; MA Jun; CUI Zhuang; ZHU Zi-long; ZHANG Cui; HU Liang-ping
2012-01-01
Background Various methods can be applied to build predictive models for the clinical data with binary outcome variable.This research aims to explore the process of constructing common predictive models,Logistic regression (LR),decision tree (DT) and multilayer perceptron (MLP),as well as focus on specific details when applying the methods mentioned above:what preconditions should be satisfied,how to set parameters of the model,how to screen variables and build accuracy models quickly and efficiently,and how to assess the generalization ability (that is,prediction performance) reliably by Monte Carlo method in the case of small sample size.Methods All the 274 patients (include 137 type 2 diabetes mellitus with diabetic peripheral neuropathy and 137 type 2 diabetes mellitus without diabetic peripheral neuropathy) from the Metabolic Disease Hospital in Tianjin participated in the study.There were 30 variables such as sex,age,glycosylated hemoglobin,etc.On account of small sample size,the classification and regression tree (CART) with the chi-squared automatic interaction detector tree (CHAID) were combined by means of the 100 times 5-7 fold stratified cross-validation to build DT.The MLP was constructed by Schwarz Bayes Criterion to choose the number of hidden layers and hidden layer units,alone with levenberg-marquardt (L-M) optimization algorithm,weight decay and preliminary training method.Subsequently,LR was applied by the best subset method with the Akaike Information Criterion (AIC) to make the best used of information and avoid overfitting.Eventually,a 10 to 100 times 3-10 fold stratified cross-validation method was used to compare the generalization ability of DT,MLP and LR in view of the areas under the receiver operating characteristic (ROC) curves (AUC).Results The AUC of DT,MLP and LR were 0.8863,0.8536 and 0.8802,respectively.As the larger the AUC of a specific prediction model is,the higher diagnostic ability presents,MLP performed optimally,and then
Cheng, Jie; Xiao, Qing; Li, Xiao-Wen; Liu, Qin-Huo; Du, Yong-Ming
2008-04-01
The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm(-1) in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to produce the test data, and the result of network test shows the MLP is robust. Meanwhile, the ISSTES algorithm was used to retrieve the temperature and emissivity form the test data. By comparing the results of MLP and ISSTES, we found the MLP can overcome the disadvantage of typical temperature and emisivity separation, although the rmse of derived emissivity using MLP is lower than the ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity of the typical temperature and emissivity separation. PMID:18619297
Chudech Losiri
2016-07-01
Full Text Available Urban expansion is considered as one of the most important problems in several developing countries. Bangkok Metropolitan Region (BMR is the urbanized and agglomerated area of Bangkok Metropolis (BM and its vicinity, which confronts the expansion problem from the center of the city. Landsat images of 1988, 1993, 1998, 2003, 2008, and 2011 were used to detect the land use and land cover (LULC changes. The demographic and economic data together with corresponding maps were used to determine the driving factors for land conversions. This study applied Cellular Automata-Markov Chain (CA-MC and Multi-Layer Perceptron-Markov Chain (MLP-MC to model LULC and urban expansions. The performance of the CA-MC and MLP-MC yielded more than 90% overall accuracy to predict the LULC, especially the MLP-MC method. Further, the annual population and economic growth rates were considered to produce the land demand for the LULC in 2014 and 2035 using the statistical extrapolation and system dynamics (SD. It was evident that the simulated map in 2014 resulting from the SD yielded the highest accuracy. Therefore, this study applied the SD method to generate the land demand for simulating LULC in 2035. The outcome showed that urban occupied the land around a half of the BMR.
In this work, multilayered perceptron neural networks (MLPNNs) were presented for the computation of the gamma-ray energy absorption buildup factors (BA) of seven thermoluminescent dosimetric (TLD) materials [LiF, BeO, Na2B4O7, CaSO4, Li2B4O7, KMgF3, Ca3(PO4)2] in the energy region 0.015–15 MeV, and for penetration depths up to 10 mfp (mean-free-path). The MLPNNs have been trained by a Levenberg–Marquardt learning algorithm. The developed model is in 99% agreement with the ANSI/ANS-6.4.3 standard data set. Furthermore, the model is fast and does not require tremendous computational efforts. The estimated BA data for TLD materials have been given with penetration depth and incident photon energy as comparative to the results of the interpolation method using the Geometrical Progression (G-P) fitting formula. - Highlights: ► Gamma-ray energy absorption buildup factors estimation in TLD materials. ► The ANN approach can be alternative to G-P fitting method for BA calculations. ► The applied model is not time-consuming and easily predicted
Simple recurrence matrix relations for multilayer anisotropic thin films.
Cojocaru, E
2000-01-01
Generalized Abelès relations for one anisotropic thin film [E. Cojocaru, Appl. Opt. 36, 2825-2829 (1997)] are developed for light propagation from an isotropic medium of incidence (with refractive index n(0)) within a multilayer anisotropic thin film coated onto an anisotropic substrate. An immersion model is used for which it is assumed that each layer is imaginatively embedded between isotropic gaps of zero thickness and refractive index n(0). This model leads to simple expressions for the resultant transmitted and reflected electric field amplitudes at interfaces. They parallel the Abelès recurrence relations for layered isotropic media. These matrix relations include multiple reflections while they deal with total fields. They can be applied directly to complex stacks of isotropic and anisotropic thin films. PMID:18337882
Robust local stability of multilayer recurrent neural networks.
Suykens, J K; De Moor, B; Vandewalle, J
2000-01-01
In this paper we derive a condition for robust local stability of multilayer recurrent neural networks with two hidden layers. The stability condition follows from linking theory about linearization, robustness analysis of linear systems under nonlinear perturbation and matrix inequalities. A characterization of the basin of attraction of the origin is given in terms of the level set of a quadratic Lyapunov function. In a similar way like for NL theory, local stability is imposed around the origin and the apparent basin of attraction is made large by applying the criterion, while the proven basin of attraction is relatively small due to conservatism of the criterion. Modifying dynamic backpropagation by the new stability condition is discussed and illustrated by simulation examples. PMID:18249754
Benrekia, Fayçal; Attari, Mokhtar; Bouhedda, Mounir
2013-01-01
This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases. PMID:23529119
Effects of Rapid Recurrent Thermal Annealing on Giant Magnetoresistance NiFe/Ag Multilayers
文岐业; 张怀武; 蒋向东; 唐晓莉; 张万里
2003-01-01
NiFe/Ag multilayers were prepared by dc sputtering onto glass-ceramic substrates directly at room temperature.The samples were thermally processed by rapid recurrent thermal anneal (RRTA). We studied the effects of RRTA on giant magnetoresistance (GMR) NiFe/Ag multilayer by controlling the anneal temperature as well as the rapid anneal cycle. The samples after three RRTA cycles have a similar annealing temperature dependence of GMR responses to the ordinary annealed samples. With the increasing anneal cycle, the GMR response improved at first and then reached an unexpected high value of 9% before descent rapidly. Microstructure study shows that this effect is ascribed to the transformation of continuous NiFe layer into discontinuous one, and then into a granular like film in a step-by-step way.
Pham, Binh Thai; Tien Bui, Dieu; Pourghasemi, Hamid Reza; Indra, Prakash; Dholakia, M. B.
2015-12-01
The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India). Firstly, a landslide inventory map with 430 landslide locations in the study area was constructed from various sources. Landslide locations were then randomly split into two parts (i) 70 % landslide locations being used for training models (ii) 30 % landslide locations being employed for validation process. Secondly, a total of eleven landslide conditioning factors including slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to lineaments, distance to rivers, and rainfall were used in the analysis to elucidate the spatial relationship between these factors and landslide occurrences. Feature selection of Linear Support Vector Machine (LSVM) algorithm was employed to assess the prediction capability of these conditioning factors on landslide models. Subsequently, the NB, MLP Neural Nets, and FT models were constructed using training dataset. Finally, success rate and predictive rate curves were employed to validate and compare the predictive capability of three used models. Overall, all the three models performed very well for landslide susceptibility assessment. Out of these models, the MLP Neural Nets and the FT models had almost the same predictive capability whereas the MLP Neural Nets (AUC = 0.850) was slightly better than the FT model (AUC = 0.849). The NB model (AUC = 0.838) had the lowest predictive capability compared to other models. Landslide susceptibility maps were final developed using these three models. These maps would be helpful to planners and engineers for the development activities and land-use planning.
In this paper, a locally recurrent neural network (LRNN) is employed for approximating the temporal evolution of a nonlinear dynamic system model of a simplified nuclear reactor. To this aim, an infinite impulse response multi-layer perceptron (IIR-MLP) is trained according to a recursive back-propagation (RBP) algorithm. The network nodes contain internal feedback paths and their connections are realized by means of IIR synaptic filters, which provide the LRNN with the necessary system state memory
Haydeé Elena Musso
2013-01-01
Full Text Available En este trabajo se realizó un estudio estadístico de variables físico químicas asociadas al fenómeno de contaminación ambiental, en particular concentración media mensual de SO2 , medidas en la ciudad Salta Capital, Argentina, simultáneamente a concentraciones de NO2 y O3 . Las series bajo estudio presentaban comportamientos dinámicos no lineales, datos atípicos y cambios estructurales, lo que hizo imposible modelarlas con tipologías econométricas tradiciones (AR, MA, ARMA, ARIMA, entre otras. Una solución eficiente que se encontró, hace uso de la teoría de los perceptrones multicapa. Mediante el modelo estructural de series de tiempo, esta solución se presenta como un proceso matemático iterativo que permite obtener un modelado final el cual tiene una muy alta confiabilidad (95%, para realizar pronoósticos a futuro sobre el comportamiento de la variable estudiada.In this paper a statistical study of phisical-chemistry variables connected with enviroment pollution, specifically SO2 monthly average concentration, measured in Salta Capital city, Argentina, together with NO2 and O3 concentrations, was made. Time series under study shown non linear dinamic behaviour, outliers and structural changes. Due to these it was impossible to use typical econometric typologies (AR, MA, ARMA, ARIMA, among others. An effective solution which uses multistep perceptrons theory was found. By using structural time series modelling, this solution is presented by an iterative mathematical process that allows us to obtain a final model with a high confidence level (95% in order to do the forecasting step on the studied variable.
Speed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons.
Panzeri, S; Rolls, E T; Battaglia, F; Lavis, R
2001-11-01
The speed of processing in the visual cortical areas can be fast, with for example the latency of neuronal responses increasing by only approximately 10 ms per area in the ventral visual system sequence V1 to V2 to V4 to inferior temporal visual cortex. This has led to the suggestion that rapid visual processing can only be based on the feedforward connections between cortical areas. To test this idea, we investigated the dynamics of information retrieval in multiple layer networks using a four-stage feedforward network modelled with continuous dynamics with integrate-and-fire neurons, and associative synaptic connections between stages with a synaptic time constant of 10 ms. Through the implementation of continuous dynamics, we found latency differences in information retrieval of only 5 ms per layer when local excitation was absent and processing was purely feedforward. However, information latency differences increased significantly when non-associative local excitation was included. We also found that local recurrent excitation through associatively modified synapses can contribute significantly to processing in as little as 15 ms per layer, including the feedforward and local feedback processing. Moreover, and in contrast to purely feed-forward processing, the contribution of local recurrent feedback was useful and approximately this rapid even when retrieval was made difficult by noise. These findings suggest that cortical information processing can benefit from recurrent circuits when the allowed processing time per cortical area is at least 15 ms long. PMID:11762898
Memristive Perceptron for Combinational Logic Classification
Lidan Wang
2013-01-01
Full Text Available The resistance of the memristor depends upon the past history of the input current or voltage; so it can function as synapse in neural networks. In this paper, a novel perceptron combined with the memristor is proposed to implement the combinational logic classification. The relationship between the memristive conductance change and the synapse weight update is deduced, and the memristive perceptron model and its synaptic weight update rule are explored. The feasibility of the novel memristive perceptron for implementing the combinational logic classification (NAND, NOR, XOR, and NXOR is confirmed by MATLAB simulation.
Identification of Non-Linear Structures using Recurrent Neural Networks
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Identification of Non-Linear Structures using Recurrent Neural Networks
Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.
1995-01-01
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....
Computational capabilities of recurrent NARX neural networks.
Siegelmann, H T; Horne, B G; Giles, C L
1997-01-01
Recently, fully connected recurrent neural networks have been proven to be computationally rich-at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with eXogenous Inputs (NARX models), and are therefore called NARX networks. As opposed to other recurrent networks, NARX networks have a limited feedback which comes only from the output neuron rather than from hidden states. They are formalized by y(t)=Psi(u(t-n(u)), ..., u(t-1), u(t), y(t-n(y)), ..., y(t-1)) where u(t) and y(t) represent input and output of the network at time t, n(u) and n(y) are the input and output order, and the function Psi is the mapping performed by a Multilayer Perceptron. We constructively prove that the NARX networks with a finite number of parameters are computationally as strong as fully connected recurrent networks and thus Turing machines. We conclude that in theory one can use the NARX models, rather than conventional recurrent networks without any computational loss even though their feedback is limited. Furthermore, these results raise the issue of what amount of feedback or recurrence is necessary for any network to be Turing equivalent and what restrictions on feedback limit computational power. PMID:18255858
A Deterministic and Polynomial Modified Perceptron Algorithm
Olof Barr
2006-01-01
Full Text Available We construct a modified perceptron algorithm that is deterministic, polynomial and also as fast as previous known algorithms. The algorithm runs in time O(mn3lognlog(1/ρ, where m is the number of examples, n the number of dimensions and ρ is approximately the size of the margin. We also construct a non-deterministic modified perceptron algorithm running in timeO(mn2lognlog(1/ρ.
Parallel strategy for optimal learning in perceptrons
We developed a parallel strategy for learning optimally specific realizable rules by perceptrons, in an online learning scenario. Our result is a generalization of the Caticha-Kinouchi (CK) algorithm developed for learning a perceptron with a synaptic vector drawn from a uniform distribution over the N-dimensional sphere, so called the typical case. Our method outperforms the CK algorithm in almost all possible situations, failing only in a denumerable set of cases. The algorithm is optimal in the sense that it saturates Bayesian bounds when it succeeds.
Hybrid Evolutionary Algorithm for Multilayer Perceptron Networks with Competetive Performance
Neruda, Roman
Los Alamitos : IEEE, 2007, s. 1620-1627. ISBN 978-1-4244-1339-3. [CEC 2007. Congress on Evolution ary Computation. Singapore (SG), 25.09.2007-28.09.2007] R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : hybrid algorithms * evolution ary learning * neural networks Subject RIV: IN - Informatics, Computer Science
Classification of fuels using multilayer perceptron neural networks
Electrical impedance data obtained with an array of conducting polymer chemical sensors was used by a neural network (ANN) to classify fuel adulteration. Real samples were classified with accuracy greater than 90% in two groups: approved and adulterated.
Learning Dynamic Classes of Events using Stacked Multilayer Perceptron Networks
Kanhabua, Nattiya; Ren, Huamin; Moeslund, Thomas B
2016-01-01
People often use a web search engine to find information about events of interest, for example, sport competitions, political elections, festivals and entertainment news. In this paper, we study a problem of detecting event-related queries, which is the first step before selecting a suitable time-aware retrieval model. In general, event-related information needs can be observed in query streams through various temporal patterns of user search behavior, e.g., spiky peaks for popular events, an...
Classification of Parking Spots Using Multilayer Perceptron Networks
FALCAO, H. S.
2013-12-01
Full Text Available This project intends to develop a prototype for the identification of free spots in open air parking area where there is a good aerial view without obstacles, allowing for the identification of occupied and free spots. We used image processing techniques and pattern recognition using Artificial Neural Networks (ANN. In order to help implement the prototype, we used Matlab. In order to simulate the parking area, we created a model so that we could acquire the images using a webcam, process them, train the neural network, classify the spots and finally, show the results. The results show that it is viable to apply pattern recognition through image capture to classify parking spots
FPGA Implementation of Multilayer Perceptron for Modeling of Photovoltaic panel
The Number of electronic applications using artificial neural network-based solutions has increased considerably in the last few years. However, their applications in photovoltaic systems are very limited. This paper introduces the preliminary result of the modeling and simulation of photovoltaic panel based on neural network and VHDL-language. In fact, an experimental database of meteorological data (irradiation, temperature) and output electrical generation signals of the PV-panel (current and voltage) has been used in this study. The inputs of the ANN-PV-panel are the daily total irradiation and mean average temperature while the outputs are the current and voltage generated from the panel. Firstly, a dataset of 4x364 have been used for training the network. Subsequently, the neural network (MLP) corresponding to PV-panel is simulated using VHDL language based on the saved weights and bias of the network. Simulation results of the trained MLP-PV panel based on Matlab and VHDL are presented. The proposed PV-panel model based ANN and VHDL permit to evaluate the performance PV-panel using only the environmental factors and involves less computational efforts, and it can be used for predicting the output electrical energy from the PV-panel
On Clifford neurons and Clifford multi-layer perceptrons.
Buchholz, Sven; Sommer, Gerald
2008-09-01
We study the framework of Clifford algebra for the design of neural architectures capable of processing different geometric entities. The benefits of this model-based computation over standard real-valued networks are demonstrated. One particular example thereof is the new class of so-called Spinor Clifford neurons. The paper provides a sound theoretical basis to Clifford neural computation. For that purpose the new concepts of isomorphic neurons and isomorphic representations are introduced. A unified training rule for Clifford MLPs is also provided. The topic of activation functions for Clifford MLPs is discussed in detail for all two-dimensional Clifford algebras for the first time. PMID:18514482
Representations of Boolean Functions by Perceptron Networks
Kůrková, Věra
Prague : Institute of Computer Science AS CR, 2014 - (Kůrková, V.; Bajer, L.; Peška, L.; Vojtáš, R.; Holeňa, M.; Nehéz, M.), s. 68-70 ISBN 978-80-87136-19-5. [ITAT 2014. European Conference on Information Technologies - Applications and Theory /14./. Demänovská dolina (SK), 25.09.2014-29.09.2014] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * Boolean functions Subject RIV: IN - Informatics, Computer Science
Learning from correlated patterns by simple perceptrons
Learning behavior of simple perceptrons is analyzed for a teacher-student scenario in which output labels are provided by a teacher network for a set of possibly correlated input patterns, and such that the teacher and student networks are of the same type. Our main concern is the effect of statistical correlations among the input patterns on learning performance. For this purpose, we extend to the teacher-student scenario a methodology for analyzing randomly labeled patterns recently developed in Shinzato and Kabashima 2008 J. Phys. A: Math. Theor. 41 324013. This methodology is used for analyzing situations in which orthogonality of the input patterns is enhanced in order to optimize the learning performance
An Efficient Rescaled Perceptron Algorithm for Conic Systems
Vempala, Santosh; Belloni, Alexandre; Freund, Robert Michael.
2009-01-01
The classical perceptron algorithm is an elementary row-action/relaxation algorithm for solving a homogeneous linear inequality system Ax > 0. A natural condition measure associated with this algorithm is the Euclidean width {tau} of the cone of feasible solutions, and the iteration complexity of the perceptron algorithm is bounded by 1/{tau}2 [see Rosenblatt, F. 1962. Principles of Neurodynamics. Spartan Books, Washington, DC]. Dunagan and Vempala [Dunagan, J., S. Vempala. 2007. A simple pol...
Elizondo, David A; Birkenhead, Ralph; Góngora, Mario; Taillard, Eric; Luyima, Patrick
2007-12-01
The Recursive Deterministic Perceptron (RDP) feed-forward multilayer neural network is a generalisation of the single layer perceptron topology. This model is capable of solving any two-class classification problem as opposed to the single layer perceptron which can only solve classification problems dealing with linearly separable sets. For all classification problems, the construction of an RDP is done automatically and convergence is always guaranteed. Three methods for constructing RDP neural networks exist: Batch, Incremental, and Modular. The Batch method has been extensively tested and it has been shown to produce results comparable with those obtained with other neural network methods such as Back Propagation, Cascade Correlation, Rulex, and Ruleneg. However, no testing has been done before on the Incremental and Modular methods. Contrary to the Batch method, the complexity of these two methods is not NP-Complete. For the first time, a study on the three methods is presented. This study will allow the highlighting of the main advantages and disadvantages of each of these methods by comparing the results obtained while building RDP neural networks with the three methods in terms of the convergence time, the level of generalisation, and the topology size. The networks were trained and tested using the following standard benchmark classification datasets: IRIS, SOYBEAN, and Wisconsin Breast Cancer. The results obtained show the effectiveness of the Incremental and the Modular methods which are as good as that of the NP-Complete Batch method but with a much lower complexity level. The results obtained with the RDP are comparable to those obtained with the backpropagation and the Cascade Correlation algorithms. PMID:17904333
On-line learning algorithms for locally recurrent neural networks.
Campolucci, P; Uncini, A; Piazza, F; Rao, B D
1999-01-01
This paper focuses on on-line learning procedures for locally recurrent neural networks with emphasis on multilayer perceptron (MLP) with infinite impulse response (IIR) synapses and its variations which include generalized output and activation feedback multilayer networks (MLN's). We propose a new gradient-based procedure called recursive backpropagation (RBP) whose on-line version, causal recursive backpropagation (CRBP), presents some advantages with respect to the other on-line training methods. The new CRBP algorithm includes as particular cases backpropagation (BP), temporal backpropagation (TBP), backpropagation for sequences (BPS), Back-Tsoi algorithm among others, thereby providing a unifying view on gradient calculation techniques for recurrent networks with local feedback. The only learning method that has been proposed for locally recurrent networks with no architectural restriction is the one by Back and Tsoi. The proposed algorithm has better stability and higher speed of convergence with respect to the Back-Tsoi algorithm, which is supported by the theoretical development and confirmed by simulations. The computational complexity of the CRBP is comparable with that of the Back-Tsoi algorithm, e.g., less that a factor of 1.5 for usual architectures and parameter settings. The superior performance of the new algorithm, however, easily justifies this small increase in computational burden. In addition, the general paradigms of truncated BPTT and RTRL are applied to networks with local feedback and compared with the new CRBP method. The simulations show that CRBP exhibits similar performances and the detailed analysis of complexity reveals that CRBP is much simpler and easier to implement, e.g., CRBP is local in space and in time while RTRL is not local in space. PMID:18252525
A multilayer extension of the similarity neural network
Buchaca Prats, David
2014-01-01
Aquest projecte ajunta idees de les radial basis functions, i el multilayer perceptron per a desenvolupar una altra arquitectura de xarxa neuronal artificial i un mètode per a poder-la entrenar. És una extensió de la similarity neural network de Lluís Belanche.
Stability of the replica symmetric solution in diluted perceptron learning
We study the role played by dilution in the average behavior of a perceptron model with continuous coupling with the replica method. We analyze the stability of the replica symmetric solution as a function of the dilution field for the generalization and memorization problems. Thanks to a Gardner-like stability analysis we show that at any fixed ratio α between the number of patterns M and the dimension N of the perceptron (α = M/N), there exists a critical dilution field hc above which the replica symmetric ansatz becomes unstable. (letter)
Chaotic diagonal recurrent neural network
Wang Xing-Yuan; Zhang Yi
2012-01-01
We propose a novel neural network based on a diagonal recurrent neural network and chaos,and its structure andlearning algorithm are designed.The multilayer feedforward neural network,diagonal recurrent neural network,and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map.The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks.
Chaotic diagonal recurrent neural network
We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)
A coherent perceptron for all-optical learning
We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem. (orig.)
Higher-order probabilistic perceptrons as Bayesian inference engines
This letter makes explicit a structural connection between the Bayes optimal classifier operating on K binary input variables and corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. Given a large and unbiased training set and an effective learning algorithm, such a neural network should be able to learn the statistics of the classification problem and match the a posteriori probabilities given by the Bayes optimal classifier. (author). 18 refs
Pengenalan Pola Pin Barcode Menggunakan Metode Backpropagation dan Metode Perceptron
Hasiholan, Ardi
2015-01-01
Pattern recognition is one of the functions of the neural networks, where objects maybe identified by their patterns. This may assist in recognition of objects which patterns are damaged. Pattern recognition in neural networkcan make by using backpropagation and perceptron methods. In Backpropagation method, the network is trained with the pattern through three phases, namely forward propagation, backward propagation, and weights adjustment phases, repeated until the termination condition is ...
A coherent perceptron for all-optical learning
Tezak, Nikolas; Mabuchi, Hideo [Stanford University, Edward L. Ginzton Laboratory, Stanford, CA (United States)
2015-12-15
We present nonlinear photonic circuit models for constructing programmable linear transformations and use these to realize a coherent perceptron, i.e., an all-optical linear classifier capable of learning the classification boundary iteratively from training data through a coherent feedback rule. Through extensive semi-classical stochastic simulations we demonstrate that the device nearly attains the theoretical error bound for a model classification problem. (orig.)
纪亚洲; 顾和和; 李保杰
2015-01-01
layer. Land class polygon is the first and the most important layer for land use database to update, so its updating model is designed on the top of the land use database adaptive updating model. To judge the change type and update strategy, 3 input conditions and 12 neurons are set up in land class polygon updating model, among which 4 neurons are responsible for judging the change type, 6 neurons are responsible for judging the spatial strategy, and 2 neurons are responsible for judging attribute strategy. Compared with land class polygon, linear feature updating model is more complicated. Therefore, linear feature updating model has 6 input conditions and 12 neurons, among which the distribution of neurons is the same as that of neurons in land class polygon updating model. Isolated feature belongs to one-dimensional element, so its updating model is relatively simple. In isolated feature updating model, 4 neurons are arranged to judge the change type, 3 neurons to judge spatial strategy, and 2 neurons to attribute strategy. The same-type neurons of adaptive updating model are organized into a network layer, and all layers are organized into a multi-layer perception network in an all-connected way. In addition, in order to realize the judgment of change type and update strategy, a threshold activation function is arranged in each neuron, and between the neurons connection weight is set up to adjust the input of neuron active function. All kinds of training methods of multi-layer perception neuron network are analyzed in a comprehensive and deep way. Change sample data of each element are collected so that updating model training can be carried out. Spatial and attribute update rules of various land use elements under the conditions of different topological relationships and different properties should be studied. And the knowledge and experience are organized into an update knowledge database. When new change survey data are input into model again, topology, property
Limitations of One-Hidden-Layer Perceptron Networks
Kůrková, Věra
Aachen & Charleston: Technical University & CreateSpace Independent Publishing Platform, 2015 - (Yaghob, J.), s. 167-171. (CEUR Workshop Proceedings. V-1422). ISBN 978-1-5151-2065-0. ISSN 1613-0073. [ITAT 2015. Conference on Theory and Practice of Information Technologies /15./. Slovenský Raj (SK), 17.09.2015-21.09.2015] R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : perceptron networks * model complexity * representations of finite mappings by neural networks Subject RIV: IN - Informatics, Computer Science
Margins, Kernels and Non-linear Smoothed Perceptrons
Ramdas, Aaditya; Peña, Javier
2015-01-01
We focus on the problem of finding a non-linear classification function that lies in a Reproducing Kernel Hilbert Space (RKHS) both from the primal point of view (finding a perfect separator when one exists) and the dual point of view (giving a certificate of non-existence), with special focus on generalizations of two classical schemes - the Perceptron (primal) and Von-Neumann (dual) algorithms. We cast our problem as one of maximizing the regularized normalized hard-margin ($\\rho$) in an RK...
Representations of highly-varying functions by perceptron networks
Kůrková, Věra
North Charleston: CreateSpace Independent Publishing Platform, 2013 - (Vinař, T.; Holeňa, M.; Lexa, M.; Peška, L.; Vojtáš, P.), s. 73-76 ISBN 978-1-4909-5208-6. [ITAT 2013. Conference on Theory and Practice of Information Technologies. Donovaly (SK), 11.09.2013-15.09.2013] R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : one-hidden-layer networks * perceptrons * Boolean functions * network complexity Subject RIV: IN - Informatics, Computer Science
Comparative Analisys of Different Approaches Towards Multilayer Percentron Training
Vališevskis, A
2001-01-01
A comparative analysis of four multilayer perceptron learning algorithms is exposed in this work: the error backpropagation algorithm and three other algorithms with fundamentally different approaches towards the improvement of convergence time. Stock exchange share price prediction is at the basis of the comparison of the algorithms. The optimal neural network topology for the solution of the above-mentioned task is determined in this work. Furthermore the forecasts concerning fo...
Generalization and capacity of extensively large two-layered perceptrons.
Rosen-Zvi, Michal; Engel, Andreas; Kanter, Ido
2002-09-01
The generalization ability and storage capacity of a treelike two-layered neural network with a number of hidden units scaling as the input dimension is examined. The mapping from the input to the hidden layer is via Boolean functions; the mapping from the hidden layer to the output is done by a perceptron. The analysis is within the replica framework where an order parameter characterizing the overlap between two networks in the combined space of Boolean functions and hidden-to-output couplings is introduced. The maximal capacity of such networks is found to scale linearly with the logarithm of the number of Boolean functions per hidden unit. The generalization process exhibits a first-order phase transition from poor to perfect learning for the case of discrete hidden-to-output couplings. The critical number of examples per input dimension, alpha(c), at which the transition occurs, again scales linearly with the logarithm of the number of Boolean functions. In the case of continuous hidden-to-output couplings, the generalization error decreases according to the same power law as for the perceptron, with the prefactor being different. PMID:12366215
Entropy landscape of solutions in the binary perceptron problem
The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a reference configuration and of solution-pairs separated by a given Hamming distance in the solution space. We evaluate the entropy at the annealed level as well as replica symmetric level and the mean field result is confirmed by the numerical simulations on single instances using the proposed message passing algorithms. From the first landscape (a random configuration as a reference), we see clearly how the solution space shrinks as more constraints are added. From the second landscape of solution-pairs, we deduce the coexistence of clustering and freezing in the solution space. (paper)
Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron
Chakraborty, Mriganka; Ghosh, Arka
2012-01-01
Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function by using quasi-newton method .This optimization leads to more accurate weight update system for mini...
Electron/pion identification in the CBM TRD using a multilayer perceptron
The problem of electron/pion identification in the CBM experiment based on the measurements of energy losses and transition radiation in the TRD detector is discussed. A possibility to solve such a problem by applying an artificial neural network (ANN) is considered. As input information for the network we used both the samples of energy losses of pions or electrons in the TRD absorbers and the 'clever' variable obtained on the basis of the original data. We show that usage of this new variable permits one to reach a reliable level of particle recognition no longer than after 10-20 training epochs; there are practically no fluctuations against the trend, and the needed level of pions suppression is obtained under the condition of a minimal loss of electrons
Application of artificial neural networks (multilayer perceptron) in reactor safety research
One of the key areas of reactor safety research are studies of reliable and safe heat removal from the reactor core and the containment, respectively, of light water reactors. Leakage accidents could carry insulating material into the containment or the building sump of the containment and the associated post-decay heat removal systems. This could obstruct systems functions. In the study titled ''Knowledge-based Modeling of Transport Processes in BWR Coolant Flows Carrying Particle Loads after Loss-of-Coolant Accidents,'' a tool is being created for engineering application which allows the deposition and retention of insulating material in the sump of the reactor containment to be estimated. Potential plant conditions in accidents can be assessed in this way. The study serves the purpose of modeling by means of data-based and knowledge-based methods. In this way, the results of experimental investigations (such as differential pressure tests of retention systems) can be used for modeling purposes. (orig.)
The reactor safety study with help of artificial neuron networks (multilayer perceptrons)
One deals with deposition of insulation large amounts on settling tank components that may result in malfunction of residual heat removal systems. Paper describes briefly simulation of pressure drops in confinement systems by means of an artificial neuron nets and compares the simulation data with the experiment ones
Visualization of learning in multilayer perceptron networks using principal component analysis.
Gallagher, M; Downs, T
2003-01-01
This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as backpropagation and can also be used to provide insight into the learning process and the nature of the error surface. PMID:18238154
A multi-layer feed-forward perceptron for microwave signals processing
Rouveure, R.; Faure, P.; Monod, M.O.
2003-01-01
This paper investigates the processing of radar signals using artificial neural networks. Today, the use of FMCW radar is considered to control the agricultural implements working depth, in order to overcome the limitations of sensors based on optical or ultrasound devices towards agricultural environment (dust, rain, etc.). The objective is to determine the radar-target distance R with a direct identification of the discrete-time radar signal Sb[n]. The neural network structure in a multi-la...
Fast lossless color image compression method using perceptron
贾克斌; 张延华; 庄新月
2004-01-01
The technique of lossless image compression plays an important role in image transmission and storage for high quality. At present, both the compression ratio and processing speed should be considered in a real-time multimedia system. A novel lossless compression algorithm is researched. A low complexity predictive model is proposed using the correlation of pixels and color components. In the meantime, perceptron in neural network is used to rectify the prediction values adaptively. It makes the prediction residuals smaller and in a small dynamic scope. Also a color space transform is used and good decorrelation is obtained in our algorithm. The compared experimental results have shown that our algorithm has a noticeably better performance than traditional algorithms. Compared to the new standard JPEG-LS, this predictive model reduces its computational complexity. And its speed is faster than the JPEG-LS with negligible performance sacrifice.
Handwritten Arabic Numeral Recognition using a Multi Layer Perceptron
Das, Nibaran; Saha, Sudip; Haque, Syed Sahidul
2010-01-01
Handwritten numeral recognition is in general a benchmark problem of Pattern Recognition and Artificial Intelligence. Compared to the problem of printed numeral recognition, the problem of handwritten numeral recognition is compounded due to variations in shapes and sizes of handwritten characters. Considering all these, the problem of handwritten numeral recognition is addressed under the present work in respect to handwritten Arabic numerals. Arabic is spoken throughout the Arab World and the fifth most popular language in the world slightly before Portuguese and Bengali. For the present work, we have developed a feature set of 88 features is designed to represent samples of handwritten Arabic numerals for this work. It includes 72 shadow and 16 octant features. A Multi Layer Perceptron (MLP) based classifier is used here for recognition handwritten Arabic digits represented with the said feature set. On experimentation with a database of 3000 samples, the technique yields an average recognition rate of 94....
How to keep the HG weights non-negative: the truncated Perceptron reweighing rule
Giorgio Magri
2015-12-01
Full Text Available The literature on error-driven learning in Harmonic Grammar (HG has adopted the Perceptron reweighing rule. Yet, this rule is not suited to HG, as it fails at ensuring non-negative weights. A variant is thus considered which truncates the updates at zero, keeping the weights non-negative. Convergence guarantees and error bounds for the original Perceptron are shown to extend to its truncated variant.
MIMO transmit scheme based on morphological perceptron with competitive learning.
Valente, Raul Ambrozio; Abrão, Taufik
2016-08-01
This paper proposes a new multi-input multi-output (MIMO) transmit scheme aided by artificial neural network (ANN). The morphological perceptron with competitive learning (MP/CL) concept is deployed as a decision rule in the MIMO detection stage. The proposed MIMO transmission scheme is able to achieve double spectral efficiency; hence, in each time-slot the receiver decodes two symbols at a time instead one as Alamouti scheme. Other advantage of the proposed transmit scheme with MP/CL-aided detector is its polynomial complexity according to modulation order, while it becomes linear when the data stream length is greater than modulation order. The performance of the proposed scheme is compared to the traditional MIMO schemes, namely Alamouti scheme and maximum-likelihood MIMO (ML-MIMO) detector. Also, the proposed scheme is evaluated in a scenario with variable channel information along the frame. Numerical results have shown that the diversity gain under space-time coding Alamouti scheme is partially lost, which slightly reduces the bit-error rate (BER) performance of the proposed MP/CL-NN MIMO scheme. PMID:27135805
Kivelä, Mikko; Barthelemy, Marc; Gleeson, James P; Moreno, Yamir; Porter, Mason A
2013-01-01
Most real and engineered systems include multiple subsystems and layers of connectivity, and it is important to take such features into account to try to obtain a complete understanding of these systems. It is thus necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts occurred several decades ago, but now the study of multilayer networks has become one of the major directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and then review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multila...
Fernández-Delgado, Manuel; Cernadas, Eva; Barro, Senén; Ribeiro, Jorge; Neves, José
2014-02-01
The Direct Kernel Perceptron (DKP) (Fernández-Delgado et al., 2010) is a very simple and fast kernel-based classifier, related to the Support Vector Machine (SVM) and to the Extreme Learning Machine (ELM) (Huang, Wang, & Lan, 2011), whose α-coefficients are calculated directly, without any iterative training, using an analytical closed-form expression which involves only the training patterns. The DKP, which is inspired by the Direct Parallel Perceptron, (Auer et al., 2008), uses a Gaussian kernel and a linear classifier (perceptron). The weight vector of this classifier in the feature space minimizes an error measure which combines the training error and the hyperplane margin, without any tunable regularization parameter. This weight vector can be translated, using a variable change, to the α-coefficients, and both are determined without iterative calculations. We calculate solutions using several error functions, achieving the best trade-off between accuracy and efficiency with the linear function. These solutions for the α coefficients can be considered alternatives to the ELM with a new physical meaning in terms of error and margin: in fact, the linear and quadratic DKP are special cases of the two-class ELM when the regularization parameter C takes the values C=0 and C=∞. The linear DKP is extremely efficient and much faster (over a vast collection of 42 benchmark and real-life data sets) than 12 very popular and accurate classifiers including SVM, Multi-Layer Perceptron, Adaboost, Random Forest and Bagging of RPART decision trees, Linear Discriminant Analysis, K-Nearest Neighbors, ELM, Probabilistic Neural Networks, Radial Basis Function neural networks and Generalized ART. Besides, despite its simplicity and extreme efficiency, DKP achieves higher accuracies than 7 out of 12 classifiers, exhibiting small differences with respect to the best ones (SVM, ELM, Adaboost and Random Forest), which are much slower. Thus, the DKP provides an easy and fast way
Statistical Mechanics of On-line Ensemble Teacher Learning through a Novel Perceptron Learning Rule
Hara, Kazuyuki; Miyoshi, Seiji
2012-06-01
In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point of ensemble teacher learning is the asymptotic behavior of the student to approach the true teacher by learning from ensemble teachers. The student performance is improved by using the Hebbian learning rule in the learning. However, the perceptron learning rule cannot improve the student performance. On the other hand, we proposed a perceptron learning rule with a margin. This learning rule is identical to the perceptron learning rule when the margin is zero and identical to the Hebbian learning rule when the margin is infinity. Thus, this rule connects the perceptron learning rule and the Hebbian learning rule continuously through the size of the margin. Using this rule, we study changes in the learning behavior from the perceptron learning rule to the Hebbian learning rule by considering several margin sizes. From the results, we show that by setting a margin of κ>0, the effect of an ensemble appears and becomes significant when a larger margin κ is used.
Quantum perceptron over a field and neural network architecture selection in a quantum computer.
da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa
2016-04-01
In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. PMID:26878722
Perceptron-like computation based on biologically-inspired neurons with heterosynaptic mechanisms
Kaluza, Pablo; Urdapilleta, Eugenio
2014-10-01
Perceptrons are one of the fundamental paradigms in artificial neural networks and a key processing scheme in supervised classification tasks. However, the algorithm they provide is given in terms of unrealistically simple processing units and connections and therefore, its implementation in real neural networks is hard to be fulfilled. In this work, we present a neural circuit able to perform perceptron's computation based on realistic models of neurons and synapses. The model uses Wang-Buzsáki neurons with coupling provided by axodendritic and axoaxonic synapses (heterosynapsis). The main characteristics of the feedforward perceptron operation are conserved, which allows to combine both approaches: whereas the classical artificial system can be used to learn a particular problem, its solution can be directly implemented in this neural circuit. As a result, we propose a biologically-inspired system able to work appropriately in a wide range of frequencies and system parameters, while keeping robust to noise and error.
Perancangan Pengenal QR (Quick Response) Code Dengan Jaringan Syaraf Tiruan Metode Perceptron
Novalia
2013-01-01
Quick Response (QR) Code is used to store important information of an item or product. QR Code has a very random pattern and can not be distinguished. QR Code can also be dirty and damaged. Research conducted on the pattern of QR Code in order to find out the information stored in the QR Code. The method used to identify patterns of QR Code is to use Artificial Neural Networks Perceptron method. Perceptron is a neural network method is often used for pattern recognition. The input to the syst...
Powell, Anna M; Nyirjesy, Paul
2014-10-01
Vulvovaginitis (VV) is one of the most commonly encountered problems by a gynecologist. Many women frequently self-treat with over-the-counter medications, and may present to their health-care provider after a treatment failure. Vulvovaginal candidiasis, bacterial vaginosis, and trichomoniasis may occur as discreet or recurrent episodes, and have been associated with significant treatment cost and morbidity. We present an update on diagnostic capabilities and treatment modalities that address recurrent and refractory episodes of VV. PMID:25220102
Herget, Philipp; O' Sullivan, Eugene J.; Romankiw, Lubomyr T.; Wang, Naigang; Webb, Bucknell C.
2016-07-05
A mechanism is provided for an integrated laminated magnetic device. A substrate and a multilayer stack structure form the device. The multilayer stack structure includes alternating magnetic layers and diode structures formed on the substrate. Each magnetic layer in the multilayer stack structure is separated from another magnetic layer in the multilayer stack structure by a diode structure.
Vassiliadis, Vassilios S.
2006-01-01
A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example case...
E. T. Venkatesh
2008-01-01
Full Text Available Study on characteristics of soil, to determine the types of crops suitable for cultivation in a particular region can increase the yield to greater extent, which minimizes the expenditures involved in irrigation and application of fertilizers. With the tested techniques available for calibrating the quality of soil and the crops suitable for cultivation in it, it is possible to determine the exact crop, irrigation patterns and even the cycle and quantity of fertilizer application. This paper dealt with the application of SOM based clustering and Artificial Intelligence techniques, to analyze the patterns of soils distributed across huge geographical area and identify the suitable types of crops for the particular soil. Estimation of exact crop(s suitable for a particular region can help stave off redundant maintenance and the inherent expenditures that would occur due to over irrigation and over usage of fertilizers, to fulfill the natural deficiencies. Our Focus is to improve the optimal utilization of innate characteristics in a soil through cultivation of appropriate crops, which will increase the volume and quality of yield, in particular for a developing country like India, where the huge majority of the population depends primarily on agriculture for livelihood.
Duckitt, Kirsten; Qureshi, Aysha
2011-01-01
Recurrent miscarriage is the spontaneous loss of three or more consecutive pregnancies with the same biological father in the first trimester; it affects 1% to 2% of women, in half of whom there is no identifiable cause. Overall, 75% of affected women will have a successful subsequent pregnancy, but this rate falls for older mothers and with increasing number of miscarriages.Antiphospholipid syndrome, with anticardiolipin or lupus anticoagulant antibodies, is present in 15% of women with r...
Perceptrons with Hebbian Learning Based on Wave Ensembles in Spatially Patterned Potentials
Espinosa-Ortega, T.; Liew, T. C. H.
2015-03-01
A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrödinger waves. Spatially patterned potentials process information by coupling different points of reciprocal space. The necessary potential shape is obtained from the Hebbian learning rule, either through exact calculation or construction from a superposition of known optical inputs. This allows implementation in a wide range of compact optical systems, including (1) any nonlinear optical system, (2) optical systems patterned by optical lithography, and (3) exciton-polariton systems with phonon or nuclear spin interactions.
Isomorphisms in Multilayer Networks
Kivelä, Mikko
2015-01-01
We extend the concept of graph isomorphisms to multilayer networks, and we identify multiple types of isomorphisms. For example, in multilayer networks with a single "aspect" (i.e., type of layering), permuting vertex labels, layer labels, and both of types of layers each yield a different type of isomorphism. We discuss how multilayer network isomorphisms naturally lead to defining isomorphisms in any type of network that can be represented as a multilayer network. This thereby yields isomorphisms for multiplex networks, temporal networks, networks with both such features, and more. We reduce each of the multilayer network isomorphism problems to a graph isomorphism problem, and we use this reduction to prove that the multilayer network isomorphism problem is computationally equally hard as the graph isomorphism problem. One can thus use software that has been developed to solve graph isomorphism problems as a practical means for solving multilayer network isomorphism problems.
Learning by random walks in the weight space of the Ising perceptron
Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of α≈0.63 for pattern length N = 101 and α≈0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of α≈0.80 for N = 101 and α≈0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density α of the learning task; at a fixed value of α, the width of the Hamming distance distribution decreases with N
Ratnadip Dey
2013-02-01
Full Text Available A Threshold Logic Unit (TLU is a mathematical function conceived as a crude model, or abstraction of biological neurons. Threshold logic units are the constitutive units in an artificial neural network. In this paper a positive clock-edge triggered T flip-flop is designed using Perceptron Learning Algorithm, which is a basic design algorithm of threshold logic units. Then this T flip-flop is used to design a two-bit up-counter that goes through the states 0, 1, 2, 3, 0, 1… Ultimately, the goal is to show how to design simple logic units based on threshold logic based perceptron concepts.
A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown
Efficient routing on multilayered communication networks
Zhou, Jie; Lai, Choy-Heng; 10.1209/0295-5075/102/28002
2013-01-01
We study the optimal routing on multilayered communication networks, which are composed of two layers of subnetworks. One is a wireless network, and the other is a wired network. We develop a simple recurrent algorithm to find an optimal routing on this kind of multilayered network, where the single-channel transmission mode and the multichannel transmission mode used on the wireless subnetwork are considered, respectively. Compared with the performance of the shortest path algorithm, our algorithm can significantly enhance the transport capacity. We show that our methods proposed in this letter could take advantage of the coupling of the two layers to the most extent, so that the wireless subnetwork could sufficiently utilize the wired subnetwork for transportation.
Multilayer dielectric diffraction gratings
Perry, Michael D.; Britten, Jerald A.; Nguyen, Hoang T.; Boyd, Robert; Shore, Bruce W.
1999-01-01
The design and fabrication of dielectric grating structures with high diffraction efficiency used in reflection or transmission is described. By forming a multilayer structure of alternating index dielectric materials and placing a grating structure on top of the multilayer, a diffraction grating of adjustable efficiency, and variable optical bandwidth can be obtained. Diffraction efficiency into the first order in reflection varying between 1 and 98 percent has been achieved by controlling the design of the multilayer and the depth, shape, and material comprising the grooves of the grating structure. Methods for fabricating these gratings without the use of ion etching techniques are described.
Effective electromagnetic shielding in multilayer printed circuit boards
Wiles, K. G.; Moe, J. L.
Multilayer printed circuit boards have proven to be recurrent abettors of electromagnetic coupling problems created by the incessantly faster response times in integrated circuit technologies. Coupling within multilayer boards has not only inhibited meeting certain EMI requirements but has also precipitated 'self-inflicted' malfunctions commonly experienced during development of avionic systems. A recent avionic system, interfacing two asynchronous processors through a fourteen-layer motherboard, permitted coupling through ground plane connector apertures of sufficient amplitude and duration as to cause unintentional intercommunication and system malfunctions. The coupling mechanism and ground plane modifications which reduced this coupling by 40 dB and eliminated the incompatibility are discussed in this paper
无
2007-01-01
Owing to continuous production lines with large amount of consecutive controls, various control signals and huge logistic relations, this paper introduced the methods and principles of the development of knowledge base in a fault diagnosis expert system that was based on machine learning by the four-layer perceptron neural network. An example was presented. By combining differential function with not differentia function and back propagation of error with back propagation of expectation, the four-layer perceptron neural network was established. And it was good for solving such a bottleneck problem in knowledge acquisition in expert system and enhancing real-time on-line diagnosis. A method of synthetic back propagation was designed, which broke the limit to non-differentiable function in BP neural network.
Implementation of a spike-based Perceptron learning rule using TiO2-x memristors
Hesham eMostafa
2015-10-01
Full Text Available Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic 'cognitive' capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO$_{2-x}$ memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.
Implementation of a spike-based perceptron learning rule using TiO2-x memristors.
Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G; Indiveri, Giacomo; Prodromakis, Themis
2015-01-01
Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode. PMID:26483629
Uezu, Tatsuya
2011-04-01
In the problem of learning under external disturbance, there is a possibility that the existence of some tolerance or flexibility in the system weakens the effect of noise and helps the system to perform more efficiently. In a previous letter, we gave one example of such phenomena in learning from stochastic rules by spherical perceptrons adopting the Gibbs algorithm using statistical mechanical methods. By the replica method, we showed that, in the output noise model, there exists an optimal temperature at which the generalization error takes its minimum for the stable replica symmetric (RS) solution. On the other hand, for other types of noise including input noise, it was shown that no such temperature exists up to the one-step replica symmetry breaking (1RSB) solution. That is, it was shown that for the asymptotic region of a large number of training sets, the RS solution becomes unstable, and the asymptotic behavior is determined by the 1RSB solution, The asymptotic expressions for learning curves were derived, and it turned out that, within the 1RSB solution, the learning curve does not depend on temperature. In this study, we give a detailed derivation of these results and also the results obtained by simulated annealing and exchange Monte Carlo simulation. The numerical results support the theoretical predictions.
A morphological perceptron with gradient-based learning for Brazilian stock market forecasting.
Araújo, Ricardo de A
2012-04-01
Several linear and non-linear techniques have been proposed to solve the stock market forecasting problem. However, a limitation arises from all these techniques and is known as the random walk dilemma (RWD). In this scenario, forecasts generated by arbitrary models have a characteristic one step ahead delay with respect to the time series values, so that, there is a time phase distortion in stock market phenomena reconstruction. In this paper, we propose a suitable model inspired by concepts in mathematical morphology (MM) and lattice theory (LT). This model is generically called the increasing morphological perceptron (IMP). Also, we present a gradient steepest descent method to design the proposed IMP based on ideas from the back-propagation (BP) algorithm and using a systematic approach to overcome the problem of non-differentiability of morphological operations. Into the learning process we have included a procedure to overcome the RWD, which is an automatic correction step that is geared toward eliminating time phase distortions that occur in stock market phenomena. Furthermore, an experimental analysis is conducted with the IMP using four complex non-linear problems of time series forecasting from the Brazilian stock market. Additionally, two natural phenomena time series are used to assess forecasting performance of the proposed IMP with other non financial time series. At the end, the obtained results are discussed and compared to results found using models recently proposed in the literature. PMID:22391234
Cox, P J; Leach, R D; Ellis, Harold
1981-01-01
One hundred consecutive recurrences following repair of inguinal hernias have been studied; 62 were direct, 30 indirect, 7 pantaloon and one a femoral hernia. Half the indirect recurrences occurred within a year of repair and probably represented failure to detect a small indirect sac. Later indirect recurrences probably represented failure to repair the internal ring. Nine of the direct hernias were medial funicular recurrences and represented failure to anchor the darn medially. The rest of...
Scattering from a multilayered chiral sphere using an iterative method
Shang, Qing-Chao; Wu, Zhen-Sen; Qu, Tan; Li, Zheng-Jun; Bai, Lu
2016-04-01
An iterative method for electromagnetic scattering from a multilayered chiral sphere is presented based on Lorenz-Mie regime. Electromagnetic fields in each region are expanded in terms of spherical vector wave functions. To calculate the scattering coefficients of the fields in outer space, an iterative form is constructed according to the coefficients equations obtained by the boundary condition on each layer. The iterative relations are expressed in forms of ratios and logarithmic derivatives of Riccati-Bessel functions, which can be calculated conveniently by their recurrence relations. The theory and codes are verified by comparing the scattered fields with those of a multilayered isotropic achiral sphere, and those of a single layered chiral sphere. Scattered fields of multilayered chiral spheres are presented and discussed, including a large sized case and a Gaussian beam incidence case.
Core reactivity estimation in space reactors using recurrent dynamic networks
A recurrent Multi Layer Perceptron (MLP) network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. This effort is part of a research program devoted in developing real-time diagnostics and predictive control techniques for large-scale complex nonlinear dynamic systems. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the Back Propagation (BP) rule. The Recurrent Dynamic Network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the matematical model of the system. There are a number of issues identified regarding the behavior of the RDN, which at this point are unresolved and require further research. Nevertheless, it is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artifical neural networks (ANNs) for recognition, classification and prediction of dynamic systems
Control of Multilayer Networks
Menichetti, Giulia; Bianconi, Ginestra
2015-01-01
The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable.
Advances in Artificial Neural Networks – Methodological Development and Application
Yanbo Huang
2009-01-01
Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a back...
Imaging recurrent parosteal osteosarcoma
The aim of this study was to document the imaging features of recurrent parosteal osteosarcoma. The clinical and imaging records of 33 patients with a parosteal osteosarcoma referred to an orthopaedic oncology service over a 17-year period were retrospectively reviewed. The mode of identification of locally recurrent tumour was noted, together with the management and clinical outcome. Five patients developed a local recurrence of their parosteal osteosarcoma ranging from 6 months to 10 years after initial surgery. In 4 patients the recurrence was first suspected clinically due to the development of a mass. In the fifth patient recurrence was first detected on routine follow-up radiography. In 4 patients the recurrence could be identified on radiography as a mineralized mass. All the recurrences were readily identified on MR imaging, despite artefacts from prostheses. The recurrences were also evident in the 3 cases in which bone scintigraphy was performed. Local recurrence of parosteal osteosarcoma is adequately detected with a combination of clinical examination and conventional radiography. MR imaging is required to stage local recurrence or where radiography has failed to confirm clinically suspected recurrence. The routine use of MR imaging to follow-up patients is of doubtful value because of the frequently long time between initial surgery and relapse. (orig.)
Recurrence Tracking Microscope
Saif, Farhan
2006-01-01
In order to probe nanostructures on a surface we present a microscope based on the quantum recurrence phenomena. A cloud of atoms bounces off an atomic mirror connected to a cantilever and exhibits quantum recurrences. The times at which the recurrences occur depend on the initial height of the bouncing atoms above the atomic mirror, and vary following the structures on the surface under investigation. The microscope has inherent advantages over existing techniques of scanning tunneling micro...
Recurrence in Quantum Mechanics
Duvenhage, Rocco
2002-01-01
We first compare the mathematical structure of quantum and classical mechanics when both are formulated in a C*-algebraic framework. By using finite von Neumann algebras, a quantum mechanical analogue of Liouville's theorem is then proposed. We proceed to study Poincare recurrence in C*-algebras by mimicking the measure theoretic setting. The results are interpreted as recurrence in quantum mechanics, similar to Poincare recurrence in classical mechanics.
Acute recurrent polyhydramnios
Rode, Line; Bundgaard, Anne; Skibsted, Lillian;
2007-01-01
Acute recurrent polyhydramnios is a rare occurrence characterized by a poor fetal outcome. This is a case report describing a 34-year-old woman presenting with acute recurrent polyhydramnios. Treatment with non-steroidal anti-inflammatory drugs (NSAID) and therapeutic amniocenteses was initiated...... an enzyme-linked immunosorbent assay. As in normal pregnancies, amniotic prolactin levels decreased by 80% from highest to lowest value in this case of resolving acute recurrent polyhydramnios....
Recurrent Escherichia coli bacteremia.
Maslow, J.N.; Mulligan, M E; Arbeit, R D
1994-01-01
Escherichia coli is the most common gram-negative organism associated with bacteremia. While recurrent E. coli urinary tract infections are well-described, recurrent E. coli bacteremia appears to be uncommon, with no episodes noted in multiple series of patients with gram-negative bacteremias. We report on 5 patients with recurrent bloodstream infections identified from a series of 163 patients with E. coli bacteremia. For each patient, the isolates from each episode were analyzed by pulsed-f...
Recurrent intracerebral hemorrhage
Shen jinsong; Lu jianhong
2000-01-01
Objective: In order to study the clinical manifestation and risk factor of recurrent intracerebral hemorrhage(ICH).Methods:The 256 patients were analysed who admitted to our hospital for intracerebral hemorrhage between 1995 and 1997.The 15(5 .86%)patients had a recurrent ICH.There were 9 men and 6 women and the mean age of the patients was 63.5 ± 6.4years at the first bleeding episode and 67.8± 8. 5 years at the second. The mean interval between the two bleeding episodes was 44.6 ± 12.5 months. The 73.3%patients were hypertensive .′The site of the first hemorrhage was ganglionic in 8 patients , ]ohar in six paients and brainstem in one .The recurrent hemorrhage occurred at a different location from the previous ICH.The most common pattern of recurrence was “ganglionic -ganglionic” (7 patients), lobar - ganglionic (3 patients), lobar-lobar(three patients), which was always observed in hypertensive patients. The outcome after the recurrent hemorrhage was usually poor. By comparison with 24 patients followed up to average 47.5± 18.7 months with isolated ICH without recurrence .Only lobar hematoma and a younger age were risk factors for recurrences whereas sex and previous hypertension were not. The mechanism of recurrence of ICH were multiple(hypertension, cerebral amyloid angiopathy).Contral of blood pressure and good living habit after the first hemorrhage may prevent ICH recurrences.
Graben, Peter beim; Fröhlich, Flavio
2015-01-01
We optimally estimate the recurrence structure of a multivariate time series by Markov chains obtained from recurrence grammars. The goodness of fit is assessed with a utility function derived from the stochastic Markov transition matrix. It assumes a local maximum for the distance threshold of the optimal recurrence grammar. We validate our approach by means of the nonlinear Lorenz system and its linearized stochastic surrogates. Finally we apply our optimization procedure to the segmentation of neurophysiological time series obtained from anesthetized animals. We propose the number of optimal recurrence domains as a statistic for classifying an animals' state of consciousness.
Recurrent Takotsubo Cardiomyopathy Related to Recurrent Thyrotoxicosis
Patel, Keval; Griffing, George T.; Hauptman, Paul J.
2016-01-01
Takotsubo cardiomyopathy, or transient left ventricular apical ballooning syndrome, is characterized by acute left ventricular dysfunction caused by transient wall-motion abnormalities of the left ventricular apex and mid ventricle in the absence of obstructive coronary artery disease. Recurrent episodes are rare but have been reported, and several cases of takotsubo cardiomyopathy have been described in the presence of hyperthyroidism. We report the case of a 55-year-old woman who had recurrent takotsubo cardiomyopathy, documented by repeat coronary angiography and evaluations of left ventricular function, in the presence of recurrent hyperthyroidism related to Graves disease. After both episodes, the patient's left ventricular function returned to normal when her thyroid function normalized. These findings suggest a possible role of thyroid-hormone excess in the pathophysiology of some patients who have takotsubo cardiomyopathy. PMID:27127432
Mobile Multilayer IPsec protocol
T.Gayathri
2009-08-01
Full Text Available A mobile user moves around and switches between wireless cells, subnets and domains, it needs to maintain the session continuity. At the same time security of signaling and transport media should not be compromised. A multi-layer security framework involving user authentication, packet based encryption and access control mechanism can provide the desired level of security to the mobile users. Supporting streaming traffic in a mobile wireless Internet is faced with several challenges due to continuous handoff experienced by a mobile user. These challenges include dynamic binding, location management, quality of service and end-to-end security for signaling and transport. Mobile users will use heterogeneous radio access networking technologies. Mobile multilayer IPsec protocol (MML IPSec extends ML-IPSec to deal with mobility and make it suitable for wireless networks. MML-IPSec is integration of ML-IPSec and mobile IP.
Multilayer polymer microspot targets
Last year the authors reported on the development of a seeded microspot x-ray diagnostic target. This target consisted of a 300-μm-diam, 2-μm-thick disk of silicon or sulfur-seeded hydrocarbon polymer nested tightly in a hole in a 2-μm-thick film of pure hydrocarbon polymer. This year they extended our work on the microspot target, fully encapsulating the microspot in what they call the multilayer polymer microspot target
Multilayer optical learning networks
Wagner, Kelvin; Psaltis, Demetri
1987-01-01
A new approach to learning in a multilayer optical neural network based on holographically interconnected nonlinear devices is presented. The proposed network can learn the interconnections that form a distributed representation of a desired pattern transformation operation. The interconnections are formed in an adaptive and self-aligning fashioias volume holographic gratings in photorefractive crystals. Parallel arrays of globally space-integrated inner products diffracted by the interconnec...
Thermopower of multilayer graphene
Hao, Lei; Lee, T. K.
2010-01-01
We systematically calculate thermopower of biased and unbiased multilayer grphene systems. The effect of screening to a bias field perpendicular to the graphene planes is taken into account self-consistently under the Hartree approximation. The model including nearest neighbor hopping and the more complete Slonczewski-Weiss-McClure (SWMcC) model are both considered for a comparison. The effect of impurity scattering is studied for monolayer and unbiased bilayer graphene and is treated in term...
Baghirli, Orkhan
2015-01-01
Wind speed forecasting is critical for wind energy conversion systems since it greatly influences the issues such as scheduling of the power systems, and dynamic control of the wind turbines. Also, it plays an essential role for siting, sizing and improving the efficiency of wind power generation systems. Due to volatile and non-stationary nature of wind speed time series, wind speed forecasting has been proven to be a challenging task that requires adamant care and caution. There are several...
Satish, S.; Rajesh, R.; Kurian, G.; Seethalekshmi, N. V.; Unni, M.; Unni, V. N.
2010-01-01
While acute renal failure secondary to intravascular hemolysis is well described in hemolytic anemias, recurrent acute renal failure as the presenting manifestation of a hemolytic anemia is rare. We report a patient with recurrent acute renal failure who was found to have paroxysmal nocturnal hemoglobinuria (PNH), on evaluation.
Neves, J C S
2015-01-01
In this work we have carried out an approach between the nonsingular scientific cosmologies (without the initial singularity, the big bang), specially the cyclic models, and the Nietzsche's thought of the eternal recurrence. Moreover, we have pointed out reasons for the Nietzsche's search for scientific proofs about the eternal recurrence in the decade of 1880's.
L Preeti
2011-01-01
Full Text Available Recurrent aphthous ulcers are common painful mucosal conditions affecting the oral cavity. Despite their high prevalence, etiopathogenesis remains unclear. This review article summarizes the clinical presentation, diagnostic criteria, and recent trends in the management of recurrent apthous stomatitis.
Casimir force in absorbing multilayers
Tomas, M. S.
2002-01-01
The Casimir effect in a dispersive and absorbing multilayered system is considered adopting the (net) vacuum-field pressure point of view to the Casimir force. Using the properties of the macroscopic field operators appropriate for absorbing systems and a convenient compact form of the Green function for a multilayer, a straightforward and transparent derivation of the Casimir force in a lossless layer of an otherwise absorbing multilayer is presented. The resulting expression in terms of the...
Recurrence Tracking Microscope
Saif, F
2006-01-01
In order to probe nanostructures on a surface we present a microscope based on the quantum recurrence phenomena. A cloud of atoms bounces off an atomic mirror connected to a cantilever and exhibits quantum recurrences. The times at which the recurrences occur depend on the initial height of the bouncing atoms above the atomic mirror, and vary following the structures on the surface under investigation. The microscope has inherent advantages over existing techniques of scanning tunneling microscope and atomic force microscope. Presently available experimental technology makes it possible to develop the device in the laboratory.
Das, Nibaran; Sarkar, Ram; Basu, Subhadip
2010-01-01
The work presents a comparative assessment of seven different feature sets for recognition of handwritten Arabic numerals using a Multi Layer Perceptron (MLP) based classifier. The seven feature sets employed here consist of shadow features, octant centroids, longest runs, angular distances, effective spans, dynamic centers of gravity, and some of their combinations. On experimentation with a database of 3000 samples, the maximum recognition rate of 95.80% is observed with both of two separate combinations of features. One of these combinations consists of shadow and centriod features, i. e. 88 features in all, and the other shadow, centroid and longest run features, i. e. 124 features in all. Out of these two, the former combination having a smaller number of features is finally considered effective for applications related to Optical Character Recognition (OCR) of handwritten Arabic numerals. The work can also be extended to include OCR of handwritten characters of Arabic alphabet.
Hyperbolic metamaterials beyond simple multilayers
Zhukovsky, Sergei; Andryieuski, Andrei; Lavrinenko, Andrei
2014-01-01
Highly corrugated surfaces, nanoparticle assemblies, and super- structured multilayers offer superior functionality in controlling propagating volume plasmons with large wave vectors.......Highly corrugated surfaces, nanoparticle assemblies, and super- structured multilayers offer superior functionality in controlling propagating volume plasmons with large wave vectors....
Controlling light with plasmonic multilayers
Orlov, Alexey A.; Zhukovsky, Sergei; Iorsh, Ivan V.;
2014-01-01
Recent years have seen a new wave of interest in layered media - namely, plasmonic multilayers - in several emerging applications ranging from transparent metals to hyperbolic metamaterials. In this paper, we review the optical properties of such subwavelength metal-dielectric multilayered metama...
Recurrent corneal perforation due to chronic graft versus host disease; a clinicopathologic report
Mehrdad Mohammadpour
2016-01-01
Conclusion: Patients with GVHD are at risk of severe dry eye and subsequent corneal vascularization. Recurrent and recalcitrant corneal perforation resistant to cyanoacrylate glue and multilayer AMT may occur. Proper systemic and ocular management alongside close collaboration with the hematologist is strongly recommended to control the condition.
RECURRENT SEASONAL ACUTE PSYCHOSIS
Agarwal, Vivek
1999-01-01
Acute psychoses have been reported to occur more frequently in summer. This is a report of seasonal recurrence of acute psychosis in a patient. This case report emphasizes towards the biological etiology of acute psychoses.
Recurrences of strange attractors
E J Ngamga; A Nandi; R Ramaswamy; M C Romano; M Thiel; J Kurths
2008-06-01
The transitions from or to strange nonchaotic attractors are investigated by recurrence plot-based methods. The techniques used here take into account the recurrence times and the fact that trajectories on strange nonchaotic attractors (SNAs) synchronize. The performance of these techniques is shown for the Heagy-Hammel transition to SNAs and for the fractalization transition to SNAs for which other usual nonlinear analysis tools are not successful.
Recurrence in acousmatic music
Seddon, Ambrose
2013-01-01
This doctoral research concerns recurrent phenomena in acousmatic works, investigating aspects of correspondence among the constituent sound materials, illuminating the temporal relationships existing among them, and providing concepts to help rationalise compositional structuring processes. While the main focus is on acousmatic music, many of the ideas developed in the research have broader scope and are relevant to other areas of music composition. The concept of recurrence is initially...
Porter, Stephen R; Scully CBE, Crispian
2007-01-01
Most people with recurrent aphthous ulcers develop a few ulcers less than 1 cm in diameter, that heal after 5 to 14 days without scarring. The causes are unknown, but risks of recurrence may decrease if the person gives up smoking.Local physical trauma may trigger ulcers in susceptible people.In 10% of sufferers, lesions are more than 1 cm in diameter and can cause scarring.
Multifocal recurrent periostitis
Two case reports of recurrent multifocal periostitis in two girls aged 15 and 16 are added to the eight cases already reported in the literature. The disease is characterised clinically by recurrent mesomelic swelling of the extremities and radiologically by periosteal thickening and sclerosis of underlying bone. Hyperglobulinaemia is the most constant biochemical finding. The bone biopsy shows no typical features. The possibility of a viral etiology is discussed. (orig.)
Multifocal recurrent periostitis
Kozlowski, K.; Anderson, R.; Tink, A.
1981-11-01
Two case reports of recurrent multifocal periostitis in two girls aged 15 and 16 are added to the eight cases already reported in the literature. The disease is characterised clinically by recurrent mesomelic swelling of the extremities and radiologically by periosteal thickening and sclerosis of underlying bone. Hyperglobulinaemia is the most constant biochemical finding. The bone biopsy shows no typical features. The possibility of a viral etiology is discussed.
Dynamic recurrent neural networks
Pearlmutter, Barak A
1990-01-01
We survey learning algorithms for recurrent neural networks with hidden units and attempt to put the various techniques into a common framework. We discuss fixpoint learning algorithms, namely recurrent backpropagation and deterministic Boltzmann Machines, and non-fixpoint algorithms, namely backpropagation through time, Elman's history cutoff nets, and Jordan's output feedback architecture. Forward propagation, an online technique that uses adjoint equations, is also discussed. In many cases...
Microchimerism in recurrent miscarriage
Hilary S Gammill; Stephenson, Mary D.; Aydelotte, Tessa M.; J. Lee Nelson
2014-01-01
Maternal–fetal cell exchange during pregnancy results in acquisition of microchimerism, which can durably persist in both recipients. Naturally acquired microchimerism may impact maternal–fetal interaction in pregnancy. We conducted studies to ask whether microchimerism that a woman acquired from her own mother is detectable before or during pregnancy in women with recurrent miscarriage. Fetal microchimerism was also assayed. Women with primary idiopathic recurrent miscarriage (n=23) and cont...
We have developed a new multilayer a-tC material that is thick stress-free, adherent, low friction, and with hardness and stiffness near that of diamond. The new a-tC material is deposited by J pulsed-laser deposition (PLD) at room temperature, and fully stress-relieved by a short thermal anneal at 600 ampersand deg;C. A thick multilayer is built up by repeated deposition and annealing steps. We measured 88 GPa hardness, 1100 GPa Young's modulus, and 0.1 friction coefficient (under high load). Significantly, these results are all well within the range reported for crystalline diamond. In fact, this material, if considered separate from crystalline diamond, is the 2nd hardest material known to man. Stress-free a-tC also has important advantages over thin film diamond; namely, it is smooth, processed at lower temperature, and can be grown on a much broader range of substrates. This breakthrough will enable a host of applications that we are actively pursuing in MEMs, sensors, LIGA, etc
Dye, Scott A.
2015-01-01
New NASA vehicles, such as Earth Departure Stage (EDS), Orion, landers, and orbiting fuel depots, need improved cryogenic propellant transfer and storage for long-duration missions. Current cryogen feed line multilayer insulation (MLI) performance is 10 times worse per area than tank MLI insulation. During each launch, cryogenic piping loses approximately 150,000 gallons (equivalent to $300,000) in boil-off during transfer, chill down, and ground hold. Quest Product Development Corp., teaming with Ball Aerospace, developed an innovative advanced insulation system, Wrapped MLI (wMLI), to provide improved thermal insulation for cryogenic feed lines. wMLI is high-performance multilayer insulation designed for cryogenic piping. It uses Quest's innovative discrete-spacer technology to control layer spacing/ density and reduce heat leak. The Phase I project successfully designed, built, and tested a wMLI prototype with a measured heat leak 3.6X lower than spiral-wrapped conventional MLI widely used for piping insulation. A wMLI prototype had a heat leak of 7.3 W/m2, or 27 percent of the heat leak of conventional MLI (26.7 W/m2). The Phase II project is further developing wMLI technology with custom, molded polymer spacers and advancing the product toward commercialization via a rigorous testing program, including developing advanced vacuuminsulated pipe for ground support equipment.
Chrzan, D.C.; Dugger, M.; Follstaedt, D.M.; Friedman, Lawrence H.; Friedmann, T.A.; Knapp, J.A.; McCarty, K.F.; Medlin, D.L.; Mirkarimi, P.B.; Missert, N.; Newcomer, P.P.; Sullivan, J.P.; Tallant, D.R.
1999-05-01
We have developed a new multilayer a-tC material that is thick stress-free, adherent, low friction, and with hardness and stiffness near that of diamond. The new a-tC material is deposited by J pulsed-laser deposition (PLD) at room temperature, and fully stress-relieved by a short thermal anneal at 600°C. A thick multilayer is built up by repeated deposition and annealing steps. We measured 88 GPa hardness, 1100 GPa Young's modulus, and 0.1 friction coefficient (under high load). Significantly, these results are all well within the range reported for crystalline diamond. In fact, this material, if considered separate from crystalline diamond, is the 2nd hardest material known to man. Stress-free a-tC also has important advantages over thin film diamond; namely, it is smooth, processed at lower temperature, and can be grown on a much broader range of substrates. This breakthrough will enable a host of applications that we are actively pursuing in MEMs, sensors, LIGA, etc.
Utilizing self-consistent Hartree-Fock calculations, several aspects of multilayers and interfaces are explored: enhancement and reduction of the local magnetic moments, magnetic coupling at the interfaces, magnetic arrangements within each film and among non-neighboring films, global symmetry of the systems, frustration, orientation of the various moments with respect to an outside applied field, and magnetic-field induced transitions. Magnetoresistance of ferromagnetic-normal-metal multilayers is found by solving the Boltzmann equation. Results explain the giant negative magnetoresistance encountered in these systems when an initial antiparallel arrangement is changed into a parallel configuration by an external magnetic field. The calculation depends on (1) geometric parameters (thicknesses of layers), (2) intrinsic metal parameters (number of conduction electrons, magnetization, and effective masses in layers), (3) bulk sample properties (conductivity relaxation times), (4) interface scattering properties (diffuse scattering versus potential scattering at the interfaces, and (5) outer surface scattering properties (specular versus diffuse surface scattering). It is found that a large negative magnetoresistance requires considerable asymmetry in interface scattering for the two spin orientations. Features of the interfaces that may produce an asymmetrical spin-dependent scattering are studied: varying interfacial geometric random roughness with no lateral coherence, correlated (quasi-periodic) roughness, and varying chemical composition of the interfaces. The interplay between these aspects of the interfaces may enhance or suppress the magnetoresistance, depending on whether it increases or decreases the asymmetry in the spin-dependent scattering of the conduction electrons
Hood, R.Q.
1994-04-01
Utilizing self-consistent Hartree-Fock calculations, several aspects of multilayers and interfaces are explored: enhancement and reduction of the local magnetic moments, magnetic coupling at the interfaces, magnetic arrangements within each film and among non-neighboring films, global symmetry of the systems, frustration, orientation of the various moments with respect to an outside applied field, and magnetic-field induced transitions. Magnetoresistance of ferromagnetic-normal-metal multilayers is found by solving the Boltzmann equation. Results explain the giant negative magnetoresistance encountered in these systems when an initial antiparallel arrangement is changed into a parallel configuration by an external magnetic field. The calculation depends on (1) geometric parameters (thicknesses of layers), (2) intrinsic metal parameters (number of conduction electrons, magnetization, and effective masses in layers), (3) bulk sample properties (conductivity relaxation times), (4) interface scattering properties (diffuse scattering versus potential scattering at the interfaces, and (5) outer surface scattering properties (specular versus diffuse surface scattering). It is found that a large negative magnetoresistance requires considerable asymmetry in interface scattering for the two spin orientations. Features of the interfaces that may produce an asymmetrical spin-dependent scattering are studied: varying interfacial geometric random roughness with no lateral coherence, correlated (quasi-periodic) roughness, and varying chemical composition of the interfaces. The interplay between these aspects of the interfaces may enhance or suppress the magnetoresistance, depending on whether it increases or decreases the asymmetry in the spin-dependent scattering of the conduction electrons.
Hyperhomocysteinemia in Recurrent Miscarriage
Objective: An elevated total plasma homocysteine level has been suggested as a possible risk factor in women suffering from recurrent pregnancy loss. The current study was undertaken to assess the association between homocysteine, folate, cobalamin (vitamin B12) and the risk of recurrent pregnancy loss. Design: Case . control study Materials and Methods: The study included 57 non-pregnant Egyptian women. They were classified according to their obstetric history into 2 groups: 32 cases with at least two consecutive miscarriages (Study group), and 25 cases with normal obstetric history (Control group). All cases were tested for plasma total homocysteine, serum folate and cobalamin (vitamin B12). Results: The fasting total homocysteine was significantly higher in the study group as compared to the control group. While the median concentrations for the vitamins studied were significantly lower in women of the study group as compared to the controls. Elevated homocysteine and reduced vitamin B12 can be considered risk factors for recurrent miscarriage with odds ratio (OR) and 95% confidence intervals (95% CI) of 1.839 (1.286, 2.63) and 1.993 (1.346, 2.951) respectively in the group of recurrent miscarriages. The OR (95% CI) in the study population for low serum folate concentrations was 1.23 (0.776, 2.256). Conclusion: Elevated homocysteine and reduced serum vitamin B12 are risk factors for recurrent miscarriage. Low serum folate did not seem a risk factor for recurrent miscarriage. Testing for homocysteine levels in women suffering from unexplained recurrent miscarriage and pre-conceptional supplementation with vitamin B12 might be beneficial to improve pregnancy outcome
Speech Recognition Method Based on Multilayer Chaotic Neural Network
REN Xiaolin; HU Guangrui
2001-01-01
In this paper,speech recognitionusing neural networks is investigated.Especially,chaotic dynamics is introduced to neurons,and a mul-tilayer chaotic neural network (MLCNN) architectureis built.A learning algorithm is also derived to trainthe weights of the network.We apply the MLCNNto speech recognition and compare the performanceof the network with those of recurrent neural net-work (RNN) and time-delay neural network (TDNN).Experimental results show that the MLCNN methodoutperforms the other neural networks methods withrespect to average recognition rate.
Multilayer optical dielectric coating
Emmett, John L.
1990-01-01
A highly damage resistant, multilayer, optical reflective coating includes alternating layers of doped and undoped dielectric material. The doping levels are low enough that there are no distinct interfaces between the doped and undoped layers so that the coating has properties nearly identical to the undoped material. The coating is fabricated at high temperature with plasma-assisted chemical vapor deposition techniques to eliminate defects, reduce energy-absorption sites, and maintain proper chemical stoichiometry. A number of differently-doped layer pairs, each layer having a thickness equal to one-quarter of a predetermined wavelength in the material are combined to form a narrowband reflective coating for a predetermined wavelength. Broadband reflectors are made by using a number of narrowband reflectors, each covering a portion of the broadband.
Multilayer graphene condenser microphone
Todorović, Dejan; Matković, Aleksandar; Milićević, Marijana; Jovanović, Djordje; Gajić, Radoš; Salom, Iva; Spasenović, Marko
2015-12-01
Vibrating membranes are the cornerstone of acoustic technology, forming the backbone of modern loudspeakers and microphones. Acoustic performance of a condenser microphone is derived mainly from the membrane’s size, surface mass and achievable static tension. The widely studied and available nickel has been a dominant membrane material for professional microphones for several decades. In this paper we introduce multilayer graphene as a membrane material for condenser microphones. The graphene device outperforms a high end commercial nickel-based microphone over a significant part of the audio spectrum, with a larger than 10 dB enhancement of sensitivity. Our experimental results are supported with numerical simulations, which also show that a 300 layer thick graphene membrane under maximum tension would offer excellent extension of the frequency range, up to 1 MHz.
Multilayer Multidimensional Extension Set Theory
CAO Shao-zhong; YANG Guo-wei; TU Xu-yan
2006-01-01
In order to study the contradiction problem of multilayer multidimensional complex systems, the concepts of extension field and stable field of intersection and union of multilayer multidimensional extension set are given. Then the related operations and properties are discussed. The results of study expand the concepts of intersection and union of extension set to a general situation, and provide the theoretical basis for production of the concepts of intersection and union of multilayer multidimensional matter element system extension set. In this way, it will be possible that matter element system theory is used to creative designs of complex systems.
Sofia Torreggiani
2016-03-01
Full Text Available Children presenting with recurrent fever may represent a diagnostic challenge. After excluding the most common etiologies, which include the consecutive occurrence of independent uncomplicated infections, a wide range of possible causes are considered. This article summarizes infectious and noninfectious causes of recurrent fever in pediatric patients. We highlight that, when investigating recurrent fever, it is important to consider age at onset, family history, duration of febrile episodes, length of interval between episodes, associated symptoms and response to treatment. Additionally, information regarding travel history and exposure to animals is helpful, especially with regard to infections. With the exclusion of repeated independent uncomplicated infections, many infective causes of recurrent fever are relatively rare in Western countries; therefore, clinicians should be attuned to suggestive case history data. It is important to rule out the possibility of an infectious process or a malignancy, in particular, if steroid therapy is being considered. After excluding an infectious or neoplastic etiology, immune-mediated and autoinflammatory diseases should be taken into consideration. Together with case history data, a careful physical exam during and between febrile episodes may give useful clues and guide laboratory investigations. However, despite a thorough evaluation, a recurrent fever may remain unexplained. A watchful follow-up is thus mandatory because new signs and symptoms may appear over time.
Torreggiani, Sofia; Filocamo, Giovanni; Esposito, Susanna
2016-01-01
Children presenting with recurrent fever may represent a diagnostic challenge. After excluding the most common etiologies, which include the consecutive occurrence of independent uncomplicated infections, a wide range of possible causes are considered. This article summarizes infectious and noninfectious causes of recurrent fever in pediatric patients. We highlight that, when investigating recurrent fever, it is important to consider age at onset, family history, duration of febrile episodes, length of interval between episodes, associated symptoms and response to treatment. Additionally, information regarding travel history and exposure to animals is helpful, especially with regard to infections. With the exclusion of repeated independent uncomplicated infections, many infective causes of recurrent fever are relatively rare in Western countries; therefore, clinicians should be attuned to suggestive case history data. It is important to rule out the possibility of an infectious process or a malignancy, in particular, if steroid therapy is being considered. After excluding an infectious or neoplastic etiology, immune-mediated and autoinflammatory diseases should be taken into consideration. Together with case history data, a careful physical exam during and between febrile episodes may give useful clues and guide laboratory investigations. However, despite a thorough evaluation, a recurrent fever may remain unexplained. A watchful follow-up is thus mandatory because new signs and symptoms may appear over time. PMID:27023528
Recurrent Escherichia coli bacteremia.
Maslow, J N; Mulligan, M E; Arbeit, R D
1994-01-01
Escherichia coli is the most common gram-negative organism associated with bacteremia. While recurrent E. coli urinary tract infections are well-described, recurrent E. coli bacteremia appears to be uncommon, with no episodes noted in multiple series of patients with gram-negative bacteremias. We report on 5 patients with recurrent bloodstream infections identified from a series of 163 patients with E. coli bacteremia. For each patient, the isolates from each episode were analyzed by pulsed-field gel electrophoresis (PFGE) and ribotyping and for the presence of E. coli virulence factors. For each of four patients, the index and recurrent episodes of bacteremia represented the same strain as defined by PFGE, and the strains were found to carry one or more virulence factors. The remaining patient, with two episodes of bloodstream infection separated by a 4-year interval, was infected with two isolates that did not carry any virulence factors and that were clonally related by ribotype analysis but differed by PFGE. All five patients had either a local host defense defect (three patients) or impaired systemic defenses (one patient) or both (one patient). Thus, recurrent E. coli bacteremia is likely to represent a multifactorial process that occurs in patients with impaired host defenses who are infected with virulent isolates. Images PMID:7910828
Torreggiani, Sofia; Filocamo, Giovanni; Esposito, Susanna
2016-01-01
Children presenting with recurrent fever may represent a diagnostic challenge. After excluding the most common etiologies, which include the consecutive occurrence of independent uncomplicated infections, a wide range of possible causes are considered. This article summarizes infectious and noninfectious causes of recurrent fever in pediatric patients. We highlight that, when investigating recurrent fever, it is important to consider age at onset, family history, duration of febrile episodes, length of interval between episodes, associated symptoms and response to treatment. Additionally, information regarding travel history and exposure to animals is helpful, especially with regard to infections. With the exclusion of repeated independent uncomplicated infections, many infective causes of recurrent fever are relatively rare in Western countries; therefore, clinicians should be attuned to suggestive case history data. It is important to rule out the possibility of an infectious process or a malignancy, in particular, if steroid therapy is being considered. After excluding an infectious or neoplastic etiology, immune-mediated and autoinflammatory diseases should be taken into consideration. Together with case history data, a careful physical exam during and between febrile episodes may give useful clues and guide laboratory investigations. However, despite a thorough evaluation, a recurrent fever may remain unexplained. A watchful follow-up is thus mandatory because new signs and symptoms may appear over time. PMID:27023528
Integrated Multilayer Insulation
Dye, Scott
2009-01-01
Integrated multilayer insulation (IMLI) is being developed as an improved alternative to conventional multilayer insulation (MLI), which is more than 50 years old. A typical conventional MLI blanket comprises between 10 and 120 metallized polymer films separated by polyester nets. MLI is the best thermal- insulation material for use in a vacuum, and is the insulation material of choice for spacecraft and cryogenic systems. However, conventional MLI has several disadvantages: It is difficult or impossible to maintain the desired value of gap distance between the film layers (and consequently, it is difficult or impossible to ensure consistent performance), and fabrication and installation are labor-intensive and difficult. The development of IMLI is intended to overcome these disadvantages to some extent and to offer some additional advantages over conventional MLI. The main difference between IMLI and conventional MLI lies in the method of maintaining the gaps between the film layers. In IMLI, the film layers are separated by what its developers call a micro-molded discrete matrix, which can be loosely characterized as consisting of arrays of highly engineered, small, lightweight, polymer (typically, thermoplastic) frames attached to, and placed between, the film layers. The term "micro-molded" refers to both the smallness of the frames and the fact that they are fabricated in a process that forms precise small features, described below, that are essential to attainment of the desired properties. The term "discrete" refers to the nature of the matrix as consisting of separate frames, in contradistinction to a unitary frame spanning entire volume of an insulation blanket.
Ferreira, B D L; Sebastião, R C O; Yoshida, M I; Mussel, W N; Fialho, S L; Barbosa, J
2016-01-01
Kinetic study by thermal decomposition of antiretroviral drugs, Efavirenz (EFV) and Lamivudine (3TC), usually present in the HIV cocktail, can be done by individual adjustment of the solid decomposition models. However, in some cases unacceptable errors are found using this methodology. To circumvent this problem, here is proposed to use a multilayer perceptron neural network (MLP), with an appropriate algorithm, which constitutes a linearization of the network by setting weights between the input layer and the intermediate one and the use of Kinetic models as activation functions of neurons in the hidden layer. The interconnection weights between that intermediate layer and output layer determines the contribution of each model in the overall fit of the experimental data. Thus, the decomposition is assumed to be a phenomenon that can occur following different kinetic processes. In the investigated data, the kinetic thermal decomposition process was best described by R1 and D4 model for all temperatures to EF...
Mukai, Koji
2014-01-01
In recent years, recurrent nova eruptions are often observed very intensely in wide range of wavelengths from radio to optical to X-rays. Here I present selected highlights from recent multi-wavelength observations. The enigma of T Pyx is at the heart of this paper. While our current understanding of CV and symbiotic star evolution can explain why certain subset of recurrent novae have high accretion rate, that of T Pyx must be greatly elevated compared to the evolutionary mean. At the same time, we have extensive data to be able to estimate how the nova envelope was ejected in T Pyx, and it turns to be a rather complex tale. One suspects that envelope ejection in recurrent and classical novae in general is more complicated than the textbook descriptions. At the end of the review, I will speculate that these two may be connected.
Multilayer thermal barrier coating systems
Vance, Steven J.; Goedjen, John G.; Sabol, Stephen M.; Sloan, Kelly M.
2000-01-01
The present invention generally describes multilayer thermal barrier coating systems and methods of making the multilayer thermal barrier coating systems. The thermal barrier coating systems comprise a first ceramic layer, a second ceramic layer, a thermally grown oxide layer, a metallic bond coating layer and a substrate. The thermal barrier coating systems have improved high temperature thermal and chemical stability for use in gas turbine applications.
Optical transmittance of multilayer graphene
Zhu, Shou-En; Yuan, Shengjun; Janssen, G. C. A. M.
2014-01-01
We study the optical transmittance of multilayer graphene films up to 65 layers thick. By combing large-scale tight-binding simulation and optical measurement on CVD multilayer graphene, the optical transmission through graphene films in the visible region is found to be solely determined by the number of graphene layers. We argue that the optical transmittance measurement is more reliable in the determination of the number of layers than the commonly used Raman Spectroscopy. Moreover, optica...
Multi-Layer and Recursive Neural Networks for Metagenomic Classification.
Ditzler, Gregory; Polikar, Robi; Rosen, Gail
2015-09-01
Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy-as that depends on the specific application-but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis. PMID:26316190
Lung Cancer Indicators Recurrence
This study describes prognostic factors for lung cancer spread and recurrence, as well as subsequent risk of death from the disease. The investigators observed that regardless of cancer stage, grade, or type of lung cancer, patients in the study were more
Dobbs, David E.
2013-01-01
A direct method is given for solving first-order linear recurrences with constant coefficients. The limiting value of that solution is studied as "n to infinity." This classroom note could serve as enrichment material for the typical introductory course on discrete mathematics that follows a calculus course.
Pedersen, Morten With
1997-01-01
Training recurrent networks is generally believed to be a difficult task. Excessive training times and lack of convergence to an acceptable solution are frequently reported. In this paper we seek to explain the reason for this from a numerical point of view and show how to avoid problems when...
Some Types of Recurrence in Finsler geometry
Soleiman, A
2016-01-01
The pullback approach to global Finsler geometry is adopted. Three classes of recurrence in Finsler geometry are introduced and investigated: simple recurrence, Ricci recurrence and concircular recurrence. Each of these classes consists of four types of recurrence. The interrelationships between the different types of recurrence are studied. The generalized concircular recurrence, as a new concept, is singled out.
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alireza Alemi
2015-08-01
Full Text Available Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the
A Three-Threshold Learning Rule Approaches the Maximal Capacity of Recurrent Neural Networks.
Alemi, Alireza; Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo
2015-08-01
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is the attractor neural network scenario, whose prototype is the Hopfield model. The model simplicity and the locality of the synaptic update rules come at the cost of a poor storage capacity, compared with the capacity achieved with perceptron learning algorithms. Here, by transforming the perceptron learning rule, we present an online learning rule for a recurrent neural network that achieves near-maximal storage capacity without an explicit supervisory error signal, relying only upon locally accessible information. The fully-connected network consists of excitatory binary neurons with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the memory patterns to be memorized are presented online as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs. Synapses corresponding to active inputs are modified as a function of the value of the local fields with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. We simulated and analyzed a network of binary neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction. The storage capacity obtained through numerical simulations is shown to be close to the value predicted by analytical calculations. We also measured the dependence of capacity on the strength of external inputs. Finally, we quantified the statistics of the resulting synaptic connectivity matrix, and found that both the fraction of zero weight synapses and the degree of symmetry of the weight matrix increase with the number of stored
José Antonio Vázquez-López
2012-06-01
Full Text Available In this article the Perceptron artificial neural network is applied as a classifier system of out of control points, in the field of contrlol chart for individual measurements. The use of geometric properties of the Perceptron as a training method is introduced, replacing in consequence to the known training methods. Some experiments with numerical databases contaminated with altered data in global average was performed, and the ability of the detection of \\out of control points" of the control chart with the implementation of the Perceptron trained by geometry was compared. The results reveal greater capacity in the Perceptron. This approach can be generalized to other types of control charts and patterns of natural and special variation, not considered in this research. // RESUMEN: En este artículo se aplica la red neuronal artificial Perceptrón como sistema clasificador de puntos fuera de control en el ámbito de la carta de control de mediciones individuales. Se introduce el uso de las propiedades geométricas de la Perceptrón como método de entrenamiento para sustituir, en consecuencia, a los métodos de entrenamiento conocidos. Se experimentó con bases de datos numéricas contaminadas con datos alterados en su media global y se comparó la capacidad de la detección de puntos fuera de control de la carta de control con la aplicación de la Perceptrón entrenada por geometría. Los resultados revelan mayor capacidad en la Perceptrón en diferentes porcentajes de contaminación. Esta propuesta puede ser generalizada a otros tipos de gráficos de control y a patrones de variación especial y natural no considerados en esta investigación.
Multilayer optical calculations
Byrnes, Steven J
2016-01-01
When light hits a multilayer planar stack, it is reflected, refracted, and absorbed in a way that can be derived from the Fresnel equations. The analysis is treated in many textbooks, and implemented in many software programs, but certain aspects of it are difficult to find explicitly and consistently worked out in the literature. Here, we derive the formulas underlying the transfer-matrix method of calculating the optical properties of these stacks, including oblique-angle incidence, absorption-vs-position profiles, and ellipsometry parameters. We discuss and explain some strange consequences of the formulas in the situation where the incident and/or final (semi-infinite) medium are absorptive, such as calculating $T>1$ in the absence of gain. We also discuss some implementation details like complex-plane branch cuts. Finally, we derive modified formulas for including one or more "incoherent" layers, i.e. very thick layers in which interference can be neglected. This document was written in conjunction with ...
Implementation of a spike-based perceptron learning rule using TiO2−x memristors
Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G.; Indiveri, Giacomo; Prodromakis, Themis
2015-01-01
Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic “cognitive” capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2−x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode. PMID:26483629
Immunomodulators to treat recurrent miscarriage
Prins, Jelmer R.; Kieffer, Tom E. C.; Scherjon, Sicco A.
2014-01-01
Recurrent miscarriage is a reproductive disorder affecting many couples. Although several factors are associated with recurrent miscarriage, in more than 50% of the cases the cause is unknown. Maladaptation of the maternal immune system is associated with recurrent miscarriage and could explain part
Recurrent hyperphosphatemic tumoural calcinosis
Amit, Sonal; Agarwal, Asha; Nigam, Anand; Rao, Yashwant Kumar
2012-01-01
Tumoural calcinosis (TC) is a benign gradually developing disorder that can occur in a variety of clinical settings, characterised by subcutaneous deposition of calcium phosphate with or without giant cell reaction. We describe a case of 11-year-old girl presenting with recurrent hard swellings in the vicinity of shoulder and hip joints associated with elevated serum phosphate and normal serum calcium levels. TC has been mainly reported from Africa, with very few cases reported from India. After the diagnosis of hyperphosphatemic TC was established, the patient was treated with oral sevelamer and is under constant follow-up to detect recurrence, if any. The present case highlights the fact that although an uncommon lesion, TC must be considered in the differential diagnosis of subcutaneous hard lump in the vicinity of a joint. PMID:23010461
Incarcerated recurrent Amyand's hernia
Quartey, Benjamin; Ugochukwu, Obinna; Kuehn, Reed; Ospina, Karen
2012-01-01
Amyand's hernia is a rarity and a recurrent case is extremely rare. A 71-year-old male with a previous history of right inguinal hernia repair presented to the emergency department with a 1-day history of pain in the right groin. A physical examination revealed a nonreducible right inguinal hernia. A computed tomography scan showed a 1.3-cm appendix with surrounding inflammation within a right inguinal hernia. An emergent right groin exploration revealed an incarcerated and injected non-perforated appendix and an indirect hernia. Appendectomy was performed through the groin incision, and the indirect hernia defect was repaired with a biological mesh (Flex-HD). We hereby present this unique case – the first reported case of recurrent Amyand's hernia and a literature review of this anatomical curiosity. PMID:23248506
Recurrent Neural Network Regularization
Zaremba, Wojciech; Sutskever, Ilya; Vinyals, Oriol
2014-01-01
We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, image caption generation, and machine translation.
Equine recurrent airway obstruction
Artur Niedźwiedź
2014-01-01
Equine Recurrent Airway Obstruction (RAO), also known as heaves or broken wind, is one of the most common disease in middle-aged horses. Inflammation of the airway is inducted by organic dust exposure. This disease is characterized by neutrophilic inflammation, bronchospasm, excessive mucus production and pathologic changes in the bronchiolar walls. Clinical signs are resolved in 3-4 weeks after environmental changes. Horses suffering from RAO are susceptible to allergens throughout their liv...
Cooijmans, Tim; Ballas, Nicolas; Laurent, César; Gülçehre, Çağlar; Courville, Aaron
2016-01-01
We propose a reparameterization of LSTM that brings the benefits of batch normalization to recurrent neural networks. Whereas previous works only apply batch normalization to the input-to-hidden transformation of RNNs, we demonstrate that it is both possible and beneficial to batch-normalize the hidden-to-hidden transition, thereby reducing internal covariate shift between time steps. We evaluate our proposal on various sequential problems such as sequence classification, language modeling an...
Recurrent confusion and hypopituitarism.
Gutowski, N J; Heron, J R
1993-01-01
Three women in late middle age had recurrent episodes of confusion which could not be explained solely on the basis of an associated infection. All three patients had latent hypopituitarism diagnosed on final presentation. Each patient had a previous history of a severe postpartum haemorrhage followed by two further pregnancies. Experienced clinicians had not made a diagnosis of confusional episodes due to hypopituitary encephalopathy because the history was not immediately available in the c...
Multilayer monochromators for neutron scattering
In an earlier paper Schoenborn, Caspar, and Kammerer (J. Appl. Cryst. 7, 508-10(1974)) reported the fabrication of thin film monochromators for neutrons. They showed that a multilayer consisting of alternating films of two materials acts as a good monochromator with large and adjustable periodicity and wide bandwidth. The diffraction properties of these multilayers have been studied with the objective of using them as monochromators, filters and polarizers for neutrons. A theoretical understanding of these multilayers has been developed by using the kinematical and dynamical approaches. In order to compare these expressions with the observed properties, the effects of beam divergence and wavelength distribution for the spectrometer have been determined. The influence of some aperiodicity on the diffraction data has also been studied within the framework of kinematical theory. (auth)
Structural reducibility of multilayer networks
de Domenico, Manlio; Nicosia, Vincenzo; Arenas, Alexandre; Latora, Vito
2015-04-01
Many complex systems can be represented as networks consisting of distinct types of interactions, which can be categorized as links belonging to different layers. For example, a good description of the full protein-protein interactome requires, for some organisms, up to seven distinct network layers, accounting for different genetic and physical interactions, each containing thousands of protein-protein relationships. A fundamental open question is then how many layers are indeed necessary to accurately represent the structure of a multilayered complex system. Here we introduce a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph. We validate our approach on synthetic benchmarks and we show that the number of informative layers in some real multilayer networks of protein-genetic interactions, social, economical and transportation systems can be reduced by up to 75%.
Thermally induced delamination of multilayers
Sørensen, Bent F.; Sarraute, S.; Jørgensen, O.; Horsewell, A.
1998-01-01
Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion coefficie......Steady-state delamination of multilayered structures, caused by stresses arising during processing due to thermal expansion mismatch, is analyzed by a fracture mechanics model based on laminate theory. It is found that inserting just a few interlayers with intermediate thermal expansion...
Neuroradiological techniques were used to elucidate pathophysiology of recurrent cerebral thrombosis. Twenty-two patients with cerebral thrombosis who suffered a second attack under stable conditions more than 22 days after the initial stroke were studied. Hypertension, diabetes mellitus, and hypercholesterolemia were also seen in 20, 8, and 12 patients, respectively. The patients were divided into three groups according to their symptoms: (I) symptoms differed between the first and second strokes (n=12); (II) initial symptoms were suddenly deteriorated (n=6); and (III) symptoms occurring in groups I and II were seen (n=4). In group I, contralateral hemiparesis or suprabulbar palsy was often associated with the initial hemiparesis. The time of recurrent stroke varied from 4 months to 9 years. CT and MRI showed not only lacunae in both hemispheres, but also deep white-matter ischemia of the centrum semi-ovale. In group II, hemiparesis or visual field defect was deteriorated early after the initial stroke. In addition, neuroimaging revealed that infarction in the posterior cerebral artery was progressed on the contralateral side, or that white matter lesion in the middle artery was enlarged in spite of small lesion in the left cerebral hemisphere. All patients in group III had deterioration of right hemiparesis associated with aphasia. CT, MRI, SPECT, and angiography indicated deep white-matter ischemia caused by main trunk lesions in the left hemisphere. Group III seemed to be equivalent to group II, except for laterality of the lesion. Neuroradiological assessment of the initial stroke may help to predict the mode of recurrence, although pathophysiology of cerebral thrombosis is complicated and varies from patient to patient. (N.K.)
Incarcerated recurrent Amyand's hernia
Benjamin Quartey; Obinna Ugochukwu; Reed Kuehn; Karen Ospina
2012-01-01
Amyand′s hernia is a rarity and a recurrent case is extremely rare. A 71-year-old male with a previous history of right inguinal hernia repair presented to the emergency department with a 1-day history of pain in the right groin. A physical examination revealed a nonreducible right inguinal hernia. A computed tomography scan showed a 1.3-cm appendix with surrounding inflammation within a right inguinal hernia. An emergent right groin exploration revealed an incarcerated and injected non-perfo...
Recurrent respiratory papillomatosis.
Venkatesan, Naren N; Pine, Harold S; Underbrink, Michael P
2012-06-01
Recurrent respiratory papillomatosis (RRP) is a rare, benign disease with no known cure. RRP is caused by infection of the upper aerodigestive tract with the human papillomavirus (HPV). Passage through the birth canal is thought to be the initial transmission event, but infection may occur in utero. HPV vaccines have helped to provide protection from cervical cancer; however, their role in the prevention of RRP is undetermined. Clinical presentation of initial symptoms of RRP may be subtle. RRP course varies, and current management focuses on surgical debulking of papillomatous lesions with or without concurrent adjuvant therapy. PMID:22588043
Equine recurrent airway obstruction
Artur Niedźwiedź
2014-10-01
Full Text Available Equine Recurrent Airway Obstruction (RAO, also known as heaves or broken wind, is one of the most common disease in middle-aged horses. Inflammation of the airway is inducted by organic dust exposure. This disease is characterized by neutrophilic inflammation, bronchospasm, excessive mucus production and pathologic changes in the bronchiolar walls. Clinical signs are resolved in 3-4 weeks after environmental changes. Horses suffering from RAO are susceptible to allergens throughout their lives, therefore they should be properly managed. In therapy the most importanthing is to eliminate dustexposure, administration of corticosteroids and use bronchodilators to improve pulmonary function.
Recurrent Miller Fisher syndrome.
Madhavan, S; Geetha; Bhargavan, P V
2004-07-01
Miller Fisher syndrome (MFS) is a variant of Guillan Barre syndrome characterized by the triad of ophthalmoplegia, ataxia and areflexia. Recurrences are exceptional with Miller Fisher syndrome. We are reporting a case with two episodes of MFS within two years. Initially he presented with partial ophthalmoplegia, ataxia. Second episode was characterized by full-blown presentation characterized by ataxia, areflexia and ophthalmoplegia. CSF analysis was typical during both episodes. Nerve conduction velocity study was fairly within normal limits. MRI of brain was within normal limits. He responded to symptomatic measures initially, then to steroids in the second episode. We are reporting the case due to its rarity. PMID:15645989
New developments in Ni/Ti multilayers
Anderson, I.; Hoghoj, P. [Institut Max von Laue - Paul Langevin (ILL), 38 - Grenoble (France)
1997-04-01
It is now 20 years since super-mirrors were first used as a neutron optical element. Since then the field of multilayer neutron-optics has matured with multilayers finding their way to application in many neutron scattering instruments. However, there is still room for progress in terms of multilayer quality, performance and application. Along with work on multilayers for neutron polarisation Ni/Ti super-mirrors have been optimised. The state-of-the-art Ni/Ti super-mirror performance and the results obtained in two neutron-optics applications of Ni/Ti multilayers are presented. (author).
Interfacial Convection in Multilayer Systems
Legros, J C; Simanovskii, I
2006-01-01
Contains an investigation of the convection in systems with interfaces. This book classifies the known types of convective instabilities in such systems, and discusses the peculiarities of multilayer systems. It also provides an overview of the variety of steady and oscillatory patterns, waves.
Multilayer Controller for Outdoor Vehicle
Reske-Nielsen, Anders; Mejnertsen, Asbjørn; Andersen, Nils Axel;
2006-01-01
A full software and hardware solution has been designed, implemented and tested for control of a small agricultural automatic tractor. The objective was to realise a user-friendly, multi-layer controller architecture for an outdoor platform. The collaborative research work was done as a part...
Multilayer Composite Pressure Vessels
DeLay, Tom
2005-01-01
A method has been devised to enable the fabrication of lightweight pressure vessels from multilayer composite materials. This method is related to, but not the same as, the method described in gMaking a Metal- Lined Composite-Overwrapped Pressure Vessel h (MFS-31814), NASA Tech Briefs, Vol. 29, No. 3 (March 2005), page 59. The method is flexible in that it poses no major impediment to changes in tank design and is applicable to a wide range of tank sizes. The figure depicts a finished tank fabricated by this method, showing layers added at various stages of the fabrication process. In the first step of the process, a mandrel that defines the size and shape of the interior of the tank is machined from a polyurethane foam or other suitable lightweight tooling material. The mandrel is outfitted with metallic end fittings on a shaft. Each end fitting includes an outer flange that has a small step to accommodate a thin layer of graphite/epoxy or other suitable composite material. The outer surface of the mandrel (but not the fittings) is covered with a suitable release material. The composite material is filament- wound so as to cover the entire surface of the mandrel from the step on one end fitting to the step on the other end fitting. The composite material is then cured in place. The entire workpiece is cut in half in a plane perpendicular to the axis of symmetry at its mid-length point, yielding two composite-material half shells, each containing half of the foam mandrel. The halves of the mandrel are removed from within the composite shells, then the shells are reassembled and bonded together with a belly band of cured composite material. The resulting composite shell becomes a mandrel for the subsequent steps of the fabrication process and remains inside the final tank. The outer surface of the composite shell is covered with a layer of material designed to be impermeable by the pressurized fluid to be contained in the tank. A second step on the outer flange of
Epidemiology of recurrent venous thrombosis
D.D. Ribeiro
2012-01-01
Full Text Available Venous thrombosis, including deep vein thrombosis and pulmonary embolism, is a common disease that frequently recurs. Recurrence can be prevented by anticoagulants, but this comes at the risk of bleeding. Therefore, assessment of the risk of recurrence is important to balance the risks and benefits of anticoagulant treatment. This review briefly outlines what is currently known about the epidemiology of recurrent venous thrombosis, and focuses in more detail on potential new risk factors for venous recurrence. The general implications of these findings in patient management are discussed.
Recurrent vulvovaginal candidiasis.
Sobel, Jack D
2016-01-01
Recurrent vulvovaginal candidiasis (RVVC) is a common cause of significant morbidity in women in all strata of society affecting millions of women worldwide. Previously, RVVC occurrence was limited by onset of menopause but the widespread use of hormone replacement therapy has extended the at-risk period. Candida albicans remains the dominant species responsible for RVVC, however optimal management of RVVC requires species determination and effective treatment measures are best if species-specific. Considerable progress has been made in understanding risk factors that determine susceptibility to RVVC, particularly genetic factors, as well as new insights into normal vaginal defense immune mechanisms and their aberrations in RVVC. While effective control of RVVC is achievable with the use of fluconazole maintenance suppressive therapy, cure of RVVC remains elusive especially in this era of fluconazole drug resistance. Vaccine development remains a critical challenge and need. PMID:26164695
Recurrent Spatial Transformer Networks
Sønderby, Søren Kaae; Sønderby, Casper Kaae; Maaløe, Lars;
2015-01-01
We integrate the recently proposed spatial transformer network (SPN) [Jaderberg et. al 2015] into a recurrent neural network (RNN) to form an RNN-SPN model. We use the RNN-SPN to classify digits in cluttered MNIST sequences. The proposed model achieves a single digit error of 1.5% compared to 2.......9% for a convolutional networks and 2.0% for convolutional networks with SPN layers. The SPN outputs a zoomed, rotated and skewed version of the input image. We investigate different down-sampling factors (ratio of pixel in input and output) for the SPN and show that the RNN-SPN model is able to down-sample the input...
A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks.
Cavalieri, Salvatore; Mirabella, Orazio
1999-01-01
The paper deals with the problem of fault tolerance in a multilayer perceptron network. Although it already possesses a reasonable fault tolerance capability, it may be insufficient in particularly critical applications. Studies carried out by the authors have shown that the traditional backpropagation learning algorithm may entail the presence of a certain number of weights with a much higher absolute value than the others. Further studies have shown that faults in these weights is the main cause of deterioration in the performance of the neural network. In other words, the main cause of incorrect network functioning on the occurrence of a fault is the non-uniform distribution of absolute values of weights in each layer. The paper proposes a learning algorithm which updates the weights, distributing their absolute values as uniformly as possible in each layer. Tests performed on benchmark test sets have shown the considerable increase in fault tolerance obtainable with the proposed approach as compared with the traditional backpropagation algorithm and with some of the most efficient fault tolerance approaches to be found in literature. PMID:12662719
Metallic multilayers at the nanoscale
Jankowski, A.F.
1994-11-01
The development of multilayer structures has been driven by a wide range of commercial applications requiring enhanced material behaviors. Innovations in physical vapor deposition technologies, in particular magnetron sputtering, have enabled the synthesis of metallic-based structures with nanoscaled layer dimensions as small as one-to-two monolayers. Parameters used in the deposition process are paramount to the Formation of these small layer dimensions and the stability of the structure. Therefore, optimization of the desired material properties must be related to assessment of the actual microstructure. Characterization techniques as x-ray diffraction and high resolution microscopy are useful to reveal the interface and layer structure-whether ordered or disordered crystalline, amorphous, compositionally abrupt or graded, and/or lattice strained Techniques for the synthesis of metallic multilayers with subnanometric layers will be reviewed with applications based on enhancing material behaviors as reflectivity and magnetic anisotropy but with emphasis on experimental studies of mechanical properties.
Anomalous magnetoresistance in Fibonacci multilayers.
Machado, L. D.; Bezerra, C. G.; Correa, M. A.; Chesman, C.; Pearson, J. E.; Hoffmann, A. (Materials Science Division); (Universidade Federal do Rio Grande do Norte)
2012-01-01
We theoretically investigated magnetoresistance curves in quasiperiodic magnetic multilayers for two different growth directions, namely, [110] and [100]. We considered identical ferromagnetic layers separated by nonmagnetic layers with two different thicknesses chosen based on the Fibonacci sequence. Using parameters for Fe/Cr multilayers, four terms were included in our description of the magnetic energy: Zeeman, cubic anisotropy, bilinear coupling, and biquadratic coupling. The minimum energy was determined by the gradient method and the equilibrium magnetization directions found were used to calculate magnetoresistance curves. By choosing spacers with a thickness such that biquadratic coupling is stronger than bilinear coupling, unusual behaviors for the magnetoresistance were observed: (i) for the [110] case, there is a different behavior for structures based on even and odd Fibonacci generations, and, more interesting, (ii) for the [100] case, we found magnetic field ranges for which the magnetoresistance increases with magnetic field.
Multilayer weighted social network model
Murase, Yohsuke; Török, János; Jo, Hang-Hyun; Kaski, Kimmo; Kertész, János
2014-11-01
Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.
Multilayer coating for high gradients
Kubo, Takayuki
2016-01-01
The multilayer coating for high gradients is reviewed. Not only the S-I-S structure, but also the S-S bilayer structure are also treated. This is an incomplete manuscript of an invited article which will be submitted to a journal. I have uploaded this version in order to help the understanding on my talk at the TESLA Technology Collaboration meeting at Saclay, France.
Polyelectrolyte Multilayers in Tissue Engineering
Detzel, Christopher J.; Larkin, Adam L.; Rajagopalan, Padmavathy
2011-01-01
The layer-by-layer assembly of sequentially adsorbed, alternating polyelectrolytes has become increasingly important over the past two decades. The ease and versatility in assembling polyelectrolyte multilayers (PEMs) has resulted in numerous wide ranging applications of these materials. More recently, PEMs are being used in biological applications ranging from biomaterials, tissue engineering, regenerative medicine, and drug delivery. The ability to manipulate the chemical, physical, surface...
Spreading processes in Multilayer Networks
Salehi, Mostafa; Sharma, Rajesh; Marzolla, Moreno; Magnani, Matteo; Siyari, Payam; Montesi, Danilo
2014-01-01
Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various discipl...
Ultra-thin multilayer capacitors.
Renk, Timothy Jerome; Monson, Todd C.
2009-06-01
The fabrication of ultra-thin lanthanum-doped lead zirconium titanate (PLZT) multilayer ceramic capacitors (MLCCs) using a high-power pulsed ion beam was studied. The deposition experiments were conducted on the RHEPP-1 facility at Sandia National Laboratories. The goal of this work was to increase the energy density of ceramic capacitors through the formation of a multilayer device with excellent materials properties, dielectric constant, and standoff voltage. For successful device construction, there are a number of challenging requirements including achieving correct stoichiometric and crystallographic composition of the deposited PLZT, as well as the creation of a defect free homogenous film. This report details some success in satisfying these requirements, although 900 C temperatures were necessary for PLZT perovskite phase formation. These temperatures were applied to a previously deposited multi-layer film which was then post-annealed to this temperature. The film exhibited mechanical distress attributable to differences in the coefficient of thermal expansion (CTE) of the various layers. This caused significant defects in the deposited films that led to shorts across devices. A follow-on single layer deposition without post-anneal produced smooth layers with good interface behavior, but without the perovskite phase formation. These issues will need to be addressed in order for ion beam deposited MLCCs to become a viable technology. It is possible that future in-situ heating during deposition may address both the CTE issue, and result in lowered processing temperatures, which in turn could raise the probability of successful MLCC formation.
Mathematical Formulation of Multilayer Networks
De Domenico, Manlio; Solé-Ribalta, Albert; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A.; Gómez, Sergio; Arenas, Alex
2013-10-01
A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems are very rich. Achieving a deep understanding of such systems necessitates generalizing “traditional” network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multilayer complex systems. In this paper, we introduce a tensorial framework to study multilayer networks, and we discuss the generalization of several important network descriptors and dynamical processes—including degree centrality, clustering coefficients, eigenvector centrality, modularity, von Neumann entropy, and diffusion—for this framework. We examine the impact of different choices in constructing these generalizations, and we illustrate how to obtain known results for the special cases of single-layer and multiplex networks. Our tensorial approach will be helpful for tackling pressing problems in multilayer complex systems, such as inferring who is influencing whom (and by which media) in multichannel social networks and developing routing techniques for multimodal transportation systems.
Interfaces in sequence permutated multilayers
Balogh, J; Bujdoso, L; Kaptas, D; Kiss, L F; Kemeny, T; Vincze, I, E-mail: baloghj@szfki.h [Research Institute for Solid State Physics and Optics, 1525 Budapest PO Box 49 (Hungary)
2010-03-01
Sequence permutation of three building block multilayers was recently suggested as a new approach in studying bottom and top interfaces formed of a given layer with either of the other two elements. It was applied to Fe-B-Ag multilayers with 5 nm Ag layers separating the Fe and the B layers. Now we examine the dependence of the chemical mixing and the consequent amorphous phase formation on the nominal thickness of the Ag layers in [2 nm B / 2nm Fe / x nm Ag]{sub 4}, 0.2{<=}x{<=}10, multilayers. The ratio of the non-alloyed Fe layer and the amorphous Fe-B interface compound changes only below x=5 nm. It is attributed to discontinuities of the Ag layer due to its three dimensional island growth over the bcc-Fe layer. The results obtained on the variation of the hyperfine field distribution of the amorhous Fe-B layers also confirm that the top interfaces of Fe with B are more B-rich than the bottom ones.
Electrical conductivity of collapsed multilayer graphene tubes
Mendoza, D.
2011-01-01
Synthesis of multilayer graphene on copper wires by a chemical vapor deposition method is reported. After copper etching, the multilayer tube collapses forming stripes of graphitic films, their electrical conductance as a function of temperature indicate a semiconductor-like behavior. Using the multilayer graphene stripes, a cross junction is built and owing to its electrical behavior we propose that a tunneling process exists in the device.
BESSY Bragg-Fresnel multilayer beam monitors
X-ray optical systems based on Bragg-Fresnel multilayer components imaging an electron beam in a storage ring with microm resolution are presented. Design concepts are compared to alternative methods, and the aberrations and limits of Bragg-Fresnel multilayer optics are discussed. Experimental results of imaging the BESSY 1 source with sub 10 microm resolution are presented and the development of a compact Bragg-Fresnel multilayer telescope as a BESSY 2 standard beam monitor is described
Multilayer Analysis and Visualization of Networks
De Domenico, Manlio; Arenas, Alex
2014-01-01
Multilayer relationships among and information about biological entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We report a methodology and a collection of algorithms for the analysis of multilayer networks in our new open-source software (muxViz). We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic and neuronal networks.
Recurrence plots and recurrence quantification analysis of human motion data
Josiński, Henryk; Michalczuk, Agnieszka; Świtoński, Adam; Szczesna, Agnieszka; Wojciechowski, Konrad
2016-06-01
The authors present exemplary application of recurrence plots, cross recurrence plots and recurrence quantification analysis for the purpose of exploration of experimental time series describing selected aspects of human motion. Time series were extracted from treadmill gait sequences which were recorded in the Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology in Bytom, Poland by means of the Vicon system. Analysis was focused on the time series representing movements of hip, knee, ankle and wrist joints in the sagittal plane.
Vacuum multilayer lamination of printed wiring boards
Wilkus, J. W.
1992-11-01
This experiment investigates vacuum multilayer lamination of rigid/flex, epoxy glass, polyimide glass, and polyimide quartz printed wiring boards. The effectiveness of the vacuum in removing entrapped air during the lamination cycle is demonstrated. The results of the experiment have also shown that vacuum lamination of epoxy glass multilayers improves the delamination resistance. Thus, epoxy glass multilayers that have been vacuum laminated will be able to withstand soldering temperatures longer without delaminating. Also, the experiment shows that vacuum multilayer lamination does not significantly change thickness, layer-to-layer registration, glass transition temperature, dielectric spacing between conductors, electrical resistance following thermal shock test, and other critical printed wiring board properties.
Magnetoresistive multilayers deposited on the AAO membranes
Malkinski, Leszek M. [Advanced Materials Research Institute, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148 (United States)]. E-mail: lmalkins@uno.edu; Chalastaras, Athanasios [Advanced Materials Research Institute, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148 (United States); Vovk, Andriy [Advanced Materials Research Institute, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148 (United States); Jung, Jin-Seung [Department of Chemistry, Kangnung National University, Kangnung 210702 (Korea, Republic of) ; Kim, Eun-Mee [Department of Chemistry, Kangnung National University, Kangnung 210702 (Korea, Republic of) ; Jun, Jong-Ho [Department of Applied Chemistry, Kunkuk University, Chungju 151747 (Korea, Republic of) ; Ventrice, Carl A. [Advanced Materials Research Institute, University of New Orleans, 2000 Lakeshore Drive, New Orleans, LA 70148 (United States)
2005-02-01
Silicon and GaAs wafers are the most commonly used substrates for deposition of giant magnetoresistive (GMR) multilayers. We explored a new type of a substrate, prepared electrochemically by anodization of aluminum sheets, for deposition of GMR multilayers. The surface of this AAO substrate consists of nanosized hemispheres organized in a regular hexagonal array. The current applied along the substrate surface intersects many magnetic layers in the multilayered structure, which results in enhancement of giant magnetoresistance effect. The GMR effect in uncoupled Co/Cu multilayers was significantly larger than the magnetoresistance of similar structures deposited on Si.
Recurrent dreams: Recurring threat simulations?
Valli, K; Revonsuo, A
2006-06-01
Zadra, Desjardins, and Marcotte (2006) have made a valuable contribution to the empirical testing of the Threat Simulation Theory (TST) (Revonsuo, 2000a) in recurrent dreams. For the most part, their results are in accordance with the theory, while some findings seem to conflict with the predictions of TST. In our commentary, we consider some alternative ways to interpret the results, and we conclude that many prominent features of most recurrent dreams seem to be manifestations of a threat simulation function, leading to repeated rehearsal of threat perception and avoidance, but a minority of recurrent dreams seem to have origins unrelated to threat simulation. PMID:16019227
STDP in recurrent neuronal networks
Matthieu Gilson
2010-09-01
Full Text Available Recent results about spike-timing-dependent plasticity (STDP in recurrently connected neurons are reviewed, with a focus on the relationship between the weight dynamics and the emergence of network structure. In particular, the evolution of synaptic weights in the two cases of incoming connections for a single neuron and recurrent connections are compared and contrasted. A theoretical framework is used that is based upon Poisson neurons with a temporally inhomogeneous firing rate and the asymptotic distribution of weights generated by the learning dynamics. Different network configurations examined in recent studies are discussed and an overview of the current understanding of STDP in recurrently connected neuronal networks is presented.
Giant recurrent retroperitoneal liposarcoma presenting as a recurrent inguinal hernia
Ajay H. Bhandarwar; Bakhshi, Girish D.; Borisa, Ashok D.; Shenoy, Sachin S.; Channabasappa G. Kori; Sameer Vora
2011-01-01
Retroperitoneal liposarcoma presenting as an inguinal hernia is a rare entity. We present the first case of Giant recurrent liposarcoma presenting as a recurrent inguinal hernia in a 40-year-old male. Physical examination showed an irreducible lump in the right inguinal region and a scar in the right lumbar and right inguinal region. Computed tomography (CT) scan of abdomen revealed it to be a retro peritoneal mass extending into the right inguinal region along and involving the cord structur...
Surgery for recurrent biliary carcinoma: results for 27 recurrent cases
Noji, Takehiro; Tsuchikawa, Takahiro; Mizota, Tomoko; Okamura, Keisuke; Nakamura, Toru; Tamoto, Eiji; Shichinohe, Toshiaki; Hirano, Satoshi
2015-01-01
Background Various chemotherapies have been used as best practice to treat recurrent biliary malignancies. Conversely, relatively few surgeries have been described for recurrent extrahepatic biliary carcinoma (RExBC), so whether surgery for RExBC is feasible has remained unclear. This retrospective study was conducted to evaluate the feasibility of surgery for RExBC. Methods From February 2000 to January 2014, a total of 27 patients, comprising 18 patients with extrahepatic cholangiocarcinoma...
Recurrent networks for wave forecasting
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper presents an application of the Artificial Neural Network, namely Backpropagation Recurrent Neural Network (BRNN) with rprop update algorithm for wave forecasting...
Recurrent ameloblastoma of the mandible
Joshi, C P; Vyas, K. C.; Deedwania, Seema; Jain, Sanjeev; Mangal, M. M.
1999-01-01
Ameloblastoma is an agressive locally recurring neoplasm of odentogenic epithelium. Here we describe a case of mandibular ameloblastoma with 12 yrs. history of local recurrences followed with history of excision twice (10 yrs. and 7 years back).
Opioids and breast cancer recurrence
Cronin-Fenton, Deirdre P; Heide-Jørgensen, Uffe; Ahern, Thomas P;
2015-01-01
BACKGROUND: Opioids may alter immune function, thereby potentially affecting cancer recurrence. The authors investigated the association between postdiagnosis opioid use and breast cancer recurrence. METHODS: Patients with incident, early stage breast cancer who were diagnosed during 1996 through...... 2008 in Denmark were identified from the Danish Breast Cancer Cooperative Group Registry. Opioid prescriptions were ascertained from the Danish National Prescription Registry. Follow-up began on the date of primary surgery for breast cancer and continued until breast cancer recurrence, death......, emigration, 10 years, or July 31, 2013, whichever occurred first. Cox regression models were used to compute hazard ratios and 95% confidence intervals associating breast cancer recurrence with opioid prescription use overall and by opioid type and strength, immunosuppressive effect, chronic use (≥6 months...
Fotiadis, Panagiotis; Polignano, Michele; Gimenez, Lucas Chavarria; Viering, Ingo; Sartori, Cinzia; Lobinger, Andreas; Pedersen, Klaus I.
2013-01-01
This paper investigates the potentials of traffic steering in the Radio Resource Control (RRC) Idle state by evaluating the Absolute Priorities (AP) framework in a multilayer Long Term Evolution (LTE) macrocell scenario. Frequency priorities are broadcast on the system information and RRC Idle...... signaling. The priority adjustment is based on both the Composite Available Capacity (CAC) and the radio conditions of the candidate layers. Compared to broadcast AP, the proposed scheme achieves better load balancing performance and improves network capacity, given that the User Equipment (UE) inactivity...
Learning Compact Recurrent Neural Networks
Lu, Zhiyun; Sindhwani, Vikas; Sainath, Tara N.
2016-01-01
Recurrent neural networks (RNNs), including long short-term memory (LSTM) RNNs, have produced state-of-the-art results on a variety of speech recognition tasks. However, these models are often too large in size for deployment on mobile devices with memory and latency constraints. In this work, we study mechanisms for learning compact RNNs and LSTMs via low-rank factorizations and parameter sharing schemes. Our goal is to investigate redundancies in recurrent architectures where compression ca...
Deep Gate Recurrent Neural Network
Gao, Yuan; Glowacka, Dorota
2016-01-01
This paper introduces two recurrent neural network structures called Simple Gated Unit (SGU) and Deep Simple Gated Unit (DSGU), which are general structures for learning long term dependencies. Compared to traditional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM, which require more than one gates to control information flow in the network, SGU and DSGU only...
Multi-Layer E-Textile Circuits
Dunne, Lucy E.; Bibeau, Kaila; Mulligan, Lucie; Frith, Ashton; Simon, Cory
2012-01-01
Stitched e-textile circuits facilitate wearable, flexible, comfortable wearable technology. However, while stitched methods of e-textile circuits are common, multi-layer circuit creation remains a challenge. Here, we present methods of stitched multi-layer circuit creation using accessible tools and techniques.
Superhard nano-multilayers and nanocomposite coatings
BAI Xiaoming; ZHENG Weitao; AN Tao
2005-01-01
This paper reviews the recent development of nano-multilayers and nanocomposite coatings. The hardening mechanisms and design of hard coating are discussed in details. Recent research on Ti/TiN and nitride/nitride multilayer, Ti-Si-N and Ti-Al-Si-N nanocomposite coatings is described, and the perspectives of the related research are proposed.
Multilayer Graphene for Waveguide Terahertz Modulator
Khromova, I.; Andryieuski, Andrei; Lavrinenko, Andrei
2014-01-01
We study terahertz to infrared electromagnetic properties of multilayer graphene/dielectric artificial medium and present a novel concept of terahertz modulation at midinfrared wavelengths. This approach allows the realization of high-speed electrically controllable terahertz modulators based...... on hollow waveguide sections filled with multilayer graphene....
Multilayer Nanoporous Graphene Membranes for Water Desalination.
Cohen-Tanugi, David; Lin, Li-Chiang; Grossman, Jeffrey C
2016-02-10
While single-layer nanoporous graphene (NPG) has shown promise as a reverse osmosis (RO) desalination membrane, multilayer graphene membranes can be synthesized more economically than the single-layer material. In this work, we build upon the knowledge gained to date toward single-layer graphene to explore how multilayer NPG might serve as a RO membrane in water desalination using classical molecular dynamic simulations. We show that, while multilayer NPG exhibits similarly promising desalination properties to single-layer membranes, their separation performance can be designed by manipulating various configurational variables in the multilayer case. This work establishes an atomic-level understanding of the effects of additional NPG layers, layer separation, and pore alignment on desalination performance, providing useful guidelines for the design of multilayer NPG membranes. PMID:26806020
Figure correction of multilayer coated optics
Chapman; Henry N. , Taylor; John S.
2010-02-16
A process is provided for producing near-perfect optical surfaces, for EUV and soft-x-ray optics. The method involves polishing or otherwise figuring the multilayer coating that has been deposited on an optical substrate, in order to correct for errors in the figure of the substrate and coating. A method such as ion-beam milling is used to remove material from the multilayer coating by an amount that varies in a specified way across the substrate. The phase of the EUV light that is reflected from the multilayer will be affected by the amount of multilayer material removed, but this effect will be reduced by a factor of 1-n as compared with height variations of the substrate, where n is the average refractive index of the multilayer.
Cold neutron interferometry using multilayer mirrors
Cold neutron interferometry using multilayer mirrors are discussed. The interferometry includes two kinds of multilayer interferometers, which are analogous optically to the Mach-Zehnder and the Jamin interferometer in classical optics. The Mach-Zehnder multilayer interferometer has been installed at JRR3M reactor of JAERI. We describe the conditions required for the Mach-Zehnder multilayer interferometer and the characteristics of the interferometer. The Jamin multilayer interferometer, called as phase echo interferometer, shows a phase echo effect analogous functionally to the spin echo principle. We describe briefly the first successful performance tests. We propose a precession phase echo interferometer and a phase echo spin interferometer, based on the coherent superposition of spin eigenstates and the phase echo effect. (author)
Antigen expression on recurrent meningioma cells
Meningiomas are intracranial brain tumours that frequently recur. Recurrence rates up to 20% in 20 years for benign meningiomas, up to 80% for atypical meningiomas and up to 100% for malignant meningiomas, have been reported. The most important prognostic factors for meningioma recurrence are meningioma grade, meningioma invasiveness and radicality of neurosurgical resection. The aim of our study was to evaluate the differences in antigenic expression on the surface of meningioma cells between recurrent and non-recurrent meningiomas. 19 recurrent meningiomas and 35 non-recurrent meningiomas were compared regarding the expression of MIB-1 antigen, progesterone receptors, cathepsin B and cathepsin L, using immunohistochemistry. MIB-1 antigen expression was higher in the recurrent meningioma group (p=0.001). No difference in progesterone receptor status between recurrent and non-recurrent meningiomas was confirmed. Immunohistochemical intensity scores for cathepsin B (p= 0.007) and cathepsin L (p<0.001) were both higher in the recurrent than in the non-recurrent meningioma group. MIB-1 antigen expression is higher in recurrent compared to non-recurrent meningiomas. There is no difference in expression of progesterone receptors between recurrent and non-recurrent meningiomas. Cathepsins B and L are expressed more in recurrent meningiomas
Recurrence of anxiety disorders and its predictors
Scholten, Willemijn D.; Batelaan, Neeltje M.; van Balkom, Anton J. L. M.; Penninx, Brenda; Smit, Johannes H.; van Oppen, Patricia
2013-01-01
Background: The chronic course of anxiety disorders and its high burden of disease are partly due to the recurrence of anxiety disorders after remission. However, knowledge about recurrence rates and predictors of recurrence is scarce. This article reports on recurrence rates of anxiety disorders an
The reported positions of 964 suspected nova eruptions in M31 recorded through the end of calendar year 2013 have been compared in order to identify recurrent nova (RN) candidates. To pass the initial screen and qualify as a RN candidate, two or more eruptions were required to be coincident within 0.′1, although this criterion was relaxed to 0.′15 for novae discovered on early photographic patrols. A total of 118 eruptions from 51 potential RN systems satisfied the screening criterion. To determine what fraction of these novae are indeed recurrent, the original plates and published images of the relevant eruptions have been carefully compared. This procedure has resulted in the elimination of 27 of the 51 progenitor candidates (61 eruptions) from further consideration as RNe, with another 8 systems (17 eruptions) deemed unlikely to be recurrent. Of the remaining 16 systems, 12 candidates (32 eruptions) were judged to be RNe, with an additional 4 systems (8 eruptions) being possibly recurrent. It is estimated that ∼4% of the nova eruptions seen in M31 over the past century are associated with RNe. A Monte Carlo analysis shows that the discovery efficiency for RNe may be as low as 10% that for novae in general, suggesting that as many as one in three nova eruptions observed in M31 arise from progenitor systems having recurrence times ≲100 yr. For plausible system parameters, it appears unlikely that RNe can provide a significant channel for the production of Type Ia supernovae
Solder fused interconnections in multilayer circuits
Voida, G.
1977-02-01
A new solder fusion process has been developed for production of multi-layer cables and multilayer printed wiring boards. The multilayer process consists of three steps: (1) the photo-etching fabrication of the basic flexcircuit, (2) the lamination bonding of several flexcircuit layers together, and (3) solder fusion interjoining of the exposed lands to provide electrical continuity. Solder fusion is the unique feature of the process. In the solder fusion process the multilayer assembly is never in contact with highly reactive chemicals which, if entrapped, can lead to corrosion and dielectric breakdown of the assembly. Accurate layer to layer registration can be accomplished with the solder fusion process. A multilayer assembly produced by solder fusion can be shaped into three-dimensional configurations. The repeatable electrical continuity of solder fused interconnections in multilayer assemblies has been confirmed by microhm resistance testing. The solder fused multilayer assembly can be used very advantageously in highly sophisticated instruments and apparatus where portability, weight, bulk, environmental stability, and high reliability are critical requirements.
Thermal stability of nanoscale metallic multilayers
Ramos, A.S., E-mail: sofia.ramos@dem.uc.pt [CEMUC, Departamento de Engenharia Mecânica, Universidade de Coimbra, 3030-788 Coimbra (Portugal); Cavaleiro, A.J.; Vieira, M.T. [CEMUC, Departamento de Engenharia Mecânica, Universidade de Coimbra, 3030-788 Coimbra (Portugal); Morgiel, J. [Institute of Metallurgy and Materials Science, Polish Academy of Sciences, Reymonta 25, 30-059 Cracow (Poland); Safran, G. [Research Institute for Technical Physics and Materials Science, Hungarian Academy of Sciences, H-1121 Budapest (Hungary)
2014-11-28
Metallic nanolayered thin films/foils, in particular Ni/Al multilayers, have been used to promote joining. The objective of this work is to evaluate the thermal stability of nanoscale metallic multilayers with potential for joining applications. Multilayers thin films with low (Ti/Al and Ni/Ti), medium (Ni/Al) and high (Pd/Al) enthalpies of exothermic reaction were prepared by dual cathode magnetron sputtering. Their thermal stability was studied by: i) differential scanning calorimetry combined with X-ray diffraction (XRD), ii) in-situ XRD using cobalt radiation, and iii) in-situ transmission electron microscopy. It was possible to detect traces of intermetallic or amorphous phases in the as-deposited short period (bilayer thickness) multilayers, except for the Ti/Al films where no reaction products that might be formed during deposition were identified. For short periods (below 20 nm) the equilibrium phases are directly achieved upon annealing, whereas for higher periods intermediate trialuminide phases are present for Ti/Al and Ni/Al multilayers. The formation of B2-NiTi from Ni/Ti multilayers occurs without the formation of intermediate phases. On the contrary, for the Pd–Al system the formation of intermediate phases was never avoided. The viability of nanoscale multilayers as “filler” materials for joining macro or microparts/devices was demonstrated. - Highlights: • Me1 and Me2 (Me—metal) alternated nanolayers deposited by magnetron sputtering • Reactive Me1/Me2 multilayer thin films with nanometric modulation period • By heat treatment the films always evolve to the equilibrium intermetallic phase. • For some Me1–Me2 systems and periods, the formation of intermediate phases occurs. • Me1/Me2 multilayer thin films can be used as filler materials to enhance joining.
The physics of multilayer networks
De Domenico, Manlio; Porter, Mason A; Arenas, Alex
2016-01-01
The study of networks plays a crucial role in investigating the structure, dynamics, and function of a wide variety of complex systems in myriad disciplines. Despite the success of traditional network analysis, standard networks provide a limited representation of these systems, which often includes different types of relationships (i.e., "multiplexity") among their constituent components and/or multiple interacting subsystems. Such structural complexity has a significant effect on both dynamics and function. Throwing away or aggregating available structural information can generate misleading results and provide a major obstacle towards attempts to understand the system under analysis. The recent "multilayer' approach for modeling networked systems explicitly allows the incorporation of multiplexity and other features of realistic networked systems. On one hand, it allows one to couple different structural relationships by encoding them in a convenient mathematical object. On the other hand, it also allows o...
Centrality in Interconnected Multilayer Networks
De Domenico, Manlio; Omodei, Elisa; Gómez, Sergio; Arenas, Alex
2013-01-01
Real-world complex systems exhibit multiple levels of relationships. In many cases, they require to be modeled by interconnected multilayer networks, characterizing interactions on several levels simultaneously. It is of crucial importance in many fields, from economics to biology, from urban planning to social sciences, to identify the most (or the less) influent nodes in a network. However, defining the centrality of actors in an interconnected structure is not trivial. In this paper, we capitalize on the tensorial formalism, recently proposed to characterize and investigate this kind of complex topologies, to show how several centrality measures -- well-known in the case of standard ("monoplex") networks -- can be extended naturally to the realm of interconnected multiplexes. We consider diagnostics widely used in different fields, e.g., computer science, biology, communication and social sciences, to cite only some of them. We show, both theoretically and numerically, that using the weighted monoplex obta...
Planar Multilayer Circuit Quantum Electrodynamics
Minev, Z. K.; Serniak, K.; Pop, I. M.; Leghtas, Z.; Sliwa, K.; Hatridge, M.; Frunzio, L.; Schoelkopf, R. J.; Devoret, M. H.
2016-04-01
Experimental quantum information processing with superconducting circuits is rapidly advancing, driven by innovation in two classes of devices, one involving planar microfabricated (2D) resonators, and the other involving machined three-dimensional (3D) cavities. We demonstrate that circuit quantum electrodynamics can be implemented in a multilayer superconducting structure that combines 2D and 3D advantages. We employ standard microfabrication techniques to pattern each layer, and rely on a vacuum gap between the layers to store the electromagnetic energy. Planar qubits are lithographically defined as an aperture in a conducting boundary of the resonators. We demonstrate the aperture concept by implementing an integrated, two-cavity-mode, one-transmon-qubit system.
Magnetic quantization in multilayer graphenes
Lin, Chiun-Yan; Ou, Yih-Jon; Chiu, Yu-Huang; Lin, Ming-Fa
2015-01-01
Essential properties of multilayer graphenes are diversified by the number of layers and the stacking configurations. For an $N$-layer system, Landau levels are divided into $N$ groups, with each identified by a dominant sublattice associated with the stacking configuration. We focus on the main characteristics of Landau levels, including the degeneracy, wave functions, quantum numbers, onset energies, field-dependent energy spectra, semiconductor-metal transitions, and crossing patterns, which are reflected in the magneto-optical spectroscopy, scanning tunneling spectroscopy, and quantum transport experiments. The Landau levels in AA-stacked graphene are responsible for multiple Dirac cones, while in AB-stacked graphene the Dirac properties depend on the number of graphene layers, and in ABC-stacked graphene the low-lying levels are related to surface states. The Landau-level mixing leads to anticrossings patterns in energy spectra, which are seen for intergroup Landau levels in AB-stacked graphene, while in...
Near field imaging from multilayer lens.
Li, Guixin; Li, Jensen; Tam, H L; Chan, C T; Cheah, K W
2011-12-01
Multilayer superlens has been reported that it had advantages over the single metal layer superlens. In this work, single silver layer and Ag-SiO2 multilayer superlens devices working at wavelength of 365 nm were fabricated using standard photolithography method. Grating objects with line/space (190 nm/190 nm) resolution could be resolved through both kinds of lens structures with working distance up to 128 nm. However, Ag-SiO2 multilayer lens shows higher transmittance and image contrast than the single silver layer device, the experimental result proves the theoretical calculation. PMID:22408982
Water distribution in multilayers of weak polyelectrolytes.
Tanchak, Oleh M; Yager, Kevin G; Fritzsche, Helmut; Harroun, Thad; Katsaras, John; Barrett, Christopher J
2006-05-23
The water localization in thin polyelectrolyte multilayers assembled from poly(acrylic acid) and poly(allylamine hydrochloride) was investigated with neutron reflectivity in an atmosphere of controlled humidity and with bulk water. Water was found to be distributed asymmetrically within the multilayer and to localize preferentially at the polymer surface. The diffusion of water into the multilayer did not completely penetrate to the substrate, but instead there appeared to be an exclusion zone near the Si substrate. These results help to explain previous observations of anomalous water transport kinetics in weak polyelectrolyte systems. PMID:16700605
Multilayer composites and manufacture of same
Holesinger, Terry G.; Jia, Quanxi
2006-02-07
The present invention is directed towards a process of depositing multilayer thin films, disk-shaped targets for deposition of multilayer thin films by a pulsed laser or pulsed electron beam deposition process, where the disk-shaped targets include at least two segments with differing compositions, and a multilayer thin film structure having alternating layers of a first composition and a second composition, a pair of the alternating layers defining a bi-layer wherein the thin film structure includes at least 20 bi-layers per micron of thin film such that an individual bi-layer has a thickness of less than about 100 nanometers.
BESSY Bragg-Fresnel multilayer beam monitors
X-ray optical systems based on Bragg-Fresnel multilayer components imaging an electron beam in a storage ring with μm resolution are presented. Design concepts are compared to alternative methods, and the aberrations and limits of Bragg-Fresnel multilayer optics are discussed. Experimental results of imaging the BESSY I source with sub-10-μm resolution are presented, and the development of a compact Bragg-Fresnel multilayer telescope as a BESSY II standard beam monitor is described. copyright 1996 American Institute of Physics
Thermionic cooling in semiconductor multilayers
Full text: A solid-state refrigerator in which electrons transport heat has advantages over the conventional vapour-cycle, compressor-based domestic refrigerator since it has no moving parts, it is low-maintenance, silent, vibration-free and does not require the use of refrigerant gases. The usual approach to making an all-electrical refrigerator is by thermoelectric refrigeration. After a period of intense research in the 1950s and 60s it was realised that the efficiency of thermoelectric devices was less than, and unlikely to exceed, that of conventional compressor units. While thermoelectric cooling has found specialised applications in cases where reliability, compactness and weight are important considerations, it does not appear that thermo-electrics will ever successfully compete in the domestic market, in spite of recent advances in the design and fabrication of thermoelectric materials. A new approach to an all-electric refrigerator is to employ thermionic emission over potential barriers. A key difference between a thermoelectric device and a thermionic device is that in the former the electrons are scattered in their motion and in the latter they are not. Thus thermionic cooling, in principle, can be much more efficient than thermoelectric cooling. A radical new realisation of the thermionic refrigerator was suggested recently in which a multilayer semiconductor structure would be used. We discuss the optimisation of such a multilayer semiconductor cooling system by considering (1) electron-phonon interactions in the barriers and electrodes; (2) the detailed treatment of thermal conductivity; (3) an exact numerical solution of the heat and energy currents (in contrast to the previous approximate analytic solutions); (4) the effect of varying layer thickness across the device; and (5) the effect of varying current density across the device
Radiotherapy for recurrent breast cancer
Clinico-radiobiological characteristics of radiotherapy for relapsed breast cancer were studied. Adequate choice of tissue mass to be exposed appeared much more important than any change in focal dose within 50-80 Gy, to achieve higher frequency of locoregional therapeutic effect. However, recurrent tumors more than 3 large lower radiosensitivity involving a sharp rise in the likelihood of dissemination. Radiotherapy for primary tumor did not affect the radiosensitivity of recurrent malignancies but slowed down the rate of its growth. Also, it might promote the dissemination acceleration
Langmuir Blodgett multilayers and related nanostructures
S S Major; S S Talwar; R S Srinivasa
2006-07-01
Langmuir Blodgett (LB) process is an important route to the development of organized molecular layered structures of a variety of organic molecules with suitably designed architecture and functionality. LB multilayers have also been used as templates and precursors to develop nano-structured thin films. In this article, studies on the molecular packing and three-dimensional structure of prototypic cadmium arachidate (CdA), zinc arachidate (ZnA) and mixed CdA–ZnA LB multilayers are presented. The formation of semiconducting nano-clusters of CdS, ZnS and CdZn1−S alloys within the organic multilayer matrix, using arachidate LB multilayers as precursors is also discussed.
Irradiated multilayer film for primal meat packaging
This patent deals with a heat-shrinkable, multilayer film suitable for use in fabricating bags for packaging primal and sub-primal meat cuts and processed meats. The multilayer film has a first outer layer of an ethylene-vinyl acetate copolymer, a core layer of a barrier film comprising vinylidene chloride-methyl acrylate copolymer, and a second outer layer of an ethylene-vinyl acetate copolymer. The multilayer film is preferably made by co-extrusion of the layers, and then it is biaxially stretched. After biaxial stretching, the multilayer film is irradiated to a dosage level of between 1 megarad and 5 megarads and heat-sealed in the form of a bag. The bag has improved storage stability characteristics
Irradiated multilayer film for primal meat packaging
This patent deals with a heat-shrinkable, multilayer film suitable for use in fabricating bags for packaging primal and sub-primal meat cuts and processed meats. The multilayer film has a first outer layer of a first ethylene-vinyl acetate copolymer, a core layer of a polyvinylidene chloride-vinyl chloride copolymer containing between about 70 weight percent and about 90 weight percent vinylidene chloride as a barrier film, and a second outer layer of a second ethylene-vinyl acetate copolymer. The multilayer film is preferably made by co-extrusion of the layers, and then it is biaxially stretched. After biaxial stretching, the entire multilayer film is substantially uniformly irradiated to a dosage level of between about 2 megarads and about 3 megarads and heat-sealed in the form of a bag. The film is not significantly discoloured by the irradiation and the bag has improved toughness properties and heat-sealing characteristics
Soft X-ray multilayers and filters
Wang Zhan Shan; Tang Wei Xing; Qin Shuji; Zhou Bing; Chen Ling Ya
2002-01-01
The periodic and non-periodic multilayers were designed by using a random number to change each layer and a suitable merit function. Ion beam sputtering and magnetron sputtering were used to fabricate various multilayers and beam splitters in soft X-ray range. The characterization of multilayers by small angle X-ray diffraction, Auger electron spectroscopy, Rutherford back scattering spectroscopy and reflectivity illustrated the multilayers had good structures and smooth interlayers. The reflectivity and transmission of a beam splitter is about 5%. The fabrication and transmission properties of Ag, Zr were studied. The Rutherford back scattering spectroscopy and auger electron spectroscopy were used to investigate the contents and distributions of impurities and influence on qualities of filters. The attenuation coefficients were corrected by the data obtained by measurements
Multilayer Kohonen network and its separability analysis
Liu, Chao-yuan; Li, Jie-Gu
1995-04-01
This paper presents a model of a multilayer Kohonen network. Because of obeying the winner- take-all learning rule and projecting high dimensional patterns into one or two dimensional space, the conventional Kohonen network has many limitations in its applications, such as pattern separability limitation and open ended limitation. Taking advantage of the innovation for learning method and its multilayer structure, the multilayer Kohonen network has the performance of nonlinear pattern partition. Owing to labeling pattern clusters with appropriate category names or numbers only, the network is an open ended system, so it is far more powerful than the conventional Kohonen network. The mechanism of the multilayer Kohonen network is explained in detail, and its nonlinear pattern separability is analyzed theoretically. As a result of an experiment made by two layer Kohonen network, a set of human head contour figures assigned into diverse by categories is shown.
Recurrence quantification analysis of chimera states
Santos, M.S. [Pós-Graduação em Ciências/Física, Universidade Estadual de Ponta Grossa, 84030-900, Ponta Grossa, PR (Brazil); Szezech, J.D., E-mail: jdanilo@gmail.com [Departamento de Matemática e Estatística, Universidade Estadual de Ponta Grossa, 84030-900, Ponta Grossa, PR (Brazil); Batista, A.M., E-mail: antoniomarcosbatista@gmail.com [Departamento de Matemática e Estatística, Universidade Estadual de Ponta Grossa, 84030-900, Ponta Grossa, PR (Brazil); Caldas, I.L. [Instituto de Física, Universidade de São Paulo, 05315-970, São Paulo, SP (Brazil); Viana, R.L.; Lopes, S.R. [Departamento de Física, Universidade Federal do Paraná, 81531-990, Curitiba, PR (Brazil)
2015-10-02
Chimera states, characterised by coexistence of coherence and incoherence in coupled dynamical systems, have been found in various physical systems, such as mechanical oscillator networks and Josephson-junction arrays. We used recurrence plots to provide graphical representations of recurrent patterns and identify chimera states. Moreover, we show that recurrence plots can be used as a diagnostic of chimera states and also to identify the chimera collapse. - Highlights: • Chimera states have been found in various physical systems. • Recurrence plots is a graphical method useful to locate recurring patterns. • We used recurrence plots to identify the chimera states. • We show also the recurrence plots can identify the chimera collapse.
Recurrence quantification analysis of chimera states
Chimera states, characterised by coexistence of coherence and incoherence in coupled dynamical systems, have been found in various physical systems, such as mechanical oscillator networks and Josephson-junction arrays. We used recurrence plots to provide graphical representations of recurrent patterns and identify chimera states. Moreover, we show that recurrence plots can be used as a diagnostic of chimera states and also to identify the chimera collapse. - Highlights: • Chimera states have been found in various physical systems. • Recurrence plots is a graphical method useful to locate recurring patterns. • We used recurrence plots to identify the chimera states. • We show also the recurrence plots can identify the chimera collapse
Ordered organic-organic multilayer growth
Forrest, Stephen R.; Lunt, Richard R.
2016-04-05
An ordered multilayer crystalline organic thin film structure is formed by depositing at least two layers of thin film crystalline organic materials successively wherein the at least two thin film layers are selected to have their surface energies within .+-.50% of each other, and preferably within .+-.15% of each other, whereby every thin film layer within the multilayer crystalline organic thin film structure exhibit a quasi-epitaxial relationship with the adjacent crystalline organic thin film.
Spherical cloaking with homogeneous isotropic multilayered structures.
Qiu, Cheng-Wei; Hu, Li; Xu, Xiaofei; Feng, Yijun
2009-04-01
We propose a practical realization of electromagnetic spherical cloaking by layered structure of homogeneous isotropic materials. By mimicking the classic anisotropic cloak by many alternating thin layers of isotropic dielectrics, the permittivity and permeability in each isotropic layer can be properly determined by effective medium theory in order to achieve invisibility. The model greatly facilitates modeling by Mie theory and realization by multilayer coating of dielectrics. Eigenmode analysis is also presented to provide insights of the discretization in multilayers. PMID:19518392
Proximity induced supercurrent in multilayer graphene
Kanda, Akinobu; Goto, Hidenori; Tanaka, Sho; Nagai, Yukitoshi; Ootuka, Youiti; Odaka, Shunsuke; Miyazaki, Hisao; Tsukagoshi, Kazuhito
2009-03-01
We report experimental study on gate-dependent superconducting proximity effect in multilayer graphene. In our sample, multilayer graphene (MLG), obtained by the micromechanical cleavage of Kish graphite, is placed on a SiO2/p^+-Si substrate, and two superconducting (Ti/Al) electrodes are connected to the top of the MLG. Dependence of the critical supercurrent on MLG length and temperature will be discussed.
Lipid layers on polyelectrolyte multilayer supports
Fischlechner, Martin; Zaulig, Markus; Meyer, Stefan; Estrela-Lopis, Irina; Cuéllar, Luis; Irigoyen, Joseba; Pescador, Paula; Brumen, Milan; Messner, Paul; Moya, Sergio; Donath, Edwin
2008-01-01
The mechanism of formation of supported lipid layers from phosphatidylcholine and phosphatidylserine vesicles in solution on polyelectrolyte multilayers was studied by a variety of experimental techniques. The interaction of zwitterionic and acidic lipid vesicles, as well as their mixtures, with polyelectrolyte supports was followed in real time by micro-gravimetry. The fabricated lipid–polyelectrolyte composite structures on top of multilayer coated colloidal particles were characterized by ...
Onychomycosis: Strategies to Minimize Recurrence.
Gupta, Aditya K; Elewski, Boni E; Rosen, Ted; Caldwell, Bryan; Pariser, David M; Kircik, Leon H; Bhatia, Neal; Tosti, Antonella
2016-03-01
Recurrence (relapse or re-infection) in onychomycosis is common, occurring in 10% to 53% of patients. However, data on prevalence is limited as few clinical studies follow patients beyond 12 months. It has been suggested that recurrence after continuous terbinafine treatment may be less common than with intermittent or continuous itraconazole therapy, probably due to the fungicidal activity of terbinafine, although these differences tended not to be significant. Relapse rates also increase with time, peaking at month 36. Although a number of factors have been suggested to play a role in recurrence, only the co-existence of diabetes has been shown to have a significant impact. Data with topical therapy is sparse; a small study showed amorolfine prophylaxis may delay recurrence. High concentrations of efinaconazole have been reported in the nail two weeks' post-treatment suggesting twice monthly prophylaxis with topical treatments may be a realistic option, and may be an important consideration in diabetic patients with onychomycosis. Data suggest that prophylaxis may need to be continued for up to three years for optimal effect. Treating tinea pedis and any immediate family members is also critical. Other preventative strategies include avoiding communal areas where infection can spread (such as swimming pools), and decontaminating footwear. J Drugs Dermatol. 2016;15(3):279-282. PMID:26954312
Recurrence Formulas for Fibonacci Sums
Brandao, Adilson J V
2008-01-01
In this article we present a new recurrence formula for a finite sum involving the Fibonacci sequence. Furthermore, we state an algorithm to compute the sum of a power series related to Fibonacci series, without the use of term-by-term differentiation theorem
Ovarian irradiation in recurrent endometriosis
We describe a case of a young woman with a history of an aplastic anaemia in which pelvic radiotherapy was used successfully in the management of a recurrent and inoperable endometriosis. The use of therapeutic pelvic or ovarian irradiation in endometriosis may be considered, when surgical and medical treatments have been exhausted and have failed. (authors)
Gadolinium induced recurrent acute pancreatitis.
Blasco-Perrin, H; Glaser, B; Pienkowski, M; Peron, J M; Payen, J L
2013-01-01
Acute pancreatitis is a sudden swelling and inflammation of the pancreas. The two most common causes are alcohol use and biliary stones. Drug-induced acute pancreatitis are rare (1.4-2%). In this present study, we present a case of recurrent acute pancreatitis induced by a specific magnetic-resonance-imaging (MRI) contrast agent called gadobenate dimeglumine. PMID:23395575
Peer Influence and Addiction Recurrence
Paul Markdissi
2009-01-01
In this paper we highlight the role of peers in the recurrence of addictive behavior. To do so, we use a simple “forward looking” model with procrastination and peers influence. Our results show that while procrastination can explain the decision to postpone rehabilitation, peers influence is essential to explain the cyclical patterns of addiction-rehabilitation-addiction.
Interpretation of Recurrent Neural Networks
Pedersen, Morten With; Larsen, Jan
1997-01-01
This paper addresses techniques for interpretation and characterization of trained recurrent nets for time series problems. In particular, we focus on assessment of effective memory and suggest an operational definition of memory. Further we discuss the evaluation of learning curves. Various nume...
Late recurrent uveitis after phacoemulsification.
Saraf Pradeep
2004-01-01
It is now assumed that recurrent late onset uveitis after phacoemulsification with intraocular lens (IOL) is due to indolent infection. Fifteen such cases were observed after uncomplicated phacoemulsification with-in-the-bag IOL implant. These cases were considered noninfective and treated medically with good visual recovery.
Process capability of etched multilayer EUV mask
Takai, Kosuke; Iida nee Sakurai, Noriko; Kamo, Takashi; Morikawa, Yasutaka; Hayashi, Naoya
2015-10-01
With shrinking pattern size at 0.33NA EUV lithography systems, mask 3D effects are expected to become stronger, such as horizontal/vertical shadowing, best focus shifts through pitch and pattern shift through focus. Etched multilayer EUV mask structures have been proposed in order to reduce mask 3D effects. It is estimated that etched multilayer type mask is also effective in reducing mask 3D effects at 0.33NA with lithographic simulation, and it is experimentally demonstrated with NXE3300 EUV Lithography system. We obtained cross-sectional TEM image of etched multilayer EUV mask pattern. It is observed that patterned multilayer width differs from pattern physical width. This means that effective reflecting width of etched multilayer pattern is smaller than pattern width measured by CD-SEM. In this work, we evaluate mask durability against both chemical and physical cleaning process to check the feasibility of etched multilayer EUV mask patterning against mask cleaning for 0.33NA EUV extension. As a result, effective width can be controlled by suitable cleaning chemicals because sidewall film works as a passivation film. And line and space pattern collapse is not detected by DUV mask pattern inspection tool after mask physical cleaning that includes both megasonic and binary spray steps with sufficient particle removal efficiency.
Clostridium difficile recurrences in Stockholm.
Sandell, Staffan; Rashid, Mamun-Ur; Jorup-Rönström, Christina; Ellström, Kristina; Nord, Carl Erik; Weintraub, Andrej
2016-04-01
Sixty-eight hospital-admitted patients with a first episode of Clostridium difficile infection (CDI) were included and followed up during 1 year. Faeces samples were collected at 1, 2, 6 and 12 months after inclusion and analyzed for the presence of C. difficile toxin B, genes for toxin A, toxin B, binary toxin and TcdC deletion by PCR. All strains were also PCR-ribotyped and the MICs of the isolates were determined against eight antimicrobial agents. In 68 patients initially included, antibiotics, clinical signs and co-morbidities were analyzed and 56 were evaluable for recurrences. The mean number of different antibiotics given during 3 months prior to inclusion was 2.6 (range 0-6). Six patients had not received any antibiotics and three of them had diagnosed inflammatory bowel disease. Thirty-two patients (57%) had either a microbiological or clinical recurrence, 16 of whom had clinical recurrences that were confirmed microbiologically (13, 23%) or unconfirmed by culture (3, 5%). Twenty-nine patients were positive in at least one of the follow-up tests, 16 had the same ribotype in follow-up tests, i.e. relapse, and 13 a different ribotype, i.e., reinfection. Most common ribotypes were 078/126, 020, 023, 026, 014/077, 001 and 005. No strain of ribotype 027 was found. Strains ribotype 078/126 and 023 were positive for binary toxin and were the strains most prone to cause recurrence. All strains were sensitive to vancomycin and metronidazole. Patients with recurrences were significantly older (p = 0.02) and all patients had a high burden of comorbidities, which could explain the high fatality rate, 26 (38%) patients died during the 1-year follow-up. PMID:26802875
A second-order learning algorithm for multilayer networks based on block Hessian matrix.
Wang, Yi Jen; Lin, Chin Teng
1998-12-01
This article proposes a new second-order learning algorithm for training the multilayer perceptron (MLP) networks. The proposed algorithm is a revised Newton's method. A forward-backward propagation scheme is first proposed for network computation of the Hessian matrix, H, of the output error function of the MLP. A block Hessian matrix, H(b), is then defined to approximate and simplify H. Several lemmas and theorems are proved to uncover the important properties of H and H(b), and verify the good approximation of H(b) to H; H(b) preserves the major properties of H. The theoretic analysis leads to the development of an efficient way for computing the inverse of H(b) recursively. In the proposed second-order learning algorithm, the least squares estimation technique is adopted to further lessen the local minimum problems. The proposed algorithm overcomes not only the drawbacks of the standard backpropagation algorithm (i.e. slow asymptotic convergence rate, bad controllability of convergence accuracy, local minimum problems, and high sensitivity to learning constant), but also the shortcomings of normal Newton's method used on the MLP, such as the lack of network implementation of H, ill representability of the diagonal terms of H, the heavy computation load of the inverse of H, and the requirement of a good initial estimate of the solution (weights). Several example problems are used to demonstrate the efficiency of the proposed learning algorithm. Extensive performance (convergence rate and accuracy) comparisons of the proposed algorithm with other learning schemes (including the standard backpropagation algorithm) are also made. PMID:12662732
Giant recurrent retroperitoneal liposarcoma presenting as a recurrent inguinal hernia
Ajay H. Bhandarwar
2011-11-01
Full Text Available Retroperitoneal liposarcoma presenting as an inguinal hernia is a rare entity. We present the first case of Giant recurrent liposarcoma presenting as a recurrent inguinal hernia in a 40-year-old male. Physical examination showed an irreducible lump in the right inguinal region and a scar in the right lumbar and right inguinal region. Computed tomography (CT scan of abdomen revealed it to be a retro peritoneal mass extending into the right inguinal region along and involving the cord structures. Wide local excision of the tumour with right orchidectomy and inguinal hernioplasty was performed. Histo-pathology confirmed it to be a liposarcoma. Patient received postoperative radio therapy. Follow up of two years has shown him to be disease free. Retroperitoneal liposarcoma can grow along cord structures into the inguinal canal and mimic an irreducible indirect inguinal hernia.
Fifth International Symposium on Recurrence Plot
Riley, Michael; Giuliani, Alessandro; Webber, Charles; Jr, Jr; Translational Recurrences : From Mathematical Theory to Real-World Applications
2014-01-01
This book features 13 papers presented at the Fifth International Symposium on Recurrence Plots, held August 2013 in Chicago, IL. It examines recent applications and developments in recurrence plots and recurrence quantifi cation analysis (RQA) with special emphasis on biological and cognitive systems and the analysis of coupled systems using cross-recurrence methods. Readers will discover new applications and insights into a range of systems provided by recurrence plot analysis and new theoretical and mathematical developments in recurrence plots. Recurrence plot based analysis is a powerful tool that operates on real-world complex systems that are nonlinear, non-stationary, noisy, of any statistical distribution, free of any particular model type, and not particularly long. Quantitative analyses promote the detection of system state changes, synchronized dynamical regimes, or classifi cation of system states. Th e book will be of interest to an interdisciplinary audience of recurrence plot users and researc...
Recurrence of hypertensive disorders of pregnancy
van Oostwaard, Miriam F; Langenveld, Josje; Schuit, Ewoud; Papatsonis, Dimitri N M; Brown, Mark A; Byaruhanga, Romano N; Bhattacharya, Sohinee; Campbell, Doris M; Chappell, Lucy C; Chiaffarino, Francesca; Crippa, Isabella; Facchinetti, Fabio; Ferrazzani, Sergio; Ferrazzi, Enrico; Figueiró-Filho, Ernesto A; Gaugler-Senden, Ingrid P M; Haavaldsen, Camilla; Lykke, Jacob A; Mbah, Alfred K; Oliveira, Vanessa M; Poston, Lucilla; Redman, Christopher W G; Salim, Raed; Thilaganathan, Baskaran; Vergani, Patrizia; Zhang, Jun; Steegers, Eric A P; Mol, Ben Willem J; Ganzevoort, Wessel
2015-01-01
OBJECTIVE: We performed an individual participant data (IPD) metaanalysis to calculate the recurrence risk of hypertensive disorders of pregnancy (HDP) and recurrence of individual hypertensive syndromes. STUDY DESIGN: We performed an electronic literature search for cohort studies that reported on...
Interference in multilayer relativistic mirrors
Mirzanejhad, Saeed; Sohbatzadeh, Farshad; Babaei, Javad; Taghipour, Meisam; Mohammadzadeh, Zahra
2015-10-01
In this paper, reflection coefficient of a relativistic ultra-thin electron multilayer is calculated using electromagnetic interference procedures. The relativistic electron layers are assumed to be formed by nonlinear plasma wake waves that constitute the electron density cusps. It is shown that the interference between successive relativistic mirrors is restricted by the condition, τ p ≫ ( 2 γ 0 ) 5 / 2 / ω p 0 , where τp is the laser pulse duration. The results showed that tailoring the pulse amplitude, incident wave frequency value, incidence angle, and plasma density leads to increasing reflection coefficient a few orders of magnitudes. This constructive interference condition can be used for increasing conversion efficiency in the reflected energy from relativistic mirrors for the purpose of generating ultra-short coherence pulses in the extreme ultraviolet and x-ray regions. We also performed reflection from relativistic thin electron layers using relativistic 1D3V electromagnetic particle-in-cell (PIC) simulation. It was found that the results of PIC simulation are in agreement with analytical considerations.
Multilayer heterostructures and their manufacture
Hammond, Scott R; Reese, Matthew; Rupert, Benjamin; Miedaner, Alexander; Curtis, Clavin; Olson, Dana; Ginley, David S
2015-11-04
A method of synthesizing multilayer heterostructures including an inorganic oxide layer residing on a solid substrate is described. Exemplary embodiments include producing an inorganic oxide layer on a solid substrate by a liquid coating process under relatively mild conditions. The relatively mild conditions include temperatures below 225.degree. C. and pressures above 9.4 mb. In an exemplary embodiment, a solution of diethyl aluminum ethoxide in anhydrous diglyme is applied to a flexible solid substrate by slot-die coating at ambient atmospheric pressure, and the diglyme removed by evaporation. An AlO.sub.x layer is formed by subjecting material remaining on the solid substrate to a relatively mild oven temperature of approximately 150.degree. C. The resulting AlO.sub.x layer exhibits relatively high light transmittance and relatively low vapor transmission rates for water. An exemplary embodiment of a flexible solid substrate is polyethylene napthalate (PEN). The PEN is not substantially adversely affected by exposure to 150.degree. C
Multilayer Piezoelectric Stack Actuator Characterization
Sherrit, Stewart; Jones, Christopher M.; Aldrich, Jack B.; Blodget, Chad; Bao, Xioaqi; Badescu, Mircea; Bar-Cohen, Yoseph
2008-01-01
Future NASA missions are increasingly seeking to use actuators for precision positioning to accuracies of the order of fractions of a nanometer. For this purpose, multilayer piezoelectric stacks are being considered as actuators for driving these precision mechanisms. In this study, sets of commercial PZT stacks were tested in various AC and DC conditions at both nominal and extreme temperatures and voltages. AC signal testing included impedance, capacitance and dielectric loss factor of each actuator as a function of the small-signal driving sinusoidal frequency, and the ambient temperature. DC signal testing includes leakage current and displacement as a function of the applied DC voltage. The applied DC voltage was increased to over eight times the manufacturers' specifications to investigate the correlation between leakage current and breakdown voltage. Resonance characterization as a function of temperature was done over a temperature range of -180C to +200C which generally exceeded the manufacturers' specifications. In order to study the lifetime performance of these stacks, five actuators from one manufacturer were driven by a 60volt, 2 kHz sine-wave for ten billion cycles. The tests were performed using a Lab-View controlled automated data acquisition system that monitored the waveform of the stack electrical current and voltage. The measurements included the displacement, impedance, capacitance and leakage current and the analysis of the experimental results will be presented.
ON-RECURRENT LORENTZIAN -KENMOTSU MANIFOLDS
SREENIVASA, G.T.; VENKATESHA, VENKATESHA; Bagewadi, C. S.; NAGANAGOUD, K.
2009-01-01
Abstract: In this paper, we study Lorentzian -Kenmotsu manifold and we shown that -recurrent Lorentzian -Kenmotsu manifold is an Einstein manifold and a pseudo-projective -recurrent Lorentzian -Kenmotsu manifold is an - Einstein manifold. And also we get the expression for 1-form A in a -recurrent Lorentzian -Kenmotsu manifold. Key words: -Kenmotsu manifold, locally pseudo-projective -symmetric manifold, -recurrent Lorentzian -Kenmotsu manifold, Einstein manifold, -Einstein manifo...
Psychological factors in recurrent genital herpes.
Green, J; Kocsis, A
1997-01-01
OBJECTIVES: To review recent research into psychological aspects of genital herpes and assess possible implications for clinical practice. METHODS: Review of all papers in the field on Medline 1985-96. RESULTS: Much attention has been paid to possible links between stress and recurrent genital herpes. There is no convincing evidence that stress in itself causes recurrences. It may be that recurrences are preceded by a prodromal period of altered mood. Patients with recurrences show considerab...
Tibell, Rasmus
2014-01-01
The need for a robust model for predicting the value of condominiums and houses are becoming more apparent as further evidence of systematic errors in existing models are presented. Traditional valuation methods fail to produce good predictions of condominium sales prices and systematic patterns in the errors linked to for example the repeat sales methodology and the hedonic pricing model have been pointed out by papers referenced in this thesis. This inability can lead to monetary problems f...
Mandal, S.
significant wave height estimation from wind speed by perceptron Kalman filtering? by A Altunkaynak and M Ozger, Ocean Engineering, 2004, 31, 1245-1255 Discussion by S Mandal* Ocean Engineering Division, National Institute of Oceanography, Dona Paula... of neural network in the study of wave transformation. REFERENCES Deo, M.C. and Naidu, C.S., 1999. Real time wave forecasting using neural networks. Ocean Engineering, 26, 191-203. Mandal, S and Prabaharan, N, 2003. An overview of the numerical...
Segmentalliverincarcerationthrougha recurrent incisional lumbar hernia
Nikolaos S. Salemis; Konstantinos Nisotakis; Stavros Gourgiotis; Efstathios Tsohataridis
2007-01-01
BACKGROUND: Lumbar hernia is a rare congenital or acquired defect of the posterior abdominal wall. The acquired type is more common and occurs mainly as an incisional defect after lfank surgery. Incarceration or strangulation of hernia contents is uncommon. METHOD: Segmental liver incarceration through a recurrent incisional lumbar defect was diagnosed in a 58 years old woman by magnetic resonance imaging. RESULTS: The patient underwent an open repair of the com-plicated hernia. An expanded polytetralfouoroethylene (e-PTFE) mesh was fashioned as a sublay prosthesis. She had an uncomplicated postoperative course. Follow-up examinations revealed no evidence of recurrence. CONCLUSIONS: Although lumbar hernia rarely results in incarceration or strangulation, early repair is necessary because of the risks of complications and the increasing dififculty in repairment as it enlarges. Surgical repair is often dififcult and challenging.
Isoniazid-Induced Recurrent Pancreatitis
Sarah Mattioni
2012-05-01
Full Text Available Context Drug induced pancreatitis are rare but potentially serious. Thus, drug withdrawal is warranted. Case report A 79-year-old woman who was treated with antituberculosis therapy for 5 weeks was admitted to our unit for pancreatitis. Usual etiologies of pancreatitis were eliminated. Because of vomiting, antituberculosis therapy was withdrawn and symptoms disappeared. Eight days later, the same treatment was reintroduced and the patient presented recurrent pancreatitis; thus, treatment was withheld again followed by disappearance of clinical and biological abnormalities. Two days later, a treatment without isoniazid was reintroduced and no recurrence of symptoms was observed. Conclusions We have experienced a case of isoniazid induced pancreatitis. This is a rare cause of pancreatitis but potentially fatal thus recognition of drug induced pancreatitis and definitive withdrawal of the drug is required.
Management of Recurrent Endometrial Carcinoma.
Ming-Shian Kao
2004-01-01
Management of recurrent endometrial carcinoma has traditionally focused on providingtargeted adjuvant therapy in select groups of patients based on their risk factors. Majorprogress has been made over the last two decades in identifying these clinical-pathologicalrisk factors, which has led to the classification of patients into different risk groups. Patientswith high-risk factors are generally treated with adjunctive radiation therapy immediatelyfollowing surgery to minimize the incidence o...
Recurrent Glioblastoma: Where we stand
Sanjoy Roy; Debarshi Lahiri; Tapas Maji; Jaydip Biswas
2015-01-01
Current first-line treatment regimens combine surgical resection and chemoradiation for Glioblastoma that provides a slight increase in overall survival. Age on its own should not be used as an exclusion criterion of glioblastoma multiforme (GBM) treatment, but performance should be factored heavily into the decision-making process for treatment planning. Despite aggressive initial treatment, most patients develop recurrent diseases which can be treated with re-resection, systemic treatment w...
Pinealitis accompanying equine recurrent uveitis.
Kalsow, C M; Dwyer, A E; Smith, A. W.; Nifong, T P
1993-01-01
There is no direct verification of pineal gland involvement in human uveitis. Specimens of pineal tissue are not available during active uveitis in human patients. Naturally occurring uveitis in horses gives us an opportunity to examine tissues during active ocular inflammation. We examined the pineal gland of a horse that was killed because it had become blind during an episode of uveitis. The clinical history and histopathology of the eyes were consistent with post-leptospiral equine recurr...
RECURRENT HAEMETEMESIS: THE MYSTERY UNFOLDS
Nayantara Rao
2014-11-01
Full Text Available This report presents the case history of a six year old child who was portrayed as having recurrent haemetemesis since two years by her mother. A detailed evaluation showed that the patient’s history was inconsistent with the clinical findings and investigations, leading to a diagnosis of Factitious Disorder by Proxy (FDbp. The report highlights the rationale for under-diagnosis of FDbp in India and challenges the conventional approach ( Parentectomy for treating FDbp.
Immunopathogenesis of recurrent vulvovaginal candidiasis.
Fidel, P. L.; Sobel, J D
1996-01-01
Recurrent vulvovaginal candidiasis (RVVC) is a prevalent opportunistic mucosal infection, caused predominantly by Candida albicans, which affects a significant number of otherwise healthy women of childbearing age. Since there are no known exogenous predisposing factors to explain the incidence of symptomatic vaginitis in most women with idiopathic RVVC, it has been postulated that these particular women suffer from an immunological abnormality that prediposes them to RVVC. Because of the inc...
Management of Recurrent Ventricular Pseudoaneurysm
Bluett, Michael; Bolling, Steven F.; Kirsh, Marvin M.
1991-01-01
A 49-year-old man suffered multiple recurrences of pseudoaneurysm following ventricular aneurysmectomy in which Teflon felt strips had been used to reinforce the closure. The pseudoaneurysm was secondary to infection of the cardiac suture line, caused by a pathogen resident in the multifilamented Teflon strips. The patient was treated successfully by removal of all residual foreign material and reinforcement of the suture line with an omental pedicle graft. (Texas Heart Institute Journal 1991...
Predicting the Risk of Venous Thromboembolism Recurrence
Heit, John A.
2012-01-01
Venous thromboembolism (VTE) is a chronic disease with a 30% ten-year recurrence rate. The highest incidence of recurrence is in the first 6 months. Active cancer significantly increases the hazard of early recurrence, and the proportions of time on standard heparin (APTT≥0.2 anti-Xa U/mL) and warfarin (INR≥2.0) treatment, significantly reduce the hazard. The acute treatment duration does not affect recurrence risk after treatment is stopped. Independent predictors of late recurrence include ...
Elastic properties of suspended multilayer WSe2
Zhang, Rui; Koutsos, Vasileios; Cheung, Rebecca
2016-01-01
We report the experimental determination of the elastic properties of suspended multilayer WSe2, a promising two-dimensional (2D) semiconducting material combined with high optical quality. The suspended WSe2 membranes have been fabricated by mechanical exfoliation of bulk WSe2 and transfer of the exfoliated multilayer WSe2 flakes onto SiO2/Si substrates pre-patterned with hole arrays. Then, indentation experiments have been performed on these membranes with an atomic force microscope. The results show that the 2D elastic modulus of the multilayer WSe2 membranes increases linearly while the prestress decreases linearly as the number of layers increases. The interlayer interaction in WSe2 has been observed to be strong enough to prevent the interlayer sliding during the indentation experiments. The Young's modulus of multilayer WSe2 (167.3 ± 6.7 GPa) is statistically independent of the thickness of the membranes, whose value is about two thirds of other most investigated 2D semiconducting transition metal dichalcogenides, namely, MoS2 and WS2. Moreover, the multilayer WSe2 can endure ˜12.4 GPa stress and ˜7.3% strain without fracture or mechanical degradation. The 2D WSe2 can be an attractive semiconducting material for application in flexible optoelectronic devices and nano-electromechanical systems.
Base Metal Co-Fired Multilayer Piezoelectrics
Lisheng Gao
2016-03-01
Full Text Available Piezoelectrics have been widely used in different kinds of applications, from the automobile industry to consumer electronics. The novel multilayer piezoelectrics, which are inspired by multilayer ceramic capacitors, not only minimize the size of the functional parts, but also maximize energy efficiency. Development of multilayer piezoelectric devices is at a significant crossroads on the way to achieving low costs, high efficiency, and excellent reliability. Concerning the costs of manufacturing multilayer piezoelectrics, the trend is to replace the costly noble metal internal electrodes with base metal materials. This paper discusses the materials development of metal co-firing and the progress of integrating current base metal chemistries. There are some significant considerations in metal co-firing multilayer piezoelectrics: retaining stoichiometry with volatile Pb and alkaline elements in ceramics, the selection of appropriate sintering agents to lower the sintering temperature with minimum impact on piezoelectric performance, and designing effective binder formulation for low pO2 burnout to prevent oxidation of Ni and Cu base metal.
Characterization of the protocrystalline silicon multilayer
Kwon, Seong Won; Kwak, Joonghwan; Lim, Koeng Su [Department of Electrical Engineering and Computer Science, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); Myong, Seung Yeop [Institute de Microtechnique (IMT), Rue AL, Breguet 2, CH-2000 Neuchatel (Switzerland)
2006-06-15
The protocrystalline silicon (pc-Si:H) multilayer solar cell is very promising owing to its fast stabilization with low degradation against light irradiation. However, the pc-Si:H multilayers have not extensively been investigated in detail on its material characteristics yet. We present the material characteristics of pc-Si:H multilayers using a transmission electron microscopy (TEM), Fourier transform infra (FTIR) spectroscopy, Raman spectroscopy, constant photocurrent method (CPM) and quantum efficiency (QE) measurement. A TEM micrograph shows that a pc-Si:H multilayer has a repeatedly layered structure consisting of low hydrogen-diluted and highly hydrogen-diluted sublayers. FTIR spectra depict the strong vibration mode at 2090cm{sup -1} which is attributed to hydrogen-rich amorphous silicon (a-Si:H) regions of highly hydrogen-diluted sublayers. Based on these results, excellent light-soaking behavior of the pc-Si:H multilayers is primarily due to the repeatedly layered structure that improves a structural order in the material. (author)
Multi-layered oriented polyfluorene films.
Nagamatsu, Shuichi; Misaki, Masahiro; Yoshida, Yuji; Azumi, Reiko; Tanigaki, Nobutaka; Yase, Kiyoshi
2009-04-30
Multilayered oriented polyfluorene (PF) films were obtained by applying thermal treatment procedure to a multilayered PF film constructed with fluorene derivatives layer formed on top of a highly oriented friction-transferred crystalline poly(9,9-dioctylfluorene) (PF8) film. The orientations in the multilayered PF films were investigated by polarized photoluminescence (PL) spectroscopy and grazing incident X-ray diffraction (GIXD) analysis. The results of the multilayered PF film constructed with spin-coated PF8 on friction-transferred PF8 indicate that the rearrangement of the upper PF8 layer is induced from the orientation of lower PF8 layer by thermal treatment at the nematic phase temperature. Polarized green emission from the multilayered oriented PF film was demonstrated using the blend of PF8 and poly(9,9-dioctylfluorene-co-benzothiadiazol) (F8BT) as green light emitter for upper layer. By this method, the polarized emission color can be tuned using polymer blends for upper layer similar to the liquid-crystalline polymer arrangement without using different materials as an underlying layer such as the rubbed polyimide. PMID:19351119
Refractive index contrast in porous silicon multilayers
Nava, R.; Mora, M.B. de la; Tagueena-Martinez, J. [Centro de Investigacion en Energia, Universidad Nacional Autonoma de Mexico, Temixco, Morelos (Mexico); Rio, J.A. del [Centro de Investigacion en Energia, Universidad Nacional Autonoma de Mexico, Temixco, Morelos (Mexico); Centro Morelense de Innovacion y Transferencia Tecnologica, Consejo de Ciencia y Tecnologia del Estado de Morelos (Mexico)
2009-07-15
Two of the most important properties of a porous silicon multilayer for photonic applications are flat interfaces and a relative large refractive index contrast between layers in the optical wavelength range. In this work, we studied the effect of the current density and HF electrolyte concentration on the refractive index of porous silicon. With the purpose of increasing the refractive index contrast in a multilayer, the refractive index of porous silicon produced at low current was studied in detail. The current density applied to produce the low porosity layers was limited in order to keep the electrolyte flow through the multilayer structure and to avoid deformation of layer interfaces. We found that an electrolyte composed of hydrofluoric acid, ethanol and glycerin in a ratio of 3:7:1 gives a refractive index contrast around 1.3/2.8 at 600 nm. Several multilayer structures with this refractive index contrast were fabricated, such as dielectric Bragg mirrors and microcavities. Reflectance spectra of the structures show the photonic quality of porous silicon multilayers produced under these electrochemical conditions. (copyright 2009 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Elastic properties of suspended multilayer WSe2
We report the experimental determination of the elastic properties of suspended multilayer WSe2, a promising two-dimensional (2D) semiconducting material combined with high optical quality. The suspended WSe2 membranes have been fabricated by mechanical exfoliation of bulk WSe2 and transfer of the exfoliated multilayer WSe2 flakes onto SiO2/Si substrates pre-patterned with hole arrays. Then, indentation experiments have been performed on these membranes with an atomic force microscope. The results show that the 2D elastic modulus of the multilayer WSe2 membranes increases linearly while the prestress decreases linearly as the number of layers increases. The interlayer interaction in WSe2 has been observed to be strong enough to prevent the interlayer sliding during the indentation experiments. The Young's modulus of multilayer WSe2 (167.3 ± 6.7 GPa) is statistically independent of the thickness of the membranes, whose value is about two thirds of other most investigated 2D semiconducting transition metal dichalcogenides, namely, MoS2 and WS2. Moreover, the multilayer WSe2 can endure ∼12.4 GPa stress and ∼7.3% strain without fracture or mechanical degradation. The 2D WSe2 can be an attractive semiconducting material for application in flexible optoelectronic devices and nano-electromechanical systems
2015-10-08
Anxiety Disorder; Depression; Fatigue; Leydig Cell Tumor; Ovarian Sarcoma; Ovarian Stromal Cancer; Pain; Peritoneal Carcinomatosis; Pseudomyxoma Peritonei; Recurrent Breast Cancer; Recurrent Cervical Cancer; Recurrent Endometrial Carcinoma; Recurrent Fallopian Tube Cancer; Recurrent Gestational Trophoblastic Tumor; Recurrent Ovarian Epithelial Cancer; Recurrent Ovarian Germ Cell Tumor; Recurrent Primary Peritoneal Cavity Cancer; Recurrent Uterine Sarcoma; Recurrent Vaginal Cancer; Recurrent Vulvar Cancer
Multilayer cladding with hyperbolic dispersion for plasmonic waveguides
Babicheva, Viktoriia; Shalaginov, Mikhail Y.; Ishii, Satoshi; Boltasseva, Alexandra; Kildishev, Alexander V.
2015-01-01
We study the properties of plasmonic waveguides with a dielectric core and multilayer metal-dielectric claddings that possess hyperbolic dispersion. The waveguides hyperbolic multilayer claddings show better performance in comparison to conventional plasmonic waveguides. © OSA 2015....
Multilayers Assembly of DNA Probe for Biosensor
谢文章; 路英杰; 隋森芳
2002-01-01
Surface plasmon resonance (SPR) was a sensitive method to study molecular interactions. Based on the specific binding, this paper presented the molecular assembly of protein-nucleic acid multilayers on the surface of a gold film. The first layer was a biotin-lipid (B-DMPE/DMPE) containing a monolayer prepared using the Langmuir-Blodgett (LB) technique. The second and third layers were avidin and DNA labeled biotin, respectively. The fourth layer was anti-DNA antibody extracted from the serum of patients with systemic lupus erythematosus (SLE). These interactions provide stability in the multilayer films of the complexes. The multilayer formation process was detected by SPR spectroscopy. The results show that the chip-based sensor system can be used for functional characterization of protein-protein and protein-DNA interactions.
Community Detection Using Multilayer Edge Mixture Model
Zhang, Han; Lai, Jian-Huang; Yu, Philip S
2016-01-01
A wide range of complex systems can be modeled as networks with corresponding constraints on the edges and nodes, which have been extensively studied in recent years. Nowadays, with the progress of information technology, systems that contain the information collected from multiple perspectives have been generated. The conventional models designed for single perspective networks fail to depict the diverse topological properties of such systems, so multilayer network models aiming at describing the structure of these networks emerge. As a major concern in network science, decomposing the networks into communities, which usually refers to closely interconnected node groups, extracts valuable information about the structure and interactions of the network. Unlike the contention of dozens of models and methods in conventional single-layer networks, methods aiming at discovering the communities in the multilayer networks are still limited. In order to help explore the community structure in multilayer networks, we...
Magnetic characterization of U/Co multilayers
With the aim of expanding the studies on 2D systems containing uranium, U/Co multilayers with layer thickness ranging from 50 to 200 A were recently prepared by dc magnetron sputtering onto glass. The multilayers were characterized by Grazing-Incidence X-Ray Diffraction (GIXRD) and Rutherford Backscattering Spectrometry (RBS). Magnetization measurements performed with a squid magnetometer showed that the multilayers have a ferromagnetic behaviour, with the magnetic signal increasing with the thickness of the layers. The analysis of magnetic anisotropy evidenced an easy magnetic direction in the film plane with large anisotropy fields, which increase with the thickness of the layers and suggests a positive contribution of surface anisotropy to the effective anisotropy Keff. (Abstract Copyright [2003], Wiley Periodicals, Inc.)
Evolutionary games on multilayer networks: A colloquium
Wang, Zhen; Szolnoki, Attila; Perc, Matjaz
2015-01-01
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling th...
Mechanical properties of polyelectrolyte multilayer microcapsules
Polyelectrolyte multilayer microcapsules were recently suggested as a new type of nanoengineered microstructures and are potentially important in many areas of science and technology. The present review focuses on the mechanics of these microstructures, emphasizing novel experimental approaches and the main experimental observations. Methods based on confocal and atomic force microscopy-osmotic buckling, osmotic swelling, and compression experiments-are detailed. Also covered is the preparation of multilayer microcapsules and various encapsulation techniques. A discussion of the theoretical models suggested is given. Special emphasis is given to the analysis of experimental data. This covers regimes of deformations, the roles of elasticity and permeability in determining the capsule stiffness, the effects of ageing, molecular weight, pH, salt concentration, and organic solvent on the multilayer shell properties, a contribution from encapsulated (charged and neutral) polymers, and more. (topical review)
Multilayer Integrated Film Bulk Acoustic Resonators
Zhang, Yafei
2013-01-01
Multilayer Integrated Film Bulk Acoustic Resonators mainly introduces the theory, design, fabrication technology and application of a recently developed new type of device, multilayer integrated film bulk acoustic resonators, at the micro and nano scale involving microelectronic devices, integrated circuits, optical devices, sensors and actuators, acoustic resonators, micro-nano manufacturing, multilayer integration, device theory and design principles, etc. These devices can work at very high frequencies by using the newly developed theory, design, and fabrication technology of nano and micro devices. Readers in fields of IC, electronic devices, sensors, materials, and films etc. will benefit from this book by learning the detailed fundamentals and potential applications of these advanced devices. Prof. Yafei Zhang is the director of the Ministry of Education’s Key Laboratory for Thin Films and Microfabrication Technology, PRC; Dr. Da Chen was a PhD student in Prof. Yafei Zhang’s research group.
Vânia Medianeira Flores Costa
2012-04-01
Full Text Available When investors decide to “adventure” through stock markets they search for a method to provide safety on making decision. In fact, there is no precise way to know which stocks will became a profitable investiment. Technical analysis is a discipline that support the investors on making decisions. Such a discipline uses a set of tools and statistical methods to forecast the market’s movement. Such a paper presents the develpment of a robotical Trade System, using a heuristic method. The system has a Neural Network multilayer perceptron, trained with an algorithm for back propagation error. Thus, approaching to the technical analysis without emotional aspects, using the Neural Network forecast on supporting the decisions of a investor on stock market. In analyzing the results of the neural network can be seen that the neural network got a result of 42.6% higher than the diagnostic of the technical analysis.Quando investidores decidem se “aventurar” pelo mercado de renda variável, como pelo mercado de ações, buscam um método de ter mais segurança na tomada de decisão. Na prática, não há como saber quais ativos tornar-se-ão um investimento lucrativo. No mercado acionário, a Análise Técnica procura auxiliar o investidor na tomada de decisão. Para isso, utiliza-se de ferramentas e de métodos estatísticos para tentar predizer os movimentos do mercado. Este artigo apresenta o desenvolvimento de um Trade System robótico, utilizando um método heurístico. O sistema conta com uma rede neural multilayer perceptron, treinada com o algoritmo de retro propagação de erro, aproximando-se da análise técnica sem o fator emoção. Ao avaliar os resultados da rede neural, pode ser visto que a mesma obteve um resultado de 42,6% maior do que o diagnóstico da análise técnica.
Reverse engineering of multi-layer films
Effendi Widjaja
2011-03-01
Full Text Available This contribution introduces the combined application of Raman microscopy and band-target entropy minimization (BTEM in order to successfully reverse-engineer a multi-layer packaging material. Three layers are identified, namely, polyethylene, a paper and talc layer (with two distinct cellulose forms, and a poly-styrene co-polymer composite containing anatase and calcite. This rapid and non-destructive approach provides a unique opportunity for the assessment of multi-layer composites, including the constitution of the additives present.
YBCO based multilayers for optoelectronic devices
YBCO based multilayers have been deposited independently by three techniques: laser ablation, inverted cylindrical target sputtering (ICM) and on-axis planar D.C. Magnetron Sputtering. The last technique is used to cover uniformly R-plane sapphire and LaAlO3 2 inch wafers with YBCO or multilayers to achieve optoelectronic devices such as infrared detectors. Very thin (- 3 nm) YSZ and MgO dielectric films have been studied as tunnel barriers for making such high Tc tunnel junctions. 14 refs., 11 figs
Preparation of multi-layer laser targets
The author describes the preparation of copper- or gold-Formvar-aluminum-Formvar multi-layer laser targets. First, copper or gold is deposited on a piece of glass, then it is covered with the Formvar film. After the copper or gold layer, together with the Formvar film, is stripped off and fitted in, aluminum is deposited. Because the Formvar is vaporized in the vaporizing chamber only once, the obtained multi-layer target is of high quality and meets the requirements for practical use
Exchange interactions in Fe/Y multilayers
The magnetization of Fe/Y multilayers has been measured as a function of temperature. A bulk-like T 3/2 temperature dependence of the magnetization is observed for all multilayers in the temperature range 5-300 K. The spin-wave constant B is found to decrease inversely with t Fe. A simple theoretical model with exchange interactions only, and with non-interacting magnons, has been used to explain the temperature dependence of the magnetization and the approximate values for the bulk exchange interaction J b, surface exchange interaction J s and the interlayer exchange interaction J I for various Fe layer thicknesses have been obtained
Variational Recurrent Auto-Encoders
Fabius, Otto; van Amersfoort, Joost R.
2014-01-01
In this paper we propose a model that combines the strengths of RNNs and SGVB: the Variational Recurrent Auto-Encoder (VRAE). Such a model can be used for efficient, large scale unsupervised learning on time series data, mapping the time series data to a latent vector representation. The model is generative, such that data can be generated from samples of the latent space. An important contribution of this work is that the model can make use of unlabeled data in order to facilitate supervised...
Recurrent abdominal pain in children.
Buch, Niyaz A; Ahmad, Sheikh Mushtaq; Ahmed, S Zubair; Ali, Syed Wazid; Charoo, B A; Hassan, Masood Ul
2002-09-01
Eighty five children with recurrent abdominal pain(RAP) were studied. Organic cause was noticed in 70 cases and non-organic in 15 cases. Giardiasis was the commonest organic cause in 57 (67.0 percent), either alone or with other parasitic infestations. Other organic causes include gallstones (4.7 percent), urinary infections (4.7 percent), esophagitis/gastritis (3.5 percent) and abdominal tuberculosis (2.3 percent). Single parent, school phobia, sibling rivalry, RAP in other family members and nocturnal enuresis are significant factors associated with nonorganic causes PMID:12368527
Uezu, Tatsuya; Kiyokawa, Shuji
2016-06-01
We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α {C})bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.
Guided wave sensing of polyelectrolyte multilayers
Horvath, R.; Pedersen, H.C.; Cuisinier, F.J.G.
2006-01-01
A planar optical waveguide configuration is proposed to monitor the buildup of thick polyelectrolyte multilayers on the surface of the waveguide in aqueous solutions. Instead of detecting the layer by the electromagnetic evanescent field the polyelectrolyte layer acts as an additional waveguiding...
Josephson plasma resonance in superconducting multilayers
Pedersen, Niels Falsig
1999-01-01
We derive an analytical solution for the josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low T-c systems with magnetic coupling between the superconducting layers, but many features of our results are more general, and thus an application to...... the recently derived plasma resonance phenomena for high T-c superconductors of the BSCCO type is discussed....
Single-order lamellar multilayer gratings
Meer, van der R.
2013-01-01
A major challenge in the soft x-ray (SXR) and eXtreme UltraViolet (XUV) spectral ranges is the ability to manipulate the incident radiation using optical elements. By patterning conventional multilayer mirrors with nanoscale structures, novel optical elements with a variety of optical properties can
Josephson plasma resonance in superconducting multilayers
Pedersen, Niels Falsig
We derive an analytical solution for the josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low T-c systems with magnetic coupling between the superconducting layers, but many features of our results are more general, and thus an application to...
Interface stress in Au/Ni multilayers
Schweitz, K.O.; Böttiger, J.; Chevallier, J.; Feidenhans'l, Robert Krarup; Nielsen, Martin Meedom; Rasmussen, Frank Berg
2000-01-01
The effect of intermixing on the apparent interface stress is studied in < 111 >-textured dc-magnetron sputtered Au/Ni multilayers by use of two methods commonly used for determining interface stress. The method using profilometry and in-plane x-ray diffraction does not take intermixing into...
Surface superconductivity in multilayered rhombohedral graphene: Supercurrent
Kopnin, N. B.
2011-09-01
The supercurrent for the surface superconductivity of a flat-band multilayered rhombohedral graphene is calculated. Despite the absence of dispersion of the excitation spectrum, the supercurrent is finite. The critical current is proportional to the zero-temperature superconducting gap, i.e., to the superconducting critical temperature and to the size of the flat band in the momentum space.
Surface superconductivity in multilayered rhombohedral graphene: Supercurrent
Kopnin, N. B.
2011-01-01
The supercurrent for the surface superconductivity of a flat-band multilayered rhombohedral graphene is calculated. Despite the absence of dispersion of the excitation spectrum, the supercurrent is finite. The critical current is proportional to the zero-temperature superconducting gap, i.e., to the superconducting critical temperature and to the size of the flat band in the momentum space.
Transmission fingerprints in quasiperiodic magnonic multilayers
Coelho, I.P. [Departamento de Ensino Superior, Instituto Federal de Educacao, Ciencia e Tecnologia do Maranhao, Imperatriz-MA 65919-050 (Brazil); Departamento de Fisica, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil); Vasconcelos, M.S. [Escola de Ciencias e Tecnologia, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil); Bezerra, C.G., E-mail: cbezerra@dfte.ufrn.br [Departamento de Fisica, Universidade Federal do Rio Grande do Norte, Natal-RN 59072-970 (Brazil)
2011-12-15
In this paper we investigated the influence of mirror symmetry on the transmission spectra of quasiperiodic magnonic multilayers arranged according to Fibonacci, Thue-Morse and double period quasiperiodic sequences. We consider that the multilayers composed of two simple cubic Heisenberg ferromagnets with bulk exchange constants J{sub A} and J{sub B} and spin quantum numbers S{sub A} and S{sub B}, respectively. The multilayer structure is surrounded by two semi-infinite slabs of a third Heisenberg ferromagnetic material with exchange constant J{sub C} and spin quantum number S{sub C}. For simplicity, the lattice constant has the same value a in each material, corresponding to epitaxial growth at the interfaces. The transfer matrix treatment was used for the exchange-dominated regime, taking into account the random phase approximation (RPA). Our numerical results illustrate the effects of mirror symmetry on (i) transmission spectra and (ii) transmission fingerprints. - Highlights: > We model quasiperiodic magnetic multilayers presenting mirror symmetry. > We investigated the allowed and forbidden bands of magnonic transmission. > Transmission return maps show the influence of mirror symmetry. > Mirror symmetry has no effect on the Fibonacci case. > Mirror symmetry does have effect on the Thue-Morse and double period cases.
Thermal Transport in Graphene and Graphene Multilayers
Alexander A. Balandin; Nika, Denis L.
2015-01-01
In this paper we review thermal properties of graphene and multilayer graphene and discuss the optothermal technique developed for the thermal conductivity measurements. We also outline different theoretical approaches used for the description of phonon transport in graphene and provide comparison with available experimental thermal conductivity data.
Is acute recurrent pancreatitis a chronic disease?
Mariani, Alberto; Testoni, Pier Alberto
2008-01-01
Whether acute recurrent pancreatitis is a chronic disease is still debated and a consensus is not still reached as demonstrated by differences in the classification of acute recurrent pancreatitis. There is major evidence for considering alcoholic pancreatitis as a chronic disease ab initio while chronic pancreatitis lesions detectable in biliary acute recurrent pancreatitis (ARP) seem a casual association. Cystic fibrosis transmembrane conductance regulator (CFTR) gene mutation, hereditary a...
Recurrent renal hyperparathyroidism due to parathyromatosis
Vulpio, Carlo; D’Errico, Giovanni; Mattoli, Maria Vittoria; Bossola, Maurizio; Lodoli, Claudio; Fadda, Guido; Bruno, Isabella; Giordano, Alessandro; Castagneto, Marco
2011-01-01
Parathyromatosis is the most severe type of recurrent secondary hyperparathyroidism (SHPT) after parathyroidectomy (PTX) in haemodialysis patients. It is difficult to completely remove all foci of parathyroid tissue and neck re-explorations are often required. Here, we report for the first time a case of recurrent SHPT due to parathyromatosis treated by radio-guided PTX. A haemodialysed 48-year-old woman with recurrent SHPT due to parathyromatosis was treated by radio-guided PTX. Preoperative...
CHROMOSOMAL ABNORMALITIES IN PATIENTS WITH RECURRENT MISCARRIAGE
Daniela Mierla; Viorica Radoi; Veronica Stoian
2012-01-01
Chromosomal abnormalities are involved in the etiology of recurrent spontaneous pregnancy loss and sub-fertility. The purpose of this study was to determine the frequency and contribution of chromosomal abnormalities in recurrent miscarriages. The results obtained and literature review are helpful in understanding the importance of cytogenetics analysis of female infertility. To investigate the distribution of chromosomal abnormalities in the Romanian population with recurrent miscarriage, ka...
Local Recurrence of Extremity Soft Tissue Sarcoma.
Guerrero, Whitney M; Deneve, Jeremiah L
2016-10-01
The management of recurrent soft tissue sarcoma is a challenging problem for clinicians and has a significant physical, mental, emotional, and oncologic impact for the patient. Despite excellent limb-preservation therapies, approximately one-quarter of patients may eventually develop recurrence of disease. How to most appropriately manage these patients is a matter of debate. Several treatment options exist, including surgical resection, irradiation, systemic chemotherapy, amputation, and regional therapies. This article highlights the management of recurrent extremity soft tissue sarcoma. PMID:27542648
Recurrence quantification analysis of chimera states
Santos, M. S.; Szezech, J. D.; Batista, A. M.; Caldas, I. L.; Viana, R. L.; Lopes, S. R.
2015-10-01
Chimera states, characterised by coexistence of coherence and incoherence in coupled dynamical systems, have been found in various physical systems, such as mechanical oscillator networks and Josephson-junction arrays. We used recurrence plots to provide graphical representations of recurrent patterns and identify chimera states. Moreover, we show that recurrence plots can be used as a diagnostic of chimera states and also to identify the chimera collapse.
Recurrent odontogenic keratocyst within the masticatory space
Lim, Su Yeon; Huh, Kyung Hoe; Yi, Won Jin; Choi, Hyun Bae; Choi, Soon Chul [School of Dentistry, Seoul National University, Seoul (Korea, Republic of)
2008-06-15
The odontogenic keratocyst (OKC) is a developmental odontogenic cyst typically occurring in the jaws. Since the first description of OKC was published in 1956, the lesion has been of particular interest because of its specific histopathologic features, high recurrence rate, and aggressive behavior. Recurrences most commonly arise within bone at the site of the original cyst. However, as lining cells may find their way into surrounding tissues either from implantation during surgery or from cortical perforation recurrences may arise at a distance from the original cyst. Here, we report a rare case of recurrent OKC which was first developed in mandible and recurred within the masticatory space.
Intravenous immunoglobulin treatment for secondary recurrent miscarriage
Christiansen, Ole Bjarne; Larsen, E. C.; Egerup, P.;
2015-01-01
OBJECTIVE: To determine whether infusions with intravenous immunoglobulin (IVIg) during early pregnancy increase live birth rate in women with secondary recurrent miscarriage compared with placebo. DESIGN: A single-centre, randomised, double-blind, placebo-controlled trial. SETTING: A tertiary...... centre for recurrent miscarriage in Copenhagen, Denmark. POPULATION: A group of 82 women with unexplained secondary recurrent miscarriage and at least four miscarriages. METHODS: Women were randomly assigned to repeated infusions with IVIg or placebo (albumin) from the time of positive pregnancy test to......, IVIg did not increase the live birth rate in patients with secondary recurrent miscarriage and the treatment cannot be recommended in clinical practice....
Recurrence and Relapse in Bipolar Mood Disorder
S Gh Mousavi
2004-06-01
Full Text Available Background: Despite the effectiveness of pharmacotherapy in acute phase of bipolar mood disorder, patients often experience relapses or recurrent episodes. Hospitalization of patients need a great deal of financial and humanistic resources which can be saved through understanding more about the rate of relapse and factors affecting this rate. Methods: In a descriptive analytical study, 380 patients with bipolar disorder who were hospitalized in psychiatric emergency ward of Noor hospital, Isfahan, Iran, were followed. Each patient was considered for; the frequency of relapse and recurrence, kind of pharmachotherapy, presence of psychotherapeutic treatments, frequency of visits by psychiatrist and the rank of present episode. Results: The overall prevalence of recurrence was 42.2%. Recurrence was lower in patients using lithium carbonate or sodium valproate or combined therapy (about 40%, compared to those using carbamazepine (80%. Recurrence was higher in patients treated with only pharmacotherapy (44.5% compared to those treated with both pharmacotherapy and psychotherapy (22.2%. Patients who were visited monthy by psychiatrist had lower rate of recurrence compared to those who had irregular visits. Conclusion: The higher rate of recurrence observed in carbamazepine therapy may be due to its adverse reactions and consequently poor compliance to this drug. Lower rates of recurrence with psychotherapy and regular visits may be related to the preventive effects of these procedures and especially to the effective management of stress. Keywords: Bipolar Mood Disorder, Recurrence, Relapse.
2016-05-26
Childhood Choroid Plexus Tumor; Childhood Ependymoblastoma; Childhood Grade III Meningioma; Childhood High-grade Cerebellar Astrocytoma; Childhood High-grade Cerebral Astrocytoma; Childhood Medulloepithelioma; Recurrent Childhood Anaplastic Astrocytoma; Recurrent Childhood Anaplastic Oligoastrocytoma; Recurrent Childhood Anaplastic Oligodendroglioma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Giant Cell Glioblastoma; Recurrent Childhood Glioblastoma; Recurrent Childhood Gliomatosis Cerebri; Recurrent Childhood Gliosarcoma; Recurrent Childhood Medulloblastoma; Recurrent Childhood Pineoblastoma; Recurrent Childhood Supratentorial Primitive Neuroectodermal Tumor
Chronic recurrent multifocal osteomyelitis (CRMO)
Chronic recurrent multifocal osteomyelitis (CRMO) is an unusual clinical entity. More than 200 cases are described in the literature and it is presented here with special reference to its radiological aspects. It is an acquired disease of the skeleton which occurs predominantly during childhood and adolescence. About ten per cent of cases begin in early or, rarely, in later adult life. This variant is described here for the first time and is discussed as 'adult CRMO'. The underlying pathology is a bland, predominantly lympho-plasma cellular osteomyelitis which is self-limiting and leads to bone sclerosis (Garre). It probably involves an abnormal immune process which follows an infection but remains clinically latent and remains aseptic and sterile. In a quarter of cases there is an association with pustulosis palmo-plantaris and its relationship with psoriatic arthropathy is discussed. The clinical, histopathological and imaging features (radiological and particularly MRT) and the bone changes are described. (orig./AJ)
Recurrent Glioblastoma: Where we stand
Sanjoy Roy
2015-01-01
Full Text Available Current first-line treatment regimens combine surgical resection and chemoradiation for Glioblastoma that provides a slight increase in overall survival. Age on its own should not be used as an exclusion criterion of glioblastoma multiforme (GBM treatment, but performance should be factored heavily into the decision-making process for treatment planning. Despite aggressive initial treatment, most patients develop recurrent diseases which can be treated with re-resection, systemic treatment with targeted agents or cytotoxic chemotherapy, reirradiation, or radiosurgery. Research into novel therapies is investigating alternative temozolomide regimens, convection-enhanced delivery, immunotherapy, gene therapy, antiangiogenic agents, poly ADP ribose polymerase inhibitors, or cancer stem cell signaling pathways. Given the aggressive and resilient nature of GBM, continued efforts to better understand GBM pathophysiology are required to discover novel targets for future therapy.
Recurrent Glioblastoma: Where we stand.
Roy, Sanjoy; Lahiri, Debarshi; Maji, Tapas; Biswas, Jaydip
2015-01-01
Current first-line treatment regimens combine surgical resection and chemoradiation for Glioblastoma that provides a slight increase in overall survival. Age on its own should not be used as an exclusion criterion of glioblastoma multiforme (GBM) treatment, but performance should be factored heavily into the decision-making process for treatment planning. Despite aggressive initial treatment, most patients develop recurrent diseases which can be treated with re-resection, systemic treatment with targeted agents or cytotoxic chemotherapy, reirradiation, or radiosurgery. Research into novel therapies is investigating alternative temozolomide regimens, convection-enhanced delivery, immunotherapy, gene therapy, antiangiogenic agents, poly ADP ribose polymerase inhibitors, or cancer stem cell signaling pathways. Given the aggressive and resilient nature of GBM, continued efforts to better understand GBM pathophysiology are required to discover novel targets for future therapy. PMID:26981507
Recurrent Primary Spinal Hydatid Cyst
Okan Turk
2015-03-01
Full Text Available Primary hydatid disease of spine is rare and spinal hydatitosis constitute only 1% of all hydatitosis. We report a case of recurrent primary intraspinal extradural hydatid cyst of the thoracic region causing progressive paraparesis. The patient was operated 16 years ago for primary spinal hydatid disease involvement and was instrumented dorsally for stabilization. The magnetic resonance imaging (MRI of thoracic spine showed a cystic lesion at T11-12 level and compressed spinal cord posterolaterally. Intraspinal cyst was excised through T11-12 laminectomy which made formerly. The early postoperative period showed a progressive improvement of his neurological deficit and he was discharged with antihelmintic treatment consisting of albendazole and amoxicillin-sulbactam combination. [Cukurova Med J 2015; 40(Suppl 1: 84-89
Pharmacotherapy of recurrent aphthous ulcers
J P Angeline Archana
2011-01-01
Full Text Available Aphthous ulcer is the most common type of ulcer affecting the oral cavity and is considered to be one of the most painful conditions. Treatment is often unsatisfactory. Newer treatment modalities are therefore being tried. Amlexanox and rebamipide are the approved drugs for painful aphthous ulcers and have been used in painful symptoms of acid peptic disease as prostaglandin enhancers. Safety and efficacy of the drugs used in the treatment of recurrent aphthous ulcers were evaluated and being used widely by most of the treating physicians choosing a modality of treatment of their experience. There is no proper treatment modality available till date. Various drugs and their efficacy with least adverse drug effects while treating the various aphthous ulcers are discussed.
Chemoradiotherapy response in recurrent rectal cancer
The efficacy of response to preoperative chemoradiotherapy (CRT) in recurrent versus primary rectal cancer has not been investigated. We compared radiological downsizing between primary and recurrent rectal cancers following CRT and determined the optimal size reduction threshold for response validated by survival outcomes. The proportional change in tumor length for primary and recurrent rectal cancers following CRT was compared using the independent sample t-test. Overall survival (OS) was calculated using the Kaplan–Meier product limit method and differences between survival for tumor size reduction thresholds of 30% (response evaluation criteria in solid tumors [RECIST]), 40%, and 50% after CRT in primary and recurrent rectal cancer groups. A total of 385 patients undergoing CRT were analyzed, 99 with recurrent rectal cancer and 286 with primary rectal cancer. The mean proportional reduction in maximum craniocaudal length was significantly higher for primary rectal tumors (33%) compared with recurrent rectal cancer (11%) (P < 0.01). There was no difference in OS for either primary or recurrent rectal cancer when ≤30% or ≤40% definitions were used. However, for both primary and recurrent tumors, significant differences in median 3-year OS were observed when a RECIST cut-off of 50% was used. OS was 99% versus 77% in primary and 100% versus 42% in recurrent rectal cancer (P = 0.002 and P = 0.03, respectively). Only patients that demonstrated >50% size reduction showed a survival benefit. Recurrent rectal cancer appears radioresistant compared with primary tumors for tumor size after CRT. Further investigation into improving/intensifying chemotherapy and radiotherapy for locally recurrent rectal cancer is justified
Recurrence models of volcanic events
An assessment of the risk of future volcanism has been conducted for isolation of high-level radioactive waste at the potential Yucca Mountain site in southern Nevada. Volcanism studies for the Yucca Mountain Site Characterization Project have progressed to a sufficient degree that it is now prudent to work toward concluding aspects of the work. An advantage of a probabilistic approach to volcanic risk is that it assigns a structured formalism to the problem. This formalism subdivides a complex issue into logical sections. The significance of uncertainty or differences in scientific opinion concerning volcanism issues can be tested for each section of a probabilistic problem. The perspective for making judgement of significance for volcanism studied are the regulatory requirements for assessing the suitability of the potential Yucca Mountain site. This paper attempts to begin the process of helping establish the probabilistic framework for making those judgement. There are three objectives. First, the authors describe the tripartite probability used to define the risk of volcanism and the geologic assumptions required for the probability model. Second, the authors examine and define the first part of this probability, the recurrence of volcanic events. Studies are reviewed from the volcanological literature where time-volume behavior of volcanic centers and fields have been evaluated. These evaluations include both conventional statistical analysis of time-series of volcanic events and applications using newly developing concepts of fractal analysis and deterministic chaos. Third, the authors tabulate past calculations and derive new values for the recurrence of volcanic events using a simple Poison model
2013-06-04
Childhood Central Nervous System Germ Cell Tumor; Childhood Extragonadal Germ Cell Tumor; Childhood Hepatoblastoma; Childhood Hepatocellular Carcinoma; Childhood High-grade Cerebral Astrocytoma; Childhood Low-grade Cerebral Astrocytoma; Childhood Malignant Ovarian Germ Cell Tumor; Childhood Malignant Testicular Germ Cell Tumor; Childhood Teratoma; Recurrent Adrenocortical Carcinoma; Recurrent Childhood Brain Stem Glioma; Recurrent Childhood Cerebellar Astrocytoma; Recurrent Childhood Cerebral Astrocytoma; Recurrent Childhood Ependymoma; Recurrent Childhood Liver Cancer; Recurrent Childhood Malignant Germ Cell Tumor; Recurrent Childhood Rhabdomyosarcoma; Recurrent Childhood Soft Tissue Sarcoma; Recurrent Childhood Visual Pathway and Hypothalamic Glioma; Recurrent Colon Cancer; Recurrent Ewing Sarcoma/Peripheral Primitive Neuroectodermal Tumor; Recurrent Nasopharyngeal Cancer; Recurrent Neuroblastoma; Recurrent Osteosarcoma; Recurrent Rectal Cancer; Recurrent Renal Cell Cancer
Design and development of multilayer vascular graft
Madhavan, Krishna
2011-07-01
Vascular graft is a widely-used medical device for the treatment of vascular diseases such as atherosclerosis and aneurysm as well as for the use of vascular access and pediatric shunt, which are major causes of mortality and morbidity in this world. Dysfunction of vascular grafts often occurs, particularly for grafts with diameter less than 6mm, and is associated with the design of graft materials. Mechanical strength, compliance, permeability, endothelialization and availability are issues of most concern for vascular graft materials. To address these issues, we have designed a biodegradable, compliant graft made of hybrid multilayer by combining an intimal equivalent, electrospun heparin-impregnated poly-epsilon-caprolactone nanofibers, with a medial equivalent, a crosslinked collagen-chitosan-based gel scaffold. The intimal equivalent is designed to build mechanical strength and stability suitable for in vivo grafting and to prevent thrombosis. The medial equivalent is designed to serve as a scaffold for the activity of the smooth muscle cells important for vascular healing and regeneration. Our results have shown that genipin is a biocompatible crosslinker to enhance the mechanical properties of collagen-chitosan based scaffolds, and the degradation time and the activity of smooth muscle cells in the scaffold can be modulated by the crosslinking degree. For vascular grafting and regeneration in vivo, an important design parameter of the hybrid multilayer is the interface adhesion between the intimal and medial equivalents. With diametrically opposite affinities to water, delamination of the two layers occurs. Physical or chemical modification techniques were thus used to enhance the adhesion. Microscopic examination and graft-relevant functional characterizations have been performed to evaluate these techniques. Results from characterization of microstructure and functional properties, including burst strength, compliance, water permeability and suture
14 CFR 91.1107 - Recurrent training.
2010-01-01
... AND GENERAL OPERATING RULES GENERAL OPERATING AND FLIGHT RULES Fractional Ownership Operations Program Management § 91.1107 Recurrent training. (a) Each program manager must ensure that each crewmember receives... 14 Aeronautics and Space 2 2010-01-01 2010-01-01 false Recurrent training. 91.1107 Section...
Examestane in advanced or recurrent endometrial carcinoma
Lindemann, Kristina; Malander, Susanne; Christensen, René dePont;
2014-01-01
We evaluated the efficacy and safety of the aromatase inhibitor exemestane in patients with advanced, persistent or recurrent endometrial carcinoma.......We evaluated the efficacy and safety of the aromatase inhibitor exemestane in patients with advanced, persistent or recurrent endometrial carcinoma....
Recurrent facial urticaria following herpes simplex labialis
Vijay Zawar
2012-01-01
Full Text Available We describe recurrent acute right-sided facial urticaria associated with herpes labialis infection in a middle-aged female patient. Antiviral medications and antihistamines not only successfully cleared the herpes infection and urticaria but also prevented further recurrences.
Risk factors of recurrent anal sphincter ruptures
Jangö, Hanna; Langhoff-Roos, J; Rosthøj, Steen;
2012-01-01
Please cite this paper as: Jangö H, Langhoff-Roos J, Rosthøj S, Sakse A. Risk factors of recurrent anal sphincter ruptures: a population-based cohort study. BJOG 2012;00:000-000 DOI: 10.1111/j.1471-0528.2012.03486.x. Objective To determine the incidence and risk factors of recurrent anal sphincter...... rupture (ASR). Design Population-based retrospective cohort study. Setting Data were taken from the National Medical Birth Registry, Denmark. Population Patients with a first and a second vaginal delivery in the time period 1997-2010. Methods Univariate analysis and multivariate logistic regression...... were used to determine risk factors of recurrent ASR. Main outcome measures The incidence of recurrent ASR and odds ratios for possible risk factors of recurrent ASR: age, body mass index, grade of ASR, birthweight, head circumference, gestational age, presentation, induction of labour, oxytocin...
6th International Symposium on Recurrence Plots
Jr, Jr; Ioana, Cornel; Marwan, Norbert
2016-01-01
The chapters in this book originate from the research work and contributions presented at the Sixth International Symposium on Recurrence Plots held in Grenoble, France in June 2015. Scientists from numerous disciplines gathered to exchange knowledge on recent applications and developments in recurrence plots and recurrence quantification analysis. This meeting was remarkable because of the obvious expansion of recurrence strategies (theory) and applications (practice) into ever-broadening fields of science. It discusses real-world systems from various fields, including mathematics, strange attractors, applied physics, physiology, medicine, environmental and earth sciences, as well as psychology and linguistics. Even readers not actively researching any of these particular systems will benefit from discovering how other scientists are finding practical non-linear solutions to specific problems. The book is of interest to an interdisciplinary audience of recurrence plot users and researchers interested in time...
Measure of Node Similarity in Multilayer Networks
Mollgaard, Anders; Dammeyer, Jesper; Jensen, Mogens H; Lehmann, Sune; Mathiesen, Joachim
2016-01-01
The weight of links in a network is often related to the similarity of the nodes. Here, we introduce a simple tunable measure for analysing the similarity of nodes across different link weights. In particular, we use the measure to analyze homophily in a group of 659 freshman students at a large university. Our analysis is based on data obtained using smartphones equipped with custom data collection software, complemented by questionnaire-based data. The network of social contacts is represented as a weighted multilayer network constructed from different channels of telecommunication as well as data on face-to-face contacts. We find that even strongly connected individuals are not more similar with respect to basic personality traits than randomly chosen pairs of individuals. In contrast, several socio-demographics variables have a significant degree of similarity. We further observe that similarity might be present in one layer of the multilayer network and simultaneously be absent in the other layers. For a...
Magnetic studies in Fe/Zn multilayers
The structural and magnetic properties of Fe/Zn films prepared by thermal evaporation have been studied by means of X-ray diffraction, vibrating-sample magnetometry and ferromagnetic resonance (FMR). For Fe layer thickness smaller than 20 A the saturation magnetization decreases with decreasing Fe thickness, which is an indication of the island growth of Zn and Fe-Zn interdiffusion at the layer interfaces. The effective field magnetization 4πMeff of the Fe/Zn multilayers was determined from the FMR data in a rotating external magnetic field. The interface anisotropy constant of the Fe/Zn multilayers, KS, is found to be 1.0 erg/cm2 at 300 K. This indicates the presence of a large perpendicular interface anisotropy and this may suggest that the largest part of KS originates from lattice misfit strain
Diffuse photon propagation in multilayered geometries
Diffuse optical tomography (DOT) is an emerging functional medical imaging modality which aims to recover the optical properties of biological tissue. The forward problem of the light propagation of DOT can be modelled in the frequency domain as a diffusion equation with Robin boundary conditions. In the case of multilayered geometries with piecewise constant parameters, the forward problem is equivalent to a set of coupled Helmholtz equations. In this paper, we present solutions for the multilayered diffuse light propagation for a three-layer concentric sphere model using a series expansion method and for a general layered geometry using the boundary element method (BEM). Results are presented comparing these solutions to an independent Monte Carlo model, and for an example three layered head model
Random walk centrality in interconnected multilayer networks
Solé-Ribalta, Albert; De Domenico, Manlio; Gómez, Sergio; Arenas, Alex
2016-06-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influent nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
Information Propagation in Clustered Multilayer Networks
Zhuang, Yong
2015-01-01
In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g., face-to-face). Therefore, understanding the dynamics of information propagation among humans calls for a multi-layer network model where an online social network is conjoined with a physical network. In this work, we initiate a study of information diffusion in a clustered multi-layer network model, where all constituent layers are random networks with high clustering. We assume that information propagates according to the SIR model and with different information transmissibility across the networks. We give results for the conditions, probability, and size of information epidemics, i.e., cases where information starts from a single individual and reaches a positive fraction of the population. We show that increasing the level of clustering in either one of the layers increas...
Network Composition from Multi-layer Data
Lerman, Kristina; Yan, Xiaoran
2016-01-01
It is common for people to access multiple social networks, for example, using phone, email, and social media. Together, the multi-layer social interactions form a "integrated social network." How can we extend well developed knowledge about single-layer networks, including vertex centrality and community structure, to such heterogeneous structures? In this paper, we approach these challenges by proposing a principled framework of network composition based on a unified dynamical process. Mathematically, we consider the following abstract problem: Given multi-layer network data and additional parameters for intra and inter-layer dynamics, construct a (single) weighted network that best integrates the joint process. We use transformations of dynamics to unify heterogeneous layers under a common dynamics. For inter-layer compositions, we will consider several cases as the inter-layer dynamics plays different roles in various social or technological networks. Empirically, we provide examples to highlight the usef...
Computerized multilevel analysis for multilayered fiber composites
Chamis, C. C.
1972-01-01
A FORTRAN 4 computer code for the micromechanics, macromechanics, and laminate analysis of multilayered fiber composite structural components is described. The code can be used either individually or as a subroutine within a complex structural analysis/synthesis program. The inputs to the code are constituent materials properties, composite geometry, and loading conditions. The outputs are various properties for ply and composite; composite structural response, including bending-stretching coupling; and composite stress analysis, including comparisons with failure criteria for combined stress. The code was used successfully in the analysis and structural synthesis of flat panels, in the buckling analysis of flat panels, in multilayered composite material failure studies, and lamination residual stresses analysis.
Random walk centrality in interconnected multilayer networks
Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex
2015-01-01
Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influential nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.
Mathematical Formulation of Multi-Layer Networks
De Domenico, Manlio; Cozzo, Emanuele; Kivelä, Mikko; Moreno, Yamir; Porter, Mason A; Gòmez, Sergio; Arenas, Alex
2013-01-01
A network representation is useful for describing the structure of a large variety of complex systems. However, most real and engineered systems have multiple subsystems and layers of connectivity, and the data produced by such systems is very rich. Achieving a deep understanding of such systems necessitates generalizing "traditional" network theory, and the newfound deluge of data now makes it possible to test increasingly general frameworks for the study of networks. In particular, although adjacency matrices are useful to describe traditional single-layer networks, such a representation is insufficient for the analysis and description of multiplex and time-dependent networks. One must therefore develop a more general mathematical framework to cope with the challenges posed by multi-layer complex systems. In this paper, we introduce a tensorial framework to study multi-layer networks, and we discuss the generalization of several important network descriptors and dynamical processes ---including degree centr...
Optics in magnetic multilayers and nanostructures
Visnovsky, Stefan
2006-01-01
In the continuing push toward optical computing, the focus remains on finding and developing the right materials. Characterizing materials, understanding the behavior of light in these materials, and being able to control the light are key players in the search for suitable optical materials. Optics in Magnetic Multilayers and Nanostructures presents an accessible introduction to optics in anisotropic magnetic media.While most of the literature presents only final results of the complicated formulae for the optics in anisotropic media, this book provides detailed explanations and full step-by-step derivations that offer insight into the procedure and reveal any approximations. Based on more than three decades of experimental research on the subject, the author explains the basic concepts of magnetooptics; nonreciprocal wave propagation; the simultaneous effect of crystalline symmetry and arbitrarily oriented magnetization on the form of permittivity tensors; spectral dependence of permittivity; multilayers at...
Perpendicular anisotropy in Fe/Ag multilayers
Fetzer, C.; Szucs, I.S.; Dezsi, I. [KFKI Research Institute for Particle and Nuclear Physics, Budapest (Hungary); Kaptas, D.; Kiss, L.F.; Vincze, I. [Research Institute for Solid State Physics and Optics, Budapest (Hungary); Balogh, J.
2008-08-15
The direction of the spontaneous magnetization changes from out of plane to in plane at around x=0.6 in[Ag(2.6 nm)/Fe(x nm)]{sub 10} multilayers (0.2{<=}x{<=}1) prepared on Si(111) substrate by vacuum evaporation. Transmission Moessbauer spectroscopy measurements of removed samples with a thick capping layer are compared to conversion electron Moessbauer spectroscopy measurements of samples on the Si substrate with a thin capping layer. The stress arising because of the application of a thick capping layer and the removal of the samples from the substrate is shown to have negligible effect on the spontaneous magnetization. The results support that the appearance of the perpendicular anisotropy below x=0.6 is an intrinsic property of Fe/Ag multilayers. (copyright 2008 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim) (orig.)
Community Detection Using Multilayer Edge Mixture Model
Zhang, Han; Wang, Chang-Dong; Lai, Jian-Huang; PHILIP S. YU
2016-01-01
A wide range of complex systems can be modeled as networks with corresponding constraints on the edges and nodes, which have been extensively studied in recent years. Nowadays, with the progress of information technology, systems that contain the information collected from multiple perspectives have been generated. The conventional models designed for single perspective networks fail to depict the diverse topological properties of such systems, so multilayer network models aiming at describin...
Multilayer Statistical Intrusion Detection in Wireless Networks
Noureddine Boudriga; Amel Meddeb-Makhlouf; Mohamed Hamdi
2008-01-01
The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs). This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The archite...
The polymorphic, multilayered and networked urbanised territory
Nielsen, Tom
2015-01-01
The discussion of the network city has in recent years been supplemented by an increasing interest in reconsidering the notion of territory. Looking into both geographical and urban design theories, we find examples of a focus on how the networks of the city not only connect them irreversibly wit...... theory. The concept of The Polymorphic, Multilayered and Networked Urbanised Territory is introduced to grasp the reality experienced in European regions outside the largest and most potent versions of contemporary cities....
Analysis of Fracture Behaviour of Multilayer Pipes
Nezbedová, E.; Knésl, Zdeněk; Vlach, B.
2007-01-01
Roč. 36, č. 5 (2007), s. 207-212. ISSN 1465-8011. [Plastic Pipes /13./. Washington, D. C., 02.10.2006-05.10.2006] R&D Projects: GA ČR GA106/07/1284 Institutional research plan: CEZ:AV0Z20410507 Keywords : multi-layer pipes Subject RIV: JL - Materials Fatigue, Friction Mechanics Impact factor: 0.431, year: 2007
Unsupervised model compression for multilayer bootstrap networks
ZHANG, XIAO-LEI
2015-01-01
Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised bootstrap methods. The framework compresses a large unsupervised bootstrap model into a small model by ta...
Surface immobilized protein multilayers for cell seeding
Brynda, Eduard; Pacherník, J.; Houska, Milan; Pientka, Zbyněk; Dvořák, P.
2005-01-01
Roč. 21, č. 17 (2005), s. 7877. ISSN 0743-7463 R&D Projects: GA ČR(CZ) GA203/02/1326; GA ČR GA102/03/0633; GA MŠk(CZ) LN00A065 Keywords : surface modification * layer-by-layer deposition * protein multilayers Subject RIV: CE - Biochemistry Impact factor: 3.705, year: 2005
Analysis of multi-layer polymer films
Paulette Guillory
2009-04-01
Full Text Available Polymer multi-layer films are used in a variety of industries. It is important both to the manufacturers of polymer films and to the industries using these films that the quality and composition be strictly controlled. The confocal analysis and high spatial resolution of Raman microscopy make this technique ideal for identifying the source and identity of defects and inclusions in polymer films.
Principles of Bragg-Fresnel multilayer optics
Aristov, V. V.; Erko, A.I.; Martynov, V.V.
1988-01-01
The paper describes the principles and theoretical models of new X-ray optical elements based on the behaviour of Bragg-Fresnel diffraction. The use of volume diffraction permits one to achieve better spatial resolution compared with conventional plane optics and bending mirrors. The construction of Bragg-Fresnel elements combines the advantages of high-resolution Fresnel optics with stability of multilayer mirrors.
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
Thermal Conduction in Graphene and Graphene Multilayers
Ghosh, Suchismita
2009-01-01
There has been increasing interest in thermal conductivity of materials motivated by the heat removal issues in electronics and by the need of fundamental science to understand heat conduction at nanoscale. This dissertation reports the results of the experimental investigation of heat conduction in graphene and graphene multilayers. Graphene is a planar single sheet of sp2–bonded carbon atoms arranged in honeycomb lattice. It reveals many unique properties, including the extraordinaril...
Ferromagnetic Resonance in Gd/Co Multilayers
A. V. Svalov; V. O. Vas'kovskiy; J. M. Barandiaran; G. V. Kurlyandskaya; L. Lezama; J. Gutiérrez; N. G. Bebenin; D. Schmool
2001-01-01
Magnetometric and ferromagnetic resonance (MFR) measurements have been performed on the polycrystalline multilayered structure, [Gd(7.5 nm)/Co(3nm)]20. The temperature dependence of magnetization of the sample suggests a compensation temperature Tcomp in the region of 240K, implying that the Co and Gd layers are antiferromagnetically aligned. The FMR curves are strongly temperature dependent, particularly in the vicinity of Tcomp.
Compact UWB Monopole for Multilayer Applications
Sanz-Izquierdo, Benito; Paul R. Young; Bai, Qiang; Batchelor, John C.
2006-01-01
A novel compact, dual layer UWB monopole antenna is presented. This low profile ultra-wideband antenna is fed by a 50 Ω shielded strip-line with an array of metal vias making the conducting walls. A printed disc monopole with a circular cut is the radiating element. The dual layer, shielded strip line feed allows for integration in multilayer technologies. The ultra-wideband, monopole characteristics of the antenna are confirmed experimentally.
Polyelectrolyte Multilayer Capsules for Medical Applications
Nazarenus, Moritz
2015-01-01
This thesis deals with the application of polymer capsules for diagnostic and therapeutic purposes in mammalian cells. The capsules comprise a multilayer shell of oppositely charged polyelectrolytes surrounding a cavity and have a size of two to five microns. Concerning diagnostics, capsules were produced to monitor the dynamics of the lysosomal pH in cancer cells. The cavities of the capsules were filled with a fluoresce...
Suppression of Brazier Effect in Multilayered Cylinders
Hiroyuki Shima; Motohiro Sato; Sung-Jin Park
2014-01-01
When a straight hollow tube having circular cross-section is bent uniformly into an arc, the cross-section tends to ovalize or flatten due to the in-plane stresses induced by bending; this ovalization phenomenon is called the Brazier effect. The present paper is aimed at theoretical formulation of the Brazier effect observed in multilayered cylinders, in which a set of thin hollow cylinders are stacked concentrically about the common axis. The results indicate that mechanical couplings betwee...
Highlights: → A method is presented to improve power system stability using IPFC. → Recurrent neural network controllers damp oscillations in a power system. → Training is based on back propagation with adaptive training parameters. → Selection of effectiveness damping control signal carried out using SVD method. -- Abstract: This paper presents a method to improve power system stability using IPFC based damping online learning recurrent neural network controllers for damping oscillations in a power system. Parameters of equipped controllers for enhancing dynamical stability at the IPFC are tuned using mathematical methods. Therefore these control parameters are often fixed and are set for particular system configurations or operating points. Multilayer recurrent neural network, which can be tuned for changing system conditions, is used in this paper for effectively damp the oscillations. Training is based on back propagation with adaptive training parameters. This controller is tested to variations in system loading and fault in the power system and its performance is compared with performance of a controller that the phase compensation method is used to set its parameters. Selection of effectiveness damping control signal for the design of robust IPFC damping controller carried out through singular value decomposition (SVD) method. Simulation studies show the superior robustness and stabilizing effect of the proposed controller in comparison with phase compensation method.
Elastic chitosan/chondroitin sulfate multilayer membranes.
Sousa, M P; Cleymand, F; Mano, J F
2016-01-01
Freestanding multilayered films were obtained using layer-by-layer (LbL) technology from the assembly of natural polyelectrolytes, namely chitosan (CHT) and chondroitin sulfate (CS). The morphology and the transparency of the membranes were evaluated. The influence of genipin (1 and 2 mg ml(-1)), a naturally-derived crosslinker agent, was also investigated in the control of the mechanical properties of the CHT/CS membranes. The water uptake ability can be tailored by changing the crosslinker concentration that also controls the Young's modulus and ultimate tensile strength. The maximum extension tends to decrease upon crosslinking with the highest genipin concentration, compromising the elastic properties of CHT/CS membranes: nevertheless, when using a lower genipin concentration, the ultimate tensile stress is similar to the non-crosslinked one, but exhibits a significantly higher modulus. Moreover, the crosslinked multilayer membranes exhibited shape memory properties, through a simple hydration action. The in vitro biological assays showed better L929 cell adhesion and proliferation when using the crosslinked membranes and confirmed the non-cytotoxicity of the developed CHT/CS membranes. Within this research work, we were able to construct freestanding biomimetic multilayer structures with tailored swelling, mechanical and biological properties that could find applicability in a variety of biomedical applications. PMID:27200488
Technique for etching monolayer and multilayer materials
Bouet, Nathalie C. D.; Conley, Raymond P.; Divan, Ralu; Macrander, Albert
2015-10-06
A process is disclosed for sectioning by etching of monolayers and multilayers using an RIE technique with fluorine-based chemistry. In one embodiment, the process uses Reactive Ion Etching (RIE) alone or in combination with Inductively Coupled Plasma (ICP) using fluorine-based chemistry alone and using sufficient power to provide high ion energy to increase the etching rate and to obtain deeper anisotropic etching. In a second embodiment, a process is provided for sectioning of WSi.sub.2/Si multilayers using RIE in combination with ICP using a combination of fluorine-based and chlorine-based chemistries and using RF power and ICP power. According to the second embodiment, a high level of vertical anisotropy is achieved by a ratio of three gases; namely, CHF.sub.3, Cl.sub.2, and O.sub.2 with RF and ICP. Additionally, in conjunction with the second embodiment, a passivation layer can be formed on the surface of the multilayer which aids in anisotropic profile generation.
Thomas, M.F.; Beesley, A.M.; Bouchenoire, L.; Brown, S.D.; Thompson, P.; Herring, A.D.F.; Lander, G.H.; Langridge, S.; Stirling, W.G.; Ward, R.C.C.; Zochowski, S.W
2004-04-28
Magnetic multilayers are known to have behaviour shaped by the intrinsic magnetic properties of their constituents and of their interactions. Multilayers composed of Uranium (5f electrons) and transition metal (3d electrons) provide the unique combination of a potentially large orbital moment with strong electronic hybridisation effects between the extended 5f states and the strongly magnetic 3d states. In this study U/Fe multilayers with layer thicknesses 20 A
Heat Transfer in High Temperature Multilayer Insulation
Daryabeigi, Kamran; Miller, Steve D.; Cunnington, George R.
2007-01-01
High temperature multilayer insulations have been investigated as an effective component of thermal-protection systems for atmospheric re-entry of reusable launch vehicles. Heat transfer in multilayer insulations consisting of thin, gold-coated, ceramic reflective foils and Saffil(TradeMark) fibrous insulation spacers was studied both numerically and experimentally. A finite volume numerical thermal model using combined conduction (gaseous and solid) and radiation in porous media was developed. A two-flux model with anisotropic scattering was used for radiation heat transfer in the fibrous insulation spacers between the reflective foils. The thermal model was validated by comparison with effective thermal conductivity measurements in an apparatus based on ASTM standard C201. Measurements were performed at environmental pressures in the range from 1x10(exp -4) to 760 torr over the temperature range from 300 to 1300 K. Four multilayer samples with nominal densities of 48 kg/cu m were tested. The first sample was 13.3 mm thick and had four evenly spaced reflective foils. The other three samples were 26.6 mm thick and utilized either one, two, or four reflective foils, located near the hot boundary with nominal foil spacing of 1.7 mm. The validated thermal model was then used to study relevant design parameters, such as reflective foil spacing and location in the stack-up and coating of one or both sides of foils.
Multilayer network decoding versatility and trust
Sarkar, Camellia; Yadav, Alok; Jalan, Sarika
2016-01-01
In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real-world systems. We analyze massive time-varying social data drawn from the largest film industry of the world under a multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher-order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. The analysis of weak ties of the industry uncovers nodes from a lower-degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real-world systems.
Evolutionary games on multilayer networks: a colloquium
Wang, Zhen; Wang, Lin; Szolnoki, Attila; Perc, Matjaž
2015-05-01
Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.
Quantum Spin Hall phase in multilayer graphene
Garcia, Noel; Lado, Jose Luis; Fernandez-Rossier, Joaquin; Theory of Nanostructures Team
2015-03-01
We address the question of whether multilayer graphene systems are Quantum Spin Hall (QSH) insulators. Since interlayer coupling coples pz orbitals to s orbitals of different layers and Spin-Orbit (SO) couples pz orbitals with px and py of opposite spins, new spins mixing channels appear in the multilayer scenario that were not present in the monolayer. These new spin-mixing channels cast a doubt on the validity of the spin-conserving Kane-Mele model for multilayers and motivates our choice of a four orbital tight-binding model in the Slater-Koster approximation with intrinsic Spin-Orbit interaction. To completely determine if the QSH phase is present we calculate for different number of layers both the Z2 invariant for different stackings (only for inversion symmetric systems), and the density of states at the edge of semi-infinite graphene ribbon with armchair termination. We find that systems with even number of layers are normal insulators while systems with odd number of layers are QSH insulators, regardless of the stacking. We acknowledge financial support by Marie-Curie-ITN 607904-SPINOGRAPH.
Automation Enhancement of Multilayer Laue Lenses
Lauer K. R.; Conley R.
2010-12-01
X-ray optics fabrication at Brookhaven National Laboratory has been facilitated by a new, state of the art magnetron sputtering physical deposition system. With its nine magnetron sputtering cathodes and substrate carrier that moves on a linear rail via a UHV brushless linear servo motor, the system is capable of accurately depositing the many thousands of layers necessary for multilayer Laue lenses. I have engineered a versatile and automated control program from scratch for the base system and many subsystems. Its main features include a custom scripting language, a fully customizable graphical user interface, wireless and remote control, and a terminal-based interface. This control system has already been successfully used in the creation of many types of x-ray optics, including several thousand layer multilayer Laue lenses.Before reaching the point at which a deposition can be run, stencil-like masks for the sputtering cathodes must be created to ensure the proper distribution of sputtered atoms. Quality of multilayer Laue lenses can also be difficult to measure, given the size of the thin film layers. I employ my knowledge of software and algorithms to further ease these previously painstaking processes with custom programs. Additionally, I will give an overview of an x-ray optic simulator package I helped develop during the summer of 2010. In the interest of keeping my software free and open, I have worked mostly with the multiplatform Python and the PyQt application framework, utilizing C and C++ where necessary.
2016-05-19
Endometrial Clear Cell Adenocarcinoma; Estrogen Receptor Negative; Ovarian Clear Cell Cystadenocarcinoma; Recurrent Fallopian Tube Carcinoma; Recurrent Ovarian Carcinoma; Recurrent Primary Peritoneal Carcinoma; Recurrent Uterine Corpus Carcinoma
Alisertib in Treating Young Patients With Recurrent or Refractory Solid Tumors or Leukemia
2016-07-20
Hepatoblastoma; Previously Treated Childhood Rhabdomyosarcoma; Recurrent Childhood Acute Lymphoblastic Leukemia; Recurrent Childhood Acute Myeloid Leukemia; Recurrent Childhood Kidney Neoplasm; Recurrent Childhood Malignant Germ Cell Tumor; Recurrent Childhood Rhabdomyosarcoma; Recurrent Childhood Soft Tissue Sarcoma; Recurrent Ewing Sarcoma/Peripheral Primitive Neuroectodermal Tumor; Recurrent Neuroblastoma; Recurrent Osteosarcoma
Treatment of Recurrent Ovarian Cancer.
Neville F. Hacker
2004-08-01
Full Text Available Recurrent ovarian cancer is a common clinical problem and the management of eachpatient must be individualized. Diagnosis is usually based on a progressively rising CA-125titre, and a CT scan of the pelvis and abdomen, together with a chest X-ray should be performed.Although there is no study to support immediate treatment in the asymptomaticpatient, our approach is to commence such patients on Tamoxifen. Chemotherapy isreserved for asymptomatic patients or those who progress on Tamoxifen. The longer thetreatment-free interval of 18-24 months. The choice of non-platinum second or subsequentline chemotherapy is based on many factors including likelihood of benefit, potential toxicity,schedule and convenience to the patient, as well as organ function and residual toxicityfrom prior treatment. Aggressive secondary cytoreductive surgery can significantly prolongsurvival in those with a disease-free interval of 24 months or more and in those in whom allmacroscopic disease can be removed. Radiation therapy to the tumour bed following resectionof localized disease may be beneficial in selected patients. Quality of life issues are particularlyimportant for this group of patients and have not been adequately studies.Communication regarding the objectives of therapy is important, and the multidisciplinaryapproach should include palliative care and psycho-social support, in addition to the moretraditional medical options.
Recurrent seizures after lidocaine ingestion.
Aminiahidashti, Hamed; Laali, Abolghasem; Nosrati, Nazanin; Jahani, Fatemeh
2015-01-01
Lidocaine has a concentration-dependent effect on seizures. Concentrations above 15 μg/mL frequently result in seizures in laboratory animals and human. We report a case of central nervous system (CNS) lidocaine toxicity and recurrent seizure after erroneous ingestion of lidocaine solution. A 4-year-old boy presented to the Emergency Department of Imam Hospital of Sari in December 2013 due to tonic-clonic generalized seizures approximately 30 min ago. 3 h before seizure, his mother gave him 2 spoons (amount 20-25 cc) lidocaine hydrochloride 2% solution instead of pediatric gripe by mistake. Seizure with generalized tonic-clonic occurred 3 times in home. Neurological examination was essentially unremarkable except for the depressed level of consciousness. Personal and medical history was unremarkable. There was no evidence of intracranial ischemic or hemorrhagic lesions in computed tomography scan. There were no further seizures, the condition of the patient remained stable, and he was discharged 2 days after admission. The use of viscous lidocaine may result in cardiovascular and CNS toxicity, particularly in children. Conservative management is the best option for treatment of lidocaine induced seizure. PMID:25709968
Immunopathogenesis of recurrent vulvovaginal candidiasis.
Fidel, P L; Sobel, J D
1996-07-01
Recurrent vulvovaginal candidiasis (RVVC) is a prevalent opportunistic mucosal infection, caused predominantly by Candida albicans, which affects a significant number of otherwise healthy women of childbearing age. Since there are no known exogenous predisposing factors to explain the incidence of symptomatic vaginitis in most women with idiopathic RVVC, it has been postulated that these particular women suffer from an immunological abnormality that prediposes them to RVVC. Because of the increased incidence of mucosal candidiasis in individuals with depressed cell-mediated immunity (CMI), defects in CMI are viewed as a possible explanation for RVVC. In this review, we attempt to place into perspective the accumulated information regarding the immunopathogenesis of RVVC, as well as to provide new immunological perspectives and hypotheses regarding potential immunological deficiencies that may predispose to RVVC and potentially other mucosal infections by the same organism. The results of both clinical studies and studies in an animal model of experimental vaginitis suggest that systemic CMI may not be the predominant host defense mechanism against C. albicans vaginal infections. Rather, locally acquired mucosal immunity, distinct from that in the peripheral circulation, is now under consideration as an important host defense at the vaginal mucosa, as well as the notion that changes in local CMI mechanism(s) may predispose to RVVC. PMID:8809464
Tumor vaccine against recurrence of hepatocellular carcinoma
Bao-Gang Peng; Li-Jiang Liang; Qiang He; Ming Kuang; Jia-Ming Lia; Ming-De Lu; Jie-Fu Huang
2005-01-01
AIM: To investigate the effects of autologous tumor vaccine on recurrence of hepatocellular carcinoma (HCC).METHODS: Sixty patients with HCC who had undergone curative resection, were randomly divided into HCC vaccine group and control group. Three vaccinations at 2-wk intervals were performed after curative hepatic resection. Delayedtype- hypersensitivity (DTH) test was performed before and after vaccination. Primary endpoints were the time of recurrence.RESULTS: Four patients in control group and 6 patients in HCC vaccine group were withdrawn from the study. The vaccine containing human autologous HCC fragments showed no essential adverse effect in a phase Ⅱ clinical trial and 17 of 24 patients developed a DTH response against the fragments. Three of 17 DTH-positive response patients and 5 of 7 DTH- negative response patients had recurrences after curative resection. After the operation,1-, 2- and 3-year recurrence rates of HCC vaccine groupwere 16.7%, 29.2% and 33.3%, respectively. But, 1-, 2- and3-year recurrence rates of the control group were 30.8%,53.8% and 61.5%, respectively. The time before the first recurrence in the vaccinated patients was significantly longer than that in the control patients (P＜0.05).CONCLUSION: Autologous tumor vaccine is of promise in decreasing recurrence of human HCC.
Inguinal hernia recurrence: Classification and approach
Campanelli Giampiero
2006-01-01
Full Text Available The authors reviewed the records of 2,468 operations of groin hernia in 2,350 patients, including 277 recurrent hernias updated to January 2005. The data obtained - evaluating technique, results and complications - were used to propose a simple anatomo-clinical classification into three types which could be used to plan the surgical strategy:Type R1: first recurrence ′high,′ oblique external, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R2: first recurrence ′low,′ direct, reducible hernia with small (< 2 cm defect in non-obese patients, after pure tissue or mesh repairType R3: all the other recurrences - including femoral recurrences; recurrent groin hernia with big defect (inguinal eventration; multirecurrent hernias; nonreducible, linked with a controlateral primitive or recurrent hernia; and situations compromised from aggravating factors (for example obesity or anyway not easily included in R1 or R2, after pure tissue or mesh repair.
Corrosion Behaviour of Annealed Cnx/Tiny Multilayers
ZHANG Guo-dong; PAN Chun-xu; FU Qiang; ZHANG Fu-ju; ZOU Yang; ZHANG Shao-hua
2004-01-01
The corrosion behaviour of annealed CNx/TiNy multilayers have been investigated using potentiodynamic test in a 0.5MH2SO4 solution. The coating has been deposited on W6Mo5Cr4V2 steel by reactive magnetron sputtering and then annealed at different temperature. The annealed multilayers showed superior corrosion resistance as compared to the as-deposited coating. The corrosion model of annealed CNx/TiNy multilayers has been set up.
Corrosion Behaviour of Annealed Cnx／Tiny Multilayers
ZHANGGuo-dong; PANChun-xu; FUQiang; ZHANGFu-ju; ZOUYang; ZHANGShao-hua
2004-01-01
The corrosion behaviour of annealed CNx/TiNy multilayers have been investigated using potentiodynamic test in a 0.5MH2SO4 solution. The coating has been deposited on W6MoSCr4V2 steel by reactive magnetron sputtering and then annealed at different temperature. The annealed multilayers showed superior corrosion resistance as compared to the as-deposited coating, The corrosion model of annealed CNx/TiNy multilayers has been set up.
Reactive multilayers fabricated by vapor deposition: A critical review
Reactive multilayer thin films are a class of energetic materials that continue to attract attention for use in joining applications and as igniters. Generally composed of two reactants, these heterogeneous solids can be stimulated by an external source to promptly release stored chemical energy in a sudden emission of light and heat. In this critical review article, results from recent investigations of these materials are discussed. Discussion begins with a brief description of the vapor deposition techniques that provide accurate control of layer thickness and film composition. More than 50 reactive film compositions have been reported to date, with most multilayers fabricated by magnetron sputter deposition or electron-beam evaporation. In subsequent sections, we review how multilayer ignition threshold, reaction rate, and total heat are tailored via thin film design. For example, planar multilayers with nanometer-scale periodicity exhibit rapid, self-sustained reactions with wavefront velocities up to 100 m/s. Numeric and analytical models have elucidated many of the fundamental processes that underlie propagating exothermic reactions while demonstrating how reaction rates vary with multilayer design. Recent, time-resolved diffraction and imaging studies have further revealed the phase transformations and the wavefront dynamics associated with propagating chemical reactions. Many reactive multilayers (e.g., Co/Al) form product phases that are consistent with published equilibrium phase diagrams, yet a few systems, such as Pt/Al, develop metastable products. The final section highlights current and emerging applications of reactive multilayers. Examples include reactive Ni(V)/Al and Pd/Al multilayers which have been developed for localized soldering of heat-sensitive components. - Highlights: • Vapor-deposited, reactive multilayers and their properties are reviewed. • This article includes discussion of various structure-property relationships.
Uniformly oriented gramicidin channels embedded in thick monodomain lecithin multilayers.
Huang, H W; Olah, G A
1987-01-01
Phosphatidylcholine multilayers, containing 20% water by total sample weight and gramicidin/lipid molar ratios up to 1:40 were aligned by low temperature annealing (less than 60 degrees C) and mechanical stressing. We were able to obtain large (greater than 80 micron thick X 40 mm2 area) monodomain defect-free multilayers containing approximately 10(17) uniformly oriented gramicidin channels. The alignment of lipid multilayers was monitored by conoscopy and polarized microscopy. The smectic d...
Synthesis and Characterization of Multilayered Diamond Coatings for Biomedical Implants
Booth, Leigh; Catledge, Shane A.; Nolen, Dustin; Raymond G. Thompson; Vohra, Yogesh K.
2011-01-01
With incredible hardness and excellent wear-resistance, nanocrystalline diamond (NCD) coatings are gaining interest in the biomedical community as articulating surfaces of structural implant devices. The focus of this study was to deposit multilayered diamond coatings of alternating NCD and microcrystalline diamond (MCD) layers on Ti-6Al-4V alloy surfaces using microwave plasma chemical vapor deposition (MPCVD) and validate the multilayer coating’s effect on toughness and adhesion. Multilayer...
Neutron diffraction studies of thin film multilayer structures
The application of neutron diffraction methods to the study of the microscopic chemical and magnetic structures of thin film multilayers is reviewed. Multilayer diffraction phenomena are described in general and in particular for the case in which one of the materials of a bilayer is ferromagnetic and the neutron beam polarized. Recent neutron diffraction measurements performed on some interesting multilayer systems are discussed. 70 refs., 5 figs
Multilayered Polymer Coated Carbon Nanotubes to Deliver Dasatinib
Moore, Thomas L.; Grimes, Stuart W.; Lewis, Robert L.; Alexis, Frank
2013-01-01
Multilayered, multifunctional polymer coatings were grafted onto carbon nanotubes (CNT) using a one-pot, ring-opening polymerization in order to control the release kinetic and therapeutic efficacy of dasatinib. Biocompatible, biodegradable multilayered coatings composed of poly(glycolide) (PGA), and poly(lactide) (PLA) were polymerized directly onto hydroxyl-functionalized CNT surfaces. Sequential addition of monomers into the reaction vessel enabled multilayered coatings of PLA-PGA, or PGA-...
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, Junyoung; Gulcehre, Caglar; Cho, Kyunghyun; Bengio, Yoshua
2014-01-01
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent unit...
Recurrent stroke: what have we learnt?
Rabia, K; Khoo, Em
2007-01-01
Stroke is the third leading cause of death, a major cause of disability in adults, and is frequently more disabling than fatal. With a decline in mortality from initial cerebral infarction and an increase in the life expectancy of the population, the number of patients with recurrent stroke and ensuing cardiovascular events will become greater. Thus it is important to find out those patients at high risk of stroke recurrence. This case report illustrates the process of recurrent stroke and the resulting disabilities and morbidities in a 42-year- old man. The role of integrated stroke rehabilitation programme is described. PMID:25606084
Prevention of Recurrent Staphylococcal Skin Infections.
Creech, C Buddy; Al-Zubeidi, Duha N; Fritz, Stephanie A
2015-09-01
Staphylococcus aureus infections pose a significant health burden. The emergence of community-associated methicillin-resistant S aureus has resulted in an epidemic of skin and soft tissue infections (SSTI), and many patients experience recurrent SSTI. As S aureus colonization is associated with subsequent infection, decolonization is recommended for patients with recurrent SSTI or in settings of ongoing transmission. S aureus infections often cluster within households, and asymptomatic carriers serve as reservoirs for transmission; therefore, a household approach to decolonization is more effective than measures performed by individuals alone. Novel strategies for the prevention of recurrent SSTI are needed. PMID:26311356
Esophageal recurrence of medullary thyroid carcinoma.
Muñoz de Nova, Jose Luis; Dworzynska, Agnieszka; Lorente-Poch, Leyre; Sancho, Juan Jose; Sitges-Serra, Antonio
2015-12-01
Medullary thyroid carcinoma (MTC) metastasizes to the regional lymph nodes and to the lungs, liver and bones. Only one case of recurrence of MTC involving the upper gastrointestinal tract has been reported so far. We describe the case of a 38-year-old woman with MTC, who developed an upper esophageal submucosal recurrence after two previous local recurrences treated surgically and one ethanol injection. After resection of the right lateral esophageal wall, calcitonin dropped by 60% and showed a doubling time >1 year. We cannot rule out the role of deep ethanol injection in the involvement of the cervical esophagus wall. PMID:26645011
Recurrence of gestational diabetes in primiparous women
Kruse, Anne R; Darling, Mette S; Hansen, Mia K L;
2015-01-01
Introduction Gestational diabetes mellitus (GDM) increases the risk for diabetes in the next pregnancy and later in life. Thus, estimating the risk of GDM in further pregnancies provides a time frame for possible preventive measures. We aimed to calculate the recurrence rate of GDM in primiparous...... women had a subsequent pregnancy and they all attended the recommended screening procedure, a 75-g oral glucose tolerance test at 14–20 (early) or 27–30 (late) weeks' gestation. The recurrence rate of GDM was 47.2%. The risk of recurrence was less in women who lost weight between the first and the...
The first example of multilayer films with thermochromic properties
A novel thermochromic multilayer film containing polyoxometalate cluster K12.5Na1.5[NaP5W30O110] has been fabricated by layer-by-layer self-assembly method. In case of the multilayer film, the color changes gradually from yellowish to blue when it is subjected to temperatures between 120 deg. C and 180 deg. C for a period of time, and the multilayer film could be bleached in air at room temperature to recover its initial state. The novel thermochromic multilayer may be of practical benefit in the development of thermosensors, which would represent promising materials for future applications
The first example of multilayer films with thermochromic properties
Jiang, Min; Wang, Enbo; Xu, Lin; Kang, Zhenhui; Lian, Suoyuan
2004-04-01
A novel thermochromic multilayer film containing polyoxometalate cluster K 12.5Na 1.5[NaP 5W 30O 110] has been fabricated by layer-by-layer self-assembly method. In case of the multilayer film, the color changes gradually from yellowish to blue when it is subjected to temperatures between 120°C and 180°C for a period of time, and the multilayer film could be bleached in air at room temperature to recover its initial state. The novel thermochromic multilayer may be of practical benefit in the development of thermosensors, which would represent promising materials for future applications.
Multi-Periodicity Induces Prominent Optical Phenomena in Plasmonic Multilayers
Orlov, Alexey A.; Krylova, A. K.; Zhukovsky, Sergei; Babicheva, Viktoriia; Belov, Pavel A.
We introduce multi-periodicity in plasmonic multilayers and develop a general theory for the description of their eigenwaves. We define the order of multi-periodicity as the number of different kinds of plasmonic interfaces present in the multilayer, and investigate the optical effects that arise...... as this order increases from one (simple periodic multilayers) to two (bi- periodic multilayers) and beyond. For example, we show the formation of additional photonic bands, multi-refringence of p -polarized light, Dirac and mixed states....
Microstructural Characterisation of Giant Magnetoresistive Co/Cu Multilayers
Antiferromagnetically-coupled Co/Cu multilayers prepared by magnetron sputtering exhibit pronounced giant magnetoresistance (GMR) effect at room temperature. Using both diffraction and imaging techniques, we studied the in-plane and out-of-plane crystallographic and layering microstructural features of these multilayers. Dominant characteristic features associated with the multilayers, such as lateral and vertical columnar grain orientations as well as layer undulations and regularity, were identified. By deliberately introducing microstructural changes to the materials system using buffer layer and heat treatment, detailed microstructural analysis have provided an insight into the dependence of GMR on microstructures of the multilayers.
Microstructural Characterisation of Giant Magnetoresistive Co/Cu Multilayers
Antiferromagnetically-coupled Co/Cu multilayers prepared by magnetron sputtering exhibit pronounced giant magnetoresistance (GMR) effect at room temperature. Using both diffraction and imaging techniques, we studied the in-plane and out-of-plane crystallographic and layering microstructural features of these multilayers. Dominant characteristic features associated with the multilayers, such as the lateral and vertical columnar grain orientations as well as layer undulations and regularity, were identified. By deliberately introducing microstructural changes to the materials system using buffer layer and heat treatment, detailed microstructural analysis had provided an insight into the dependence of GMR on the microstructures of the multilayers.
Building a Chaotic Proved Neural Network
Bahi, Jacques M; Salomon, Michel
2011-01-01
Chaotic neural networks have received a great deal of attention these last years. In this paper we establish a precise correspondence between the so-called chaotic iterations and a particular class of artificial neural networks: global recurrent multi-layer perceptrons. We show formally that it is possible to make these iterations behave chaotically, as defined by Devaney, and thus we obtain the first neural networks proven chaotic. Several neural networks with different architectures are trained to exhibit a chaotical behavior.
Artificial neural networks applied to forecasting time series
Montaño Moreno, Juan José; Palmer Pol, Alfonso; Muñoz Gracia, María del Pilar
2011-01-01
This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparativ...
Aim: To evaluate recurrence rate and associated risk factors for recurrence after ethanol ablation (EA) in patients with predominantly cystic thyroid nodules. Materials and methods: This observational study was approved by the Ethics Committee of the Institutional Review Board and informed consent for procedures was obtained. From April 2009 to April 2013, 107 consecutive patients with predominantly cystic nodules were treated using EA. Recurrence was defined as nodules showing a residual solid portion with internal vascularity, cosmetic problems remaining, or persistent symptoms, and patients who requested additional therapy to resolve their symptomatic or cosmetic problems. Delayed recurrence was defined as treated nodules that showed no recurrent features at 1 month, but showed newly developed recurrent features during the longer follow-up period. Multivariate analysis was used for variables to demonstrate the independent factors related to volume reduction. Results: One month after EA, 18.7% of patients (20/107) showed recurrence. Among 87 patients with non-recurrence, 24.1% (21/87) showed delayed recurrence. The total recurrence rate was 38.3% (41/107). Patients with recurrence (n = 41) were treated using radiofrequency ablation (n = 28), second EA (n = 4), and refused further treatment (n = 9). These patients responded well to repeat EA and radiofrequency ablation. Multivariate analysis demonstrated that the initial nodule volume (>20 ml; p < 0.036) and vascularity (grade >1; p < 0.049) were independent predictors of volume reduction at last follow-up. Conclusions: The results revealed that although EA seemed to be effective during the initial period, delayed recurrence should be considered during longer-term follow-up. The independent predictors of recurrence were initial volume (>20 ml) and vascularity. - Highlights: • Ethanol ablation showed unsatisfactory results in 18.7% of patient at one month. • Delayed recurrence was observed in 24
Mindfulness Therapy May Help Ease Recurrent Depression
... page: https://medlineplus.gov/news/fullstory_158537.html Mindfulness Therapy May Help Ease Recurrent Depression Review of ... 27, 2016 WEDNESDAY, April 27, 2016 (HealthDay News) -- Mindfulness therapy may help reduce the risk of repeated ...
Poincaré recurrences of DNA sequences
Frahm, K. M.; Shepelyansky, D. L.
2012-01-01
We analyze the statistical properties of Poincaré recurrences of Homo sapiens, mammalian, and other DNA sequences taken from the Ensembl Genome data base with up to 15 billion base pairs. We show that the probability of Poincaré recurrences decays in an algebraic way with the Poincaré exponent β≈4 even if the oscillatory dependence is well pronounced. The correlations between recurrences decay with an exponent ν≈0.6 that leads to an anomalous superdiffusive walk. However, for Homo sapiens sequences, with the largest available statistics, the diffusion coefficient converges to a finite value on distances larger than one million base pairs. We argue that the approach based on Poncaré recurrences determines new proximity features between different species and sheds a new light on their evolution history.
Risk of Seizure Recurrence with Neurocysticercosis
J Gordon Millichap
2002-01-01
The risk of seizure recurrence after a first seizure due to neurocysticercosis (NC) was evaluated in a prospective study of 77 patients at the School of Medicine and Research Institute, University of Cuenca, Ecuador, and Columbia University, New York.
Ocean wave forecasting using recurrent neural networks
Mandal, S.; Prabaharan, N.
, merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...
Postoperative recurrence after VATS for spontaneous pneumothorax
A total of 88 cases of 81 patients with spontaneous pneumothorax treated at the hospital from March 1992 to August 2001 were subjected to a study of examining preoperative chest CT and thoracographic findings from the standpoint of postoperative recurrence. Preoperative chest CT and thoracography were conducted in 82 cases and 41 cases (including 25 cases with continuous air leakage), respectively. Eight (9.1%) patients developed recurrence of pneumothorax, and three patients of them underwent reoperation. Considering the intraoperative findings, newly formed bullae appeared to be a cause of recurrence. Resulting from these examinations, we conclude that it is difficult to predict the risk factor for postoperative recurrence at this time, in addition, it is important that the area of air leakage can be confirmed by thoracoscopic findings. (author)
Dietary and medical management of recurrent nephrolithiasis.
Shah, Silvi; Calle, Juan Camilo
2016-06-01
Dietary approaches and medical treatment can prevent recurrence of urinary stones. Some interventions are appropriate for all types of stones, but there are particular risk factors that may need directed therapy. PMID:27281259
Recurrence quantification analysis theory and best practices
Jr, Jr; Marwan, Norbert
2015-01-01
The analysis of recurrences in dynamical systems by using recurrence plots and their quantification is still an emerging field. Over the past decades recurrence plots have proven to be valuable data visualization and analysis tools in the theoretical study of complex, time-varying dynamical systems as well as in various applications in biology, neuroscience, kinesiology, psychology, physiology, engineering, physics, geosciences, linguistics, finance, economics, and other disciplines. This multi-authored book intends to comprehensively introduce and showcase recent advances as well as established best practices concerning both theoretical and practical aspects of recurrence plot based analysis. Edited and authored by leading researcher in the field, the various chapters address an interdisciplinary readership, ranging from theoretical physicists to application-oriented scientists in all data-providing disciplines.
Recurrent encephalic hemorrhage associated with cocaine abuse
We report a case of recurrent intracerebral hemorrhage secondary to cocaine abuse in a patient with no other predisposing factors. The hemorrhages were located both supra- and infratentorially. (orig.)
Mindfulness Therapy May Help Ease Recurrent Depression
... nlm.nih.gov/medlineplus/news/fullstory_158537.html Mindfulness Therapy May Help Ease Recurrent Depression Review of ... 27, 2016 WEDNESDAY, April 27, 2016 (HealthDay News) -- Mindfulness therapy may help reduce the risk of repeated ...
Is acute recurrent pancreatitis a chronic disease?
Alberto Mariani; Pier Alberto Testoni
2008-01-01
Whether acute recurrent pancreaUtis is a chronic disease is still debated and a consensus is not still reached as demonstrated by differences in the classification of acute recurrent pancreatitis.There is major evidence for considering alcoholic pancreatitis as a chronic disease ab initio while chronic pancreatitis lesions detectable in biliary acute recurrent pancreatitis (ARP) seem a casual association.Cystic fibrosis transmembrane con ductance regulator (CFTR) gene mutation,hereditary and obstructive pancreatitis seem an acute disease that progress to chronic pancreatitis,likely as a consequence of the activation and proliferation of pancreatic stellate cells that produce and activate collagen and therefore fibrosis.From the diagnostic point of view,in patients with acute recurrent pancreatitis Endoscopic ultrasound (EUS) seems the more reliable technique for an accurate evaluation and follow-up of some ductal and parenchymal abnormalities suspected for early chronic pancreatitis.
The half plane UIPT is recurrent
Angel, Omer; Ray, Gourab
2016-01-01
We prove that the half plane version of the uniform infinite planar triangulation (UIPT) is recurrent. The key ingredients of the proof are a construction of a new full plane extension of the half plane UIPT, based on a natural decomposition of the half plane UIPT into independent layers, and an extension of previous methods for proving recurrence of weak local limits (still using circle packings).
Inguinal hernia recurrence: Classification and approach
Campanelli Giampiero; Pettinari Diego; Cavalli Marta; Avesani Ettore
2006-01-01
The authors reviewed the records of 2,468 operations of groin hernia in 2,350 patients, including 277 recurrent hernias updated to January 2005. The data obtained - evaluating technique, results and complications - were used to propose a simple anatomo-clinical classification into three types which could be used to plan the surgical strategy:Type R1: first recurrence ′high,′ oblique external, reducible hernia with small (< 2 cm) defect in non-obese patients, after pure tissu...
Optimizing and Contrasting Recurrent Neural Network Architectures
Krause, Ben
2015-01-01
Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series. However, it remains to be determined what optimization techniques and recurrent architectures can be used to best realize this potential. The experiments presented take a deep look into Hessian free optimization, a powerful second order optimization method that has shown promising results, but still does not enjoy widespread use. This algorithm was used to train to a number of RNN arch...
Recurrent aphthous stomatitis and Helicobacter pylori
Gomes, Carolina-Cavaliéri; Gomez, Ricardo-Santiago; Zina, Lívia-Guimarães; Amaral, Fabrício-Rezende
2016-01-01
Background Recurrent aphthous stomatitis (RAS) is a recurrent painful ulcerative disorder that commonly affects the oral mucosa. Local and systemic factors such as trauma, food sensitivity, nutritional deficiencies, systemic conditions, immunological disorders and genetic polymorphisms are associated with the development of the disease. Helicobacter pylori (H. pylori) is a gram-negative, microaerophile bacteria, that colonizes the gastric mucosa and it was previously suggested to be involved ...
TODDLER WITH RECURRENT ABDOMINAL PAIN: MIGRAINE?
Amit; Vaishali
2014-01-01
Abdominal migraine is a migraine variant, causing chronic idiopathic recurrent abdominal pain in 4-15% of children. It is usually seen between the ages of seven to twelve years and is more common in girls, with peak prevalence at the age of ten years. We report a 3 year old girl suffering from recurrent abdominal pain since 1½ years of age, who underwent extensive investigations as well as diagnostic laparotomy with appendectomy, and was ultimately diagnosed to have abdomi...
Recurrence of adrenal aldosterone-producing adenoma
Calvo-Romero, J. M.; Ramos-Salado, J. L.
2000-01-01
Conn's syndrome (adrenal aldosterone-producing adenoma) and bilateral adrenal hyperplasia are the most common causes of primary aldosteronism. The treatment of choice for patients with aldosterone-producing adenoma is unilateral total adrenalectomy. Recurrence after adequate surgery is exceptional. We present a patient with recurrence of an aldosterone-producing adenoma in the right adrenal gland 9 years after adenomectomy of a aldosterone-producing adenoma in the same adrenal gland. We concl...
Current surgery of retinal detachment recurrence. Review
V. D. Zakharov
2012-01-01
Full Text Available this review presents a detailed analysis and an experience of surgical treatment of retinal detachment recurrence associated with light silicone oil tamponade of vitreous cavity. Approaches and variants of treatment were described in the historical aspect and till now. there are considered general and particular issues in case of retinal detachment recurrence appearance, expediency and volume of intraoperative manipulations, time of operation and choice of temporary substitute of vitreous body for a purpose of postoperative tamponade of vitreous cavity.
Heterogeneous recurrence monitoring and control of nonlinear stochastic processes
Recurrence is one of the most common phenomena in natural and engineering systems. Process monitoring of dynamic transitions in nonlinear and nonstationary systems is more concerned with aperiodic recurrences and recurrence variations. However, little has been done to investigate the heterogeneous recurrence variations and link with the objectives of process monitoring and anomaly detection. Notably, nonlinear recurrence methodologies are based on homogeneous recurrences, which treat all recurrence states in the same way as black dots, and non-recurrence is white in recurrence plots. Heterogeneous recurrences are more concerned about the variations of recurrence states in terms of state properties (e.g., values and relative locations) and the evolving dynamics (e.g., sequential state transitions). This paper presents a novel approach of heterogeneous recurrence analysis that utilizes a new fractal representation to delineate heterogeneous recurrence states in multiple scales, including the recurrences of both single states and multi-state sequences. Further, we developed a new set of heterogeneous recurrence quantifiers that are extracted from fractal representation in the transformed space. To that end, we integrated multivariate statistical control charts with heterogeneous recurrence analysis to simultaneously monitor two or more related quantifiers. Experimental results on nonlinear stochastic processes show that the proposed approach not only captures heterogeneous recurrence patterns in the fractal representation but also effectively monitors the changes in the dynamics of a complex system
FDG-PET probe-guided surgery for recurrent retroperitoneal testicular tumor recurrences
Jong, J.S. de; van Ginkel, R.J.; Slart, R.H.J.A.; Lemstra, C.L.; Paans, A.M.J.; Mulder, N. H.; Hoekstra, H.J.
2010-01-01
Abstract Aim Tumor marker based recurrences of previously treated testicular cancer are generally detected with CT-scan. They sometimes cannot be visualized with conventional morphologic imaging. FDG-PET has the ability to detect these recurrences. PET probe-guided surgery, may facilitate the extent of surgery and optimize the surgical resection. Methods Three patient with resectable 2nd or 3rd recurrent testicular cancer based on elevated tumor mar...
Recurrence relations for spin foam vertices
We study recurrence relations for various Wigner 3nj-symbols and the non-topological 10j-symbol. For the 6j- and the 15j-symbols which correspond to basic amplitudes of 3d and 4d topological spin foam models, recurrence relations are obtained from the invariance under Pachner moves and can be interpreted as quantizations of the constraints of the underlying classical field theories. We also derive recurrences from the action of holonomy operators on spin network functionals, making a more precise link between the topological Pachner moves and the classical constraints. Interestingly, our recurrence relations apply to any SU(2) invariant symbol, depending on the cycles of the corresponding spin network graph. Another method is used for non-topological objects such as the 10j-symbol and pseudo-isosceles 6j-symbols. The recurrence relations are also interpreted in terms of elementary geometric properties. Finally, we discuss the extension of the recurrences to take into account boundary states which leads to equations similar to Ward identities for correlation functions in the Barrett-Crane model.
Management of recurrent postoperative fungal endophthalmitis
Anand Vinekar
2014-01-01
Full Text Available Aim: To report the management of recurrent postoperative fungal endophthalmitis (POFE after failed pars plana vitrectomy (PPV and antifungal therapy. Settings and Design: Tertiary Care Referral Centre in North India. Retrospective, single institution, interventional case-series. Materials and Methods: Six patients with microbiologically proven recurrent post-operative fungal endophthalmitis refractory to conventional management were included. The final recurrence was managed with intraocular lens (IOL explantation and re-PPV. Main outcome measures included preserved globe anatomy, visual acuity and retinal status. ′Anatomical success′ was defined as preserved anatomy of the globe, and absence of signs of inflammation. ′Functional success′ was defined as an attached retina and a best corrected visual acuity of better than 20/400. Results: Of the six cases of POFE, five were culture positive [Aspergillus flavus (1, Aspergillus fumigatus (2, Candida albicans (1 and Candida glabrata (1] and one was smear positive for yeast. All recurred (mean recurrences, 4 despite a mean of 2.17 PPVs and intravitreal amphotericin B. No recurrences were observed after IOL explantation with re - PPV (median follow-up, 37 months. Pre-study defined criteria for successful ′anatomical′ and ′functional′ outcomes were achieved in 83.3% and 50% respectively. Conclusion: This report highlights the effective role of combined IOL explantation with PPV in managing recurrent POFE.
Dipole radiation in a multilayer geometry
There are several kinds of experiments that can be done with multilayer stacks of dielectric media which require an understanding of light emission by sources within the stack for their analysis. These experiments may involve, for example, light-emitting tunnel junctions, Raman scattering in Kretschmann and other multilayered geometries, and Rayleigh scattering by small amounts of surface or interface roughness, either alone or in combination with other processes. A set of electromagnetic Green's functions for a multilayer stack of isotropic dielectric media [D. L. Mills and A. A. Maradudin, Phys. Rev. B 12, 2943 (1975)] gives the electric fields produced everywhere by a point source of current oscillating at a frequency f. These Green's functions can thus be used to solve this type of problem. In this paper we show how these Green's functions can be written in terms of 2 x 2 transfer matrices of the type commonly used to find the fields in a dielectric stack due to an incident plane wave. With this simplification we can easily evaluate the Green's functions for a stack with an arbitrary number of layers. We further show that, when the electric fields generated by a point source within the stack are evaluated far away, they can be written directly in terms of the electric fields that would be generated at the location of the current source by plane waves incident from the direction of the observation point. We show that this follows from the Lorentz reciprocity theorem. Thus, in this case the formalism of Green's functions is not needed
Recurrence of cancer in the breast is an important problem in breast-conserving therapy. We evaluated risk factors for recurrence from the viewpoint of recurrence type and outcome after recurrence. Of 533 cases of breast cancer treated with breast-conserving surgery from April 1989 through July 2000, disease in 66 recurred (12.4%) and were classified as 23 cases of breast recurrence only, 16 cases of both breast recurrence and distant metastasis, and 27 cases of distant metastasis only. The clinical factors examined included age, lymphatic invasion, nodal status, extensive intraductal component (EIC), proliferative activity, and estrogen receptor (ER) status. Of the 39 cases of breast recurrence, 19 had intramammary tumors and 20 had extramammary tumors of the skin, subcutaneous tissue, or muscle, including 8 cases with inflammatory breast recurrence. Multivariate analysis showed that factors correlated with breast recurrence were age, ER status, proliferative activity, and surgical margin. EIC-comedo was related to intramammary recurrence, whereas lymphatic invasion and nodal status were related to extramammary recurrence. Postoperative irradiation was an effective treatment for tumors in young women and tumors with positive margins or a comedo component. Outcome after breast recurrence depended on nodal status at primary operation, and survival rates were worst in patients with inflammatory breast recurrence. In conclusion, age, EIC-comedo status, the surgical margin, and negative ER status were correlated with breast recurrence. Countermeasures against these factors should be investigated. (author)
Measure of Node Similarity in Multilayer Networks
Møllgaard, Anders; Zettler, Ingo; Dammeyer, Jesper;
2016-01-01
The weight of links in a network is often related to the similarity of thenodes. Here, we introduce a simple tunable measure for analysing the similarityof nodes across different link weights. In particular, we use the measure toanalyze homophily in a group of 659 freshman students at a large...... university.Our analysis is based on data obtained using smartphones equipped with customdata collection software, complemented by questionnaire-based data. The networkof social contacts is represented as a weighted multilayer network constructedfrom different channels of telecommunication as well as data on...
Fracture mechanics parameters of multilayer pipes
Šestáková, Lucie; Náhlík, Luboš; Hutař, Pavel; Knésl, Zdeněk
2007-01-01
Roč. 1, č. 1 (2007), s. 299-306. ISSN 1802-680X. [Výpočtová mechanika 2007. Hrad Nečtiny, 05.11.2007-07.11.2007] R&D Projects: GA ČR GA101/05/0227; GA ČR GD106/05/H008 Institutional research plan: CEZ:AV0Z20410507 Keywords : multilayer pipe s * K-calibration * T- stress * finite element method Subject RIV: JL - Materials Fatigue, Friction Mechanics
Fracture mechanics parameters of multilayer pipes
Šestáková L.
2007-10-01
Full Text Available Multilayer pipes consisting of different materials are frequently used in praxis because of partial improvement of the properties of pipe systems. To estimate lifetime of these pipes the basic fracture parameters have to be determined. In this work finite element calculations are applied in order to estimate the stress intensity factor K and T-stress values for a new type of non-homogenous C-shape specimen. The application of calculated K and T values to laboratory estimation of fracture toughness and its transferability to real pipe system is discussed.
Josephson plasma resonance in superconducting multilayers
Pedersen, Niels Falsig; Sakai, S
1998-01-01
the recently derived plasma resonance phenomena for high-T-c superconductors of the Bi2Sr2CaCu2Ox type is discussed. Our approach allows us to give full details of the different plasma resonance excitations, and we also predict the existence of new nonlinear effects, so far only identified in single......We derive an analytical solution for the Josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low-T-c systems with magnetic coupling between the superconducting layers. but many features of our results are more general, and thus an application to...
Effective Interactions between Multilayered Ionic Microgels
Clemens Hanel
2014-12-01
Full Text Available Using a one-component reduction formalism, we calculate the effective interactions and the counterion density profiles for microgels that feature a multilayered shell structure. We follow a strategy that involves second order perturbation theory and obtain analytical expressions for the effective interactions by modeling the layers of the particles as linear superpostion of homogeneously charged spheres. The general method is applied to the important case of core–shell microgels and compared with the well-known results for a microgel that can be approximated by a macroscopic, and homogeneously charged, spherical macroion.
Ceramic-Metal Interfaces in Multilayer Actuators
Engell, John; Pedersen, Henrik Guldberg; Andersen, Bjørn;
1996-01-01
quality and strength of this interface. In the case of a weak ceramic-metal interface, delaminations will occur under severe working conditions.Work has been carried out on a commercial PZT type ceramic and various types of Pt electrode paste. The present study involves characterization of the interface......Multilayer actuators consist of a number of piezoelectric or electrostrictive ceramic layers, separated by thin metal electrodes. Thus, the ceramic-metal interface plays an even more important role than for bulk piezoceramics. The performance and durability of the actuator depends closely on the...
Engineering aspects of multilayer piezoceramic actuators
With the increasing demand for multilayer ceramic chip components a full understanding of the co-firing of ceramics with metal electrodes becomes important. In the present work the processing of a piezoelectric monolithic actuator by stacking and cofiring Ag-Pd electroded tape cast layers was studied. The inter-diffusion and microstructure of the co-fired interface of PZT ferroelectrics and Ag-Pd metal electrode were examined by scanning electron microscopy (SEM) and energy-dispersive microanalysis. No strong structural distortions and interdiffusion were observed at the co-fired ceramic-electrode interface
Ceramic-Metal Interfaces in Multilayer Actuators
Engell, John; Pedersen, Henrik Guldberg; Andersen, Bjørn; James, Andrew S.
1996-01-01
Multilayer actuators consist of a number of piezoelectric or electrostrictive ceramic layers, separated by thin metal electrodes. Thus, the ceramic-metal interface plays an even more important role than for bulk piezoceramics. The performance and durability of the actuator depends closely on the...... quality and strength of this interface. In the case of a weak ceramic-metal interface, delaminations will occur under severe working conditions.Work has been carried out on a commercial PZT type ceramic and various types of Pt electrode paste. The present study involves characterization of the interface...
Engineering aspects of multilayer piezoceramic actuators
Golovnin, V. A.; Kaplunov, I. A.; Ivanova, A. I.; Grechishkin, R. M.
2013-12-01
With the increasing demand for multilayer ceramic chip components a full understanding of the co-firing of ceramics with metal electrodes becomes important. In the present work the processing of a piezoelectric monolithic actuator by stacking and cofiring Ag-Pd electroded tape cast layers was studied. The inter-diffusion and microstructure of the co-fired interface of PZT ferroelectrics and Ag-Pd metal electrode were examined by scanning electron microscopy (SEM) and energy-dispersive microanalysis. No strong structural distortions and interdiffusion were observed at the co-fired ceramic-electrode interface.
Optics and multilayer coatings for EUVL systems
Soufli, R; Bajt, S; Hudyma, R M; Taylor, J S
2008-03-21
EUV lithography (EUVL) employs illumination wavelengths around 13.5 nm, and in many aspects it is considered an extension of optical lithography, which is used for the high-volume manufacturing (HVM) of today's microprocessors. The EUV wavelength of illumination dictates the use of reflective optical elements (mirrors) as opposed to the refractive lenses used in conventional lithographic systems. Thus, EUVL tools are based on all-reflective concepts: they use multilayer (ML) coated optics for their illumination and projection systems, and they have a ML-coated reflective mask.
Supervised Learning in Multilayer Spiking Neural Networks
Sporea, Ioana
2012-01-01
The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.
Analysis of multi-layer ERBS spectra
Marmitt, G.G. [Atomic and Molecular Physics Laboratories, Research School of Physics and Engineering, The Australian National University, Canberra 0200 (Australia); Instituto de Fisica da Universidade Federal do Rio Grande do Sul, Avenida Bento Goncalves 9500, 91501-970 Porto Alegre, RS (Brazil); Rosa, L.F.S. [Instituto de Fisica da Universidade Federal do Rio Grande do Sul, Avenida Bento Goncalves 9500, 91501-970 Porto Alegre, RS (Brazil); Nandi, S.K. [Electronic Materials Engineering Department, Research School of Physics and Engineering, The Australian National University, Canberra 0200 (Australia); Research School of Astronomy and Astrophysics, The Australian National University, Canberra, ACT 2611 (Australia); Department of Physics, University of Chittagong, Chittagong 4331 (Bangladesh); Vos, M., E-mail: maarten.vos@anu.edu.au [Atomic and Molecular Physics Laboratories, Research School of Physics and Engineering, The Australian National University, Canberra 0200 (Australia)
2015-07-15
Highlights: • Electron Rutherford backscattering (ERBS) spectra are presented. • The spectra are fitted based on physical meaningful quantities. • Very consistent results are obtained for spectra taken under different conditions. • This establishes that ERBS can be used to measure film thicknesses. - Abstract: A systematic way of analysis of multi-layer electron Rutherford backscattering spectra is described. The approach uses fitting in terms of physical meaningful parameters. Simultaneous analysis then becomes possible for spectra taken at different incoming energies and measurement geometries. Examples are given to demonstrate the level of detail that can be resolved by this technique.
Josephson plasma resonance in superconducting multilayers
Pedersen, Niels Falsig; Sakai, S
1998-01-01
We derive an analytical solution for the Josephson plasma resonance of superconducting multilayers. This analytical solution is derived mainly for low-T-c systems with magnetic coupling between the superconducting layers. but many features of our results are more general, and thus an application to...... the recently derived plasma resonance phenomena for high-T-c superconductors of the Bi2Sr2CaCu2Ox type is discussed. Our approach allows us to give full details of the different plasma resonance excitations, and we also predict the existence of new nonlinear effects, so far only identified in single...
MULTI-LAYER GRID REFINEMENT METHOD
TSUN-ZEE MAI
2012-07-01
Full Text Available The uniform grid scheme has been widely used to solve a partial differential equation. Due to the extreme large linear systems generated by the uniform grid scheme, a lot of computation time is required. To improve the efficiency of the uniform grid scheme, a more economical method is desirable. In this paper, we propose a multi-layer grid refinement method for solving a partial different equation over a rectangular domain with Dirichlet boundary conditions. Numerical experiments demonstrate that the efficiency has been improved significantly, and the accuracy is satisfactory.
Multilayer mirror interferometer for very cold neutrons at KUR
Multilayer mirror interferometer for very cold neutrons has been in progress for these years at KUR. This device is a kind of Mach-Zehnder interferometer using multilayer neutron monochromators as optical elements. We describe an adjusting methods of the mirror position with very high precision. (author)
A MULTILAYER BIOCHEMICAL DRY DEPOSITION MODEL 1. MODEL FORMULATION
A multilayer biochemical dry deposition model has been developed based on the NOAA Multilayer Model (MLM) to study gaseous exchanges between the soil, plants, and the atmosphere. Most of the parameterizations and submodels have been updated or replaced. The numerical integration ...
Thermoelectric power of multilayer compositions of aluminium and carbon nanotubes
Changing the thermoelectric power monolayer and multilayer aluminium foil and multilayer foils compositions of aluminium and carbon nanotubes is the deformation ε < 60% due to the scattering of conduction electrons at dislocations and ε=(70 ...96)% - due to their scattering on the boundaries between the layers (thermoelectric size effect)
Multilayer tape cast SOFC – Effect of anode sintering temperature
Hauch, Anne; Birkl, Christoph; Brodersen, Karen; Jørgensen, Peter Stanley
2012-01-01
Multilayer tape casting (MTC) is considered a promising, cost-efficient, up-scalable shaping process for production of planar anode supported solid oxide fuel cells (SOFC). Multilayer tape casting of the three layers comprising the half cell (anode support/active anode/electrolyte) can potentially...