Learning Methods for Radial Basis Functions Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Kudová, Petra
2005-01-01
Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005
Radial basis function neural network in fault detection of automotive ...
African Journals Online (AJOL)
Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
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Agus Arif
2014-11-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.
On-line learning in radial basis functions networks
Freeman, Jason; Saad, David
1997-01-01
An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives ...
Complexity of Gaussian-Radial-Basis Networks Approximating Smooth Functions
Czech Academy of Sciences Publication Activity Database
Kainen, P.C.; Kůrková, Věra; Sanguineti, M.
2009-01-01
Roč. 25, č. 1 (2009), s. 63-74 ISSN 0885-064X R&D Projects: GA ČR GA201/08/1744 Institutional research plan: CEZ:AV0Z10300504 Keywords : Gaussian-radial-basis-function networks * rates of approximation * model complexity * variation norms * Bessel and Sobolev norms * tractability of approximation Subject RIV: IN - Informatics, Computer Science Impact factor: 1.227, year: 2009
Radial Basis Function Networks for Conversion of Sound Spectra
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Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Neuronal spike sorting based on radial basis function neural networks
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Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Organisms modeling: The question of radial basis function networks
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Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
Satisfiability of logic programming based on radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-01-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems
Satisfiability of logic programming based on radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
A metric for the Radial Basis Function Network - Application on Real Radar Data
Heiden, R. van der; Groen, F.C.A.
1996-01-01
A Radial Basis Functions (RBF) network for pattern recognition is considered. Classification with such a network is based on distances between patterns, so a metric is always present. Using real radar data, the Euclidean metric is shown to perform poorly - a metric based on the so called Box-Cox
Directory of Open Access Journals (Sweden)
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
Burken, John J.
2005-01-01
This viewgraph presentation reviews the use of a Robust Servo Linear Quadratic Regulator (LQR) and a Radial Basis Function (RBF) Neural Network in reconfigurable flight control designs in adaptation to a aircraft part failure. The method uses a robust LQR servomechanism design with model Reference adaptive control, and RBF neural networks. During the failure the LQR servomechanism behaved well, and using the neural networks improved the tracking.
An enhanced radial basis function network for short-term electricity price forecasting
International Nuclear Information System (INIS)
Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang
2010-01-01
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)
Radial basis function neural network for power system load-flow
International Nuclear Information System (INIS)
Karami, A.; Mohammadi, M.S.
2008-01-01
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Machine learning of radial basis function neural network based on Kalman filter: Introduction
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Vuković Najdan L.
2014-01-01
Full Text Available This paper analyzes machine learning of radial basis function neural network based on Kalman filtering. Three algorithms are derived: linearized Kalman filter, linearized information filter and unscented Kalman filter. We emphasize basic properties of these estimation algorithms, demonstrate how their advantages can be used for optimization of network parameters, derive mathematical models and show how they can be applied to model problems in engineering practice.
Ryu, Duchwan; Liang, Faming; Mallick, Bani K.
2013-01-01
be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle
A prediction method for the wax deposition rate based on a radial basis function neural network
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Ying Xie
2017-06-01
Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.
Radial basis function neural networks with sequential learning MRAN and its applications
Sundararajan, N; Wei Lu Ying
1999-01-01
This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
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Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
International Nuclear Information System (INIS)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-01-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
Hong, Xia
2006-07-01
In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.
DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel
2016-03-29
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA
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Alisson C. D. de Souza
2014-09-01
Full Text Available This paper proposes a parallel fixed point radial basis function (RBF artificial neural network (ANN, implemented in a field programmable gate array (FPGA trained online with a least mean square (LMS algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx, with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA.
Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks
Ali Reza Zirak; Sobhan Roshani
2016-01-01
A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...
Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman
2017-02-01
The soil sorption partition coefficient logK oc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logK oc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logK oc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year. Copyright © 2016 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin
2006-01-01
High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)
Learning Errors by Radial Basis Function Neural Networks and Regularization Networks
Czech Academy of Sciences Publication Activity Database
Neruda, Roman; Vidnerová, Petra
2009-01-01
Roč. 1, č. 2 (2009), s. 49-57 ISSN 2005-4262 R&D Projects: GA MŠk(CZ) 1M0567 Institutional research plan: CEZ:AV0Z10300504 Keywords : neural network * RBF networks * regularization * learning Subject RIV: IN - Informatics, Computer Science http://www.sersc.org/journals/IJGDC/vol2_no1/5.pdf
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Jian Hu
2016-05-01
Full Text Available Uncertainties, including parametric uncertainties and uncertain nonlinearities, always exist in positioning servo systems driven by a hydraulic actuator, which would degrade their tracking accuracy. In this article, an integrated control scheme, which combines adaptive robust control together with radial basis function neural network–based disturbance observer, is proposed for high-accuracy motion control of hydraulic systems. Not only parametric uncertainties but also uncertain nonlinearities (i.e. nonlinear friction, external disturbances, and/or unmodeled dynamics are taken into consideration in the proposed controller. The above uncertainties are compensated, respectively, by adaptive control and radial basis function neural network, which are ultimately integrated together by applying feedforward compensation technique, in which the global stabilization of the controller is ensured via a robust feedback path. A new kind of parameter and weight adaptation law is designed on the basis of Lyapunov stability theory. Furthermore, the proposed controller obtains an expected steady performance even if modeling uncertainties exist, and extensive simulation results in various working conditions have proven the high performance of the proposed control scheme.
Reconstruction of Daily Sea Surface Temperature Based on Radial Basis Function Networks
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Zhihong Liao
2017-11-01
Full Text Available A radial basis function network (RBFN method is proposed to reconstruct daily Sea surface temperatures (SSTs with limited SST samples. For the purpose of evaluating the SSTs using this method, non-biased SST samples in the Pacific Ocean (10°N–30°N, 115°E–135°E are selected when the tropical storm Hagibis arrived in June 2014, and these SST samples are obtained from the Reynolds optimum interpolation (OI v2 daily 0.25° SST (OISST products according to the distribution of AVHRR L2p SST and in-situ SST data. Furthermore, an improved nearest neighbor cluster (INNC algorithm is designed to search for the optimal hidden knots for RBFNs from both the SST samples and the background fields. Then, the reconstructed SSTs from the RBFN method are compared with the results from the OI method. The statistical results show that the RBFN method has a better performance of reconstructing SST than the OI method in the study, and that the average RMSE is 0.48 °C for the RBFN method, which is quite smaller than the value of 0.69 °C for the OI method. Additionally, the RBFN methods with different basis functions and clustering algorithms are tested, and we discover that the INNC algorithm with multi-quadric function is quite suitable for the RBFN method to reconstruct SSTs when the SST samples are sparsely distributed.
Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.
2012-04-01
Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-09-18
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality.
Mohamed Yacin, S; Srinivasa Chakravarthy, V; Manivannan, M
2011-11-01
Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Li, Bo; Rui, Xiaoting
2018-01-01
Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.
Le, Nguyen-Quoc-Khanh; Nguyen, Trinh-Trung-Duong; Ou, Yu-Yen
2017-05-01
The electron transport proteins have an important role in storing and transferring electrons in cellular respiration, which is the most proficient process through which cells gather energy from consumed food. According to the molecular functions, the electron transport chain components could be formed with five complexes with several different electron carriers and functions. Therefore, identifying the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. This work includes two phases for discriminating electron transport proteins from transport proteins and classifying categories of five complexes in electron transport proteins. In the first phase, the performances from PSSM with AAIndex feature set were successful in identifying electron transport proteins in transport proteins with achieved sensitivity of 73.2%, specificity of 94.1%, and accuracy of 91.3%, with MCC of 0.64 for independent data set. With the second phase, our method can approach a precise model for identifying of five complexes with different molecular functions in electron transport proteins. The PSSM with AAIndex properties in five complexes achieved MCC of 0.51, 0.47, 0.42, 0.74, and 1.00 for independent data set, respectively. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins. Copyright © 2017 Elsevier Inc. All rights reserved.
Radial basis function networks applied to DNBR calculation in digital core protection systems
International Nuclear Information System (INIS)
Lee, Gyu-Cheon; Heung Chang, Soon
2003-01-01
The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about ±10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction.
Kumudha, P; Venkatesan, R
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
Directory of Open Access Journals (Sweden)
Tatar Afshin
2016-03-01
Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.
Directory of Open Access Journals (Sweden)
Eyad K Almaita
2017-03-01
Keywords: Energy efficiency, Power quality, Radial basis function, neural networks, adaptive, harmonic. Article History: Received Dec 15, 2016; Received in revised form Feb 2nd 2017; Accepted 13rd 2017; Available online How to Cite This Article: Almaita, E.K and Shawawreh J.Al (2017 Improving Stability and Convergence for Adaptive Radial Basis Function Neural Networks Algorithm (On-Line Harmonics Estimation Application. International Journal of Renewable Energy Develeopment, 6(1, 9-17. http://dx.doi.org/10.14710/ijred.6.1.9-17
Kayri, Murat
2015-01-01
The objective of this study is twofold: (1) to investigate the factors that affect the success of university students by employing two artificial neural network methods (i.e., multilayer perceptron [MLP] and radial basis function [RBF]); and (2) to compare the effects of these methods on educational data in terms of predictive ability. The…
Abdelkarim M. Ertiame; D. W. Yu; D. L. Yu; J. B. Gomm
2015-01-01
In this paper, a robust fault detection and isolation (FDI) scheme is developed to monitor a multivariable nonlinear chemical process called the Chylla-Haase polymerization reactor, when it is under the cascade PI control. The scheme employs a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is emplo...
Mixtures of truncated basis functions
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2012-01-01
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar t...
Directory of Open Access Journals (Sweden)
Ángel Gutiérrez
2015-04-01
Full Text Available The data available in the average clinical study of a disease is very often small. This is one of the main obstacles in the application of neural networks to the classification of biological signals used for diagnosing diseases. A rule of thumb states that the number of parameters (weights that can be used for training a neural network should be around 15% of the available data, to avoid overlearning. This condition puts a limit on the dimension of the input space. Different authors have used different approaches to solve this problem, like eliminating redundancy in the data, preprocessing the data to find centers for the radial basis functions, or extracting a small number of features that were used as inputs. It is clear that the classification would be better the more features we could feed into the network. The approach utilized in this paper is incrementing the number of training elements with randomly expanding training sets. This way the number of original signals does not constraint the dimension of the input set in the radial basis network. Then we train the network using the method that minimizes the error function using the gradient descent algorithm and the method that uses the particle swarm optimization technique. A comparison between the two methods showed that for the same number of iterations on both methods, the particle swarm optimization was faster, it was learning to recognize only the sick people. On the other hand, the gradient method was not as good in general better at identifying those people.
Directory of Open Access Journals (Sweden)
Ali Mansourkhaki
2018-01-01
Full Text Available Noise pollution is a level of environmental noise which is considered as a disturbing and annoying phenomenon for human and wildlife. It is one of the environmental problems which has not been considered as harmful as the air and water pollution. Compared with other pollutants, the attempts to control noise pollution have largely been unsuccessful due to the inadequate knowledge of its effectson humans, as well as the lack of clear standards in previous years. However, with an increase of traveling vehicles, the adverse impact of increasing noise pollution on human health is progressively emerging. Hence, investigators all around the world are seeking to findnew approaches for predicting, estimating and controlling this problem and various models have been proposed. Recently, developing learning algorithms such as neural network has led to novel solutions for this challenge. These algorithms provide intelligent performance based on the situations and input data, enabling to obtain the best result for predicting noise level. In this study, two types of neural networks – multilayer perceptron and radial basis function – were developed for predicting equivalent continuous sound level (LA eq by measuring the traffivolume, average speed and percentage of heavy vehicles in some roads in west and northwest of Tehran. Then, their prediction results were compared based on the coefficienof determination (R 2 and the Mean Squared Error (MSE. Although both networks are of high accuracy in prediction of noise level, multilayer perceptron neural network based on selected criteria had a better performance.
International Nuclear Information System (INIS)
Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam
2007-01-01
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
Chen, Qian; Liu, Guohai; Xu, Dezhi; Xu, Liang; Xu, Gaohong; Aamir, Nazir
2018-05-01
This paper proposes a new decoupled control for a five-phase in-wheel fault-tolerant permanent magnet (IW-FTPM) motor drive, in which radial basis function neural network inverse (RBF-NNI) and internal model control (IMC) are combined. The RBF-NNI system is introduced into original system to construct a pseudo-linear system, and IMC is used as a robust controller. Hence, the newly proposed control system incorporates the merits of the IMC and RBF-NNI methods. In order to verify the proposed strategy, an IW-FTPM motor drive is designed based on dSPACE real-time control platform. Then, the experimental results are offered to verify that the d-axis current and the rotor speed are successfully decoupled. Besides, the proposed motor drive exhibits strong robustness even under load torque disturbance.
Directory of Open Access Journals (Sweden)
Feilu Wang
2014-04-01
Full Text Available Research on tactile sensors to enhance their flexibility and ability of multi- dimensional information detection is a key issue to develop humanoid robots. In view of that the tactile sensor is often affected by noise, this paper adds different white Gaussian noises (WGN into the ideal model of flexible tactile sensors based on conductive rubber purposely, then improves the standard radial basis function neural network (RNFNN to deal with the noises. The modified RBFNN is applied to approximate and decouple the mapping relationship between row-column resistance with WGNs and three-dimensional deformation. Numerical experiments demonstrate that the decoupling result of the deformation for the sensor is quite good. The results show that the improved RBFNN which doesn’t rely on the mathematical model of the system has good anti-noise ability and robustness.
Zhou, Jingwen; Xu, Zhenghong; Chen, Shouwen
2013-04-01
The thuringiensin abiotic degradation processes in aqueous solution under different conditions, with a pH range of 5.0-9.0 and a temperature range of 10-40°C, were systematically investigated by an exponential decay model and a radius basis function (RBF) neural network model, respectively. The half-lives of thuringiensin calculated by the exponential decay model ranged from 2.72 d to 16.19 d under the different conditions mentioned above. Furthermore, an RBF model with accuracy of 0.1 and SPREAD value 5 was employed to model the degradation processes. The results showed that the model could simulate and predict the degradation processes well. Both the half-lives and the prediction data showed that thuringiensin was an easily degradable antibiotic, which could be an important factor in the evaluation of its safety. Copyright © 2012 Elsevier Ltd. All rights reserved.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
A new scheme to estimate the moment of inertia in the servo motor drive system in very low speed is proposed in this paper. The speed estimation scheme in most servo drive systems for low speed operation is sensitive to the variation of machine parameter, especially the moment of inertia....... To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...... in the sense of Lyapunov. The effectiveness of the proposed inertia estimation is verified by simulations and experiments. It is concluded that the speed control performance in low speed region is improved with the proposed disturbance observer using RBFN....
Meng, Qinggang; Lee, M. H.
2007-03-01
Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.
Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong
2008-06-01
A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.
Anitha, J.; Vijila, C. Kezi Selva; Hemanth, D. Jude
2010-02-01
Diabetic retinopathy (DR) is a chronic eye disease for which early detection is highly essential to avoid any fatal results. Image processing of retinal images emerge as a feasible tool for this early diagnosis. Digital image processing techniques involve image classification which is a significant technique to detect the abnormality in the eye. Various automated classification systems have been developed in the recent years but most of them lack high classification accuracy. Artificial neural networks are the widely preferred artificial intelligence technique since it yields superior results in terms of classification accuracy. In this work, Radial Basis function (RBF) neural network based bi-level classification system is proposed to differentiate abnormal DR Images and normal retinal images. The results are analyzed in terms of classification accuracy, sensitivity and specificity. A comparative analysis is performed with the results of the probabilistic classifier namely Bayesian classifier to show the superior nature of neural classifier. Experimental results show promising results for the neural classifier in terms of the performance measures.
Directory of Open Access Journals (Sweden)
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole-position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro-mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real-time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
Yang, Yanchao; Jiang, Hong; Liu, Congbin; Lan, Zhongli
2013-03-01
Cognitive radio (CR) is an intelligent wireless communication system which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service. The core technology for CR is the design of cognitive engine, which introduces reasoning and learning methods in the field of artificial intelligence, to achieve the perception, adaptation and learning capability. Considering the dynamical wireless environment and demands, this paper proposes a design of cognitive engine based on the rough sets (RS) and radial basis function neural network (RBF_NN). The method uses experienced knowledge and environment information processed by RS module to train the RBF_NN, and then the learning model is used to reconfigure communication parameters to allocate resources rationally and improve system performance. After training learning model, the performance is evaluated according to two benchmark functions. The simulation results demonstrate the effectiveness of the model and the proposed cognitive engine can effectively achieve the goal of learning and reconfiguration in cognitive radio.
Directory of Open Access Journals (Sweden)
HU Qi-guo
2017-01-01
Full Text Available For reducing the vehicle compartment low frequency noise, the Optimal Latin hypercube sampling method was applied to perform experimental design for sampling in the factorial design space. The thickness parameters of the panels with larger acoustic contribution was considered as factors, as well as the vehicle mass, seventh rank modal frequency of body, peak sound pressure of test point and sound pressure root-mean-square value as responses. By using the RBF(radial basis function neuro-network method, an approximation model of four responses about six factors was established. Further more, error analysis of established approximation model was performed in this paper. To optimize the panel’s thickness parameter, the adaptive simulated annealing algorithm was im-plemented. Optimization results show that the peak sound pressure of driver’s head was reduced by 4.45dB and 5.47dB at frequency 158HZ and 134Hz respec-tively. The test point pressure were significantly reduced at other frequency as well. The results indicate that through the optimization the vehicle interior cavity noise was reduced effectively, and the acoustical comfort of the vehicle was im-proved significantly.
Directory of Open Access Journals (Sweden)
Seng-Chi Chen
2014-01-01
Full Text Available Studies on active magnetic bearing (AMB systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC. The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC, the parameters of which are adjusted using a radial basis function neural network (RBFNN, is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede
2005-01-01
A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...
Application of radial basis neural network for state estimation of ...
African Journals Online (AJOL)
An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...
Functional Basis of Microorganism Classification.
Zhu, Chengsheng; Delmont, Tom O; Vogel, Timothy M; Bromberg, Yana
2015-08-01
Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with
Functional Basis of Microorganism Classification
Zhu, Chengsheng; Delmont, Tom O.; Vogel, Timothy M.; Bromberg, Yana
2015-01-01
Correctly identifying nearest “neighbors” of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned
Ghasemi, Nahid; Aghayari, Reza; Maddah, Heydar
2018-06-01
The present study aims at predicting and optimizing exergetic efficiency of TiO2-Al2O3/water nanofluid at different Reynolds numbers, volume fractions and twisted ratios using Artificial Neural Networks (ANN) and experimental data. Central Composite Design (CCD) and cascade Radial Basis Function (RBF) were used to display the significant levels of the analyzed factors on the exergetic efficiency. The size of TiO2-Al2O3/water nanocomposite was 20-70 nm. The parameters of ANN model were adapted by a training algorithm of radial basis function (RBF) with a wide range of experimental data set. Total mean square error and correlation coefficient were used to evaluate the results which the best result was obtained from double layer perceptron neural network with 30 neurons in which total Mean Square Error(MSE) and correlation coefficient (R2) were equal to 0.002 and 0.999, respectively. This indicated successful prediction of the network. Moreover, the proposed equation for predicting exergetic efficiency was extremely successful. According to the optimal curves, the optimum designing parameters of double pipe heat exchanger with inner twisted tape and nanofluid under the constrains of exergetic efficiency 0.937 are found to be Reynolds number 2500, twisted ratio 2.5 and volume fraction( v/v%) 0.05.
Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei
2017-06-01
To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (Plogistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Gomes, Carla Regina; Canedo Medeiros, Jose Antonio Carlos
2015-01-01
Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis
International Nuclear Information System (INIS)
Wu Xue-Dong; Liu Wei-Ting; Zhu Zhi-Yu; Wang Yao-Nan
2011-01-01
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Baasch, B.; M"uller, H.; von Dobeneck, T.
2018-04-01
In this work we present a new methodology to predict grain-size distributions from geophysical data. Specifically, electric conductivity and magnetic susceptibility of seafloor sediments recovered from electromagnetic profiling data are used to predict grain-size distributions along shelf-wide survey lines. Field data from the NW Iberian shelf are investigated and reveal a strong relation between the electromagnetic properties and grain-size distribution. The here presented workflow combines unsupervised and supervised machine learning techniques. Nonnegative matrix factorisation is used to determine grain-size end-members from sediment surface samples. Four end-members were found which well represent the variety of sediments in the study area. A radial-basis function network modified for prediction of compositional data is then used to estimate the abundances of these end-members from the electromagnetic properties. The end-members together with their predicted abundances are finally back transformed to grain-size distributions. A minimum spatial variation constraint is implemented in the training of the network to avoid overfitting and to respect the spatial distribution of sediment patterns. The predicted models are tested via leave-one-out cross-validation revealing high prediction accuracy with coefficients of determination (R2) between 0.76 and 0.89. The predicted grain-size distributions represent the well-known sediment facies and patterns on the NW Iberian shelf and provide new insights into their distribution, transition and dynamics. This study suggests that electromagnetic benthic profiling in combination with machine learning techniques is a powerful tool to estimate grain-size distribution of marine sediments.
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Directory of Open Access Journals (Sweden)
Nan YU
2014-09-01
Full Text Available The interference signal in magneto-hydro-dynamics (MHD may be the disturbance from the power supply, the equipment itself, or the electromagnetic radiation. Interference signal mixed in normal signal, brings difficulties for signal analysis and processing. Recently proposed S-Transform algorithm combines advantages of short time Fourier transform and wavelet transform. It uses Fourier kernel and wavelet like Gauss window whose width is inversely proportional to the frequency. Therefore, S-Transform algorithm not only preserves the phase information of the signals but also has variable resolution like wavelet transform. This paper proposes a new method to establish a MHD signal classifier using S-transform algorithm and radial basis function neural network (RBFNN. Because RBFNN centers ascertained by k-means clustering algorithm probably are the local optimum, this paper analyzes the characteristics of k-means clustering algorithm and proposes an improved k-means clustering algorithm called GCW (Group-cluster-weight k-means clustering algorithm to improve the centers distribution. The experiment results show that the improvement greatly enhances the RBFNN performance.
The Matlab Radial Basis Function Toolbox
Directory of Open Access Journals (Sweden)
Scott A. Sarra
2017-03-01
Full Text Available Radial Basis Function (RBF methods are important tools for scattered data interpolation and for the solution of Partial Differential Equations in complexly shaped domains. The most straight forward approach used to evaluate the methods involves solving a linear system which is typically poorly conditioned. The Matlab Radial Basis Function toolbox features a regularization method for the ill-conditioned system, extended precision floating point arithmetic, and symmetry exploitation for the purpose of reducing flop counts of the associated numerical linear algebra algorithms.
Coded Network Function Virtualization
DEFF Research Database (Denmark)
Al-Shuwaili, A.; Simone, O.; Kliewer, J.
2016-01-01
Network function virtualization (NFV) prescribes the instantiation of network functions on general-purpose network devices, such as servers and switches. While yielding a more flexible and cost-effective network architecture, NFV is potentially limited by the fact that commercial off......-the-shelf hardware is less reliable than the dedicated network elements used in conventional cellular deployments. The typical solution for this problem is to duplicate network functions across geographically distributed hardware in order to ensure diversity. In contrast, this letter proposes to leverage channel...... coding in order to enhance the robustness on NFV to hardware failure. The proposed approach targets the network function of uplink channel decoding, and builds on the algebraic structure of the encoded data frames in order to perform in-network coding on the signals to be processed at different servers...
Doubly stochastic radial basis function methods
Yang, Fenglian; Yan, Liang; Ling, Leevan
2018-06-01
We propose a doubly stochastic radial basis function (DSRBF) method for function recoveries. Instead of a constant, we treat the RBF shape parameters as stochastic variables whose distribution were determined by a stochastic leave-one-out cross validation (LOOCV) estimation. A careful operation count is provided in order to determine the ranges of all the parameters in our methods. The overhead cost for setting up the proposed DSRBF method is O (n2) for function recovery problems with n basis. Numerical experiments confirm that the proposed method not only outperforms constant shape parameter formulation (in terms of accuracy with comparable computational cost) but also the optimal LOOCV formulation (in terms of both accuracy and computational cost).
Tensor Basis Neural Network v. 1.0 (beta)
Energy Technology Data Exchange (ETDEWEB)
2017-03-28
This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.
Fast radial basis functions for engineering applications
Biancolini, Marco Evangelos
2017-01-01
This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF: multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...
Spherical radial basis functions, theory and applications
Hubbert, Simon; Morton, Tanya M
2015-01-01
This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...
Bioprinting: Functional droplet networks
Durmus, Naside Gozde; Tasoglu, Savas; Demirci, Utkan
2013-06-01
Tissue-mimicking printed networks of droplets separated by lipid bilayers that can be functionalized with membrane proteins are able to spontaneously fold and transmit electrical currents along predefined paths.
Network Function Virtualisation
Aakarshan Singh; Kamal Grover; Palak Bansal; Taranveer Singh Seekhon
2017-01-01
This paper is written to give basic knowledge of Network function virtualisation in network system. In this paper the work on NFV done till now has been collaborated. It describes how the challenges faced by industry lead to NFV and what is meaning of NFV and NFV architecture model. It also explains NFV Infrastructure is managed and the forwarding path on which packets traverse in NFV. A relationship of NFV with SDN and current research ongoing on NFV policies is discussed.
Learning Mixtures of Truncated Basis Functions from Data
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Pérez-Bernabé, Inmaculada
2014-01-01
In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing a ke...... propose an alternative learning method that relies on the cumulative distribution function of the data. Empirical results demonstrate the usefulness of the approaches: Even though the methods produce estimators that are slightly poorer than the state of the art (in terms of log......In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing......-likelihood), they are significantly faster, and therefore indicate that the MoTBF framework can be used for inference and learning in reasonably sized domains. Furthermore, we show how a particular sub- class of MoTBF potentials (learnable by the proposed methods) can be exploited to significantly reduce complexity during inference....
Han, Ping; Luan, Feng; Yan, Xizu; Gao, Yuan; Liu, Huitao
2012-01-01
A method for the separation and determination of honokiol and magnolol in Magnolia officinalis and its medicinal preparation is developed by capillary zone electrophoresis and response surface methodology. The concentration of borate, content of organic modifier, and applied voltage are selected as variables. The optimized conditions (i.e., 16 mmol/L sodium tetraborate at pH 10.0, 11% methanol, applied voltage of 25 kV and UV detection at 210 nm) are obtained and successfully applied to the analysis of honokiol and magnolol in Magnolia officinalis and Huoxiang Zhengqi Liquid. Good separation is achieved within 6 min. The limits of detection are 1.67 µg/mL for honokiol and 0.83 µg/mL for magnolol, respectively. In addition, an artificial neural network with “3-7-1” structure based on the ratio of peak resolution to the migration time of the later component (Rs/t) given by Box-Behnken design is also reported, and the predicted results are in good agreement with the values given by the mathematic software and the experimental results. PMID:22291059
Le, Nguyen-Quoc-Khanh; Ou, Yu-Yen
2016-07-30
transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.
Green function on product networks
Arauz Lombardía, Cristina; Carmona Mejías, Ángeles; Encinas Bachiller, Andrés Marcos
2012-01-01
Our objective is to determine the Green function of product networks in terms of the Green function of one of the factor networks and the eigenvalues and eigenfunctions of the Schr odinger operator of the other factor network, which we consider that are known. Moreover, we use these results to obtain the Green function of spider networks in terms of Green functions over cicles and paths. Peer Reviewed
A genetic basis for functional hypothalamic amenorrhea.
Caronia, L.M.; Martin, C.; Welt, C.K.; Sykiotis, G.P.; Quinton, R.; Thambundit, A.; Avbelj, M.; Dhruvakumar, S.; Plummer, L.; Hughes, V.A.; Seminara, S.B.; Boepple, P.A.; Sidis, Y.; Crowley, W.F.; Martin, K.A.
2011-01-01
Background: Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogon...
A Genetic Basis for Functional Hypothalamic Amenorrhea
Caronia, Lisa M.; Martin, Cecilia; Welt, Corrine K.; Sykiotis, Gerasimos P.; Quinton, Richard; Thambundit, Apisadaporn; Avbelj, Magdalena; Dhruvakumar, Sadhana; Plummer, Lacey; Hughes, Virginia A.; Seminara, Stephanie B.; Boepple, Paul A.; Sidis, Yisrael; Crowley, William F.; Martin, Kathryn A.; Hall, Janet E.; Pitteloud, Nelly
2011-01-01
BACKGROUND Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogonadotropic hypogonadism, a congenital form of GnRH deficiency, are associated with hypothalamic amenorrhea. METHODS We analyzed the coding sequence of genes associated with idiopathic hypogonadotropic hypogonadism in 55 women with hypothalamic amenorrhea and performed in vitro studies of the identified mutations. RESULTS Six heterozygous mutations were identified in 7 of the 55 patients with hypothalamic amenorrhea: two variants in the fibroblast growth factor receptor 1 gene FGFR1 (G260E and R756H), two in the prokineticin receptor 2 gene PROKR2 (R85H and L173R), one in the GnRH receptor gene GNRHR (R262Q), and one in the Kall-mann syndrome 1 sequence gene KAL1 (V371I). No mutations were found in a cohort of 422 controls with normal menstrual cycles. In vitro studies showed that FGFR1 G260E, FGFR1 R756H, and PROKR2 R85H are loss-of-function mutations, as has been previously shown for PROKR2 L173R and GNRHR R262Q. CONCLUSIONS Rare variants in genes associated with idiopathic hypogonadotropic hypogonadism are found in women with hypothalamic amenorrhea, suggesting that these mutations may contribute to the variable susceptibility of women to the functional changes in GnRH secretion that characterize hypothalamic amenorrhea. Our observations provide evidence for the role of rare variants in common multifactorial disease. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT00494169.) PMID:21247312
A genetic basis for functional hypothalamic amenorrhea.
Caronia, Lisa M; Martin, Cecilia; Welt, Corrine K; Sykiotis, Gerasimos P; Quinton, Richard; Thambundit, Apisadaporn; Avbelj, Magdalena; Dhruvakumar, Sadhana; Plummer, Lacey; Hughes, Virginia A; Seminara, Stephanie B; Boepple, Paul A; Sidis, Yisrael; Crowley, William F; Martin, Kathryn A; Hall, Janet E; Pitteloud, Nelly
2011-01-20
Functional hypothalamic amenorrhea is a reversible form of gonadotropin-releasing hormone (GnRH) deficiency commonly triggered by stressors such as excessive exercise, nutritional deficits, or psychological distress. Women vary in their susceptibility to inhibition of the reproductive axis by such stressors, but it is unknown whether this variability reflects a genetic predisposition to hypothalamic amenorrhea. We hypothesized that mutations in genes involved in idiopathic hypogonadotropic hypogonadism, a congenital form of GnRH deficiency, are associated with hypothalamic amenorrhea. We analyzed the coding sequence of genes associated with idiopathic hypogonadotropic hypogonadism in 55 women with hypothalamic amenorrhea and performed in vitro studies of the identified mutations. Six heterozygous mutations were identified in 7 of the 55 patients with hypothalamic amenorrhea: two variants in the fibroblast growth factor receptor 1 gene FGFR1 (G260E and R756H), two in the prokineticin receptor 2 gene PROKR2 (R85H and L173R), one in the GnRH receptor gene GNRHR (R262Q), and one in the Kallmann syndrome 1 sequence gene KAL1 (V371I). No mutations were found in a cohort of 422 controls with normal menstrual cycles. In vitro studies showed that FGFR1 G260E, FGFR1 R756H, and PROKR2 R85H are loss-of-function mutations, as has been previously shown for PROKR2 L173R and GNRHR R262Q. Rare variants in genes associated with idiopathic hypogonadotropic hypogonadism are found in women with hypothalamic amenorrhea, suggesting that these mutations may contribute to the variable susceptibility of women to the functional changes in GnRH secretion that characterize hypothalamic amenorrhea. Our observations provide evidence for the role of rare variants in common multifactorial disease. (Funded by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT00494169.).
Using piecewise sinusoidal basis functions to blanket multiple wire segments
CSIR Research Space (South Africa)
Lysko, AA
2009-06-01
Full Text Available This paper discusses application of the piecewise sinusoidal (PWS) basis function (BF) over a chain of several wire segments, for example as a multiple domain basis function. The usage of PWS BF is compared to results based on the piecewise linear...
Localizing and placement of network node functions in a network
Strijkers, R.J.; Meulenhoff, P.J.
2014-01-01
The invention enables placement and use of a network node function in a second network node instead of using the network node function in a first network node. The network node function is e.g. a server function or a router function. The second network node is typically located in or close to the
DEFF Research Database (Denmark)
Madsen, Per Printz
1998-01-01
The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...
Industrial entrepreneurial network: Structural and functional analysis
Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.
2016-12-01
Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.
Functional Module Analysis for Gene Coexpression Networks with Network Integration.
Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K
2015-01-01
Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.
Network-based functional enrichment
Directory of Open Access Journals (Sweden)
Poirel Christopher L
2011-11-01
Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are
Bounds on Rates of Variable-Basis and Neural-Network Approximation
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Sanguineti, M.
2001-01-01
Roč. 47, č. 6 (2001), s. 2659-2665 ISSN 0018-9448 R&D Projects: GA ČR GA201/00/1482 Institutional research plan: AV0Z1030915 Keywords : approximation by variable-basis functions * bounds on rates of approximation * complexity of neural networks * high-dimensional optimal decision problems Subject RIV: BA - General Mathematics Impact factor: 2.077, year: 2001
Świetlicka, Izabela; Muszyński, Siemowit; Marzec, Agata
2015-04-01
The presented work covers the problem of developing a method of extruded bread classification with the application of artificial neural networks. Extruded flat graham, corn, and rye breads differening in water activity were used. The breads were subjected to the compression test with simultaneous registration of acoustic signal. The amplitude-time records were analyzed both in time and frequency domains. Acoustic emission signal parameters: single energy, counts, amplitude, and duration acoustic emission were determined for the breads in four water activities: initial (0.362 for rye, 0.377 for corn, and 0.371 for graham bread), 0.432, 0.529, and 0.648. For classification and the clustering process, radial basis function, and self-organizing maps (Kohonen network) were used. Artificial neural networks were examined with respect to their ability to classify or to cluster samples according to the bread type, water activity value, and both of them. The best examination results were achieved by the radial basis function network in classification according to water activity (88%), while the self-organizing maps network yielded 81% during bread type clustering.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Directory of Open Access Journals (Sweden)
Khang Jie Liew
Full Text Available This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Point Set Denoising Using Bootstrap-Based Radial Basis Function.
Liew, Khang Jie; Ramli, Ahmad; Abd Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study.
Closed fringe demodulation using phase decomposition by Fourier basis functions.
Kulkarni, Rishikesh; Rastogi, Pramod
2016-06-01
We report a new technique for the demodulation of a closed fringe pattern by representing the phase as a weighted linear combination of a certain number of linearly independent Fourier basis functions in a given row/column at a time. A state space model is developed with the weights of the basis functions as the elements of the state vector. The iterative extended Kalman filter is effectively utilized for the robust estimation of the weights. A coarse estimate of the fringe density based on the fringe frequency map is used to determine the initial row/column to start with and subsequently the optimal number of basis functions. The performance of the proposed method is evaluated with several noisy fringe patterns. Experimental results are also reported to support the practical applicability of the proposed method.
Speech/Nonspeech Detection Using Minimal Walsh Basis Functions
Directory of Open Access Journals (Sweden)
Pwint Moe
2007-01-01
Full Text Available This paper presents a new method to detect speech/nonspeech components of a given noisy signal. Employing the combination of binary Walsh basis functions and an analysis-synthesis scheme, the original noisy speech signal is modified first. From the modified signals, the speech components are distinguished from the nonspeech components by using a simple decision scheme. Minimal number of Walsh basis functions to be applied is determined using singular value decomposition (SVD. The main advantages of the proposed method are low computational complexity, less parameters to be adjusted, and simple implementation. It is observed that the use of Walsh basis functions makes the proposed algorithm efficiently applicable in real-world situations where processing time is crucial. Simulation results indicate that the proposed algorithm achieves high-speech and nonspeech detection rates while maintaining a low error rate for different noisy conditions.
The Anatomical Basis for Dystonia: The Motor Network Model
Directory of Open Access Journals (Sweden)
H.A. Jinnah
2017-10-01
Full Text Available Background: The dystonias include a clinically and etiologically very diverse group of disorders. There are both degenerative and non-degenerative subtypes resulting from genetic or acquired causes. Traditionally, all dystonias have been viewed as disorders of the basal ganglia. However, there has been increasing appreciation for involvement of other brain regions including the cerebellum, thalamus, midbrain, and cortex. Much of the early evidence for these other brain regions has come from studies of animals, but multiple recent studies have been done with humans, in an effort to confirm or refute involvement of these other regions. The purpose of this article is to review the new evidence from animals and humans regarding the motor network model, and to address the issues important to translational neuroscience.Methods: The English literature was reviewed for articles relating to the neuroanatomical basis for various types of dystonia in both animals and humans.Results: There is evidence from both animals and humans that multiple brain regions play an important role in various types of dystonia. The most direct evidence for specific brain regions comes from animal studies using pharmacological, lesion, or genetic methods. In these studies, experimental manipulations of specific brain regions provide direct evidence for involvement of the basal ganglia, cerebellum, thalamus and other regions. Additional evidence also comes from human studies using neuropathological, neuroimaging, non-invasive brain stimulation, and surgical interventions. In these studies, the evidence is less conclusive, because discriminating the regions that cause dystonia from those that reflect secondary responses to abnormal movements is more challenging.Discussion: Overall, the evidence from both animals and humans suggests that different regions may play important roles in different subtypes of dystonia. The evidence so far provides strong support for the motor
Gaussian basis functions for highly oscillatory scattering wavefunctions
Mant, B. P.; Law, M. M.
2018-04-01
We have applied a basis set of distributed Gaussian functions within the S-matrix version of the Kohn variational method to scattering problems involving deep potential energy wells. The Gaussian positions and widths are tailored to the potential using the procedure of Bačić and Light (1986 J. Chem. Phys. 85 4594) which has previously been applied to bound-state problems. The placement procedure is shown to be very efficient and gives scattering wavefunctions and observables in agreement with direct numerical solutions. We demonstrate the basis function placement method with applications to hydrogen atom–hydrogen atom scattering and antihydrogen atom–hydrogen atom scattering.
Aging and functional brain networks
International Nuclear Information System (INIS)
Tomasi D.; Volkow, N.D.
2012-01-01
Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.
Optimal Piecewise Linear Basis Functions in Two Dimensions
Energy Technology Data Exchange (ETDEWEB)
Brooks III, E D; Szoke, A
2009-01-26
We use a variational approach to optimize the center point coefficients associated with the piecewise linear basis functions introduced by Stone and Adams [1], for polygonal zones in two Cartesian dimensions. Our strategy provides optimal center point coefficients, as a function of the location of the center point, by minimizing the error induced when the basis function interpolation is used for the solution of the time independent diffusion equation within the polygonal zone. By using optimal center point coefficients, one expects to minimize the errors that occur when these basis functions are used to discretize diffusion equations, or transport equations in optically thick zones (where they approach the solution of the diffusion equation). Our optimal center point coefficients satisfy the requirements placed upon the basis functions for any location of the center point. We also find that the location of the center point can be optimized, but this requires numerical calculations. Curiously, the optimum center point location is independent of the values of the dependent variable on the corners only for quadrilaterals.
Optimal choice of basis functions in the linear regression analysis
International Nuclear Information System (INIS)
Khotinskij, A.M.
1988-01-01
Problem of optimal choice of basis functions in the linear regression analysis is investigated. Step algorithm with estimation of its efficiency, which holds true at finite number of measurements, is suggested. Conditions, providing the probability of correct choice close to 1 are formulated. Application of the step algorithm to analysis of decay curves is substantiated. 8 refs
Radial Basis Function Based Quadrature over Smooth Surfaces
2016-03-24
Radial Basis Functions φ(r) Piecewise Smooth (Conditionally Positive Definite) MN Monomial |r|2m+1 TPS thin plate spline |r|2mln|r| Infinitely Smooth...smooth surfaces using polynomial interpolants, while [27] couples Thin - Plate Spline interpolation (see table 1) with Green’s integral formula [29
Large-Eddy Simulation Using Projection onto Local Basis Functions
Pope, S. B.
In the traditional approach to LES for inhomogeneous flows, the resolved fields are obtained by a filtering operation (with filter width Delta). The equations governing the resolved fields are then partial differential equations, which are solved numerically (on a grid of spacing h). For an LES computation of a given magnitude (i.e., given h), there are conflicting considerations in the choice of Delta: to resolve a large range of turbulent motions, Delta should be small; to solve the equations with numerical accuracy, Delta should be large. In the alternative approach advanced here, this conflict is avoided. The resolved fields are defined by projection onto local basis functions, so that the governing equations are ordinary differential equations for the evolution of the basis-function coefficients. There is no issue of numerical spatial discretization errors. A general methodology for modelling the effects of the residual motions is developed. The model is based directly on the basis-function coefficients, and its effect is to smooth the fields where their rates of change are not well resolved by the basis functions. Demonstration calculations are performed for Burgers' equation.
Higher-Order Hierarchical Legendre Basis Functions in Applications
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter
2007-01-01
The higher-order hierarchical Legendre basis functions have been developed for eﬀective solution of integral equations with the method of moments. They are derived from orthogonal Legendre polynomials modiﬁed to enforce normal continuity between neighboring mesh elements, while preserving a high...
Diffusion Forecasting Model with Basis Functions from QR-Decomposition
Harlim, John; Yang, Haizhao
2017-12-01
The diffusion forecasting is a nonparametric approach that provably solves the Fokker-Planck PDE corresponding to Itô diffusion without knowing the underlying equation. The key idea of this method is to approximate the solution of the Fokker-Planck equation with a discrete representation of the shift (Koopman) operator on a set of basis functions generated via the diffusion maps algorithm. While the choice of these basis functions is provably optimal under appropriate conditions, computing these basis functions is quite expensive since it requires the eigendecomposition of an N× N diffusion matrix, where N denotes the data size and could be very large. For large-scale forecasting problems, only a few leading eigenvectors are computationally achievable. To overcome this computational bottleneck, a new set of basis functions constructed by orthonormalizing selected columns of the diffusion matrix and its leading eigenvectors is proposed. This computation can be carried out efficiently via the unpivoted Householder QR factorization. The efficiency and effectiveness of the proposed algorithm will be shown in both deterministically chaotic and stochastic dynamical systems; in the former case, the superiority of the proposed basis functions over purely eigenvectors is significant, while in the latter case forecasting accuracy is improved relative to using a purely small number of eigenvectors. Supporting arguments will be provided on three- and six-dimensional chaotic ODEs, a three-dimensional SDE that mimics turbulent systems, and also on the two spatial modes associated with the boreal winter Madden-Julian Oscillation obtained from applying the Nonlinear Laplacian Spectral Analysis on the measured Outgoing Longwave Radiation.
Surface interpolation with radial basis functions for medical imaging
International Nuclear Information System (INIS)
Carr, J.C.; Beatson, R.K.; Fright, W.R.
1997-01-01
Radial basis functions are presented as a practical solution to the problem of interpolating incomplete surfaces derived from three-dimensional (3-D) medical graphics. The specific application considered is the design of cranial implants for the repair of defects, usually holes, in the skull. Radial basis functions impose few restrictions on the geometry of the interpolation centers and are suited to problems where interpolation centers do not form a regular grid. However, their high computational requirements have previously limited their use to problems where the number of interpolation centers is small (<300). Recently developed fast evaluation techniques have overcome these limitations and made radial basis interpolation a practical approach for larger data sets. In this paper radial basis functions are fitted to depth-maps of the skull's surface, obtained from X-ray computed tomography (CT) data using ray-tracing techniques. They are used to smoothly interpolate the surface of the skull across defect regions. The resulting mathematical description of the skull's surface can be evaluated at any desired resolution to be rendered on a graphics workstation or to generate instructions for operating a computer numerically controlled (CNC) mill
Quantum phase space with a basis of Wannier functions
Fang, Yuan; Wu, Fan; Wu, Biao
2018-02-01
A quantum phase space with Wannier basis is constructed: (i) classical phase space is divided into Planck cells; (ii) a complete set of Wannier functions are constructed with the combination of Kohn’s method and Löwdin method such that each Wannier function is localized at a Planck cell. With these Wannier functions one can map a wave function unitarily onto phase space. Various examples are used to illustrate our method and compare it to Wigner function. The advantage of our method is that it can smooth out the oscillations in wave functions without losing any information and is potentially a better tool in studying quantum-classical correspondence. In addition, we point out that our method can be used for time-frequency analysis of signals.
Compactly Supported Basis Functions as Support Vector Kernels for Classification.
Wittek, Peter; Tan, Chew Lim
2011-10-01
Wavelet kernels have been introduced for both support vector regression and classification. Most of these wavelet kernels do not use the inner product of the embedding space, but use wavelets in a similar fashion to radial basis function kernels. Wavelet analysis is typically carried out on data with a temporal or spatial relation between consecutive data points. We argue that it is possible to order the features of a general data set so that consecutive features are statistically related to each other, thus enabling us to interpret the vector representation of an object as a series of equally or randomly spaced observations of a hypothetical continuous signal. By approximating the signal with compactly supported basis functions and employing the inner product of the embedding L2 space, we gain a new family of wavelet kernels. Empirical results show a clear advantage in favor of these kernels.
Application of the Characteristic Basis Function Method Using CUDA
Directory of Open Access Journals (Sweden)
Juan Ignacio Pérez
2014-01-01
Full Text Available The characteristic basis function method (CBFM is a popular technique for efficiently solving the method of moments (MoM matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA, and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit’s (GPU’s memory.
Radial basis function and its application in tourism management
Hu, Shan-Feng; Zhu, Hong-Bin; Zhao, Lei
2018-05-01
In this work, several applications and the performances of the radial basis function (RBF) are briefly reviewed at first. After that, the binomial function combined with three different RBFs including the multiquadric (MQ), inverse quadric (IQ) and inverse multiquadric (IMQ) distributions are adopted to model the tourism data of Huangshan in China. Simulation results showed that all the models match very well with the sample data. It is found that among the three models, the IMQ-RBF model is more suitable for forecasting the tourist flow.
Caffeine Modulates Attention Network Function
Brunye, Tad T.; Mahoney, Caroline R.; Lieberman, Harris R.; Taylor, Holly A.
2010-01-01
The present work investigated the effects of caffeine (0 mg, 100 mg, 200 mg, 400 mg) on a flanker task designed to test Posner's three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). "Cognitive neuroscience of attention". New York, NY: Guilford Press]. In a placebo-controlled, double-blind…
SATWG networked quality function deployment
Brown, Don
1992-01-01
The initiative of this work is to develop a cooperative process for continual evolution of an integrated, time phased avionics technology plan that involves customers, technologists, developers, and managers. This will be accomplished by demonstrating a computer network technology to augment the Quality Function Deployment (QFD). All results are presented in viewgraph format.
Functional asynchronous networks: Factorization of dynamics and function
Directory of Open Access Journals (Sweden)
Bick Christian
2016-01-01
Full Text Available In this note we describe the theory of functional asynchronous networks and one of the main results, the Modularization of Dynamics Theorem, which for a large class of functional asynchronous networks gives a factorization of dynamics in terms of constituent subnetworks. For these networks we can give a complete description of the network function in terms of the function of the events comprising the network and thereby answer a question originally raised by Alon in the context of biological networks.
METHODS OF TEXT INFORMATION CLASSIFICATION ON THE BASIS OF ARTIFICIAL NEURAL AND SEMANTIC NETWORKS
Directory of Open Access Journals (Sweden)
L. V. Serebryanaya
2016-01-01
Full Text Available The article covers the use of perseptron, Hopfild artificial neural network and semantic network for classification of text information. Network training algorithms are studied. An algorithm of inverse mistake spreading for perceptron network and convergence algorithm for Hopfild network are implemented. On the basis of the offered models and algorithms automatic text classification software is developed and its operation results are evaluated.
Wave forecasting in near real time basis by neural network
Digital Repository Service at National Institute of Oceanography (India)
Rao, S.; Mandal, S.; Prabaharan, N.
., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...
A Bayesian spatial model for neuroimaging data based on biologically informed basis functions.
Huertas, Ismael; Oldehinkel, Marianne; van Oort, Erik S B; Garcia-Solis, David; Mir, Pablo; Beckmann, Christian F; Marquand, Andre F
2017-11-01
The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data are characterised as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state. This model is estimated using a Bayesian framework which accurately quantifies uncertainty and automatically finds the most accurate and parsimonious combination of basis functions describing the data. We demonstrate the utility of this framework by predicting quantitative SPECT images of striatal dopamine function and we compare a variety of basis sets including generic isotropic functions, anatomical representations of the striatum derived from structural MRI, and two different soft functional parcellations of the striatum derived from resting-state fMRI (rfMRI). We found that a combination of ∼50 multiscale functional basis functions accurately represented the striatal dopamine activity, and that functional basis functions derived from an advanced parcellation technique known as Instantaneous Connectivity Parcellation (ICP) provided the most parsimonious models of dopamine function. Importantly, functional basis functions derived from resting fMRI were more accurate than both structural and generic basis sets in representing dopamine function in the striatum for a fixed model order. We demonstrate the translational validity of our framework by constructing classification models for discriminating parkinsonian disorders and their subtypes. Here, we show that ICP approach is the only basis set that performs well across all comparisons and performs better overall than the classical voxel-based approach
Practical auxiliary basis implementation of Rung 3.5 functionals
International Nuclear Information System (INIS)
Janesko, Benjamin G.; Scalmani, Giovanni; Frisch, Michael J.
2014-01-01
Approximate exchange-correlation functionals for Kohn-Sham density functional theory often benefit from incorporating exact exchange. Exact exchange is constructed from the noninteracting reference system's nonlocal one-particle density matrix γ(r -vector ,r -vector ′). Rung 3.5 functionals attempt to balance the strengths and limitations of exact exchange using a new ingredient, a projection of γ(r -vector ,r -vector ′) onto a semilocal model density matrix γ SL (ρ(r -vector ),∇ρ(r -vector ),r -vector −r -vector ′). γ SL depends on the electron density ρ(r -vector ) at reference point r -vector , and is closely related to semilocal model exchange holes. We present a practical implementation of Rung 3.5 functionals, expanding the r -vector −r -vector ′ dependence of γ SL in an auxiliary basis set. Energies and energy derivatives are obtained from 3D numerical integration as in standard semilocal functionals. We also present numerical tests of a range of properties, including molecular thermochemistry and kinetics, geometries and vibrational frequencies, and bandgaps and excitation energies. Rung 3.5 functionals typically provide accuracy intermediate between semilocal and hybrid approximations. Nonlocal potential contributions from γ SL yield interesting successes and failures for band structures and excitation energies. The results enable and motivate continued exploration of Rung 3.5 functional forms
Computational network design from functional specifications
Peng, Chi Han; Yang, Yong Liang; Bao, Fan; Fink, Daniel; Yan, Dongming; Wonka, Peter; Mitra, Niloy J.
2016-01-01
of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
The Gaussian radial basis function method for plasma kinetic theory
Energy Technology Data Exchange (ETDEWEB)
Hirvijoki, E., E-mail: eero.hirvijoki@chalmers.se [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden); Candy, J.; Belli, E. [General Atomics, PO Box 85608, San Diego, CA 92186-5608 (United States); Embréus, O. [Department of Applied Physics, Chalmers University of Technology, SE-41296 Gothenburg (Sweden)
2015-10-30
Description of a magnetized plasma involves the Vlasov equation supplemented with the non-linear Fokker–Planck collision operator. For non-Maxwellian distributions, the collision operator, however, is difficult to compute. In this Letter, we introduce Gaussian Radial Basis Functions (RBFs) to discretize the velocity space of the entire kinetic system, and give the corresponding analytical expressions for the Vlasov and collision operator. Outlining the general theory, we also highlight the connection to plasma fluid theories, and give 2D and 3D numerical solutions of the non-linear Fokker–Planck equation. Applications are anticipated in both astrophysical and laboratory plasmas. - Highlights: • A radically new method to address the velocity space discretization of the non-linear kinetic equation of plasmas. • Elegant and physically intuitive, flexible and mesh-free. • Demonstration of numerical solution of both 2-D and 3-D non-linear Fokker–Planck relaxation problem.
Adaptive radial basis function mesh deformation using data reduction
Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.
2016-09-01
Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited
Global sensitivity analysis using a Gaussian Radial Basis Function metamodel
International Nuclear Information System (INIS)
Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua
2016-01-01
Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.
Generation of Optimal Basis Functions for Reconstruction of Power Distribution
Energy Technology Data Exchange (ETDEWEB)
Park, Moonghu [Sejong Univ., Seoul (Korea, Republic of)
2014-05-15
This study proposes GMDH to find not only the best functional form but also the optimal parameters those describe the power distribution most accurately. A total of 1,060 cases of axially 1-dimensional core power distributions of 20-nodes are generated by 3-dimensional core analysis code covering BOL to EOL core burnup histories to validate the method. Axially five-point box powers at in-core detectors are considered as measurements. The reconstructed axial power shapes using GMDH method are compared to the reference power shapes. The results show that the proposed method is very robust and accurate compared with spline fitting method. It is shown that the GMDH analysis can give optimal basis functions for core power shape reconstruction. The in-core measurements are the 5 detector snapshots and the 20-node power distribution is successfully reconstructed. The effectiveness of the method is demonstrated by comparing the results of spline fitting for BOL, saddle and top-skewed power shapes.
The Method of Building a Network of Online Showcases on the Basis of the MVC Architecture
Directory of Open Access Journals (Sweden)
Pursky Oleg I.
2017-10-01
Full Text Available A method to build a network of online showcases that support a large number of customer orders and visits, which meets the current performance standards and the reliability of Internet solutions in the sphere electronic commerce, has been developed. The method involves the creation of a typical showcase and the implementation for the information management system of a showcases network of an own database operating on the data from the central management information system with the two-way data replication. A mechanism for «cloning» the online showcases, which are part of the network, and their quick integration with the business processes of enterprise and a management system based on a typical showcase, has been proposed. The development of typical online showcases is implemented on the basis of MVC concept (Model-View-Controller, the ASP.NET MVC Framework Technology, and the visual templates of web pages, thus ensuring that the algorithms for the behavior of objects are independent of both the objects themselves and their visual representation. This enhances the development of e-commerce projects significantly, speeds up the implementation process, and provides a high degree of flexibility and functionality of the online showcases.
Network function virtualization concepts and applicability in 5G networks
Zhang, Ying
2018-01-01
A horizontal view of newly emerged technologies in the field of network function virtualization (NFV), introducing the open source implementation efforts that bring NFV from design to reality This book explores the newly emerged technique of network function virtualization (NFV) through use cases, architecture, and challenges, as well as standardization and open source implementations. It is the first systematic source of information about cloud technologies' usage in the cellular network, covering the interplay of different technologies, the discussion of different design choices, and its impact on our future cellular network. Network Function Virtualization: Concepts and Applicability in 5G Networks reviews new technologies that enable NFV, such as Software Defined Networks (SDN), network virtualization, and cloud computing. It also provides an in-depth investigation of the most advanced open source initiatives in this area, including OPNFV, Openstack, and Opendaylight. Finally, this book goes beyond li...
Meshfree Local Radial Basis Function Collocation Method with Image Nodes
Energy Technology Data Exchange (ETDEWEB)
Baek, Seung Ki; Kim, Minjae [Pukyong National University, Busan (Korea, Republic of)
2017-07-15
We numerically solve two-dimensional heat diffusion problems by using a simple variant of the meshfree local radial-basis function (RBF) collocation method. The main idea is to include an additional set of sample nodes outside the problem domain, similarly to the method of images in electrostatics, to perform collocation on the domain boundaries. We can thereby take into account the temperature profile as well as its gradients specified by boundary conditions at the same time, which holds true even for a node where two or more boundaries meet with different boundary conditions. We argue that the image method is computationally efficient when combined with the local RBF collocation method, whereas the addition of image nodes becomes very costly in case of the global collocation. We apply our modified method to a benchmark test of a boundary value problem, and find that this simple modification reduces the maximum error from the analytic solution significantly. The reduction is small for an initial value problem with simpler boundary conditions. We observe increased numerical instability, which has to be compensated for by a sufficient number of sample nodes and/or more careful parameter choices for time integration.
Complex basis functions for molecular resonances: Methodology and applications
White, Alec; McCurdy, C. William; Head-Gordon, Martin
The computation of positions and widths of metastable electronic states is a challenge for molecular electronic structure theory because, in addition to the difficulty of the many-body problem, such states obey scattering boundary conditions. These resonances cannot be addressed with naïve application of traditional bound state electronic structure theory. Non-Hermitian electronic structure methods employing complex basis functions is one way that we may rigorously treat resonances within the framework of traditional electronic structure theory. In this talk, I will discuss our recent work in this area including the methodological extension from single determinant SCF-based approaches to highly correlated levels of wavefunction-based theory such as equation of motion coupled cluster and many-body perturbation theory. These approaches provide a hierarchy of theoretical methods for the computation of positions and widths of molecular resonances. Within this framework, we may also examine properties of resonances including the dependence of these parameters on molecular geometry. Some applications of these methods to temporary anions and dianions will also be discussed.
Wavelets as basis functions in electronic structure calculations
International Nuclear Information System (INIS)
Chauvin, C.
2005-11-01
This thesis is devoted to the definition and the implementation of a multi-resolution method to determine the fundamental state of a system composed of nuclei and electrons. In this work, we are interested in the Density Functional Theory (DFT), which allows to express the Hamiltonian operator with the electronic density only, by a Coulomb potential and a non-linear potential. This operator acts on orbitals, which are solutions of the so-called Kohn-Sham equations. Their resolution needs to express orbitals and density on a set of functions owing both physical and numerical properties, as explained in the second chapter. One can hardly satisfy these two properties simultaneously, that is why we are interested in orthogonal and bi-orthogonal wavelets basis, whose properties of interpolation are presented in the third chapter. We present in the fourth chapter three dimensional solvers for the Coulomb's potential, using not only the preconditioning property of wavelets, but also a multigrid algorithm. Determining this potential allows us to solve the self-consistent Kohn-Sham equations, by an algorithm presented in chapter five. The originality of our method consists in the construction of the stiffness matrix, combining a Galerkin formulation and a collocation scheme. We analyse the approximation properties of this method in case of linear Hamiltonian, such as harmonic oscillator and hydrogen, and present convergence results of the DFT for small electrons. Finally we show how orbital compression reduces considerably the number of coefficients to keep, while preserving a good accuracy of the fundamental energy. (author)
Hierarchical organization of brain functional networks during visual tasks.
Zhuo, Zhao; Cai, Shi-Min; Fu, Zhong-Qian; Zhang, Jie
2011-09-01
The functional network of the brain is known to demonstrate modular structure over different hierarchical scales. In this paper, we systematically investigated the hierarchical modular organizations of the brain functional networks that are derived from the extent of phase synchronization among high-resolution EEG time series during a visual task. In particular, we compare the modular structure of the functional network from EEG channels with that of the anatomical parcellation of the brain cortex. Our results show that the modular architectures of brain functional networks correspond well to those from the anatomical structures over different levels of hierarchy. Most importantly, we find that the consistency between the modular structures of the functional network and the anatomical network becomes more pronounced in terms of vision, sensory, vision-temporal, motor cortices during the visual task, which implies that the strong modularity in these areas forms the functional basis for the visual task. The structure-function relationship further reveals that the phase synchronization of EEG time series in the same anatomical group is much stronger than that of EEG time series from different anatomical groups during the task and that the hierarchical organization of functional brain network may be a consequence of functional segmentation of the brain cortex.
Computational network design from functional specifications
Peng, Chi Han
2016-07-11
Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.
Yang, Jingjing; Cox, Dennis D; Lee, Jong Soo; Ren, Peng; Choi, Taeryon
2017-12-01
Functional data are defined as realizations of random functions (mostly smooth functions) varying over a continuum, which are usually collected on discretized grids with measurement errors. In order to accurately smooth noisy functional observations and deal with the issue of high-dimensional observation grids, we propose a novel Bayesian method based on the Bayesian hierarchical model with a Gaussian-Wishart process prior and basis function representations. We first derive an induced model for the basis-function coefficients of the functional data, and then use this model to conduct posterior inference through Markov chain Monte Carlo methods. Compared to the standard Bayesian inference that suffers serious computational burden and instability in analyzing high-dimensional functional data, our method greatly improves the computational scalability and stability, while inheriting the advantage of simultaneously smoothing raw observations and estimating the mean-covariance functions in a nonparametric way. In addition, our method can naturally handle functional data observed on random or uncommon grids. Simulation and real studies demonstrate that our method produces similar results to those obtainable by the standard Bayesian inference with low-dimensional common grids, while efficiently smoothing and estimating functional data with random and high-dimensional observation grids when the standard Bayesian inference fails. In conclusion, our method can efficiently smooth and estimate high-dimensional functional data, providing one way to resolve the curse of dimensionality for Bayesian functional data analysis with Gaussian-Wishart processes. © 2017, The International Biometric Society.
Connectomics and neuroticism : an altered functional network organization
Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard; Ormel, Johan; Riese, Harriëtte; Aleman, André
The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network
Functional modules by relating protein interaction networks and gene expression.
Tornow, Sabine; Mewes, H W
2003-11-01
Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.
Modelling the dependability in Network Function Virtualisation
Lin, Wenqi
2017-01-01
Network Function Virtualization has been brought up to allow the TSPs to have more possibilities and flexibilities to provision services with better load optimizing, energy utilizing and dynamic scaling. Network functions will be decoupled from the underlying dedicated hardware into software instances that run on commercial off-the-shelf servers. However, the development is still at an early stage and the dependability concerns raise by the virtualization of the network functions are touched ...
Bruni, S.; Llombart Juan, N.; Neto, A.; Gerini, G.; Maci, S.
2004-01-01
A general algorithm for the analysis of microstrip coupled leaky wave slot antennas was discussed. The method was based on the construction of physically appealing entire domain Methods of Moments (MoM) basis function that allowed a consistent reduction of the number of unknowns and of total
The functional consequences of mutualistic network architecture.
Directory of Open Access Journals (Sweden)
José M Gómez
Full Text Available The architecture and properties of many complex networks play a significant role in the functioning of the systems they describe. Recently, complex network theory has been applied to ecological entities, like food webs or mutualistic plant-animal interactions. Unfortunately, we still lack an accurate view of the relationship between the architecture and functioning of ecological networks. In this study we explore this link by building individual-based pollination networks from eight Erysimum mediohispanicum (Brassicaceae populations. In these individual-based networks, each individual plant in a population was considered a node, and was connected by means of undirected links to conspecifics sharing pollinators. The architecture of these unipartite networks was described by means of nestedness, connectivity and transitivity. Network functioning was estimated by quantifying the performance of the population described by each network as the number of per-capita juvenile plants produced per population. We found a consistent relationship between the topology of the networks and their functioning, since variation across populations in the average per-capita production of juvenile plants was positively and significantly related with network nestedness, connectivity and clustering. Subtle changes in the composition of diverse pollinator assemblages can drive major consequences for plant population performance and local persistence through modifications in the structure of the inter-plant pollination networks.
Functional and nonfunctional testing of ATM networks
Ricardo, Manuel; Ferreira, M. E. P.; Guimaraes, Francisco E.; Mamede, J.; Henriques, M.; da Silva, Jorge A.; Carrapatoso, E.
1995-02-01
ATM network will support new multimedia services that will require new protocols, those services and protocols will need different test strategies and tools. In this paper, the concepts of functional and non-functional testers of ATM networks are discussed, a multimedia service and its requirements are presented and finally, a summary description of an ATM network and of the test tool that will be used to validate it are presented.
Main concept of local area network protection on the basis of the SAAM 'TRAFFIC'
International Nuclear Information System (INIS)
Vasil'ev, P.M.; Kryukov, Yu.A.; Kuptsov, S.I.; Ivanov, V.V.; Koren'kov, V.V.
2002-01-01
In our previous paper we developed a system for acquisition, analysis and management of the network traffic (SAAM 'Traffic') for a segment of the JINR local area computer network (JINR LAN). In our present work we consider well-known scenarios of attacks on local area networks and propose protection methods based on the SAAM 'Traffic'. Although the system for LAN protection is installed on a router computer, it is not analogous to the firewall scheme and, thus, it does not hinder the performance of distributed network applications. This provides a possibility to apply such an approach to GRID-technologies, where network protection on the firewall basis can not be basically used. (author)
Directory of Open Access Journals (Sweden)
Lixia Pei
Full Text Available The clinical application of Traditional Chinese medicine (TCM, using several herbs in combination (called formulas, has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L. Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas.
Directory of Open Access Journals (Sweden)
Barbara Martini
2016-06-01
Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.
Directory of Open Access Journals (Sweden)
M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
52 Sociocultural Competence as a Basis for Functional Education: A ...
African Journals Online (AJOL)
User
2010-10-17
Oct 17, 2010 ... This paper examines the relevance of socio-cultural competence in functional ... non-material expressions of the people as well as the processes with which .... themselves in all kinds of immorality, smoking, stealing, drug ...
Institute of Scientific and Technical Information of China (English)
朱国俊; 冯建军; 郭鹏程; 罗兴锜
2014-01-01
, the Bezier curve was used to parameterize the hydrofoil. The Latin Hypercube experiment design method was used to select the sample points in the design space which were used for training the Radial Basis Function neural network. The hydrodynamic performance for each sample was calculated by the computational fluid dynamic method, and then the Radial Basis Function neural network would be trained by these sample points. After the neural network had been trained, the multi-point optimization method of hydrofoil was solved by combining the NSGA-II method and the Radial Basis Function neural network. The method mentioned above was applied to the optimal design of NACA63-815 hydrofoil, and the optimization problems of the hydrofoil in three typical conditions in which the attack angle is 0, 6º and 12º were mainly studied in this paper. After optimization, two optimized hydrofoils were selected in the Pareto solution to compare with initial, which were named Optimal A and Optimal B. According to the CFD simulation, the optimized hydrofoil’s performance was gotten and compared with the initial hydrofoil. By comparison, it was found that the drag coefficient of the optimized hydrofoil in the three conditions are less than or equal to the initial. Moreover, the lift-drag ratios of the Optimal A hydrofoil in which the attack angles is 0, 6° and 12° have been improve by 4.6%, 4.4%and 22.8%respectively. And the lift-drag ratios of the Optimal B hydrofoils have also been improved by 6.6%, 3.8%and 16.6%respectively. In addition, according to the comparison of the optimized and initial hydrofoil’s pressure coefficient at the 12°attack angle, it can be found that the optimized hydrofoil can effectively suppress the stall phenomenon. Finally, two conclusions can be drawn from the optimal results. Firstly, use the Radial Basis Function neural network to replace the CFD simulation can effectively decrease the time that the optimization cycle spent. Secondly, the hydrofoil
Studies in the method of correlated basis functions. Pt. 3
International Nuclear Information System (INIS)
Krotscheck, E.; Clark, J.W.
1980-01-01
A variational theory of pairing phenomena is presented for systems like neutron matter and liquid 3 He. The strong short-range correlations among the particles in these systems are incorporated into the trial states describing normal and pair-condensed phases, via a correlation operator F. The resulting theory has the same basic structure as that ordinarily applied for weak two-body interactions; in place of the pairing matrix elements of the bare interaction one finds certain effective pairing matrix elements Psub(kl), and modified single particle energies epsilon (k) appear. Detailed prescriptions are given for the construction of the Psub(kl) and epsilon (k) in terms of off-diagonal and diagonal matrix elements of the Hamiltonian and unit operators in a correlated basis of normal states. An exact criterion for instability of the assumed normal phase with respect to pair condensation is derived for general F. This criterion is investigated numerically for the special case if Jastrow correlations, the required normal-state quantities being evaluated by integral equation techniques which extend the Fermi hypernetted-chain scheme. In neutron matter, an instability with respect to 1 S 0 pairing is found in the low-density region, in concert with the predictions of Yang and Clark. In liquid 3 He, there is some indication of a 3 P 0 pairing instability in the vicinity of the experimental equilibrium density. (orig.)
Structural basis and functions of abscisic acid receptors PYLs
Zhang, Xing L.; Jiang, Lun; Xin, Qi; Liu, Yang; Tan, Jian X.; Chen, Zhong Z.
2015-01-01
Abscisic acid (ABA) plays a key role in many developmental processes and responses to adaptive stresses in plants. Recently, a new family of nucleocytoplasmic PYR/PYL/RCAR (PYLs) has been identified as bona fide ABA receptors. PYLs together with protein phosphatases type-2C (PP2Cs), Snf1 (Sucrose-non-fermentation 1)-related kinases subfamily 2 (SnRK2s) and downstream substrates constitute the core ABA signaling network. Generally, PP2Cs inactivate SnRK2s kinases by physical interaction and direct dephosphorylation. Upon ABA binding, PYLs change their conformations and then contact and inhibit PP2Cs, thus activating SnRK2s. Here, we reviewed the recent progress in research regarding the structures of the core signaling pathways of ABA, including the (+)-ABA, (−)-ABA and ABA analogs pyrabactin as well as 6AS perception by PYLs, SnRK2s mimicking PYLs in binding PP2Cs. PYLs inhibited PP2Cs in both the presence and absence of ABA and activated SnRK2s. The present review elucidates multiple ABA signal perception and transduction by PYLs, which might shed light on how to design small chemical compounds for improving plant performance in the future. PMID:25745428
Mandal, Sudhansu S.; Mukherjee, Sutirtha; Ray, Koushik
2018-03-01
A method for determining the ground state of a planar interacting many-electron system in a magnetic field perpendicular to the plane is described. The ground state wave-function is expressed as a linear combination of a set of basis functions. Given only the flux and the number of electrons describing an incompressible state, we use the combinatorics of partitioning the flux among the electrons to derive the basis wave-functions as linear combinations of Schur polynomials. The procedure ensures that the basis wave-functions form representations of the angular momentum algebra. We exemplify the method by deriving the basis functions for the 5/2 quantum Hall state with a few particles. We find that one of the basis functions is precisely the Moore-Read Pfaffian wave function.
New MoM code incorporating multiple domain basis functions
CSIR Research Space (South Africa)
Lysko, AA
2011-08-01
Full Text Available piecewise linear approximation of geometry. This often leads to an unnecessarily great number of unknowns used to model relatively small loop and spiral antennas, coils and other curved structures. This is because the program creates a dense mesh... to accelerate computation of the elements of the impedance matrix and showed acceleration factor exceeding an order of magnitude, subject to a high accuracy requirement. 3. On Code Functionality and Application Results The package of programs was written...
Connectomics and neuroticism: an altered functional network organization.
Servaas, Michelle N; Geerligs, Linda; Renken, Remco J; Marsman, Jan-Bernard C; Ormel, Johan; Riese, Harriëtte; Aleman, André
2015-01-01
The personality trait neuroticism is a potent risk marker for psychopathology. Although the neurobiological basis remains unclear, studies have suggested that alterations in connectivity may underlie it. Therefore, the aim of the current study was to shed more light on the functional network organization in neuroticism. To this end, we applied graph theory on resting-state functional magnetic resonance imaging (fMRI) data in 120 women selected based on their neuroticism score. Binary and weighted brain-wide graphs were constructed to examine changes in the functional network structure and functional connectivity strength. Furthermore, graphs were partitioned into modules to specifically investigate connectivity within and between functional subnetworks related to emotion processing and cognitive control. Subsequently, complex network measures (ie, efficiency and modularity) were calculated on the brain-wide graphs and modules, and correlated with neuroticism scores. Compared with low neurotic individuals, high neurotic individuals exhibited a whole-brain network structure resembling more that of a random network and had overall weaker functional connections. Furthermore, in these high neurotic individuals, functional subnetworks could be delineated less clearly and the majority of these subnetworks showed lower efficiency, while the affective subnetwork showed higher efficiency. In addition, the cingulo-operculum subnetwork demonstrated more ties with other functional subnetworks in association with neuroticism. In conclusion, the 'neurotic brain' has a less than optimal functional network organization and shows signs of functional disconnectivity. Moreover, in high compared with low neurotic individuals, emotion and salience subnetworks have a more prominent role in the information exchange, while sensory(-motor) and cognitive control subnetworks have a less prominent role.
Avian magnetic compass: Its functional properties and physical basis
Directory of Open Access Journals (Sweden)
Roswitha WILTSCHKO, Wolfgang WILTSCHKO
2010-06-01
Full Text Available The avian magnetic compass was analyzed in bird species of three different orders – Passeriforms, Columbiforms and Galliforms – and in three different behavioral contexts, namely migratory orientation, homing and directional conditioning. The respective findings indicate similar functional properties: it is an inclination compass that works only within a functional window around the ambient magnetic field intensity; it tends to be lateralized in favor of the right eye, and it is wavelength-dependent, requiring light from the short-wavelength range of the spectrum. The underlying physical mechanisms have been identified as radical pair processes, spin-chemical reactions in specialized photopigments. The iron-based receptors in the upper beak do not seem to be involved. The existence of the same type of magnetic compass in only very distantly related bird species suggests that it may have been present already in the common ancestors of all modern birds, where it evolved as an all-purpose compass mechanism for orientation within the home range [Current Zoology 56 (3: 265–276, 2010].
International Nuclear Information System (INIS)
Brown, James; Carrington, Tucker
2016-01-01
In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier’s symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.
Brown, James; Carrington, Tucker
2016-06-28
In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier's symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.
Development of the brain's functional network architecture.
Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L
2010-12-01
A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.
Human brain networks function in connectome-specific harmonic waves.
Atasoy, Selen; Donnelly, Isaac; Pearson, Joel
2016-01-21
A key characteristic of human brain activity is coherent, spatially distributed oscillations forming behaviour-dependent brain networks. However, a fundamental principle underlying these networks remains unknown. Here we report that functional networks of the human brain are predicted by harmonic patterns, ubiquitous throughout nature, steered by the anatomy of the human cerebral cortex, the human connectome. We introduce a new technique extending the Fourier basis to the human connectome. In this new frequency-specific representation of cortical activity, that we call 'connectome harmonics', oscillatory networks of the human brain at rest match harmonic wave patterns of certain frequencies. We demonstrate a neural mechanism behind the self-organization of connectome harmonics with a continuous neural field model of excitatory-inhibitory interactions on the connectome. Remarkably, the critical relation between the neural field patterns and the delicate excitation-inhibition balance fits the neurophysiological changes observed during the loss and recovery of consciousness.
Maaskant, R.; Mittra, R.; Tijhuis, A.G.; Graglia, R.D.
2007-01-01
This paper describes a novel technique for generating the characteristic basis functions (CBFs) used to represent the surface currents on finite arrays of electrically interconnected antenna elements. The CBFs are high-level basis functions, defined on subdomains in which the original problem is
The Neural Basis of Typewriting: A Functional MRI Study.
Higashiyama, Yuichi; Takeda, Katsuhiko; Someya, Yoshiaki; Kuroiwa, Yoshiyuki; Tanaka, Fumiaki
2015-01-01
To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI) study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.
The Neural Basis of Typewriting: A Functional MRI Study.
Directory of Open Access Journals (Sweden)
Yuichi Higashiyama
Full Text Available To investigate the neural substrate of typewriting Japanese words and to detect the difference between the neural substrate of typewriting and handwriting, we conducted a functional magnetic resonance imaging (fMRI study in 16 healthy volunteers. All subjects were skillful touch typists and performed five tasks: a typing task, a writing task, a reading task, and two control tasks. Three brain regions were activated during both the typing and the writing tasks: the left superior parietal lobule, the left supramarginal gyrus, and the left premotor cortex close to Exner's area. Although typing and writing involved common brain regions, direct comparison between the typing and the writing task revealed greater left posteromedial intraparietal cortex activation in the typing task. In addition, activity in the left premotor cortex was more rostral in the typing task than in the writing task. These findings suggest that, although the brain circuits involved in Japanese typewriting are almost the same as those involved in handwriting, there are brain regions that are specific for typewriting.
Factorization of products of discontinuous functions applied to Fourier-Bessel basis.
Popov, Evgeny; Nevière, Michel; Bonod, Nicolas
2004-01-01
The factorization rules of Li [J. Opt. Soc. Am. A 13, 1870 (1996)] are generalized to a cylindrical geometry requiring the use of a Bessel function basis. A theoretical study confirms the validity of the Laurent rule when a product of two continuous functions or of one continuous and one discontinuous function is factorized. The necessity of applying the so-called inverse rule in factorizing a continuous product of two discontinuous functions in a truncated basis is demonstrated theoretically and numerically.
DEFF Research Database (Denmark)
Darden, Safi-Kirstine; Steffensen, Lise K.; Dabelsteen, Torben
2008-01-01
In species where individuals are widely spaced instantaneous signals cannot readily form the basis of communication networks, that is several individuals within signalling range of each other. However, markings, signals that remain in the environment after the signaller has left, may fulfil...... this role. In this study, we have investigated the possible function of swift fox, Vulpes velox, latrines, collections of scat, urine and possibly other secretions, in a communication network context. We found that latrines had higher frequencies of occurrence inside the core (defined as the 50% kernel...... contour) of a pair's home-range when compared with outside the core and in areas of a pair's home-range that overlapped with neighbouring individuals when compared with those areas that did not overlap with neighbours. These were also the two areas where latrines were most likely to reoccur in the next...
Explicit appropriate basis function method for numerical solution of stiff systems
International Nuclear Information System (INIS)
Chen, Wenzhen; Xiao, Hongguang; Li, Haofeng; Chen, Ling
2015-01-01
Highlights: • An explicit numerical method called the appropriate basis function method is presented. • The method differs from the power series method for obtaining approximate numerical solutions. • Two cases show the method is fit for linear and nonlinear stiff systems. • The method is very simple and effective for most of differential equation systems. - Abstract: In this paper, an explicit numerical method, called the appropriate basis function method, is presented. The explicit appropriate basis function method differs from the power series method because it employs an appropriate basis function such as the exponential function, or periodic function, other than a polynomial, to obtain approximate numerical solutions. The method is successful and effective for the numerical solution of the first order ordinary differential equations. Two examples are presented to show the ability of the method for dealing with linear and nonlinear systems of differential equations
Functional brain networks in schizophrenia: a review
Directory of Open Access Journals (Sweden)
Vince D Calhoun
2009-08-01
Full Text Available Functional magnetic resonance imaging (fMRI has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their
Hellweg, Arnim; Rappoport, Dmitrij
2015-01-14
We report optimized auxiliary basis sets for use with the Karlsruhe segmented contracted basis sets including moderately diffuse basis functions (Rappoport and Furche, J. Chem. Phys., 2010, 133, 134105) in resolution-of-the-identity (RI) post-self-consistent field (post-SCF) computations for the elements H-Rn (except lanthanides). The errors of the RI approximation using optimized auxiliary basis sets are analyzed on a comprehensive test set of molecules containing the most common oxidation states of each element and do not exceed those of the corresponding unaugmented basis sets. During these studies an unsatisfying performance of the def2-SVP and def2-QZVPP auxiliary basis sets for Barium was found and improved sets are provided. We establish the versatility of the def2-SVPD, def2-TZVPPD, and def2-QZVPPD basis sets for RI-MP2 and RI-CC (coupled-cluster) energy and property calculations. The influence of diffuse basis functions on correlation energy, basis set superposition error, atomic electron affinity, dipole moments, and computational timings is evaluated at different levels of theory using benchmark sets and showcase examples.
Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm
Directory of Open Access Journals (Sweden)
G. Trejo-Caballero
2015-01-01
Full Text Available Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.
Privacy-preserving Network Functionality Outsourcing
Shi, Junjie; Zhang, Yuan; Zhong, Sheng
2015-01-01
Since the advent of software defined networks ({SDN}), there have been many attempts to outsource the complex and costly local network functionality, i.e. the middlebox, to the cloud in the same way as outsourcing computation and storage. The privacy issues, however, may thwart the enterprises' willingness to adopt this innovation since the underlying configurations of these middleboxes may leak crucial and confidential information which can be utilized by attackers. To address this new probl...
Histone chaperone networks shaping chromatin function
DEFF Research Database (Denmark)
Hammond, Colin; Strømme, Caroline Bianchi; Huang, Hongda
2017-01-01
and fate, which affects all chromosomal processes, including gene expression, chromosome segregation and genome replication and repair. Here, we review the distinct structural and functional properties of the expanding network of histone chaperones. We emphasize how chaperones cooperate in the histone...... chaperone network and via co-chaperone complexes to match histone supply with demand, thereby promoting proper nucleosome assembly and maintaining epigenetic information by recycling modified histones evicted from chromatin....
Polarized DIS Structure Functions from Neural Networks
International Nuclear Information System (INIS)
Del Debbio, L.; Guffanti, A.; Piccione, A.
2007-01-01
We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g 1 p (x,Q 2 )
Nicotine increases brain functional network efficiency.
Wylie, Korey P; Rojas, Donald C; Tanabe, Jody; Martin, Laura F; Tregellas, Jason R
2012-10-15
Despite the use of cholinergic therapies in Alzheimer's disease and the development of cholinergic strategies for schizophrenia, relatively little is known about how the system modulates the connectivity and structure of large-scale brain networks. To better understand how nicotinic cholinergic systems alter these networks, this study examined the effects of nicotine on measures of whole-brain network communication efficiency. Resting state fMRI was acquired from fifteen healthy subjects before and after the application of nicotine or placebo transdermal patches in a single blind, crossover design. Data, which were previously examined for default network activity, were analyzed with network topology techniques to measure changes in the communication efficiency of whole-brain networks. Nicotine significantly increased local efficiency, a parameter that estimates the network's tolerance to local errors in communication. Nicotine also significantly enhanced the regional efficiency of limbic and paralimbic areas of the brain, areas which are especially altered in diseases such as Alzheimer's disease and schizophrenia. These changes in network topology may be one mechanism by which cholinergic therapies improve brain function. Published by Elsevier Inc.
Accurate correlation energies in one-dimensional systems from small system-adapted basis functions
Baker, Thomas E.; Burke, Kieron; White, Steven R.
2018-02-01
We propose a general method for constructing system-dependent basis functions for correlated quantum calculations. Our construction combines features from several traditional approaches: plane waves, localized basis functions, and wavelets. In a one-dimensional mimic of Coulomb systems, it requires only 2-3 basis functions per electron to achieve high accuracy, and reproduces the natural orbitals. We illustrate its effectiveness for molecular energy curves and chains of many one-dimensional atoms. We discuss the promise and challenges for realistic quantum chemical calculations.
Hierarchical modularity in human brain functional networks
Directory of Open Access Journals (Sweden)
David Meunier
2009-10-01
Full Text Available The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules” decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.
Multiscale finite element methods for high-contrast problems using local spectral basis functions
Efendiev, Yalchin
2011-02-01
In this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.
Directory of Open Access Journals (Sweden)
Yuri B. Tebekin
2011-11-01
Full Text Available The article is devoted to the problem of the quality management for multiphase processes on the basis of the probabilistic approach. Method with continuous response functions is offered from the application of the method of Lagrange multipliers.
Advanced Functionalities for Highly Reliable Optical Networks
DEFF Research Database (Denmark)
An, Yi
This thesis covers two research topics concerning optical solutions for networks e.g. avionic systems. One is to identify the applications for silicon photonic devices for cost-effective solutions in short-range optical networks. The other one is to realise advanced functionalities in order...... to increase the availability of highly reliable optical networks. A cost-effective transmitter based on a directly modulated laser (DML) using a silicon micro-ring resonator (MRR) to enhance its modulation speed is proposed, analysed and experimentally demonstrated. A modulation speed enhancement from 10 Gbit...... interconnects and network-on-chips. A novel concept of all-optical protection switching scheme is proposed, where fault detection and protection trigger are all implemented in the optical domain. This scheme can provide ultra-fast establishment of the protection path resulting in a minimum loss of data...
Combustion monitoring of a water tube boiler using a discriminant radial basis network.
Sujatha, K; Pappa, N
2011-01-01
This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Modelling and prediction for chaotic fir laser attractor using rational function neural network.
Cho, S
2001-02-01
Many real-world systems such as irregular ECG signal, volatility of currency exchange rate and heated fluid reaction exhibit highly complex nonlinear characteristic known as chaos. These chaotic systems cannot be retreated satisfactorily using linear system theory due to its high dimensionality and irregularity. This research focuses on prediction and modelling of chaotic FIR (Far InfraRed) laser system for which the underlying equations are not given. This paper proposed a method for prediction and modelling a chaotic FIR laser time series using rational function neural network. Three network architectures, TDNN (Time Delayed Neural Network), RBF (radial basis function) network and the RF (rational function) network, are also presented. Comparisons between these networks performance show the improvements introduced by the RF network in terms of a decrement in network complexity and better ability of predictability.
Smooth function approximation using neural networks.
Ferrari, Silvia; Stengel, Robert F
2005-01-01
An algebraic approach for representing multidimensional nonlinear functions by feedforward neural networks is presented. In this paper, the approach is implemented for the approximation of smooth batch data containing the function's input, output, and possibly, gradient information. The training set is associated to the network adjustable parameters by nonlinear weight equations. The cascade structure of these equations reveals that they can be treated as sets of linear systems. Hence, the training process and the network approximation properties can be investigated via linear algebra. Four algorithms are developed to achieve exact or approximate matching of input-output and/or gradient-based training sets. Their application to the design of forward and feedback neurocontrollers shows that algebraic training is characterized by faster execution speeds and better generalization properties than contemporary optimization techniques.
Schizophrenia classification using functional network features
Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle
2012-03-01
This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.
Distributed Function Calculation over Noisy Networks
Directory of Open Access Journals (Sweden)
Zhidun Zeng
2016-01-01
Full Text Available Considering any connected network with unknown initial states for all nodes, the nearest-neighbor rule is utilized for each node to update its own state at every discrete-time step. Distributed function calculation problem is defined for one node to compute some function of the initial values of all the nodes based on its own observations. In this paper, taking into account uncertainties in the network and observations, an algorithm is proposed to compute and explicitly characterize the value of the function in question when the number of successive observations is large enough. While the number of successive observations is not large enough, we provide an approach to obtain the tightest possible bounds on such function by using linear programing optimization techniques. Simulations are provided to demonstrate the theoretical results.
EEG classification of emotions using emotion-specific brain functional network.
Gonuguntla, V; Shafiq, G; Wang, Y; Veluvolu, K C
2015-08-01
The brain functional network perspective forms the basis to relate mechanisms of brain functions. This work analyzes the network mechanisms related to human emotion based on synchronization measure - phase-locking value in EEG to formulate the emotion specific brain functional network. Based on network dissimilarities between emotion and rest tasks, most reactive channel pairs and the reactive band corresponding to emotions are identified. With the identified most reactive pairs, the subject-specific functional network is formed. The identified subject-specific and emotion-specific dynamic network pattern show significant synchrony variation in line with the experiment protocol. The same network pattern are then employed for classification of emotions. With the study conducted on the 4 subjects, an average classification accuracy of 62 % was obtained with the proposed technique.
Directory of Open Access Journals (Sweden)
A. Vasilis Vasiliauskas
2010-12-01
Full Text Available .The days when a buyer was forced to choose from what is being offered have passed. These days, buyers demand a product that would answer their exclusive expectations at a time of their preference and at an acceptable price. Therefore, manufacturers aiming to survive the competition battle have to rethink their operation strategies. Special importance is attached to the process of development and reconstruction of supply chains, and the process which may feature particularities, depending on the branch of industry. Automobile manufacturing is the biggest the fastest industry developing across the globe. New automobiles are listed as luxury commodities and are, therefore, subjected to very strict requirements with regard to various logistic operations and technologies, which are vital for ensuring efficient automobile delivery to the final users. Due to the growing demand for brand-new automobiles and the distance to the user, automobile manufacturers are constantly searching for solutions to the development and support of an efficient distribution network. Strategy shaping of distribution network requires evaluation of a number of criteria, which influence the distribution system. The article analyzes the development of automobile distribution networks on the basis of multi-criteria evaluation of distribution channels.
Functional model of biological neural networks.
Lo, James Ting-Ho
2010-12-01
A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.
Large-Scale Functional Brain Network Reorganization During Taoist Meditation.
Jao, Tun; Li, Chia-Wei; Vértes, Petra E; Wu, Changwei Wesley; Achard, Sophie; Hsieh, Chao-Hsien; Liou, Chien-Hui; Chen, Jyh-Horng; Bullmore, Edward T
2016-02-01
Meditation induces a distinct and reversible mental state that provides insights into brain correlates of consciousness. We explored brain network changes related to meditation by graph theoretical analysis of resting-state functional magnetic resonance imaging data. Eighteen Taoist meditators with varying levels of expertise were scanned using a within-subjects counterbalanced design during resting and meditation states. State-related differences in network topology were measured globally and at the level of individual nodes and edges. Although measures of global network topology, such as small-worldness, were unchanged, meditation was characterized by an extensive and expertise-dependent reorganization of the hubs (highly connected nodes) and edges (functional connections). Areas of sensory cortex, especially the bilateral primary visual and auditory cortices, and the bilateral temporopolar areas, which had the highest degree (or connectivity) during the resting state, showed the biggest decrease during meditation. Conversely, bilateral thalamus and components of the default mode network, mainly the bilateral precuneus and posterior cingulate cortex, had low degree in the resting state but increased degree during meditation. Additionally, these changes in nodal degree were accompanied by reorganization of anatomical orientation of the edges. During meditation, long-distance longitudinal (antero-posterior) edges increased proportionally, whereas orthogonal long-distance transverse (right-left) edges connecting bilaterally homologous cortices decreased. Our findings suggest that transient changes in consciousness associated with meditation introduce convergent changes in the topological and spatial properties of brain functional networks, and the anatomical pattern of integration might be as important as the global level of integration when considering the network basis for human consciousness.
Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model
Directory of Open Access Journals (Sweden)
Yazid Edwar
2014-07-01
Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.
Modeling multivariate time series on manifolds with skew radial basis functions.
Jamshidi, Arta A; Kirby, Michael J
2011-01-01
We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.
Arithmetic functions in torus and tree networks
Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos M.
2007-12-25
Methods and systems for performing arithmetic functions. In accordance with a first aspect of the invention, methods and apparatus are provided, working in conjunction of software algorithms and hardware implementation of class network routing, to achieve a very significant reduction in the time required for global arithmetic operation on the torus. Therefore, it leads to greater scalability of applications running on large parallel machines. The invention involves three steps in improving the efficiency and accuracy of global operations: (1) Ensuring, when necessary, that all the nodes do the global operation on the data in the same order and so obtain a unique answer, independent of roundoff error; (2) Using the topology of the torus to minimize the number of hops and the bidirectional capabilities of the network to reduce the number of time steps in the data transfer operation to an absolute minimum; and (3) Using class function routing to reduce latency in the data transfer. With the method of this invention, every single element is injected into the network only once and it will be stored and forwarded without any further software overhead. In accordance with a second aspect of the invention, methods and systems are provided to efficiently implement global arithmetic operations on a network that supports the global combining operations. The latency of doing such global operations are greatly reduced by using these methods.
Bruni, S.; Llombart, N.; Neto, A.; Gerini, G.; Maci, S.
2004-01-01
A method is proposed for the analysis of arrays of linear printed antennas. After the formulation of pertinent set of integral equations, the appropriate equivalent currents of the Method of Moments are represented in terms of two sets of entire domain basis functions. These functions synthesize on
Hong, X; Harris, C J
2000-01-01
This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.
Function approximation of tasks by neural networks
International Nuclear Information System (INIS)
Gougam, L.A.; Chikhi, A.; Mekideche-Chafa, F.
2008-01-01
For several years now, neural network models have enjoyed wide popularity, being applied to problems of regression, classification and time series analysis. Neural networks have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. The latter is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. In a previous contribution, we have used a well known simplified architecture to show that it provides a reasonably efficient, practical and robust, multi-frequency analysis. We have investigated the universal approximation theory of neural networks whose transfer functions are: sigmoid (because of biological relevance), Gaussian and two specified families of wavelets. The latter have been found to be more appropriate to use. The aim of the present contribution is therefore to use a m exican hat wavelet a s transfer function to approximate different tasks relevant and inherent to various applications in physics. The results complement and provide new insights into previously published results on this problem
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter
2004-01-01
An efficient higher-order method of moments (MoM) solution of volume integral equations is presented. The higher-order MoM solution is based on higher-order hierarchical Legendre basis functions and higher-order geometry modeling. An unstructured mesh composed of 8-node trilinear and/or curved 27...... of magnitude in comparison to existing higher-order hierarchical basis functions. Consequently, an iterative solver can be applied even for high expansion orders. Numerical results demonstrate excellent agreement with the analytical Mie series solution for a dielectric sphere as well as with results obtained...
Exponential Convergence for Numerical Solution of Integral Equations Using Radial Basis Functions
Directory of Open Access Journals (Sweden)
Zakieh Avazzadeh
2014-01-01
Full Text Available We solve some different type of Urysohn integral equations by using the radial basis functions. These types include the linear and nonlinear Fredholm, Volterra, and mixed Volterra-Fredholm integral equations. Our main aim is to investigate the rate of convergence to solve these equations using the radial basis functions which have normic structure that utilize approximation in higher dimensions. Of course, the use of this method often leads to ill-posed systems. Thus we propose an algorithm to improve the results. Numerical results show that this method leads to the exponential convergence for solving integral equations as it was already confirmed for partial and ordinary differential equations.
Yeh, Wei-Chang
Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.
Network structure shapes spontaneous functional connectivity dynamics.
Shen, Kelly; Hutchison, R Matthew; Bezgin, Gleb; Everling, Stefan; McIntosh, Anthony R
2015-04-08
The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture. Copyright © 2015 the authors 0270-6474/15/355579-10$15.00/0.
Directory of Open Access Journals (Sweden)
Kihara Daisuke
2010-05-01
Full Text Available Abstract Background A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance. Results Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum (malaria. The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted. Conclusion The analyses demonstrate that applying high confidence predictions from PFP
Institute of Scientific and Technical Information of China (English)
ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang
2012-01-01
Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.
Fast generation of macro basis functions for LEGO through the adaptive cross approximation
Lancellotti, V.
2015-01-01
We present a method for the fast generation of macro basis functions in the context of the linear embedding via Green's operators approach (LEGO) which is a domain decomposition technique based on the combination of electromagnetic bricks in turn described by means of scattering operators. We show
Method of applying single higher order polynomial basis function over multiple domains
CSIR Research Space (South Africa)
Lysko, AA
2010-03-01
Full Text Available A novel method has been devised where one set of higher order polynomial-based basis functions can be applied over several wire segments, thus permitting to decouple the number of unknowns from the number of segments, and so from the geometrical...
New Method for Mesh Moving Based on Radial Basis Function Interpolation
De Boer, A.; Van der Schoot, M.S.; Bijl, H.
2006-01-01
A new point-by-point mesh movement algorithm is developed for the deformation of unstructured grids. The method is based on using radial basis function, RBFs, to interpolate the displacements of the boundary nodes to the whole flow mesh. A small system of equations has to be solved, only involving
Least square fitting of low resolution gamma ray spectra with cubic B-spline basis functions
International Nuclear Information System (INIS)
Zhu Menghua; Liu Lianggang; Qi Dongxu; You Zhong; Xu Aoao
2009-01-01
In this paper, the least square fitting method with the cubic B-spline basis functions is derived to reduce the influence of statistical fluctuations in the gamma ray spectra. The derived procedure is simple and automatic. The results show that this method is better than the convolution method with a sufficient reduction of statistical fluctuation. (authors)
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions
Novosad, Philip; Reader, Andrew J.
2016-06-01
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral
Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks
Directory of Open Access Journals (Sweden)
Lindsay eRutter
2013-07-01
Full Text Available Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.
The correlation of metrics in complex networks with applications in functional brain networks
Li, C.; Wang, H.; De Haan, W.; Stam, C.J.; Van Mieghem, P.F.A.
2011-01-01
An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we
Allocating resources between network nodes for providing a network node function
Strijkers, R.J.; Meulenhoff, P.J.
2014-01-01
The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a
Computer network defense through radial wave functions
Malloy, Ian J.
The purpose of this research is to synthesize basic and fundamental findings in quantum computing, as applied to the attack and defense of conventional computer networks. The concept focuses on uses of radio waves as a shield for, and attack against traditional computers. A logic bomb is analogous to a landmine in a computer network, and if one was to implement it as non-trivial mitigation, it will aid computer network defense. As has been seen in kinetic warfare, the use of landmines has been devastating to geopolitical regions in that they are severely difficult for a civilian to avoid triggering given the unknown position of a landmine. Thus, the importance of understanding a logic bomb is relevant and has corollaries to quantum mechanics as well. The research synthesizes quantum logic phase shifts in certain respects using the Dynamic Data Exchange protocol in software written for this work, as well as a C-NOT gate applied to a virtual quantum circuit environment by implementing a Quantum Fourier Transform. The research focus applies the principles of coherence and entanglement from quantum physics, the concept of expert systems in artificial intelligence, principles of prime number based cryptography with trapdoor functions, and modeling radio wave propagation against an event from unknown parameters. This comes as a program relying on the artificial intelligence concept of an expert system in conjunction with trigger events for a trapdoor function relying on infinite recursion, as well as system mechanics for elliptic curve cryptography along orbital angular momenta. Here trapdoor both denotes the form of cipher, as well as the implied relationship to logic bombs.
Directory of Open Access Journals (Sweden)
V. A. Lazarenko
2017-01-01
Full Text Available Purpose. To develop an artificial neural network for diagnosing and predicting the development of cholecystitis based on an analysis of data on risk factors, and to explore the possibilities of its application in real clinical practice.Materials and methods. The collection of materials was held in at the hospitals of the city of Kursk and included a survey of 488 patients with hepatopancreatoduodenal diseases. 203 patients were suffering from cholecystitis, in 285 patients the diagnosis of cholecystitis was excluded. Analysis of risk factors’ data (such as sex, age, bad habits, profession, family relationships, etc. was carried out using an internally developed artificial neural network (multilayer perceptron with hyperbolic tangent as the activation function. The computer program “System of Intellectual Analysis and Diagnosis of Diseases” was registered in accordance with established procedure (Certificate No. 2017613090.Results. The use of neural network analysis of data on risk factors in comparison with the processing of information that forms a clinical picture allows the diagnosis of a potential disease with cholecystitis before the onset of symptoms. The training of the artificial neural network with a quantitative output coding the age of probable hospitalization made it possible to generate an array of values, signifficantly (α ≤ 0.001 not differing from the empirical data. The difference between the mean calculated and mean empirical values was 0.45 for the training set and 1.75 for the clinical approbation group. The mean absolute error was within the range of 1.87–2.07 years.Conclusion. 1. The proposed new approach to the diagnosis and prognosis of cholecystitis has demonstrated its effectiveness, which is confirmed in clinical approbation by the levels of sensitivity (94.44%, m = 2.26 and specificity (80.6%, m = 3.9.2. The error in predicting the age of probable hospitalization of patients with cholecystitis did not
A conceptual basis to encode and detect organic functional groups in XML.
Sankar, Punnaivanam; Krief, Alain; Vijayasarathi, Durairaj
2013-06-01
A conceptual basis to define and detect organic functional groups is developed. The basic model of a functional group is termed as a primary functional group and is characterized by a group center composed of one or more group center atoms bonded to terminal atoms and skeletal carbon atoms. The generic group center patterns are identified from the structures of known functional groups. Accordingly, a chemical ontology 'Font' is developed to organize the existing functional groups as well as the new ones to be defined by the chemists. The basic model is extended to accommodate various combinations of primary functional groups as functional group assemblies. A concept of skeletal group is proposed to define the characteristic groups composed of only carbon atoms to be regarded as equivalent to functional groups. The combination of primary functional groups with skeletal groups is categorized as skeletal group assembly. In order to make the model suitable for reaction modeling purpose, a Graphical User Interface (GUI) is developed to define the functional groups and to encode in XML format appropriate to detect them in chemical structures. The system is capable of detecting multiple instances of primary functional groups as well as the overlapping poly-functional groups as the respective assemblies. Copyright © 2013 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.
Chen, Yaojing; Chen, Kewei; Zhang, Junying; Li, Xin; Shu, Ni; Wang, Jun; Zhang, Zhanjun; Reiman, Eric M
2015-03-13
As the Apolipoprotein E (APOE) ɛ4 allele is a major genetic risk factor for sporadic Alzheimer's disease (AD), which has been suggested as a disconnection syndrome manifested by the disruption of white matter (WM) integrity and functional connectivity (FC), elucidating the subtle brain structural and functional network changes in cognitively normal ɛ4 carriers is essential for identifying sensitive neuroimaging based biomarkers and understanding the preclinical AD-related abnormality development. We first constructed functional network on the basis of resting-state functional magnetic resonance imaging and a structural network on the basis of diffusion tensor image. Using global, local and nodal efficiencies of these two networks, we then examined (i) the differences of functional and WM structural network between cognitively normal ɛ4 carriers and non-carriers simultaneously, (ii) the sensitivity of these indices as biomarkers, and (iii) their relationship to behavior measurements, as well as to cholesterol level. For ɛ4 carriers, we found reduced global efficiency significantly in WM and marginally in FC, regional FC dysfunctions mainly in medial temporal areas, and more widespread for WM network. Importantly, the right parahippocampal gyrus (PHG.R) was the only region with simultaneous functional and structural damage, and the nodal efficiency of PHG.R in WM network mediates the APOE ɛ4 effect on memory function. Finally, the cholesterol level correlated with WM network differently than with the functional network in ɛ4 carriers. Our results demonstrated ɛ4-specific abnormal structural and functional patterns, which may potentially serve as biomarkers for early detection before the onset of the disease.
Feller, David; Dixon, David A
2018-03-08
Two recent papers in this journal called into question the suitability of the correlation consistent basis sets for density functional theory (DFT) calculations, because the sets were designed for correlated methods such as configuration interaction, perturbation theory, and coupled cluster theory. These papers focused on the ability of the correlation consistent and other basis sets to reproduce total energies, atomization energies, and dipole moments obtained from "quasi-exact" multiwavelet results. Undesirably large errors were observed for the correlation consistent basis sets. One of the papers argued that basis sets specifically optimized for DFT methods were "essential" for obtaining high accuracy. In this work we re-examined the performance of the correlation consistent basis sets by resolving problems with the previous calculations and by making more appropriate basis set choices for the alkali and alkaline-earth metals and second-row elements. When this is done, the statistical errors with respect to the benchmark values and with respect to DFT optimized basis sets are greatly reduced, especially in light of the relatively large intrinsic error of the underlying DFT method. When judged with respect to high-quality Feller-Peterson-Dixon coupled cluster theory atomization energies, the PBE0 DFT method used in the previous studies exhibits a mean absolute deviation more than a factor of 50 larger than the quintuple zeta basis set truncation error.
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Meincke, Peter; Breinbjerg, Olav
2007-01-01
The problem of electromagnetic scattering by composite metallic and dielectric objects is solved using the coupled volume-surface integral equation (VSIE). The method of moments (MoM) based on higher-order hierarchical Legendre basis functions and higher-order curvilinear geometrical elements...... with the analytical Mie series solution. Scattering by more complex metal-dielectric objects are also considered to compare the presented technique with other numerical methods....
Resting state functional connectivity: its physiological basis and application in neuropharmacology.
Lu, Hanbing; Stein, Elliot A
2014-09-01
Brain structures do not work in isolation; they work in concert to produce sensory perception, motivation and behavior. Systems-level network activity can be investigated by resting state magnetic resonance imaging (rsMRI), an emerging neuroimaging technique that assesses the synchrony of the brain's ongoing spontaneous activity. Converging evidence reveals that rsMRI is able to consistently identify distinct spatiotemporal patterns of large-scale brain networks. Dysregulation within and between these networks has been implicated in a number of neurodegenerative and neuropsychiatric disorders, including Alzheimer's disease and drug addiction. Despite wide application of this approach in systems neuroscience, the physiological basis of these fluctuations remains incompletely understood. Here we review physiological studies in electrical, metabolic and hemodynamic fluctuations that are most pertinent to the rsMRI signal. We also review recent applications to neuropharmacology - specifically drug effects on resting state fluctuations. We speculate that the mechanisms governing spontaneous fluctuations in regional oxygenation availability likely give rise to the observed rsMRI signal. We conclude by identifying several open questions surrounding this technique. This article is part of the Special Issue Section entitled 'Neuroimaging in Neuropharmacology'. Published by Elsevier Ltd.
Kullmann, Stephanie; Pape, Anna-Antonia; Heni, Martin; Ketterer, Caroline; Schick, Fritz; Häring, Hans-Ulrich; Fritsche, Andreas; Preissl, Hubert; Veit, Ralf
2013-05-01
In order to adequately explore the neurobiological basis of eating behavior of humans and their changes with body weight, interactions between brain areas or networks need to be investigated. In the current functional magnetic resonance imaging study, we examined the modulating effects of stimulus category (food vs. nonfood), caloric content of food, and body weight on the time course and functional connectivity of 5 brain networks by means of independent component analysis in healthy lean and overweight/obese adults. These functional networks included motor sensory, default-mode, extrastriate visual, temporal visual association, and salience networks. We found an extensive modulation elicited by food stimuli in the 2 visual and salience networks, with a dissociable pattern in the time course and functional connectivity between lean and overweight/obese subjects. Specifically, only in lean subjects, the temporal visual association network was modulated by the stimulus category and the salience network by caloric content, whereas overweight and obese subjects showed a generalized augmented response in the salience network. Furthermore, overweight/obese subjects showed changes in functional connectivity in networks important for object recognition, motivational salience, and executive control. These alterations could potentially lead to top-down deficiencies driving the overconsumption of food in the obese population.
Allocating resources between network nodes for providing a network node function
Strijkers, R.J.; Meulenhoff, P.J.
2014-01-01
The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a server (e.g. application server, a proxy server, cloud location, router). Examples of the second network node are an application server, a cloud location or a router. The available resources may b...
Application of natural basis functions to soft x-ray tomography
International Nuclear Information System (INIS)
Ingesson, L.
2000-03-01
Natural basis functions (NBFs), also known as natural pixels in the literature, have been applied in tomographic reconstructions of simulated measurements for the JET soft x-ray system, which has a total of about 200 detectors spread over 6 directions. Various types of NBFs, i.e. normal, generalized and orthonormal NBFs, are reviewed. The number of basis functions is roughly equal to the number of measurements. Therefore, little a priori information is required as regularization and truncated singular-value decomposition can be used for the tomographic inversion. The results of NBFs are compared with reconstructions by the same solution technique using local basis functions (LBFs), and with the reconstructions of a conventional constrained-optimization tomography method with many more LBFs that requires more a priori information. Although the results of the conventional method are superior due to the a priori information, the results of the NBF and other LBF methods are reasonable and show the main features. Therefore, NBFs are a promising way to assess whether features in reconstructions are real or artefacts resulting from the a priori information. Of the NBFs, regular triangular (generalized) NBFs give the most acceptable reconstructions, much better than traditional square pixels, although the reconstructions with pyramid-shaped LBFs are also reasonable and have slightly smaller reconstruction errors. A more-regular (virtual) viewing geometry improves the reconstructions. However, simulations with a viewing geometry with a total of 480 channels spread over 12 directions clearly show that a priori information still improves the reconstructions considerably. (author)
Rational quadratic trigonometric Bézier curve based on new basis with exponential functions
Directory of Open Access Journals (Sweden)
Wu Beibei
2017-06-01
Full Text Available We construct a rational quadratic trigonometric Bézier curve with four shape parameters by introducing two exponential functions into the trigonometric basis functions in this paper. It has the similar properties as the rational quadratic Bézier curve. For given control points, the shape of the curve can be flexibly adjusted by changing the shape parameters and the weight. Some conics can be exactly represented when the control points, the shape parameters and the weight are chosen appropriately. The C0, C1 and C2 continuous conditions for joining two constructed curves are discussed. Some examples are given.
Light Manipulation in Metallic Nanowire Networks with Functional Connectivity
Galinski, Henning; Fratalocchi, Andrea; Dö beli, Max; Capasso, Federico
2016-01-01
Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model
DevOps for network function virtualisation: an architectural approach
Karl, H.; Draexler, S.; Peuster, M.; Galis, A.; Bredel, M.; Ramos, A.; Martrat, J.; Siddiqui, M. S.; Van Rossem, S.; Tavernier, W.; Xilouris, G.
2016-01-01
The Service Programming and Orchestration for Virtualised Software Networks (SONATA) project targets both the flexible programmability of software networks and the optimisation of their deployments by means of integrating Development and Operations in order to accelerate industry adoption of software networks and reduce time-to-market for networked services. SONATA supports network function chaining and orchestration, making service platforms modular and easier to customise to the needs of di...
Directory of Open Access Journals (Sweden)
Misun Ahn
2017-12-01
Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.
Impulsive generalized function synchronization of complex dynamical networks
International Nuclear Information System (INIS)
Zhang, Qunjiao; Chen, Juan; Wan, Li
2013-01-01
This Letter investigates generalized function synchronization of continuous and discrete complex networks by impulsive control. By constructing the reasonable corresponding impulsively controlled response networks, some criteria and corollaries are derived for the generalized function synchronization between the impulsively controlled complex networks, continuous and discrete networks are both included. Furthermore, the generalized linear synchronization and nonlinear synchronization are respectively illustrated by several examples. All the numerical simulations demonstrate the correctness of the theoretical results
Identification of Resting State Networks Involved in Executive Function.
Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W
2016-06-01
The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function.
Boverman, Gregory; Miller, Eric L.; Brooks, Dana H.; Fang, Qianqian; Carp, S. A.; Selb, J. J.; Boas, David A.
2007-02-01
In the course of our experiments imaging the compressed breast in conjunction with digital tomosynthesis, we have noted that significant changes in tissue optical properties, on the order of 5%, occur during our imaging protocol. These changes seem to consistent with changes both in total Hemoglobin concentration as well as in oxygen saturation, as was the case for our standalone breast compression study, which made use of reflectance measurements. Simulation experiments show the importance of taking into account the temporal dynamics in the image reconstruction, and demonstrate the possibility of imaging the spatio-temporal dynamics of oxygen saturation and total Hemoglobin in the breast. In the image reconstruction, we make use of spatio-temporal basis functions, specifically a voxel basis for spatial imaging, and a cubic spline basis in time, and we reconstruct the spatio-temporal images using the entire data set simultaneously, making use of both absolute and relative measurements in the cost function. We have modified the sequence of sources used in our imaging acquisition protocol to improve our temporal resolution, and preliminary results are shown for normal subjects.
Yurchenko, Sergei N; Yachmenev, Andrey; Ovsyannikov, Roman I
2017-09-12
We present a general, numerically motivated approach to the construction of symmetry-adapted basis functions for solving ro-vibrational Schrödinger equations. The approach is based on the property of the Hamiltonian operator to commute with the complete set of symmetry operators and, hence, to reflect the symmetry of the system. The symmetry-adapted ro-vibrational basis set is constructed numerically by solving a set of reduced vibrational eigenvalue problems. In order to assign the irreducible representations associated with these eigenfunctions, their symmetry properties are probed on a grid of molecular geometries with the corresponding symmetry operations. The transformation matrices are reconstructed by solving overdetermined systems of linear equations related to the transformation properties of the corresponding wave functions on the grid. Our method is implemented in the variational approach TROVE and has been successfully applied to many problems covering the most important molecular symmetry groups. Several examples are used to illustrate the procedure, which can be easily applied to different types of coordinates, basis sets, and molecular systems.
Directory of Open Access Journals (Sweden)
Ekkehard Krüger
2015-05-01
Full Text Available The paper presents the group theory of optimally-localized and symmetry-adapted Wannier functions in a crystal of any given space group G or magnetic group M. Provided that the calculated band structure of the considered material is given and that the symmetry of the Bloch functions at all of the points of symmetry in the Brillouin zone is known, the paper details whether or not the Bloch functions of particular energy bands can be unitarily transformed into optimally-localized Wannier functions symmetry-adapted to the space group G, to the magnetic group M or to a subgroup of G or M. In this context, the paper considers usual, as well as spin-dependent Wannier functions, the latter representing the most general definition of Wannier functions. The presented group theory is a review of the theory published by one of the authors (Ekkehard Krüger in several former papers and is independent of any physical model of magnetism or superconductivity. However, it is suggested to interpret the special symmetry of the optimally-localized Wannier functions in the framework of a nonadiabatic extension of the Heisenberg model, the nonadiabatic Heisenberg model. On the basis of the symmetry of the Wannier functions, this model of strongly-correlated localized electrons makes clear predictions of whether or not the system can possess superconducting or magnetic eigenstates.
Plumley, Joshua A; Dannenberg, J J
2011-06-01
We evaluate the performance of ten functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D, and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-density functional theory (non-DFT) molecular orbital (MO) calculations and to experimental results. Several of the smaller basis sets lead to qualitatively incorrect geometries when optimized on a normal potential energy surface (PES). This problem disappears when the optimization is performed on a counterpoise (CP) corrected PES. The calculated interaction energies (ΔEs) with the largest basis sets vary from -4.42 (B97D) to -5.19 (B2PLYPD) kcal/mol for the different functionals. Small basis sets generally predict stronger interactions than the large ones. We found that, because of error compensation, the smaller basis sets gave the best results (in comparison to experimental and high-level non-DFT MO calculations) when combined with a functional that predicts a weak interaction with the largest basis set. As many applications are complex systems and require economical calculations, we suggest the following functional/basis set combinations in order of increasing complexity and cost: (1) D95(d,p) with B3LYP, B97D, M06, or MPWB1k; (2) 6-311G(d,p) with B3LYP; (3) D95++(d,p) with B3LYP, B97D, or MPWB1K; (4) 6-311++G(d,p) with B3LYP or B97D; and (5) aug-cc-pVDZ with M05-2X, M06-2X, or X3LYP. Copyright © 2011 Wiley Periodicals, Inc.
Advanced functional network analysis in the geosciences: The pyunicorn package
Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen
2013-04-01
Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.
Functional alignment of regulatory networks: a study of temperate phages.
Directory of Open Access Journals (Sweden)
Ala Trusina
2005-12-01
Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.
Molecular basis of the functional heterogeneity of the muscarinic acetylcholine receptor
International Nuclear Information System (INIS)
Numa, S.; Fukuda, K.; Kubo, T.; Maeda, A.; Akiba, I.; Bujo, H.; Nakai, J.; Mishina, M.; Higashida, H.
1988-01-01
The muscarinic acetylcholine receptor (mAChR) mediates a variety of cellular responses, including inhibition of adenylate cyclase, breakdown of phosphoinositides, and modulation of potassium channels, through the action of guanine-nucleotide-binding regulatory proteins (G proteins). The question then arises as to whether multiple mAChR species exist that are responsible for the various biochemical and physiological effects. In fact, pharmacologically distinguishable forms of the mAChR occur in different tissues and have been provisionally classified into M 1 (I), M 2 cardiac (II), and M 2 glandular (III) subtypes on the basis of their difference in apparent affinity for antagonists. Here, the authors have made attempts to understand the molecular basis of the functional heterogeneity of the mAChR, using recombinant DNA technology
The position value for partition function form network games
Nouweland, van den C.G.A.M.; Slikker, M.
We use the axiomatization of the position value for network situations in van den Nouweland and Slikker (2012) to define a position value for partition function form network situations. We do this by generalizing the axioms to the partition function form value function setting as studied in Navarro
Analysis of neural networks through base functions
van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Chen, Zhaoxue; Chen, Hao
2014-01-01
A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.
Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study.
Wang, Ping; Zhu, Xing-Ting; Qi, Zhigang; Huang, Silin; Li, Hui-Jie
2017-01-01
Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI) study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs) and twenty non-video game players (NVGPs) of 60 years of age or older participated in the present study, and there are no significant differences in age ( t = 0.62, p = 0.536), gender ratio ( t = 1.29, p = 0.206) and years of education ( t = 1.92, p = 0.062) between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.
Neural Basis of Enhanced Executive Function in Older Video Game Players: An fMRI Study
Directory of Open Access Journals (Sweden)
Ping Wang
2017-11-01
Full Text Available Video games have been found to have positive influences on executive function in older adults; however, the underlying neural basis of the benefits from video games has been unclear. Adopting a task-based functional magnetic resonance imaging (fMRI study targeted at the flanker task, the present study aims to explore the neural basis of the improved executive function in older adults with video game experiences. Twenty video game players (VGPs and twenty non-video game players (NVGPs of 60 years of age or older participated in the present study, and there are no significant differences in age (t = 0.62, p = 0.536, gender ratio (t = 1.29, p = 0.206 and years of education (t = 1.92, p = 0.062 between VGPs and NVGPs. The results show that older VGPs present significantly better behavioral performance than NVGPs. Older VGPs activate greater than NVGPs in brain regions, mainly in frontal-parietal areas, including the right dorsolateral prefrontal cortex, the left supramarginal gyrus, the right angular gyrus, the right precuneus and the left paracentral lobule. The present study reveals that video game experiences may have positive influences on older adults in behavioral performance and the underlying brain activation. These results imply the potential role that video games can play as an effective tool to improve cognitive ability in older adults.
Modelling of coil-loaded wire antenna using composite multiple domain basis functions
CSIR Research Space (South Africa)
Lysko, AA
2010-03-01
Full Text Available - tional Electromagnetics, Artech House, 2001. 3. Rogers, S. D. and C. M. Butler, \\An e–cient curved-wire integral equation solution technique," IEEE Trans. Ant. and Propag., 70{79, Vol. 49, Jan. 2001. 4. Mosig, J. and E. Suter, \\A multilevel divide.... 8. Wan, J. X., J. Lei, and C.-H. Liang, \\An e–cient analysis of large-scale periodic microstrip antenna arrays using the characteristic basis function method," Progress In Electromagnetics Research, PIER 50, 61{81, 2005. 9. Taguchi, M., K...
CSIR Research Space (South Africa)
Bogaers, Alfred EJ
2016-10-01
Full Text Available words, gB = [ φBA PB ] [ MAA PA P TA 0 ]−1 [ gA 0 ] . (15) NAME: DEFINITION C0 compactly supported piecewise polynomial (C0): (1− (||x|| /r))2+ C2 compactly supported piecewise polynomial (C2): (1− (||x|| /r))4+ (4 (||x|| /r) + 1) Thin-plate spline (TPS... a numerical comparison to Kriging and the moving least-squares method, see Krishnamurthy [16]). RBF interpolation is based on fitting a series of splines, or basis functions to interpolate information from one point cloud to another. Let us assume we...
KARHUNEN-LOÈVE Basis Functions of Kolmogorov Turbulence in the Sphere
Mathar, Richard J.
In support of modeling atmospheric turbulence, the statistically independent Karhunen-Loève modes of refractive indices with isotropic Kolmogorov spectrum of the covariance are calculated inside a sphere of fixed radius, rendered as series of 3D Zernike functions. Many of the symmetry arguments of the well-known associated 2D problem for the circular input pupil remain valid. The technique of efficient diagonalization of the eigenvalue problem in wavenumber space is founded on the Fourier representation of the 3D Zernike basis, and extensible to the von-Kármán power spectrum.
International Nuclear Information System (INIS)
Davidson, G.; Palmer, T.S.
2005-01-01
In 1975, Wachspress developed basis functions that can be constructed upon very general zone shapes, including convex polygons and polyhedra, as well as certain zone shapes with curved sides and faces. Additionally, Adams has recently shown that weight functions with certain properties will produce solutions with full-resolution. Wachspress rational functions possess those necessary properties. Here we present methods to construct and integrate Wachspress rational functions on quadrilaterals. We also present an asymptotic analysis of a discontinuous finite element discretization on quadrilaterals, and we present 3 numerical results that confirm the predictions of our analysis. In the first test problem, we showed that Wachspress rational functions could give robust solutions for a strongly heterogeneous problem with both orthogonal and skewed meshes. This strongly heterogenous problem contained thick, diffusive regions, and the discretization provided full-resolution solutions. In the second test problem, we confirmed our asymptotic analysis by demonstrating that the transport solution will converge to the diffusion solution as the problem is made increasingly thick and diffusive. In the third test problem, we demonstrated that bilinear discontinuous based transport and Wachspress rational function based transport converge in the one-mesh limit
Joint Modelling of Structural and Functional Brain Networks
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten
-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...
Functional classification of the Gauteng provincial road network ...
African Journals Online (AJOL)
The built environment consists of various land uses and activities connected by a road network. The efficiency and effectiveness of the road network directly impacts economic growth and societal movement. This study involved the functional classification of the Gauteng provincial road network using the South African Road ...
A new chaotic Hopfield network with piecewise linear activation function
International Nuclear Information System (INIS)
Peng-Sheng, Zheng; Wan-Sheng, Tang; Jian-Xiong, Zhang
2010-01-01
This paper presents a new chaotic Hopfield network with a piecewise linear activation function. The dynamic of the network is studied by virtue of the bifurcation diagram, Lyapunov exponents spectrum and power spectrum. Numerical simulations show that the network displays chaotic behaviours for some well selected parameters
Light Manipulation in Metallic Nanowire Networks with Functional Connectivity
Galinski, Henning
2016-12-27
Guided by ideas from complex systems, a new class of network metamaterials is introduced for light manipulation, which are based on the functional connectivity among heterogeneous subwavelength components arranged in complex networks. The model system is a nanonetwork formed by dealloying a metallic thin film. The connectivity of the network is deterministically controlled, enabling the formation of tunable absorbing states.
Stochastic Geometric Network Models for Groups of Functional and Structural Connectomes
Friedman, Eric J.; Landsberg, Adam S.; Owen, Julia P.; Li, Yi-Ou; Mukherjee, Pratik
2014-01-01
Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. Current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of networks' density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high “smallworldness” beyond that arising from geometric and degree considerations alone. PMID:25067815
GRACE L1b inversion through a self-consistent modified radial basis function approach
Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette
2016-04-01
Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically
Building functional networks of spiking model neurons.
Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin
2016-03-01
Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.
Machine learning (ML)-guided OPC using basis functions of polar Fourier transform
Choi, Suhyeong; Shim, Seongbo; Shin, Youngsoo
2016-03-01
With shrinking feature size, runtime has become a limitation of model-based OPC (MB-OPC). A few machine learning-guided OPC (ML-OPC) have been studied as candidates for next-generation OPC, but they all employ too many parameters (e.g. local densities), which set their own limitations. We propose to use basis functions of polar Fourier transform (PFT) as parameters of ML-OPC. Since PFT functions are orthogonal each other and well reflect light phenomena, the number of parameters can significantly be reduced without loss of OPC accuracy. Experiments demonstrate that our new ML-OPC achieves 80% reduction in OPC time and 35% reduction in the error of predicted mask bias when compared to conventional ML-OPC.
Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1
Energy Technology Data Exchange (ETDEWEB)
Luna, Antonio [Health Time Group, Jaen (Spain); University Hospitals, Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Radiology; Vilanova, Joan C. [Clinica Girona - Hospital Sta. Caterina, Girona (Spain); Hygino da Cruz, L. Celso Jr. [CDPI and IRM, Rio de Janeiro, RJ (Brazil). Dept. of Radiology; Rossi, Santiago E. (ed.) [Centro de Diagnostico, Buenos Aires (Argentina)
2014-07-01
Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.
Cellular and molecular basis of chronic constipation: taking the functional/idiopathic label out.
Bassotti, Gabrio; Villanacci, Vincenzo; Creţoiu, Dragos; Creţoiu, Sanda Maria; Becheanu, Gabriel
2013-07-14
In recent years, the improvement of technology and the increase in knowledge have shifted several strongly held paradigms. This is particularly true in gastroenterology, and specifically in the field of the so-called "functional" or "idiopathic" disease, where conditions thought for decades to be based mainly on alterations of visceral perception or aberrant psychosomatic mechanisms have, in fact, be reconducted to an organic basis (or, at the very least, have shown one or more demonstrable abnormalities). This is particularly true, for instance, for irritable bowel syndrome, the prototype entity of "functional" gastrointestinal disorders, where low-grade inflammation of both mucosa and myenteric plexus has been repeatedly demonstrated. Thus, researchers have also investigated other functional/idiopathic gastrointestinal disorders, and found that some organic ground is present, such as abnormal neurotransmission and myenteric plexitis in esophageal achalasia and mucosal immune activation and mild eosinophilia in functional dyspepsia. Here we show evidence, based on our own and other authors' work, that chronic constipation has several abnormalities reconductable to alterations in the enteric nervous system, abnormalities mainly characterized by a constant decrease of enteric glial cells and interstitial cells of Cajal (and, sometimes, of enteric neurons). Thus, we feel that (at least some forms of) chronic constipation should no more be considered as a functional/idiopathic gastrointestinal disorder, but instead as a true enteric neuropathic abnormality.
Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1
International Nuclear Information System (INIS)
Luna, Antonio; Hygino da Cruz, L. Celso Jr.
2014-01-01
Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.
The NOVO Network: the original scientific basis for its establishment and our R&D vision
Winkel, Jørgen; Edwards, Kasper; Dellve, L.; Schiller, B.; Westgaard, Rolf H.
2017-01-01
The NOVO network is a Nordic non-governmental professional association whose aims are to foster the scientific progress, knowledge and development of the working environment within Healthcare as an integrated part of production system development. The vision is a “Nordic Model for Sustainable Systems” in the healthcare sector. It was founded in 2006 in Copenhagen and was financially supported by the Nordic Council of Ministers from 2007 to 2015. The motivation to establish the NOVO Network ar...
Development of the disable software reporting system on the basis of the neural network
Gavrylenko, S.; Babenko, O.; Ignatova, E.
2018-04-01
The PE structure of malicious and secure software is analyzed, features are highlighted, binary sign vectors are obtained and used as inputs for training the neural network. A software model for detecting malware based on the ART-1 neural network was developed, optimal similarity coefficients were found, and testing was performed. The obtained research results showed the possibility of using the developed system of identifying malicious software in computer systems protection systems
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Directory of Open Access Journals (Sweden)
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450
Enhancing the Functional Content of Eukaryotic Protein Interaction Networks
Pandey, Gaurav; Arora, Sonali; Manocha, Sahil; Whalen, Sean
2014-01-01
Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks. PMID:25275489
Plumley, Joshua A.; Dannenberg, J. J.
2011-01-01
We evaluate the performance of nine functionals (B3LYP, M05, M05-2X, M06, M06-2X, B2PLYP, B2PLYPD, X3LYP, B97D and MPWB1K) in combination with 16 basis sets ranging in complexity from 6-31G(d) to aug-cc-pV5Z for the calculation of the H-bonded water dimer with the goal of defining which combinations of functionals and basis sets provide a combination of economy and accuracy for H-bonded systems. We have compared the results to the best non-DFT molecular orbital calculations and to experimenta...
International Nuclear Information System (INIS)
Kollmar, Christian; Neese, Frank
2014-01-01
The role of the static Kohn-Sham (KS) response function describing the response of the electron density to a change of the local KS potential is discussed in both the theory of the optimized effective potential (OEP) and the so-called inverse Kohn-Sham problem involving the task to find the local KS potential for a given electron density. In a general discussion of the integral equation to be solved in both cases, it is argued that a unique solution of this equation can be found even in case of finite atomic orbital basis sets. It is shown how a matrix representation of the response function can be obtained if the exchange-correlation potential is expanded in terms of a Schmidt-orthogonalized basis comprising orbitals products of occupied and virtual orbitals. The viability of this approach in both OEP theory and the inverse KS problem is illustrated by numerical examples
State-related functional integration and functional segregation brain networks in schizophrenia.
Yu, Qingbao; Sui, Jing; Kiehl, Kent A; Pearlson, Godfrey; Calhoun, Vince D
2013-11-01
Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder. © 2013.
Dynamics of functional failures and recovery in complex road networks
Zhan, Xianyuan; Ukkusuri, Satish V.; Rao, P. Suresh C.
2017-11-01
We propose a new framework for modeling the evolution of functional failures and recoveries in complex networks, with traffic congestion on road networks as the case study. Differently from conventional approaches, we transform the evolution of functional states into an equivalent dynamic structural process: dual-vertex splitting and coalescing embedded within the original network structure. The proposed model successfully explains traffic congestion and recovery patterns at the city scale based on high-resolution data from two megacities. Numerical analysis shows that certain network structural attributes can amplify or suppress cascading functional failures. Our approach represents a new general framework to model functional failures and recoveries in flow-based networks and allows understanding of the interplay between structure and function for flow-induced failure propagation and recovery.
Functional Basis for Efficient Physical Layer Classical Control in Quantum Processors
Ball, Harrison; Nguyen, Trung; Leong, Philip H. W.; Biercuk, Michael J.
2016-12-01
The rapid progress seen in the development of quantum-coherent devices for information processing has motivated serious consideration of quantum computer architecture and organization. One topic which remains open for investigation and optimization relates to the design of the classical-quantum interface, where control operations on individual qubits are applied according to higher-level algorithms; accommodating competing demands on performance and scalability remains a major outstanding challenge. In this work, we present a resource-efficient, scalable framework for the implementation of embedded physical layer classical controllers for quantum-information systems. Design drivers and key functionalities are introduced, leading to the selection of Walsh functions as an effective functional basis for both programing and controller hardware implementation. This approach leverages the simplicity of real-time Walsh-function generation in classical digital hardware, and the fact that a wide variety of physical layer controls, such as dynamic error suppression, are known to fall within the Walsh family. We experimentally implement a real-time field-programmable-gate-array-based Walsh controller producing Walsh timing signals and Walsh-synthesized analog waveforms appropriate for critical tasks in error-resistant quantum control and noise characterization. These demonstrations represent the first step towards a unified framework for the realization of physical layer controls compatible with large-scale quantum-information processing.
Ortigue, S; Bianchi-Demicheli, F; Hamilton, A F de C; Grafton, S T
2007-07-01
Throughout the ages, love has been defined as a motivated and goal-directed mechanism with explicit and implicit mechanisms. Recent evidence demonstrated that the explicit representation of love recruits subcorticocortical pathways mediating reward, emotion, and motivation systems. However, the neural basis of the implicit (unconscious) representation of love remains unknown. To assess this question, we combined event-related functional magnetic resonance imaging (fMRI) with a behavioral subliminal priming paradigm embedded in a lexical decision task. In this task, the name of either a beloved partner, a neutral friend, or a passionate hobby was subliminally presented before a target stimulus (word, nonword, or blank), and participants were required to decide if the target was a word or not. Behavioral results showed that subliminal presentation of either a beloved's name (love prime) or a passion descriptor (passion prime) enhanced reaction times in a similar fashion. Subliminal presentation of a friend's name (friend prime) did not show any beneficial effects. Functional results showed that subliminal priming with a beloved's name (as opposed to either a friend's name or a passion descriptor) specifically recruited brain areas involved in abstract representations of others and the self, in addition to motivation circuits shared with other sources of passion. More precisely, love primes recruited the fusiform and angular gyri. Our findings suggest that love, as a subliminal prime, involves a specific neural network that surpasses a dopaminergic-motivation system.
Evidence for Functional Networks within the Human Brain's White Matter.
Peer, Michael; Nitzan, Mor; Bick, Atira S; Levin, Netta; Arzy, Shahar
2017-07-05
Investigation of the functional macro-scale organization of the human cortex is fundamental in modern neuroscience. Although numerous studies have identified networks of interacting functional modules in the gray-matter, limited research was directed to the functional organization of the white-matter. Recent studies have demonstrated that the white-matter exhibits blood oxygen level-dependent signal fluctuations similar to those of the gray-matter. Here we used these signal fluctuations to investigate whether the white-matter is organized as functional networks by applying a clustering analysis on resting-state functional MRI (RSfMRI) data from white-matter voxels, in 176 subjects (of both sexes). This analysis indicated the existence of 12 symmetrical white-matter functional networks, corresponding to combinations of white-matter tracts identified by diffusion tensor imaging. Six of the networks included interhemispheric commissural bridges traversing the corpus callosum. Signals in white-matter networks correlated with signals from functional gray-matter networks, providing missing knowledge on how these distributed networks communicate across large distances. These findings were replicated in an independent subject group and were corroborated by seed-based analysis in small groups and individual subjects. The identified white-matter functional atlases and analysis codes are available at http://mind.huji.ac.il/white-matter.aspx Our results demonstrate that the white-matter manifests an intrinsic functional organization as interacting networks of functional modules, similarly to the gray-matter, which can be investigated using RSfMRI. The discovery of functional networks within the white-matter may open new avenues of research in cognitive neuroscience and clinical neuropsychiatry. SIGNIFICANCE STATEMENT In recent years, functional MRI (fMRI) has revolutionized all fields of neuroscience, enabling identifications of functional modules and networks in the human
Unveiling protein functions through the dynamics of the interaction network.
Directory of Open Access Journals (Sweden)
Irene Sendiña-Nadal
Full Text Available Protein interaction networks have become a tool to study biological processes, either for predicting molecular functions or for designing proper new drugs to regulate the main biological interactions. Furthermore, such networks are known to be organized in sub-networks of proteins contributing to the same cellular function. However, the protein function prediction is not accurate and each protein has traditionally been assigned to only one function by the network formalism. By considering the network of the physical interactions between proteins of the yeast together with a manual and single functional classification scheme, we introduce a method able to reveal important information on protein function, at both micro- and macro-scale. In particular, the inspection of the properties of oscillatory dynamics on top of the protein interaction network leads to the identification of misclassification problems in protein function assignments, as well as to unveil correct identification of protein functions. We also demonstrate that our approach can give a network representation of the meta-organization of biological processes by unraveling the interactions between different functional classes.
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-01-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one’s avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one’s avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603
Rational design of functional and tunable oscillating enzymatic networks
Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.
2015-02-01
Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.
International Nuclear Information System (INIS)
Fomenko, O.V.; Moloshnaya, E.S.; Ul'yanova, Yu.E.
2014-01-01
The authors propose the technique of designing a portable grounding contour for protection and control systems, relay protection and automation (RPA) systems made on the basis of digital equipment in networks of an operational direct current to increase the reliability of their functioning under the conditions of influence of strong hindrances and improvement of an electromagnetic environment [ru
Merrison-Hort, Robert; Soffe, Stephen R; Borisyuk, Roman
2018-01-01
Although, in most animals, brain connectivity varies between individuals, behaviour is often similar across a species. What fundamental structural properties are shared across individual networks that define this behaviour? We describe a probabilistic model of connectivity in the hatchling Xenopus tadpole spinal cord which, when combined with a spiking model, reliably produces rhythmic activity corresponding to swimming. The probabilistic model allows calculation of structural characteristics that reflect common network properties, independent of individual network realisations. We use the structural characteristics to study examples of neuronal dynamics, in the complete network and various sub-networks, and this allows us to explain the basis for key experimental findings, and make predictions for experiments. We also study how structural and functional features differ between detailed anatomical connectomes and those generated by our new, simpler, model (meta-model). PMID:29589828
Specific neural basis of Chinese idioms processing: an event-related functional MRI study
International Nuclear Information System (INIS)
Chen Shaoqi; Zhang Yanzhen; Xiao Zhuangwei; Zhang Xuexin
2007-01-01
Objective: To address the neural basis of Chinese idioms processing with different kinds of stimuli using an event-related fMRI design. Methods: Sixteen native Chinese speakers were asked to perform a semantic decision task during fMRI scanning. Three kinds of stimuli were used: Real idioms (Real-idiom condition); Literally plausible phrases (Pseudo-idiom condition, the last character of a real idiom was replaced by a character with similar meaning); Literally implausible strings (Non-idiom condition, the last character of a real idiom was replaced by a character with unrelated meaning). Reaction time and correct rate were recorded at the same time. Results: The error rate was 2.6%, 5.2% and 0.9% (F=3.51, P 0.05) for real idioms, pseudo-idioms and wrong idioms, respectively. Similar neural network was activated in all of the three conditions. However, the right hippocampus was only activated in the real idiom condition, and significant activations were found in anterior portion of left inferior frontal gyms (BA47) in real-and pseudo-idiom conditions, but not in non-idiom condition. Conclusion: The right hippocampus plays a specific role in the particular wording of the Chinese idioms. And the left anterior inferior frontal gyms (BA47) may be engaged in the semantic processing of Chinese idioms. The results support the notion that there were specific neural bases for Chinese idioms processing. (authors)
A Game for Energy-Aware Allocation of Virtualized Network Functions
Directory of Open Access Journals (Sweden)
Roberto Bruschi
2016-01-01
Full Text Available Network Functions Virtualization (NFV is a network architecture concept where network functionality is virtualized and separated into multiple building blocks that may connect or be chained together to implement the required services. The main advantages consist of an increase in network flexibility and scalability. Indeed, each part of the service chain can be allocated and reallocated at runtime depending on demand. In this paper, we present and evaluate an energy-aware Game-Theory-based solution for resource allocation of Virtualized Network Functions (VNFs within NFV environments. We consider each VNF as a player of the problem that competes for the physical network node capacity pool, seeking the minimization of individual cost functions. The physical network nodes dynamically adjust their processing capacity according to the incoming workload, by means of an Adaptive Rate (AR strategy that aims at minimizing the product of energy consumption and processing delay. On the basis of the result of the nodes’ AR strategy, the VNFs’ resource sharing costs assume a polynomial form in the workflows, which admits a unique Nash Equilibrium (NE. We examine the effect of different (unconstrained and constrained forms of the nodes’ optimization problem on the equilibrium and compare the power consumption and delay achieved with energy-aware and non-energy-aware strategy profiles.
Scholastic performance and functional connectivity of brain networks in children.
Directory of Open Access Journals (Sweden)
Laura Chaddock-Heyman
Full Text Available One of the keys to understanding scholastic success is to determine the neural processes involved in school performance. The present study is the first to use a whole-brain connectivity approach to explore whether functional connectivity of resting state brain networks is associated with scholastic performance in seventy-four 7- to 9-year-old children. We demonstrate that children with higher scholastic performance across reading, math and language have more integrated and interconnected resting state networks, specifically the default mode network, salience network, and frontoparietal network. To add specificity, core regions of the dorsal attention and visual networks did not relate to scholastic performance. The results extend the cognitive role of brain networks in children as well as suggest the importance of network connectivity in scholastic success.
Functional clustering in hippocampal cultures: relating network structure and dynamics
International Nuclear Information System (INIS)
Feldt, S; Dzakpasu, R; Olariu, E; Żochowski, M; Wang, J X; Shtrahman, E
2010-01-01
In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures
Functional network macroscopes for probing past and present Earth system dynamics (Invited)
Donges, J. F.
2013-12-01
The Earth, as viewed from a physicist's perspective, is a dynamical system of great complexity. Functional complex networks are inferred from observational data and model runs or constructed on the basis of theoretical considerations. Representing statistical interdependencies or causal interactions between objects (e.g., Earth system subdomains, processes, or local field variables), functional complex networks are conceptually well-suited for naturally addressing some of the fundamental questions of Earth system analysis concerning, among others, major dynamical patterns, teleconnections, and feedback loops in the planetary machinery, as well as critical elements such as thresholds, bottlenecks, and switches. The first part of this talk concerns complex network theory and network-based time series analysis. Regarding complex network theory, the novel contributions include consistent frameworks for analyzing the topology of (i) general networks of interacting networks and (ii) networks with vertices of heterogeneously distributed weights, as well as (iii) an analytical theory for describing spatial networks. In the realm of time series analysis, (i) recurrence network analysis is put forward as a theoretically founded, nonlinear technique for the study of single, but possibly multivariate time series. (ii) Coupled climate networks are introduced as an exploratory tool of data analysis for quantitatively characterizing the intricate statistical interdependency structure within and between several fields of time series. The second part presents applications for detecting dynamical transitions (tipping points) in time series and studying bottlenecks in the atmosphere's general circulation structure. The analysis of paleoclimate data reveals a possible influence of large-scale shifts in Plio-Pleistocene African climate variability on events in human evolution. This presentation summarizes the contents of the dissertation titled "Functional network macroscopes for
The neural basis of human social values: evidence from functional MRI.
Zahn, Roland; Moll, Jorge; Paiva, Mirella; Garrido, Griselda; Krueger, Frank; Huey, Edward D; Grafman, Jordan
2009-02-01
Social values are composed of social concepts (e.g., "generosity") and context-dependent moral sentiments (e.g., "pride"). The neural basis of this intricate cognitive architecture has not been investigated thus far. Here, we used functional magnetic resonance imaging while subjects imagined their own actions toward another person (self-agency) which either conformed or were counter to a social value and were associated with pride or guilt, respectively. Imagined actions of another person toward the subjects (other-agency) in accordance with or counter to a value were associated with gratitude or indignation/anger. As hypothesized, superior anterior temporal lobe (aTL) activity increased with conceptual detail in all conditions. During self-agency, activity in the anterior ventromedial prefrontal cortex correlated with pride and guilt, whereas activity in the subgenual cingulate solely correlated with guilt. In contrast, indignation/anger activated lateral orbitofrontal-insular cortices. Pride and gratitude additionally evoked mesolimbic and basal forebrain activations. Our results demonstrate that social values emerge from coactivation of stable abstract social conceptual representations in the superior aTL and context-dependent moral sentiments encoded in fronto-mesolimbic regions. This neural architecture may provide the basis of our ability to communicate about the meaning of social values across cultural contexts without limiting our flexibility to adapt their emotional interpretation.
Dynamic neural networking as a basis for plasticity in the control of heart rate.
Kember, G; Armour, J A; Zamir, M
2013-01-21
A model is proposed in which the relationship between individual neurons within a neural network is dynamically changing to the effect of providing a measure of "plasticity" in the control of heart rate. The neural network on which the model is based consists of three populations of neurons residing in the central nervous system, the intrathoracic extracardiac nervous system, and the intrinsic cardiac nervous system. This hierarchy of neural centers is used to challenge the classical view that the control of heart rate, a key clinical index, resides entirely in central neuronal command (spinal cord, medulla oblongata, and higher centers). Our results indicate that dynamic networking allows for the possibility of an interplay among the three populations of neurons to the effect of altering the order of control of heart rate among them. This interplay among the three levels of control allows for different neural pathways for the control of heart rate to emerge under different blood flow demands or disease conditions and, as such, it has significant clinical implications because current understanding and treatment of heart rate anomalies are based largely on a single level of control and on neurons acting in unison as a single entity rather than individually within a (plastically) interconnected network. Copyright © 2012 Elsevier Ltd. All rights reserved.
Functional Topology of Evolving Urban Drainage Networks
Yang, Soohyun; Paik, Kyungrock; McGrath, Gavan S.; Urich, Christian; Krueger, Elisabeth; Kumar, Praveen; Rao, P. Suresh C.
2017-11-01
We investigated the scaling and topology of engineered urban drainage networks (UDNs) in two cities, and further examined UDN evolution over decades. UDN scaling was analyzed using two power law scaling characteristics widely employed for river networks: (1) Hack's law of length (L)-area (A) [L∝Ah] and (2) exceedance probability distribution of upstream contributing area (δ) [P>(A≥δ>)˜aδ-ɛ]. For the smallest UDNs ((A≥δ>) plots for river networks are abruptly truncated, those for UDNs display exponential tempering [P>(A≥δ>)=aδ-ɛexp>(-cδ>)]. The tempering parameter c decreases as the UDNs grow, implying that the distribution evolves in time to resemble those for river networks. However, the power law exponent ɛ for large UDNs tends to be greater than the range reported for river networks. Differences in generative processes and engineering design constraints contribute to observed differences in the evolution of UDNs and river networks, including subnet heterogeneity and nonrandom branching.
New strong motion network in Georgia: basis for specifying seismic hazard
Kvavadze, N.; Tsereteli, N. S.
2017-12-01
Risk created by hazardous natural events is closely related to sustainable development of the society. Global observations have confirmed tendency of growing losses resulting from natural disasters, one of the most dangerous and destructive if which are earthquakes. Georgia is located in seismically active region. So, it is imperative to evaluate probabilistic seismic hazard and seismic risk with proper accuracy. National network of Georgia includes 35 station all of which are seismometers. There are significant gaps in strong motion recordings, which essential for seismic hazard assessment. To gather more accelerometer recordings, we have built a strong motion network distributed on the territory of Georgia. The network includes 6 stations for now, with Basalt 4x datalogger and strong motion sensor Episensor ES-T. For each site, Vs30 and soil resonance frequencies have been measured. Since all but one station (Tabakhmelam near Tbilisi), are located far from power and internet lines special system was created for instrument operation. Solar power is used to supply the system with electricity and GSM/LTE modems for internet access. VPN tunnel was set up using Raspberry pi, for two-way communication with stations. Tabakhmela station is located on grounds of Ionosphere Observatory, TSU and is used as a hub for the network. This location also includes a broadband seismometer and VLF electromagnetic waves observation antenna, for possible earthquake precursor studies. On server, located in Tabakhmela, the continues data is collected from all the stations, for later use. The recordings later will be used in different seismological and engineering problems, namely selecting and creating GMPE model for Caucasus, for probabilistic seismic hazard and seismic risk evaluation. These stations are a start and in the future expansion of strong motion network is planned. Along with this, electromagnetic wave observations will continue and additional antennas will be implemented
Leclerc, Arnaud; Carrington, Tucker
2014-05-07
We propose an iterative method for computing vibrational spectra that significantly reduces the memory cost of calculations. It uses a direct product primitive basis, but does not require storing vectors with as many components as there are product basis functions. Wavefunctions are represented in a basis each of whose functions is a sum of products (SOP) and the factorizable structure of the Hamiltonian is exploited. If the factors of the SOP basis functions are properly chosen, wavefunctions are linear combinations of a small number of SOP basis functions. The SOP basis functions are generated using a shifted block power method. The factors are refined with a rank reduction algorithm to cap the number of terms in a SOP basis function. The ideas are tested on a 20-D model Hamiltonian and a realistic CH3CN (12 dimensional) potential. For the 20-D problem, to use a standard direct product iterative approach one would need to store vectors with about 10(20) components and would hence require about 8 × 10(11) GB. With the approach of this paper only 1 GB of memory is necessary. Results for CH3CN agree well with those of a previous calculation on the same potential.
Radial basis functions in mathematical modelling of flow boiling in minichannels
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Hożejowska Sylwia
2017-01-01
Full Text Available The paper addresses heat transfer processes in flow boiling in a vertical minichannel of 1.7 mm depth with a smooth heated surface contacting fluid. The heated element for FC-72 flowing in a minichannel was a 0.45 mm thick plate made of Haynes-230 alloy. An infrared camera positioned opposite the central, axially symmetric part of the channel measured the plate temperature. K-type thermocouples and pressure converters were installed at the inlet and outlet of the minichannel. In the study radial basis functions were used to solve a problem concerning heat transfer in a heated plate supplied with the controlled direct current. According to the model assumptions, the problem is treated as twodimensional and governed by the Poisson equation. The aim of the study lies in determining the temperature field and the heat transfer coefficient. The results were verified by comparing them with those obtained by the Trefftz method.
Directory of Open Access Journals (Sweden)
Huaiqing Zhang
2014-01-01
Full Text Available The spectral leakage has a harmful effect on the accuracy of harmonic analysis for asynchronous sampling. This paper proposed a time quasi-synchronous sampling algorithm which is based on radial basis function (RBF interpolation. Firstly, a fundamental period is evaluated by a zero-crossing technique with fourth-order Newton’s interpolation, and then, the sampling sequence is reproduced by the RBF interpolation. Finally, the harmonic parameters can be calculated by FFT on the synchronization of sampling data. Simulation results showed that the proposed algorithm has high accuracy in measuring distorted and noisy signals. Compared to the local approximation schemes as linear, quadric, and fourth-order Newton interpolations, the RBF is a global approximation method which can acquire more accurate results while the time-consuming is about the same as Newton’s.
Bubin, Sergiy; Adamowicz, Ludwik
2006-06-14
In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programmed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.
Bubin, Sergiy; Adamowicz, Ludwik
2006-06-01
In this work we present analytical expressions for Hamiltonian matrix elements with spherically symmetric, explicitly correlated Gaussian basis functions with complex exponential parameters for an arbitrary number of particles. The expressions are derived using the formalism of matrix differential calculus. In addition, we present expressions for the energy gradient that includes derivatives of the Hamiltonian integrals with respect to the exponential parameters. The gradient is used in the variational optimization of the parameters. All the expressions are presented in the matrix form suitable for both numerical implementation and theoretical analysis. The energy and gradient formulas have been programed and used to calculate ground and excited states of the He atom using an approach that does not involve the Born-Oppenheimer approximation.
Big geo data surface approximation using radial basis functions: A comparative study
Majdisova, Zuzana; Skala, Vaclav
2017-12-01
Approximation of scattered data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for big scattered datasets in n-dimensional space. It is a non-separable approximation, as it is based on the distance between two points. This method leads to the solution of an overdetermined linear system of equations. In this paper the RBF approximation methods are briefly described, a new approach to the RBF approximation of big datasets is presented, and a comparison for different Compactly Supported RBFs (CS-RBFs) is made with respect to the accuracy of the computation. The proposed approach uses symmetry of a matrix, partitioning the matrix into blocks and data structures for storage of the sparse matrix. The experiments are performed for synthetic and real datasets.
Radial Basis Function (RBF Interpolation and Investigating its Impact on Rainfall Duration Mapping
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Hassan Derakhshan
2012-01-01
Full Text Available The missing data in database must be reproduced primarily by appropriate interpolation techniques. Radial basis function (RBF interpolators can play a significant role in data completion of precipitation mapping. Five RBF techniques were engaged to be employed in compensating the missing data in event-wised dataset of Upper Paramatta River Catchment in the western suburbs of Sydney, Australia. The related shape parameter, C, of RBFs was optimized for first event of database during a cross-validation process. The Normalized mean square error (NMSE, percent average estimation error (PAEE and coefficient of determination (R2 were the statistics used as validation tools. Results showed that the multiquadric RBF technique with the least error, best suits compensation of the related database.
THE ALGORITHM OF MESHFREE METHOD OF RADIAL BASIS FUNCTIONS IN TASKS OF UNDERGROUND HYDROMECHANICS
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N. V. Medvid
2016-01-01
Full Text Available A Mathematical model of filtering consolidation in the body of soil dam with conduit andwashout zone in two-dimensional case is considered. The impact of such technogenic factors as temperature, salt concentration, subsidence of upper boundary and interior points of the dam with time is taken into account. The software to automate the calculation of numerical solution of the boundary problem by radial basis functions has been created, which enables to conduct numerical experiments by varying the input parameters and shape. The influence of the presence of conduit and washout zone on the pressure, temperature and concentration of salts in the dam body at different time intervals isinvestigated. A number of numerical experiments is conducted and the analysis of dam accidents is performed.
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Sharma Rakesh
2004-05-01
Full Text Available Abstract Functional magnetic resonance imaging (fMRI is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities.
International Nuclear Information System (INIS)
Havu, V.; Blum, V.; Havu, P.; Scheffler, M.
2009-01-01
We consider the problem of developing O(N) scaling grid-based operations needed in many central operations when performing electronic structure calculations with numeric atom-centered orbitals as basis functions. We outline the overall formulation of localized algorithms, and specifically the creation of localized grid batches. The choice of the grid partitioning scheme plays an important role in the performance and memory consumption of the grid-based operations. Three different top-down partitioning methods are investigated, and compared with formally more rigorous yet much more expensive bottom-up algorithms. We show that a conceptually simple top-down grid partitioning scheme achieves essentially the same efficiency as the more rigorous bottom-up approaches.
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Ruslan Skrynkovskyy
2017-12-01
Full Text Available The purpose of the article is to improve the model of the enterprise (institution, organization management process on the basis of general management functions. The graphic model of the process of management according to the process-structured management is presented. It has been established that in today's business environment, the model of the management process should include such general management functions as: 1 controlling the achievement of results; 2 planning based on the main goal; 3 coordination and corrective actions (in the system of organization of work and production; 4 action as a form of act (conscious, volitional, directed; 5 accounting system (accounting, statistical, operational-technical and managerial; 6 diagnosis (economic, legal with such subfunctions as: identification of the state and capabilities; analysis (economic, legal, systemic with argumentation; assessment of the state, trends and prospects of development. The prospect of further research in this direction is: 1 the formation of a system of interrelation of functions and management methods, taking into account the presented research results; 2 development of the model of effective and efficient communication business process of the enterprise.
Polarization functions for the modified m6-31G basis sets for atoms Ga through Kr.
Mitin, Alexander V
2013-09-05
The 2df polarization functions for the modified m6-31G basis sets of the third-row atoms Ga through Kr (Int J Quantum Chem, 2007, 107, 3028; Int J. Quantum Chem, 2009, 109, 1158) are proposed. The performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets were examined in molecular calculations carried out by the density functional theory (DFT) method with B3LYP hybrid functional, Møller-Plesset perturbation theory of the second order (MP2), quadratic configuration interaction method with single and double substitutions and were compared with those for the known 6-31G basis sets as well as with the other similar 641 and 6-311G basis sets with and without polarization functions. Obtained results have shown that the performances of the m6-31G, m6-31G(d,p), and m6-31G(2df,p) basis sets are better in comparison with the performances of the known 6-31G, 6-31G(d,p) and 6-31G(2df,p) basis sets. These improvements are mainly reached due to better approximations of different electrons belonging to the different atomic shells in the modified basis sets. Applicability of the modified basis sets in thermochemical calculations is also discussed. © 2013 Wiley Periodicals, Inc.
Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas
2015-08-07
A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of "low-cost" electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT methods
International Nuclear Information System (INIS)
Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas
2015-01-01
A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of “low-cost” electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT
International Nuclear Information System (INIS)
Lin Lin; Lu Jianfeng; Ying Lexing; Weinan, E
2012-01-01
Kohn–Sham density functional theory is one of the most widely used electronic structure theories. In the pseudopotential framework, uniform discretization of the Kohn–Sham Hamiltonian generally results in a large number of basis functions per atom in order to resolve the rapid oscillations of the Kohn–Sham orbitals around the nuclei. Previous attempts to reduce the number of basis functions per atom include the usage of atomic orbitals and similar objects, but the atomic orbitals generally require fine tuning in order to reach high accuracy. We present a novel discretization scheme that adaptively and systematically builds the rapid oscillations of the Kohn–Sham orbitals around the nuclei as well as environmental effects into the basis functions. The resulting basis functions are localized in the real space, and are discontinuous in the global domain. The continuous Kohn–Sham orbitals and the electron density are evaluated from the discontinuous basis functions using the discontinuous Galerkin (DG) framework. Our method is implemented in parallel and the current implementation is able to handle systems with at least thousands of atoms. Numerical examples indicate that our method can reach very high accuracy (less than 1 meV) with a very small number (4–40) of basis functions per atom.
Evidence for a Functional Hierarchy of Association Networks.
Choi, Eun Young; Drayna, Garrett K; Badre, David
2018-05-01
Patient lesion and neuroimaging studies have identified a rostral-to-caudal functional gradient in the lateral frontal cortex (LFC) corresponding to higher-order (complex or abstract) to lower-order (simple or concrete) cognitive control. At the same time, monkey anatomical and human functional connectivity studies show that frontal regions are reciprocally connected with parietal and temporal regions, forming parallel and distributed association networks. Here, we investigated the link between the functional gradient of LFC regions observed during control tasks and the parallel, distributed organization of association networks. Whole-brain fMRI task activity corresponding to four orders of hierarchical control [Badre, D., & D'Esposito, M. Functional magnetic resonance imaging evidence for a hierarchical organization of the prefrontal cortex. Journal of Cognitive Neuroscience, 19, 2082-2099, 2007] was compared with a resting-state functional connectivity MRI estimate of cortical networks [Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106, 1125-1165, 2011]. Critically, at each order of control, activity in the LFC and parietal cortex overlapped onto a common association network that differed between orders. These results are consistent with a functional organization based on separable association networks that are recruited during hierarchical control. Furthermore, corticostriatal functional connectivity MRI showed that, consistent with their participation in functional networks, rostral-to-caudal LFC and caudal-to-rostral parietal regions had similar, order-specific corticostriatal connectivity that agreed with a striatal gating model of hierarchical rule use. Our results indicate that hierarchical cognitive control is subserved by parallel and distributed association networks, together forming
CSIR Research Space (South Africa)
Lysko, AA
2009-06-01
Full Text Available The paper introduces a method to cover several wire segments with a single basis function, describes related practical algorithms, and gives some results. The process involves three steps: identifying chains of wire segments, splitting the chains...
The correlation of metrics in complex networks with applications in functional brain networks
International Nuclear Information System (INIS)
Li, C; Wang, H; Van Mieghem, P; De Haan, W; Stam, C J
2011-01-01
An increasing number of network metrics have been applied in network analysis. If metric relations were known better, we could more effectively characterize networks by a small set of metrics to discover the association between network properties/metrics and network functioning. In this paper, we investigate the linear correlation coefficients between widely studied network metrics in three network models (Bárabasi–Albert graphs, Erdös–Rényi random graphs and Watts–Strogatz small-world graphs) as well as in functional brain networks of healthy subjects. The metric correlations, which we have observed and theoretically explained, motivate us to propose a small representative set of metrics by including only one metric from each subset of mutually strongly dependent metrics. The following contributions are considered important. (a) A network with a given degree distribution can indeed be characterized by a small representative set of metrics. (b) Unweighted networks, which are obtained from weighted functional brain networks with a fixed threshold, and Erdös–Rényi random graphs follow a similar degree distribution. Moreover, their metric correlations and the resultant representative metrics are similar as well. This verifies the influence of degree distribution on metric correlations. (c) Most metric correlations can be explained analytically. (d) Interestingly, the most studied metrics so far, the average shortest path length and the clustering coefficient, are strongly correlated and, thus, redundant. Whereas spectral metrics, though only studied recently in the context of complex networks, seem to be essential in network characterizations. This representative set of metrics tends to both sufficiently and effectively characterize networks with a given degree distribution. In the study of a specific network, however, we have to at least consider the representative set so that important network properties will not be neglected
MODEL OF FUNCTIONING OF TELECOMMUNICATION EQUIPMENT FOR SOFTWARE-CONFIGURATED NETWORKS
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Konstantin E. Samouylov
2018-03-01
Full Text Available A mathematical model of the functioning of the switch of a software defined networks is constructed in the form of a queuing network consisting of two queuing systems: the first simulates an input data buffer and a device for reading information from the header of the packet; the second is a table for addressing the switch of a software defined networks. The receipt of data in software defined networks has a probabilistic character in their deterministic processing in communication channels and switching nodes. Therefore, this mathematical model of the functioning of the switch of a software defined networks was built on the basis of queuing systems and networks. The stream of requests flowing into the network was divided into two Poisson streams of various types of applications, the first of which corresponded to the packets that came to the control port of the switch (from the controller, and the second flow to the remaining packets arriving on the switch. The flow corresponding to the packets arriving at the switch from the controller has a relative priority over the flow from the remaining arriving packets As a result, formulas were obtained for calculating the performance indicators of this telecommunications equipment such as average waiting queues for priority and non-priority applications, the probability of loss of applications for each phase of the switch. Based on the received quality of service indicators for this telecommunications equipment, it is possible to assess the stability of switches in software defined networks for various information impacts.
A new diffusion nodal method based on analytic basis function expansion
International Nuclear Information System (INIS)
Noh, J.M.; Cho, N.Z.
1993-01-01
The transverse integration procedure commonly used in most advanced nodal methods results in some limitations. The first is that the transverse leakage term that appears in the transverse integration procedure must be appropriately approximated. In most advanced nodal methods, this term is expanded in a quadratic polynomial. The second arises when reconstructing the pinwise flux distribution within a node. The available one-dimensional flux shapes from nodal calculation in each spatial direction cannot be used directly in the flux reconstruction. Finally, the transverse leakage defined for a hexagonal node becomes so complicated as not to be easily handled and contains nonphysical singular terms. In this paper, a new nodal method called the analytic function expansion nodal (AFEN) method is described for both the rectangular geometry and the hexagonal geometry in order to overcome these limitations. This method does not solve the transverse-integrated one-dimensional diffusion equations but instead solves directly the original multidimensional diffusion equation within a node. This is a accomplished by expanding the solution (or the intranodal homogeneous flux distribution) in terms of nonseparable analytic basis functions satisfying the diffusion equation at any point in the node
Response functions of a superlattice with a basis: A model for oxide superconductors
International Nuclear Information System (INIS)
Griffin, A.
1988-01-01
The new high-T/sub c/ oxide superconductors appear to be superlattice structures with a basis composed of metallic sheets as well as metallic chains. Using a simple free-electron-gas model for the sheets and chains, we obtain the dielectric function ε(q,ω) of such a multilayer system within the random-phase approximation (RPA). We give results valid for arbitrary wave vector q appropriate to sheets and chains (as in the orthorhombic phase of Y-Ba-Cu-O) as well as for two different kinds of sheets (such as may be present in the Bi-Ca-Sr-Cu-O superconductors). The occurrence of acoustic plasmons is a general phenomenon in such superlattices, as shown by an alternative formulation based on the exact response functions for the individual sheets and chains, in which only the interchain (sheet) Coulomb interaction is treated in the RPA. These results generalize the long-wavelength expressions recently given in the literature. We also briefly discuss the analogous results for two arrays of mutually perpendicular chains, such as found in Hg chain compounds
Solution to PDEs using radial basis function finite-differences (RBF-FD) on multiple GPUs
International Nuclear Information System (INIS)
Bollig, Evan F.; Flyer, Natasha; Erlebacher, Gordon
2012-01-01
This paper presents parallelization strategies for the radial basis function-finite difference (RBF-FD) method. As a generalized finite differencing scheme, the RBF-FD method functions without the need for underlying meshes to structure nodes. It offers high-order accuracy approximation and scales as O(N) per time step, with N being with the total number of nodes. To our knowledge, this is the first implementation of the RBF-FD method to leverage GPU accelerators for the solution of PDEs. Additionally, this implementation is the first to span both multiple CPUs and multiple GPUs. OpenCL kernels target the GPUs and inter-processor communication and synchronization is managed by the Message Passing Interface (MPI). We verify our implementation of the RBF-FD method with two hyperbolic PDEs on the sphere, and demonstrate up to 9x speedup on a commodity GPU with unoptimized kernel implementations. On a high performance cluster, the method achieves up to 7x speedup for the maximum problem size of 27,556 nodes.
International Nuclear Information System (INIS)
Choi, Sunghwan; Hong, Kwangwoo; Kim, Jaewook; Kim, Woo Youn
2015-01-01
We developed a self-consistent field program based on Kohn-Sham density functional theory using Lagrange-sinc functions as a basis set and examined its numerical accuracy for atoms and molecules through comparison with the results of Gaussian basis sets. The result of the Kohn-Sham inversion formula from the Lagrange-sinc basis set manifests that the pseudopotential method is essential for cost-effective calculations. The Lagrange-sinc basis set shows faster convergence of the kinetic and correlation energies of benzene as its size increases than the finite difference method does, though both share the same uniform grid. Using a scaling factor smaller than or equal to 0.226 bohr and pseudopotentials with nonlinear core correction, its accuracy for the atomization energies of the G2-1 set is comparable to all-electron complete basis set limits (mean absolute deviation ≤1 kcal/mol). The same basis set also shows small mean absolute deviations in the ionization energies, electron affinities, and static polarizabilities of atoms in the G2-1 set. In particular, the Lagrange-sinc basis set shows high accuracy with rapid convergence in describing density or orbital changes by an external electric field. Moreover, the Lagrange-sinc basis set can readily improve its accuracy toward a complete basis set limit by simply decreasing the scaling factor regardless of systems
The Efficiency of a Small-World Functional Brain Network
Institute of Scientific and Technical Information of China (English)
ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan
2012-01-01
We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.
Structural and functional networks in complex systems with delay.
Eguíluz, Víctor M; Pérez, Toni; Borge-Holthoefer, Javier; Arenas, Alex
2011-05-01
Functional networks of complex systems are obtained from the analysis of the temporal activity of their components, and are often used to infer their unknown underlying connectivity. We obtain the equations relating topology and function in a system of diffusively delay-coupled elements in complex networks. We solve exactly the resulting equations in motifs (directed structures of three nodes) and in directed networks. The mean-field solution for directed uncorrelated networks shows that the clusterization of the activity is dominated by the in-degree of the nodes, and that the locking frequency decreases with increasing average degree. We find that the exponent of a power law degree distribution of the structural topology γ is related to the exponent of the associated functional network as α=(2-γ)(-1) for γ<2. © 2011 American Physical Society
International Nuclear Information System (INIS)
Roshani, G.H.; Nazemi, E.; Roshani, M.M.
2017-01-01
In this paper, a novel method is proposed for predicting the density of liquid phase in stratified regime of liquid-gas two phase flows by utilizing dual modality densitometry technique and artificial neural network (ANN) model of radial basis function (RBF). The detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors for registering transmitted and scattered photons. At the first step, a Monte Carlo simulation model was utilized to obtain the optimum position for the scattering detector in dual modality densitometry configuration. At the next step, an experimental setup was designed based on obtained optimum position for detectors from simulation in order to generate the required data for training and testing the ANN. The results show that the proposed approach could be successfully applied for predicting the density of liquid phase in stratified regime of gas-liquid two phase flows with mean relative error (MRE) of less than 0.701. - Highlights: • Density of liquid phase in stratified regime of two phase flows was predicted. • Combination of dual modality densitometry technique and ANN was utilized. • Detection system includes a 137 Cs radioactive source and two NaI(Tl) detectors. • MCNP simulation was done to obtain the optimum position for the scattering detector. • An experimental setup was designed to generate the required data for training the ANN.
Functional brain connectivity is predictable from anatomic network's Laplacian eigen-structure.
Abdelnour, Farras; Dayan, Michael; Devinsky, Orrin; Thesen, Thomas; Raj, Ashish
2018-05-15
How structural connectivity (SC) gives rise to functional connectivity (FC) is not fully understood. Here we mathematically derive a simple relationship between SC measured from diffusion tensor imaging, and FC from resting state fMRI. We establish that SC and FC are related via (structural) Laplacian spectra, whereby FC and SC share eigenvectors and their eigenvalues are exponentially related. This gives, for the first time, a simple and analytical relationship between the graph spectra of structural and functional networks. Laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series. A small number of Laplacian eigenmodes are shown to be sufficient to reconstruct FC matrices, serving as basis functions. This approach is fast, and requires no time-consuming simulations. It was tested on two empirical SC/FC datasets, and was found to significantly outperform generative model simulations of coupled neural masses. Copyright © 2018. Published by Elsevier Inc.
The functional and structural neural basis of individual differences in loss aversion.
Canessa, Nicola; Crespi, Chiara; Motterlini, Matteo; Baud-Bovy, Gabriel; Chierchia, Gabriele; Pantaleo, Giuseppe; Tettamanti, Marco; Cappa, Stefano F
2013-09-04
Decision making under risk entails the anticipation of prospective outcomes, typically leading to the greater sensitivity to losses than gains known as loss aversion. Previous studies on the neural bases of choice-outcome anticipation and loss aversion provided inconsistent results, showing either bidirectional mesolimbic responses of activation for gains and deactivation for losses, or a specific amygdala involvement in processing losses. Here we focused on loss aversion with the aim to address interindividual differences in the neural bases of choice-outcome anticipation. Fifty-six healthy human participants accepted or rejected 104 mixed gambles offering equal (50%) chances of gaining or losing different amounts of money while their brain activity was measured with functional magnetic resonance imaging (fMRI). We report both bidirectional and gain/loss-specific responses while evaluating risky gambles, with amygdala and posterior insula specifically tracking the magnitude of potential losses. At the individual level, loss aversion was reflected both in limbic fMRI responses and in gray matter volume in a structural amygdala-thalamus-striatum network, in which the volume of the "output" centromedial amygdala nuclei mediating avoidance behavior was negatively correlated with monetary performance. We conclude that outcome anticipation and ensuing loss aversion involve multiple neural systems, showing functional and structural individual variability directly related to the actual financial outcomes of choices. By supporting the simultaneous involvement of both appetitive and aversive processing in economic decision making, these results contribute to the interpretation of existing inconsistencies on the neural bases of anticipating choice outcomes.
International Nuclear Information System (INIS)
Filippov, G.F.; Ovcharenko, V.I.; Teryoshin, Yu.V.
1980-01-01
For near-magnetic nuclei, the matrix elements of the central exchange nucleon-nucleon interaction potential energy operator between the generating functions of the total basis of the Sn are obtained. The basis states are highest weigt vectorsp(2,R) irreducible representatio of the SO(3) irredicible representation and in addition, have a definite O(A-1) symmetry. The Sp(2,R) basis generating matrix elements simplify essentially the problem of calculating the spectrum of collective excitations of the atomic nucleus over an intrinsic function of definite O(A-1) symmetry
Methylphenidate Modulates Functional Network Connectivity to Enhance Attention
Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.
2016-01-01
Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. SIGNIFICANCE STATEMENT Recent work identified a promising neuromarker of sustained attention based on whole
International Nuclear Information System (INIS)
Guan, Xuemei; Zhu, Yuren; Song, Wenlong
2016-01-01
According to the characteristics of wood dyeing, we propose a predictive model of pigment formula for wood dyeing based on Radial Basis Function (RBF) neural network. In practical application, however, it is found that the number of neurons in the hidden layer of RBF neural network is difficult to determine. In general, we need to test several times according to experience and prior knowledge, which is lack of a strict design procedure on theoretical basis. And we also don’t know whether the RBF neural network is convergent. This paper proposes a peak density function to determine the number of neurons in the hidden layer. In contrast to existing approaches, the centers and the widths of the radial basis function are initialized by extracting the features of samples. So the uncertainty caused by random number when initializing the training parameters and the topology of RBF neural network is eliminated. The average relative error of the original RBF neural network is 1.55% in 158 epochs. However, the average relative error of the RBF neural network which is improved by peak density function is only 0.62% in 50 epochs. Therefore, the convergence rate and approximation precision of the RBF neural network are improved significantly.
Predicting Protein Function via Semantic Integration of Multiple Networks.
Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong
2016-01-01
Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.
Omega-Harmonic Functions and Inverse Conductivity Problems on Networks
National Research Council Canada - National Science Library
Berenstein, Carlos A; Chung, Soon-Yeong
2003-01-01
.... To do this, they introduce an elliptic operator DELTA omega and an omega-harmonic function on the graph, with its physical interpretation being the diffusion equation on the graph, which models an electric network...
Intelligent Network Management and Functional Cerebellum Synthesis
Loebner, Egon E.
1989-01-01
Transdisciplinary modeling of the cerebellum across histology, physiology, and network engineering provides preliminary results at three organization levels: input/output links to central nervous system networks; links between the six neuron populations in the cerebellum; and computation among the neurons of the populations. Older models probably underestimated the importance and role of climbing fiber input which seems to supply write as well as read signals, not just to Purkinje but also to basket and stellate neurons. The well-known mossy fiber-granule cell-Golgi cell system should also respond to inputs originating from climbing fibers. Corticonuclear microcomplexing might be aided by stellate and basket computation and associate processing. Technological and scientific implications of the proposed cerebellum model are discussed.
Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf
2018-04-01
Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.
The NOVO Network: the original scientific basis for its establishment and our R&D vision
DEFF Research Database (Denmark)
Winkel, Jørgen; Edwards, Kasper; Dellve, L.
2017-01-01
Systems” in the healthcare sector. It was founded in 2006 in Copenhagen and was financially supported by the Nordic Council of Ministers from 2007 to 2015. The motivation to establish the NOVO Network arose when reviewing the literature regarding opportunities to create sustainable production systems....... This work was initiated year 2002 and resultet in a systematic review published 2011 (Westgaard and Winkel 2011). Already in 2006 it was concluded that ergonomic interventions have limited musculoskeletal and mental health effects in a long-range perspective while rationalization has predominant negative...... be challenged due to the frequent negative impact of rationalization on ergonomics and vice versa. This call for dialogue processes between the stakeholders taking more holistic systems perspectives. Dialogue-based change processes may be more common in the Nordic countries compared to other parts of the world...
Memory and neural networks on the basis of color centers in solids.
Winnacker, Albrecht; Osvet, Andres
2009-11-01
Optical data recording is one of the most widely used and efficient systems of memory in the non-living world. The application of color centers in this context offers not only systems of high speed in writing and read-out due to a high degree of parallelism in data handling but also a possibility to set up models of neural networks. In this way, systems with a high potential for image processing, pattern recognition and logical operations can be constructed. A limitation to storage density is given by the diffraction limit of optical data recording. It is shown that this limitation can at least in principle be overcome by the principle of spectral hole burning, which results in systems of storage capacities close to the human brain system.
A Mapping Between Structural and Functional Brain Networks.
Meier, Jil; Tewarie, Prejaas; Hillebrand, Arjan; Douw, Linda; van Dijk, Bob W; Stufflebeam, Steven M; Van Mieghem, Piet
2016-05-01
The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.
Statistical Network Analysis for Functional MRI: Mean Networks and Group Comparisons.
Directory of Open Access Journals (Sweden)
Cedric E Ginestet
2014-05-01
Full Text Available Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN, or by directly computing the mean network, provided that a metric has been specified on the space of all networks with a given number of nodes. In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.
Extracting neuronal functional network dynamics via adaptive Granger causality analysis.
Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash
2018-04-24
Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.
McClements, David Julian; Gumus, Cansu Ekin
2016-08-01
There is increasing consumer pressure for commercial products that are more natural, sustainable, and environmentally friendly, including foods, cosmetics, detergents, and personal care products. Industry has responded by trying to identify natural alternatives to synthetic functional ingredients within these products. The focus of this review article is on the replacement of synthetic surfactants with natural emulsifiers, such as amphiphilic proteins, polysaccharides, biosurfactants, phospholipids, and bioparticles. In particular, the physicochemical basis of emulsion formation and stabilization by natural emulsifiers is discussed, and the benefits and limitations of different natural emulsifiers are compared. Surface-active polysaccharides typically have to be used at relatively high levels to produce small droplets, but the droplets formed are highly resistant to environmental changes. Conversely, surface-active proteins are typically utilized at low levels, but the droplets formed are highly sensitive to changes in pH, ionic strength, and temperature. Certain phospholipids are capable of producing small oil droplets during homogenization, but again the droplets formed are highly sensitive to changes in environmental conditions. Biosurfactants (saponins) can be utilized at low levels to form fine oil droplets that remain stable over a range of environmental conditions. Some nature-derived nanoparticles (e.g., cellulose, chitosan, and starch) are effective at stabilizing emulsions containing relatively large oil droplets. Future research is encouraged to identify, isolate, purify, and characterize new types of natural emulsifier, and to test their efficacy in food, cosmetic, detergent, personal care, and other products. Copyright © 2016 Elsevier B.V. All rights reserved.
Wang, Zhiheng
2014-12-10
A meshless local radial basis function method is developed for two-dimensional incompressible Navier-Stokes equations. The distributed nodes used to store the variables are obtained by the philosophy of an unstructured mesh, which results in two main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization of equations is performed conveniently with the supporting nodes. The algebraic system is solved by a semi-implicit pseudo-time method, in which the convective and source terms are explicitly marched by the Runge-Kutta method, and the diffusive terms are implicitly solved. The proposed method is validated by several benchmark problems, including natural convection in a square cavity, the lid-driven cavity flow, and the natural convection in a square cavity containing a circular cylinder, and very good agreement with the existing results are obtained.
Gaussian Radial Basis Function for Efficient Computation of Forest Indirect Illumination
Abbas, Fayçal; Babahenini, Mohamed Chaouki
2018-06-01
Global illumination of natural scenes in real time like forests is one of the most complex problems to solve, because the multiple inter-reflections between the light and material of the objects composing the scene. The major problem that arises is the problem of visibility computation. In fact, the computing of visibility is carried out for all the set of leaves visible from the center of a given leaf, given the enormous number of leaves present in a tree, this computation performed for each leaf of the tree which also reduces performance. We describe a new approach that approximates visibility queries, which precede in two steps. The first step is to generate point cloud representing the foliage. We assume that the point cloud is composed of two classes (visible, not-visible) non-linearly separable. The second step is to perform a point cloud classification by applying the Gaussian radial basis function, which measures the similarity in term of distance between each leaf and a landmark leaf. It allows approximating the visibility requests to extract the leaves that will be used to calculate the amount of indirect illumination exchanged between neighbor leaves. Our approach allows efficiently treat the light exchanges in the scene of a forest, it allows a fast computation and produces images of good visual quality, all this takes advantage of the immense power of computation of the GPU.
S-pairing in neutron matter: I. Correlated basis function theory
International Nuclear Information System (INIS)
Fabrocini, Adelchi; Fantoni, Stefano; Illarionov, Alexey Yu.; Schmidt, Kevin E.
2008-01-01
S-wave pairing in neutron matter is studied within an extension of correlated basis function (CBF) theory to include the strong, short range spatial correlations due to realistic nuclear forces and the pairing correlations of the Bardeen, Cooper and Schrieffer (BCS) approach. The correlation operator contains central as well as tensor components. The correlated BCS scheme of [S. Fantoni, Nucl. Phys. A 363 (1981) 381], developed for simple scalar correlations, is generalized to this more realistic case. The energy of the correlated pair condensed phase of neutron matter is evaluated at the two-body order of the cluster expansion, but considering the one-body density and the corresponding energy vertex corrections at the first order of the Power Series expansion. Based on these approximations, we have derived a system of Euler equations for the correlation factors and for the BCS amplitudes, resulting in correlated nonlinear gap equations, formally close to the standard BCS ones. These equations have been solved for the momentum independent part of several realistic potentials (Reid, Argonne v 14 and Argonne v 8 ' ) to stress the role of the tensor correlations and of the many-body effects. Simple Jastrow correlations and/or the lack of the density corrections enhance the gap with respect to uncorrelated BCS, whereas it is reduced according to the strength of the tensor interaction and following the inclusion of many-body contributions
Directory of Open Access Journals (Sweden)
Misganaw Abebe
2017-11-01
Full Text Available Springback in multi-point dieless forming (MDF is a common problem because of the small deformation and blank holder free boundary condition. Numerical simulations are widely used in sheet metal forming to predict the springback. However, the computational time in using the numerical tools is time costly to find the optimal process parameters value. This study proposes radial basis function (RBF to replace the numerical simulation model by using statistical analyses that are based on a design of experiment (DOE. Punch holding time, blank thickness, and curvature radius are chosen as effective process parameters for determining the springback. The Latin hypercube DOE method facilitates statistical analyses and the extraction of a prediction model in the experimental process parameter domain. Finite element (FE simulation model is conducted in the ABAQUS commercial software to generate the springback responses of the training and testing samples. The genetic algorithm is applied to find the optimal value for reducing and compensating the induced springback for the different blank thicknesses using the developed RBF prediction model. Finally, the RBF numerical result is verified by comparing with the FE simulation result of the optimal process parameters and both results show that the springback is almost negligible from the target shape.
FMFinder: A Functional Module Detector for PPI Networks
Directory of Open Access Journals (Sweden)
M. Modi
2017-10-01
Full Text Available Bioinformatics is an integrated area of data mining, statistics and computational biology. Protein-Protein Interaction (PPI network is the most important biological process in living beings. In this network a protein module interacts with another module and so on, forming a large network of proteins. The same set of proteins which takes part in the organic courses of biological actions is detected through the Function Module Detection method. Clustering process when applied in PPI networks is made of proteins which are part of a larger communication network. As a result of this, we can define the limits for module detection as well as clarify the construction of a PPI network. For understating the bio-mechanism of various living beings, a detailed study of FMFinder detection by clustering process is called for.
Functional Interaction Network Construction and Analysis for Disease Discovery.
Wu, Guanming; Haw, Robin
2017-01-01
Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.
Imaging structural and functional brain networks in temporal lobe epilepsy
Bernhardt, Boris C.; Hong, SeokJun; Bernasconi, Andrea; Bernasconi, Neda
2013-01-01
Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy. PMID:24098281
Using function approximation to determine neural network accuracy
International Nuclear Information System (INIS)
Wichman, R.F.; Alexander, J.
2013-01-01
Many, if not most, control processes demonstrate nonlinear behavior in some portion of their operating range and the ability of neural networks to model non-linear dynamics makes them very appealing for control. Control of high reliability safety systems, and autonomous control in process or robotic applications, however, require accurate and consistent control and neural networks are only approximators of various functions so their degree of approximation becomes important. In this paper, the factors affecting the ability of a feed-forward back-propagation neural network to accurately approximate a non-linear function are explored. Compared to pattern recognition using a neural network for function approximation provides an easy and accurate method for determining the network's accuracy. In contrast to other techniques, we show that errors arising in function approximation or curve fitting are caused by the neural network itself rather than scatter in the data. A method is proposed that provides improvements in the accuracy achieved during training and resulting ability of the network to generalize after training. Binary input vectors provided a more accurate model than with scalar inputs and retraining using a small number of the outlier x,y pairs improved generalization. (author)
Imaging structural and functional brain networks in temporal lobe epilepsy
Directory of Open Access Journals (Sweden)
Boris eBernhardt
2013-10-01
Full Text Available Early imaging studies in temporal lobe epilepsy (TLE focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.
Density-dependence of functional spiking networks in vitro
Energy Technology Data Exchange (ETDEWEB)
Ham, Michael I [Los Alamos National Laboratory; Gintautuas, Vadas [Los Alamos National Laboratory; Rodriguez, Marko A [Los Alamos National Laboratory; Bettencourt, Luis M A [Los Alamos National Laboratory; Bennett, Ryan [UNIV OF NORTH TEXAS; Santa Maria, Cara L [UNIV OF NORTH TEXAS
2008-01-01
During development, the mammalian brain differentiates into specialized regions with unique functional abilities. While many factors contribute to this functional specialization, we explore the effect neuronal density can have on neuronal interactions. Two types of networks, dense (50,000 neurons and glia support cells) and sparse (12,000 neurons and glia support cells), are studied. A competitive first response model is applied to construct activation graphs that represent pairwise neuronal interactions. By observing the evolution of these graphs during development in vitro we observe that dense networks form activation connections earlier than sparse networks, and that link-!llltropy analysis of the resulting dense activation graphs reveals that balanced directional connections dominate. Information theoretic measures reveal in addition that early functional information interactions (of order 3) are synergetic in both dense and sparse networks. However, during development in vitro, such interactions become redundant in dense, but not sparse networks. Large values of activation graph link-entropy correlate strongly with redundant ensembles observed in the dense networks. Results demonstrate differences between dense and sparse networks in terms of informational groups, pairwise relationships, and activation graphs. These differences suggest that variations in cell density may result in different functional specialization of nervous system tissue also in vivo.
Imaging structural and functional brain networks in temporal lobe epilepsy.
Bernhardt, Boris C; Hong, Seokjun; Bernasconi, Andrea; Bernasconi, Neda
2013-10-01
Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.
Sex differences in normal age trajectories of functional brain networks.
Scheinost, Dustin; Finn, Emily S; Tokoglu, Fuyuze; Shen, Xilin; Papademetris, Xenophon; Hampson, Michelle; Constable, R Todd
2015-04-01
Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. © 2014 Wiley Periodicals, Inc.
A framework for interpreting functional networks in schizophrenia
Directory of Open Access Journals (Sweden)
Peter eWilliamson
2012-06-01
Full Text Available Some promising genetic correlates of schizophrenia have emerged in recent years but none explain more than a small fraction of cases. The challenge of our time is to characterize the neuronal networks underlying schizophrenia and other neuropsychiatric illnesses. It has been proposed that schizophrenia arises from a uniquely human brain network associated with directed effort including the dorsal anterior and posterior cingulate cortex, auditory cortex, and hippocampus and while mood disorders arise from a different brain network associated with emotional encoding including the ventral anterior cingulate cortex, orbital frontal cortex, and amygdala. Both interact with a representation network including the frontal and temporal poles and the fronto-insular cortex, allowing the representation of the thoughts, feelings and actions of self and others. This paper reviews recent morphological and functional literature in light of the proposed networks underlying these disorders. It is suggested that there is considerable support for the involvement of the directed effort network in schizophrenia from studies of brain structure with voxel-based morphometry (VBM and diffusion tensor imaging (DTI. While early studies of resting brain networks are inconclusive, functional magnetic resonance imaging imaging (fMRI studies of task-related networks clearly implicate these regions. In keeping with the model, functional deficits in regions associated with directed effort and self-monitoring are associated with structural anomalies in action-related regions in schizophrenic patients. VBM, DTI, fMRI studies of mood disordered patients support the involvement of a different network associated with emotional encoding. The distinction between disorders is enhanced by combining structural and functional data. It is concluded that brain networks associated with directed effort are particularly vulnerable to failure in the human brain leading to the symptoms of
Gökharman, Fatma Dilek; Aydın, Sonay; Fatihoğlu, Erdem; Koşar, Pınar Nercis
2017-12-19
Background/aim: Head injuries are commonly seen in the pediatric population. Noncontrast enhanced cranial CT is the method of choice to detect possible traumatic brain injury (TBI). Concerns about ionizing radiation exposure make the evaluation more challenging. The aim of this study was to evaluate the effectiveness of the Pediatric Emergency Care Applied Research Network (PECARN) rules in predicting clinically important TBI and to determine the amount of medical resource waste and unnecessary radiation exposure.Materials and methods: This retrospective study included 1041 pediatric patients presented to the emergency department. The patients were divided into subgroups of "appropriate for cranial CT", "not appropriate for cranial CT" and "cranial CT/observation of patient; both are appropriate". To determine the effectiveness of the PECARN rules, data were analyzed according to the presence of pathological findings Results: "Appropriate for cranial CT" results can predict pathology presence 118,056-fold compared to the "not appropriate for cranial CT" results. With "cranial CT/observation of patient; both are appropriate" results, pathology presence was predicted 11,457-fold compared to "not appropriate for cranial CT" results.Conclusion: PECARN rules can predict pathology presence successfully in pediatric TBI. Using PECARN can decrease resource waste and exposure to ionizing radiation.
Hyper-connectivity of functional networks for brain disease diagnosis.
Jie, Biao; Wee, Chong-Yaw; Shen, Dinggang; Zhang, Daoqiang
2016-08-01
Exploring structural and functional interactions among various brain regions enables better understanding of pathological underpinnings of neurological disorders. Brain connectivity network, as a simplified representation of those structural and functional interactions, has been widely used for diagnosis and classification of neurodegenerative diseases, especially for Alzheimer's disease (AD) and its early stage - mild cognitive impairment (MCI). However, the conventional functional connectivity network is usually constructed based on the pairwise correlation among different brain regions and thus ignores their higher-order relationships. Such loss of high-order information could be important for disease diagnosis, since neurologically a brain region predominantly interacts with more than one other brain regions. Accordingly, in this paper, we propose a novel framework for estimating the hyper-connectivity network of brain functions and then use this hyper-network for brain disease diagnosis. Here, the functional connectivity hyper-network denotes a network where each of its edges representing the interactions among multiple brain regions (i.e., an edge can connect with more than two brain regions), which can be naturally represented by a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI (R-fMRI) time series by using sparse representation. Then, we extract three sets of brain-region specific features from the connectivity hyper-networks, and further exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results on both MCI dataset and attention deficit hyperactivity disorder (ADHD) dataset demonstrate that, compared with the conventional connectivity network-based methods, the proposed method can not only improve the classification performance, but also help
Garrido Ortiz, Pablo; Sørensen, Chres W.; Lucani Roetter, Daniel Enrique; Agüero Calvo, Ramón
2016-01-01
Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and comp...
Functional neural networks underlying response inhibition in adolescents and adults.
Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D
2007-07-19
This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.
Functional segregation and integration within fronto-parietal networks.
Parlatini, Valeria; Radua, Joaquim; Dell'Acqua, Flavio; Leslie, Anoushka; Simmons, Andy; Murphy, Declan G; Catani, Marco; Thiebaut de Schotten, Michel
2017-02-01
Experimental data on monkeys and functional studies in humans support the existence of a complex fronto-parietal system activating for cognitive and motor tasks, which may be anatomically supported by the superior longitudinal fasciculus (SLF). Advanced tractography methods have recently allowed the separation of the three branches of the SLF but are not suitable for their functional investigation. In order to gather comprehensive information about the functional organisation of these fronto-parietal connections, we used an innovative method, which combined tractography of the SLF in the largest dataset so far (129 participants) with 14 meta-analyses of functional magnetic resonance imaging (fMRI) studies. We found that frontal and parietal functions can be clustered into a dorsal spatial/motor network associated with the SLF I, and a ventral non-spatial/motor network associated with the SLF III. Further, all the investigated functions activated a middle network mostly associated with the SLF II. Our findings suggest that dorsal and ventral fronto-parietal networks are segregated but also share regions of activation, which may support flexible response properties or conscious processing. In sum, our novel combined approach provided novel findings on the functional organisation of fronto-parietal networks, and may be successfully applied to other brain connections. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
A Basis Function Approach to Simulate Storm Surge Events for Coastal Flood Risk Assessment
Wu, Wenyan; Westra, Seth; Leonard, Michael
2017-04-01
Storm surge is a significant contributor to flooding in coastal and estuarine regions, especially when it coincides with other flood producing mechanisms, such as extreme rainfall. Therefore, storm surge has always been a research focus in coastal flood risk assessment. Often numerical models have been developed to understand storm surge events for risk assessment (Kumagai et al. 2016; Li et al. 2016; Zhang et al. 2016) (Bastidas et al. 2016; Bilskie et al. 2016; Dalledonne and Mayerle 2016; Haigh et al. 2014; Kodaira et al. 2016; Lapetina and Sheng 2015), and assess how these events may change or evolve in the future (Izuru et al. 2015; Oey and Chou 2016). However, numeric models often require a lot of input information and difficulties arise when there are not sufficient data available (Madsen et al. 2015). Alternative, statistical methods have been used to forecast storm surge based on historical data (Hashemi et al. 2016; Kim et al. 2016) or to examine the long term trend in the change of storm surge events, especially under climate change (Balaguru et al. 2016; Oh et al. 2016; Rueda et al. 2016). In these studies, often the peak of surge events is used, which result in the loss of dynamic information within a tidal cycle or surge event (i.e. a time series of storm surge values). In this study, we propose an alternative basis function (BF) based approach to examine the different attributes (e.g. peak and durations) of storm surge events using historical data. Two simple two-parameter BFs were used: the exponential function and the triangular function. High quality hourly storm surge record from 15 tide gauges around Australia were examined. It was found that there are significantly location and seasonal variability in the peak and duration of storm surge events, which provides additional insights in coastal flood risk. In addition, the simple form of these BFs allows fast simulation of storm surge events and minimises the complexity of joint probability
Uncovering Biological Network Function via Graphlet Degree Signatures
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Nataša Pržulj
2008-01-01
Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassiﬁed proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting speciﬁc proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassiﬁed proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.
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Delong Zhang
Full Text Available Many studies have demonstrated that the pathophysiology and clinical symptoms of Parkinson's disease (PD are inhomogeneous. However, the symptom-specific intrinsic neural activities underlying the PD subtypes are still not well understood. Here, 15 tremor-dominant PD patients, 10 non-tremor-dominant PD patients, and 20 matched normal controls (NCs were recruited and underwent resting-state functional magnetic resonance imaging (fMRI. Functional brain networks were constructed based on randomly generated anatomical templates with and without the cerebellum. The regional network efficiencies (i.e., the local and global efficiencies were further measured and used to distinguish subgroups of PD patients (i.e., with tremor-dominant PD and non-tremor-dominant PD from the NCs using linear discriminant analysis. The results demonstrate that the subtype-specific functional networks were small-world-organized and that the network regional efficiency could discriminate among the individual PD subgroups and the NCs. Brain regions involved in distinguishing between the study groups included the basal ganglia (i.e., the caudate and putamen, limbic regions (i.e., the hippocampus and thalamus, the cerebellum, and other cerebral regions (e.g., the insula, cingulum, and calcarine sulcus. In particular, the performances of the regional local efficiency in the functional network were better than those of the global efficiency, and the performances of global efficiency were dependent on the inclusion of the cerebellum in the analysis. These findings provide new evidence for the neurological basis of differences between PD subtypes and suggest that the cerebellum may play different roles in the pathologies of different PD subtypes. The present study demonstrated the power of the combination of graph-based network analysis and discrimination analysis in elucidating the neural basis of different PD subtypes.
Memory functions reveal structural properties of gene regulatory networks
Perez-Carrasco, Ruben
2018-01-01
Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs. PMID:29470492
International Nuclear Information System (INIS)
Kaldellis, J.K.
2008-01-01
Most medium and small islands of the Aegean Archipelagos face serious infrastructure problems, strongly related with the limited electrical energy available at extremely high cost. On the other hand, the area is characterized by very high wind speeds and abundant solar energy, thus the exploitation of the available renewable energy sources (RES) may significantly contribute to the fulfillment of the local societies energy demand at minimum environmental and macroeconomic cost. However, the stochastic availability of wind energy and the variable availability of solar energy, the daily and seasonal electricity demand fluctuations, as well as the limited local electrical network capacity result in serious restrictions concerning the maximum renewable power penetration. In this context, the present paper investigates the possibility of creating a combined electricity generation facility based on the exploitation of wind or/and solar potential of an area as well as on the utilization of an appropriate energy storage configuration in order to replace the existing thermal power stations with rational investment requirements. For this purpose, the major parameters of the proposed integrated configuration are firstly calculated and its financial viability is accordingly analyzed. One of the main targets of the proposed solution is to maximize the RES exploitation of the area at a minimum electricity generation cost, while special emphasis is given in order to select the most cost-efficient energy storage device available. According to the results obtained the proposed solution is not only financially attractive but also improves the quality of the electricity offered to the local communities, substituting the expensive and heavily polluting existing thermal power stations
Disrupted functional brain networks in autistic toddlers
Boersma, M.; Kemner, C.; Reus, M.A. de; Collin, G; Snijders, T.M.; Hofman, D.; Buitelaar, J.K.; Stam, C.J.; Heuvel, M.P. van den
2013-01-01
Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions
Zhao, Yongli; Hu, Liyazhou; Wang, Wei; Li, Yajie; Zhang, Jie
2017-01-01
With the continuous opening of resource acquisition and application, there are a large variety of network hardware appliances deployed as the communication infrastructure. To lunch a new network application always implies to replace the obsolete devices and needs the related space and power to accommodate it, which will increase the energy and capital investment. Network function virtualization1 (NFV) aims to address these problems by consolidating many network equipment onto industry standard elements such as servers, switches and storage. Many types of IT resources have been deployed to run Virtual Network Functions (vNFs), such as virtual switches and routers. Then how to deploy NFV in optical transport networks is a of great importance problem. This paper focuses on this problem, and gives an implementation architecture of NFV-enabled optical transport networks based on Software Defined Optical Networking (SDON) with the procedure of vNFs call and return. Especially, an implementation solution of NFV-enabled optical transport node is designed, and a parallel processing method for NFV-enabled OTN nodes is proposed. To verify the performance of NFV-enabled SDON, the protocol interaction procedures of control function virtualization and node function virtualization are demonstrated on SDON testbed. Finally, the benefits and challenges of the parallel processing method for NFV-enabled OTN nodes are simulated and analyzed.
Hemispheric asymmetry of electroencephalography-based functional brain networks.
Jalili, Mahdi
2014-11-12
Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.
Directory of Open Access Journals (Sweden)
Gisele Tessari Santos
2009-08-01
Full Text Available A large number of financial engineering problems involve non-linear equations with non-linear or time-dependent boundary conditions. Despite available analytical solutions, many classical and modified forms of the well-known Black-Scholes (BS equation require fast and accurate numerical solutions. This work introduces the radial basis function (RBF method as applied to the solution of the BS equation with non-linear boundary conditions, related to path-dependent barrier options. Furthermore, the diffusional method for solving advective-diffusive equations is explored as to its effectiveness to solve BS equations. Cubic and Thin-Plate Spline (TPS radial basis functions were employed and evaluated as to their effectiveness to solve barrier option problems. The numerical results, when compared against analytical solutions, allow affirming that the RBF method is very accurate and easy to be implemented. When the RBF method is applied, the diffusional method leads to the same results as those obtained from the classical formulation of Black-Scholes equation.Muitos problemas de engenharia financeira envolvem equações não-lineares com condições de contorno não-lineares ou dependentes do tempo. Apesar de soluções analíticas disponíveis, várias formas clássicas e modificadas da conhecida equação de Black-Scholes (BS requerem soluções numéricas rápidas e acuradas. Este trabalho introduz o método de função de base radial (RBF aplicado à solução da equação BS com condições de contorno não-lineares relacionadas a opções de barreira dependentes da trajetória. Além disso, explora-se o método difusional para solucionar equações advectivo-difusivas quanto à sua efetividade para solucionar equações BS. Utilizam-se funções de base radial Cúbica e Thin-Plate Spline (TPS, aplicadas à solução de problemas de opções de barreiras. Os resultados numéricos, quando comparados com as soluções analíticas, permitem afirmar
Dynamic reconfiguration of human brain functional networks through neurofeedback.
Haller, Sven; Kopel, Rotem; Jhooti, Permi; Haas, Tanja; Scharnowski, Frank; Lovblad, Karl-Olof; Scheffler, Klaus; Van De Ville, Dimitri
2013-11-01
Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (1) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. Copyright © 2013 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Casa, L D C; Krueger, P S
2013-01-01
Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to
Development of large-scale functional brain networks in children.
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Kaustubh Supekar
2009-07-01
Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.
Development of large-scale functional brain networks in children.
Supekar, Kaustubh; Musen, Mark; Menon, Vinod
2009-07-01
The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.
Fuertinger, Stefan
2015-01-01
Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. PMID:25673742
Simonyan, Kristina; Fuertinger, Stefan
2015-04-01
Speech production is one of the most complex human behaviors. Although brain activation during speaking has been well investigated, our understanding of interactions between the brain regions and neural networks remains scarce. We combined seed-based interregional correlation analysis with graph theoretical analysis of functional MRI data during the resting state and sentence production in healthy subjects to investigate the interface and topology of functional networks originating from the key brain regions controlling speech, i.e., the laryngeal/orofacial motor cortex, inferior frontal and superior temporal gyri, supplementary motor area, cingulate cortex, putamen, and thalamus. During both resting and speaking, the interactions between these networks were bilaterally distributed and centered on the sensorimotor brain regions. However, speech production preferentially recruited the inferior parietal lobule (IPL) and cerebellum into the large-scale network, suggesting the importance of these regions in facilitation of the transition from the resting state to speaking. Furthermore, the cerebellum (lobule VI) was the most prominent region showing functional influences on speech-network integration and segregation. Although networks were bilaterally distributed, interregional connectivity during speaking was stronger in the left vs. right hemisphere, which may have underlined a more homogeneous overlap between the examined networks in the left hemisphere. Among these, the laryngeal motor cortex (LMC) established a core network that fully overlapped with all other speech-related networks, determining the extent of network interactions. Our data demonstrate complex interactions of large-scale brain networks controlling speech production and point to the critical role of the LMC, IPL, and cerebellum in the formation of speech production network. Copyright © 2015 the American Physiological Society.
Linear response calculation using the canonical-basis TDHFB with a schematic pairing functional
International Nuclear Information System (INIS)
Ebata, Shuichiro; Nakatsukasa, Takashi; Yabana, Kazuhiro
2011-01-01
A canonical-basis formulation of the time-dependent Hartree-Fock-Bogoliubov (TDHFB) theory is obtained with an approximation that the pair potential is assumed to be diagonal in the time-dependent canonical basis. The canonical-basis formulation significantly reduces the computational cost. We apply the method to linear-response calculations for even-even nuclei. E1 strength distributions for proton-rich Mg isotopes are systematically calculated. The calculation suggests strong Landau damping of giant dipole resonance for drip-line nuclei.
Khvostichenko, Daria; Choi, Andrew; Boulatov, Roman
2008-04-24
We investigated the effect of several computational variables, including the choice of the basis set, application of symmetry constraints, and zero-point energy (ZPE) corrections, on the structural parameters and predicted ground electronic state of model 5-coordinate hemes (iron(II) porphines axially coordinated by a single imidazole or 2-methylimidazole). We studied the performance of B3LYP and B3PW91 with eight Pople-style basis sets (up to 6-311+G*) and B97-1, OLYP, and TPSS functionals with 6-31G and 6-31G* basis sets. Only hybrid functionals B3LYP, B3PW91, and B97-1 reproduced the quintet ground state of the model hemes. With a given functional, the choice of the basis set caused up to 2.7 kcal/mol variation of the quintet-triplet electronic energy gap (DeltaEel), in several cases, resulting in the inversion of the sign of DeltaEel. Single-point energy calculations with triple-zeta basis sets of the Pople (up to 6-311G++(2d,2p)), Ahlrichs (TZVP and TZVPP), and Dunning (cc-pVTZ) families showed the same trend. The zero-point energy of the quintet state was approximately 1 kcal/mol lower than that of the triplet, and accounting for ZPE corrections was crucial for establishing the ground state if the electronic energy of the triplet state was approximately 1 kcal/mol less than that of the quintet. Within a given model chemistry, effects of symmetry constraints and of a "tense" structure of the iron porphine fragment coordinated to 2-methylimidazole on DeltaEel were limited to 0.3 kcal/mol. For both model hemes the best agreement with crystallographic structural data was achieved with small 6-31G and 6-31G* basis sets. Deviation of the computed frequency of the Fe-Im stretching mode from the experimental value with the basis set decreased in the order: nonaugmented basis sets, basis sets with polarization functions, and basis sets with polarization and diffuse functions. Contraction of Pople-style basis sets (double-zeta or triple-zeta) affected the results
Transiently chaotic neural networks with piecewise linear output functions
Energy Technology Data Exchange (ETDEWEB)
Chen, S.-S. [Department of Mathematics, National Taiwan Normal University, Taipei, Taiwan (China); Shih, C.-W. [Department of Applied Mathematics, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu, Taiwan (China)], E-mail: cwshih@math.nctu.edu.tw
2009-01-30
Admitting both transient chaotic phase and convergent phase, the transiently chaotic neural network (TCNN) provides superior performance than the classical networks in solving combinatorial optimization problems. We derive concrete parameter conditions for these two essential dynamic phases of the TCNN with piecewise linear output function. The confirmation for chaotic dynamics of the system results from a successful application of the Marotto theorem which was recently clarified. Numerical simulation on applying the TCNN with piecewise linear output function is carried out to find the optimal solution of a travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function.
A candidate multimodal functional genetic network for thermal adaptation
Directory of Open Access Journals (Sweden)
Katharina C. Wollenberg Valero
2014-09-01
Full Text Available Vertebrate ectotherms such as reptiles provide ideal organisms for the study of adaptation to environmental thermal change. Comparative genomic and exomic studies can recover markers that diverge between warm and cold adapted lineages, but the genes that are functionally related to thermal adaptation may be difficult to identify. We here used a bioinformatics genome-mining approach to predict and identify functions for suitable candidate markers for thermal adaptation in the chicken. We first established a framework of candidate functions for such markers, and then compiled the literature on genes known to adapt to the thermal environment in different lineages of vertebrates. We then identified them in the genomes of human, chicken, and the lizard Anolis carolinensis, and established a functional genetic interaction network in the chicken. Surprisingly, markers initially identified from diverse lineages of vertebrates such as human and fish were all in close functional relationship with each other and more associated than expected by chance. This indicates that the general genetic functional network for thermoregulation and/or thermal adaptation to the environment might be regulated via similar evolutionarily conserved pathways in different vertebrate lineages. We were able to identify seven functions that were statistically overrepresented in this network, corresponding to four of our originally predicted functions plus three unpredicted functions. We describe this network as multimodal: central regulator genes with the function of relaying thermal signal (1, affect genes with different cellular functions, namely (2 lipoprotein metabolism, (3 membrane channels, (4 stress response, (5 response to oxidative stress, (6 muscle contraction and relaxation, and (7 vasodilation, vasoconstriction and regulation of blood pressure. This network constitutes a novel resource for the study of thermal adaptation in the closely related nonavian reptiles and
DEFF Research Database (Denmark)
Klinting, Emil Lund; Thomsen, Bo; Godtliebsen, Ian Heide
. This results in a decreased number of single point calculations required during the potential construction. Especially the Morse-like fit-basis functions are of interest, when combined with rectilinear hybrid optimized and localized coordinates (HOLCs), which can be generated as orthogonal transformations......The overall shape of a molecular energy surface can be very different for different molecules and different vibrational coordinates. This means that the fit-basis functions used to generate an analytic representation of a potential will be met with different requirements. It is therefore worthwhile...... single point calculations when constructing the molecular potential. We therefore present a uniform framework that can handle general fit-basis functions of any type which are specified on input. This framework is implemented to suit the black-box nature of the ADGA in order to avoid arbitrary choices...
Density functional and neural network analysis
DEFF Research Database (Denmark)
Jalkanen, K. J.; Suhai, S.; Bohr, Henrik
1997-01-01
Density functional theory (DFT) calculations have been carried out for hydrated L-alanine, L-alanyl-L-alanine and N-acetyl L-alanine N'-methylamide and examined with respect to the effect of water on the structure, the vibrational frequencies, vibrational absorption (VA) and vibrational circular...
Quan, Austin; Osorio, Ivan; Ohira, Toru; Milton, John
2011-12-01
Resonance can occur in bistable dynamical systems due to the interplay between noise and delay (τ) in the absence of a periodic input. We investigate resonance in a two-neuron model with mutual time-delayed inhibitory feedback. For appropriate choices of the parameters and inputs three fixed-point attractors co-exist: two are stable and one is unstable. In the absence of noise, delay-induced transient oscillations (referred to herein as DITOs) arise whenever the initial function is tuned sufficiently close to the unstable fixed-point. In the presence of noisy perturbations, DITOs arise spontaneously. Since the correlation time for the stationary dynamics is ˜τ, we approximated a higher order Markov process by a three-state Markov chain model by rescaling time as t → 2sτ, identifying the states based on whether the sub-intervals were completely confined to one basin of attraction (the two stable attractors) or straddled the separatrix, and then determining the transition probability matrix empirically. The resultant Markov chain model captured the switching behaviors including the statistical properties of the DITOs. Our observations indicate that time-delayed and noisy bistable dynamical systems are prone to generate DITOs as switches between the two attractors occur. Bistable systems arise transiently in situations when one attractor is gradually replaced by another. This may explain, for example, why seizures in certain epileptic syndromes tend to occur as sleep stages change.
Dissociable intrinsic functional networks support noun-object and verb-action processing.
Yang, Huichao; Lin, Qixiang; Han, Zaizhu; Li, Hongyu; Song, Luping; Chen, Lingjuan; He, Yong; Bi, Yanchao
2017-12-01
The processing mechanism of verbs-actions and nouns-objects is a central topic of language research, with robust evidence for behavioral dissociation. The neural basis for these two major word and/or conceptual classes, however, remains controversial. Two experiments were conducted to study this question from the network perspective. Experiment 1 found that nodes of the same class, obtained through task-evoked brain imaging meta-analyses, were more strongly connected with each other than nodes of different classes during resting-state, forming segregated network modules. Experiment 2 examined the behavioral relevance of these intrinsic networks using data from 88 brain-damaged patients, finding that across patients the relative strength of functional connectivity of the two networks significantly correlated with the noun-object vs. verb-action relative behavioral performances. In summary, we found that verbs-actions and nouns-objects are supported by separable intrinsic functional networks and that the integrity of such networks accounts for the relative noun-object- and verb-action-selective deficits. Copyright © 2017 Elsevier Inc. All rights reserved.
Functional network integrity presages cognitive decline in preclinical Alzheimer disease.
Buckley, Rachel F; Schultz, Aaron P; Hedden, Trey; Papp, Kathryn V; Hanseeuw, Bernard J; Marshall, Gad; Sepulcre, Jorge; Smith, Emily E; Rentz, Dorene M; Johnson, Keith A; Sperling, Reisa A; Chhatwal, Jasmeer P
2017-07-04
To examine the utility of resting-state functional connectivity MRI (rs-fcMRI) measurements of network integrity as a predictor of future cognitive decline in preclinical Alzheimer disease (AD). A total of 237 clinically normal older adults (aged 63-90 years, Clinical Dementia Rating 0) underwent baseline β-amyloid (Aβ) imaging with Pittsburgh compound B PET and structural and rs-fcMRI. We identified 7 networks for analysis, including 4 cognitive networks (default, salience, dorsal attention, and frontoparietal control) and 3 noncognitive networks (primary visual, extrastriate visual, motor). Using linear and curvilinear mixed models, we used baseline connectivity in these networks to predict longitudinal changes in preclinical Alzheimer cognitive composite (PACC) performance, both alone and interacting with Aβ burden. Median neuropsychological follow-up was 3 years. Baseline connectivity in the default, salience, and control networks predicted longitudinal PACC decline, unlike connectivity in the dorsal attention and all noncognitive networks. Default, salience, and control network connectivity was also synergistic with Aβ burden in predicting decline, with combined higher Aβ and lower connectivity predicting the steepest curvilinear decline in PACC performance. In clinically normal older adults, lower functional connectivity predicted more rapid decline in PACC scores over time, particularly when coupled with increased Aβ burden. Among examined networks, default, salience, and control networks were the strongest predictors of rate of change in PACC scores, with the inflection point of greatest decline beyond the fourth year of follow-up. These results suggest that rs-fcMRI may be a useful predictor of early, AD-related cognitive decline in clinical research settings. © 2017 American Academy of Neurology.
DISTRIBUTED RC NETWORKS WITH RATIONAL TRANSFER FUNCTIONS,
A distributed RC circuit analogous to a continuously tapped transmission line can be made to have a rational short-circuit transfer admittance and...one rational shortcircuit driving-point admittance. A subcircuit of the same structure has a rational open circuit transfer impedance and one rational ...open circuit driving-point impedance. Hence, rational transfer functions may be obtained while considering either generator impedance or load
Brookhaven Reactor Experiment Control Facility, a distributed function computer network
International Nuclear Information System (INIS)
Dimmler, D.G.; Greenlaw, N.; Kelley, M.A.; Potter, D.W.; Rankowitz, S.; Stubblefield, F.W.
1975-11-01
A computer network for real-time data acquisition, monitoring and control of a series of experiments at the Brookhaven High Flux Beam Reactor has been developed and has been set into routine operation. This reactor experiment control facility presently services nine neutron spectrometers and one x-ray diffractometer. Several additional experiment connections are in progress. The architecture of the facility is based on a distributed function network concept. A statement of implementation and results is presented
Remote synchronization reveals network symmetries and functional modules.
Nicosia, Vincenzo; Valencia, Miguel; Chavez, Mario; Díaz-Guilera, Albert; Latora, Vito
2013-04-26
We study a Kuramoto model in which the oscillators are associated with the nodes of a complex network and the interactions include a phase frustration, thus preventing full synchronization. The system organizes into a regime of remote synchronization where pairs of nodes with the same network symmetry are fully synchronized, despite their distance on the graph. We provide analytical arguments to explain this result, and we show how the frustration parameter affects the distribution of phases. An application to brain networks suggests that anatomical symmetry plays a role in neural synchronization by determining correlated functional modules across distant locations.
Rational function systems and electrical networks with multiparameters
Lu, KaiSheng
2012-01-01
To overcome the problems of system theory and network theory over real field, this book uses matrices over the field F(z) of rational functions in multiparameters describing coefficient matrices of systems and networks and makes systems and network description over F(z) and researches their structural properties: reducible condition of a class of matrices over F(z) and their characteristic polynomial; type1 matrix and two basic properties; variable replacement conditions for independent parameters; structural controllability and observability of linear systems over F(z); separability, reducibi
Stahn, Kirsten; Lehnertz, Klaus
2017-12-01
We aim at identifying factors that may affect the characteristics of evolving weighted networks derived from empirical observations. To this end, we employ various chains of analysis that are often used in field studies for a data-driven derivation and characterization of such networks. As an example, we consider fully connected, weighted functional brain networks before, during, and after epileptic seizures that we derive from multichannel electroencephalographic data recorded from epilepsy patients. For these evolving networks, we estimate clustering coefficient and average shortest path length in a time-resolved manner. Lastly, we make use of surrogate concepts that we apply at various levels of the chain of analysis to assess to what extent network characteristics are dominated by properties of the electroencephalographic recordings and/or the evolving weighted networks, which may be accessible more easily. We observe that characteristics are differently affected by the unavoidable referencing of the electroencephalographic recording, by the time-series-analysis technique used to derive the properties of network links, and whether or not networks were normalized. Importantly, for the majority of analysis settings, we observe temporal evolutions of network characteristics to merely reflect the temporal evolutions of mean interaction strengths. Such a property of the data may be accessible more easily, which would render the weighted network approach—as used here—as an overly complicated description of simple aspects of the data.
Function of local networks in palliative care: a Dutch view.
Nikbakht-Van de Sande, C V M Vahedi; van der Rijt, C C D; Visser, A Ph; ten Voorde, M A; Pruyn, J F A
2005-08-01
Although network formation is considered an effective method of stimulating the integrated delivery of palliative care, scientific evidence on the usefulness of network formation is scarce. In 1998 the Ministry of Health of The Netherlands started a 5-year stimulation program on palliative care by founding and funding six regional Centres for the Development of Palliative Care. These centers were structured around pivotal organizations such as university hospitals and comprehensive cancer centers. As part of the stimulation program a locoregional network model was introduced within each center for the Development of Palliative Care to integrate palliative care services in the Dutch health care system. We performed a study on network formation in the southwestern area of The Netherlands with 2.4 million inhabitants. The study aimed to answer the following questions: (1) how do networks in palliative care develop, which care providers participate and how do they function? (2) which are the achievements of the palliative care networks as perceived by their participants? (3) which are the success factors of the palliative care networks according to their participants and which factors predict the achievements? Between September 2000 and January 2004 eight local palliative care networks in the region of the Center for Development of Palliative Care-Rotterdam (southwestern area of The Netherlands) were closely followed to gain information on their characteristics and developmental course. At the start of the study semistructured interviews were held with the coordinators of the eight networks. The information from these interviews and from the network documents were used to constitute a questionnaire to assess the opinions and experiences of the network participants. According to the vast majority of responders, the most important reason to install the networks was the lack of integration between the existing local health care services. The networks were initiated to
International Nuclear Information System (INIS)
Guerra, Fabio A.; Coelho, Leandro dos S.
2008-01-01
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting
Functional brain networks develop from a "local to distributed" organization.
Directory of Open Access Journals (Sweden)
Damien A Fair
2009-05-01
Full Text Available The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI, graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength between regions close in anatomical space and 'integration' (an increased correlation strength between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults
Functional brain networks develop from a "local to distributed" organization.
Fair, Damien A; Cohen, Alexander L; Power, Jonathan D; Dosenbach, Nico U F; Church, Jessica A; Miezin, Francis M; Schlaggar, Bradley L; Petersen, Steven E
2009-05-01
The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have
The brain's default network: anatomy, function, and relevance to disease.
Buckner, Randy L; Andrews-Hanna, Jessica R; Schacter, Daniel L
2008-03-01
Thirty years of brain imaging research has converged to define the brain's default network-a novel and only recently appreciated brain system that participates in internal modes of cognition. Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment. Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system. Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others. Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems. The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation. The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations. These two subsystems converge on important nodes of integration including the posterior cingulate cortex. The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world. We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer's disease.
Functional evolution of new and expanded attention networks in humans.
Patel, Gaurav H; Yang, Danica; Jamerson, Emery C; Snyder, Lawrence H; Corbetta, Maurizio; Ferrera, Vincent P
2015-07-28
Macaques are often used as a model system for invasive investigations of the neural substrates of cognition. However, 25 million years of evolution separate humans and macaques from their last common ancestor, and this has likely substantially impacted the function of the cortical networks underlying cognitive processes, such as attention. We examined the homology of frontoparietal networks underlying attention by comparing functional MRI data from macaques and humans performing the same visual search task. Although there are broad similarities, we found fundamental differences between the species. First, humans have more dorsal attention network areas than macaques, indicating that in the course of evolution the human attention system has expanded compared with macaques. Second, potentially homologous areas in the dorsal attention network have markedly different biases toward representing the contralateral hemifield, indicating that the underlying neural architecture of these areas may differ in the most basic of properties, such as receptive field distribution. Third, despite clear evidence of the temporoparietal junction node of the ventral attention network in humans as elicited by this visual search task, we did not find functional evidence of a temporoparietal junction in macaques. None of these differences were the result of differences in training, experimental power, or anatomical variability between the two species. The results of this study indicate that macaque data should be applied to human models of cognition cautiously, and demonstrate how evolution may shape cortical networks.
The structural and functional brain networks that support human social networks.
Noonan, M P; Mars, R B; Sallet, J; Dunbar, R I M; Fellows, L K
2018-02-20
Social skills rely on a specific set of cognitive processes, raising the possibility that individual differences in social networks are related to differences in specific brain structural and functional networks. Here, we tested this hypothesis with multimodality neuroimaging. With diffusion MRI (DMRI), we showed that differences in structural integrity of particular white matter (WM) tracts, including cingulum bundle, extreme capsule and arcuate fasciculus were associated with an individual's social network size (SNS). A voxel-based morphology analysis demonstrated correlations between gray matter (GM) volume and SNS in limbic and temporal lobe regions. These structural changes co-occured with functional network differences. As a function of SNS, dorsomedial and dorsolateral prefrontal cortex showed altered resting-state functional connectivity with the default mode network (DMN). Finally, we integrated these three complementary methods, interrogating the relationship between social GM clusters and specific WM and resting-state networks (RSNs). Probabilistic tractography seeded in these GM nodes utilized the SNS-related WM pathways. Further, the spatial and functional overlap between the social GM clusters and the DMN was significantly closer than other control RSNs. These integrative analyses provide convergent evidence of the role of specific circuits in SNS, likely supporting the adaptive behavior necessary for success in extensive social environments. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.
Functional brain networks underlying detection and integration of disconfirmatory evidence.
Lavigne, Katie M; Metzak, Paul D; Woodward, Todd S
2015-05-15
Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e
Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
Directory of Open Access Journals (Sweden)
Xu Lei
Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.
Directory of Open Access Journals (Sweden)
Sadra Sadeh
2015-06-01
Full Text Available In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational
International Nuclear Information System (INIS)
Feng Weiguo; Wang Hongwei; Wu Xiang
1989-12-01
Based on the real space Correlated-Basis-Functions theory and the collective oscillation behaviour of the electron gas with effective Coulomb interaction, the many body wave function is obtained for the quasi-two-dimensional electron system in the semiconductor inversion layer. The pair-correlation function and the correlation energy of the system have been calculated by the integro-differential method in this paper. The comparison with the other previous theoretical results is also made. The new theoretical approach and its numerical results show that the pair-correlation functions are definitely positive and satisfy the normalization condition. (author). 10 refs, 2 figs
Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen
2018-06-13
Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.
Czech Academy of Sciences Publication Activity Database
Bucha, B.; Bezděk, Aleš; Sebera, Josef; Janak, J.
2015-01-01
Roč. 36, č. 6 (2015), s. 773-801 ISSN 0169-3298 R&D Projects: GA ČR GA13-36843S Grant - others:SAV(SK) VEGA 1/0954/15 Institutional support: RVO:67985815 Keywords : spherical radial basis functions * spherical harmonics * geopotential Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics Impact factor: 3.622, year: 2015
Piret, Cé cile
2012-01-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper
Joosten, S.M.M.; van den Berg, Klaas; Mulder, F.
1990-01-01
Aan de Universiteit Twente is de afgelopen vier jaar geëxperimenteerd met een inleidende kursus in het programmeren, op basis van funktioneel programmeren. Dit artikel gaat in op het waarom van deze aanpak, de doelstelling van het programmeeronderwijs, de opzet en inhoud van de kursusonderdelen en
Hutto, Clayton; Briscoe, Erica; Trewhitt, Ethan
2012-01-01
Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing.
Reveal genes functionally associated with ACADS by a network study.
Chen, Yulong; Su, Zhiguang
2015-09-15
Establishing a systematic network is aimed at finding essential human gene-gene/gene-disease pathway by means of network inter-connecting patterns and functional annotation analysis. In the present study, we have analyzed functional gene interactions of short-chain acyl-coenzyme A dehydrogenase gene (ACADS). ACADS plays a vital role in free fatty acid β-oxidation and regulates energy homeostasis. Modules of highly inter-connected genes in disease-specific ACADS network are derived by integrating gene function and protein interaction data. Among the 8 genes in ACADS web retrieved from both STRING and GeneMANIA, ACADS is effectively conjoined with 4 genes including HAHDA, HADHB, ECHS1 and ACAT1. The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with ACADS are HAHDA, HADHB, ECHS1 and ACAT1. Interestingly, the ontological aspect of genes in the ACADS network reveals that ACADS, HAHDA and HADHB play equally vital roles in fatty acid metabolism. The gene ACAT1 together with ACADS indulges in ketone metabolism. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of ACADS, HAHDA, HADHB, ECHS1 and ACAT1 not only with lipid metabolism but also with infant death syndrome, skeletal myopathy, acute hepatic encephalopathy, Reye-like syndrome, episodic ketosis, and metabolic acidosis. The current study presents a comprehensible layout of ACADS network, its functional strategies and candidate disease approach associated with ACADS network. Copyright © 2015 Elsevier B.V. All rights reserved.
Functional networks inference from rule-based machine learning models.
Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume
2016-01-01
Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The
Enhanced Network Efficiency of Functional Brain Networks in Primary Insomnia Patients
Directory of Open Access Journals (Sweden)
Xiaofen Ma
2018-02-01
Full Text Available Accumulating evidence from neuroimaging studies suggests that primary insomnia (PI affects interregional neural coordination of multiple interacting functional brain networks. However, a complete understanding of the whole-brain network organization from a system-level perspective in PI is still lacking. To this end, we investigated in topological organization changes in brain functional networks in PI. 36 PI patients and 38 age-, sex-, and education-matched healthy controls were recruited. All participants underwent a series of neuropsychological assessments and resting-state functional magnetic resonance imaging scans. Individual whole-brain functional network were constructed and analyzed using graph theory-based network approaches. There were no significant differences with respect to age, sex, or education between groups (P > 0.05. Graph-based analyses revealed that participants with PI had a significantly higher total number of edges (P = 0.022, global efficiency (P = 0.014, and normalized global efficiency (P = 0.002, and a significantly lower normalized local efficiency (P = 0.042 compared with controls. Locally, several prefrontal and parietal regions, the superior temporal gyrus, and the thalamus exhibited higher nodal efficiency in participants with PI (P < 0.05, false discovery rate corrected. In addition, most of these regions showed increased functional connectivity in PI patients (P < 0.05, corrected. Finally, altered network efficiency was correlated with neuropsychological variables of the Epworth Sleepiness Scale and Insomnia Severity Index in patients with PI. PI is associated with abnormal organization of large-scale functional brain networks, which may account for memory and emotional dysfunction in people with PI. These findings provide novel implications for neural substrates associated with PI.
Understanding emergent functions in self-assembled fibrous networks
Sinko, Robert; Keten, Sinan
2015-09-01
Understanding self-assembly processes of nanoscale building blocks and characterizing their properties are both imperative for designing new hierarchical, network materials for a wide range of structural, optoelectrical, and transport applications. Although the characterization and choices of these material building blocks have been well studied, our understanding of how to precisely program a specific morphology through self-assembly still must be significantly advanced. In the recent study by Xie et al (2015 Nanotechnology 26 205602), the self-assembly of end-functionalized nanofibres is investigated using a coarse-grained molecular model and offers fundamental insight into how to control the structural morphology of nanofibrous networks. Varying nanoscale networks are observed when the molecular interaction strength is changed and the findings suggest that self-assembly through the tuning of molecular interactions is a key strategy for designing nanostructured networks with specific topologies.
Construction of functional linkage gene networks by data integration.
Linghu, Bolan; Franzosa, Eric A; Xia, Yu
2013-01-01
Networks of functional associations between genes have recently been successfully used for gene function and disease-related research. A typical approach for constructing such functional linkage gene networks (FLNs) is based on the integration of diverse high-throughput functional genomics datasets. Data integration is a nontrivial task due to the heterogeneous nature of the different data sources and their variable accuracy and completeness. The presence of correlations between data sources also adds another layer of complexity to the integration process. In this chapter we discuss an approach for constructing a human FLN from data integration and a subsequent application of the FLN to novel disease gene discovery. Similar approaches can be applied to nonhuman species and other discovery tasks.
Pro-cognitive drug effects modulate functional brain network organization
Giessing, Carsten; Thiel, Christiane M.
2012-01-01
Previous studies document that cholinergic and noradrenergic drugs improve attention, memory and cognitive control in healthy subjects and patients with neuropsychiatric disorders. In humans neural mechanisms of cholinergic and noradrenergic modulation have mainly been analyzed by investigating drug-induced changes of task-related neural activity measured with functional magnetic resonance imaging (fMRI). Endogenous neural activity has often been neglected. Further, although drugs affect the coupling between neurons, only a few human studies have explicitly addressed how drugs modulate the functional connectome, i.e., the functional neural interactions within the brain. These studies have mainly focused on synchronization or correlation of brain activations. Recently, there are some drug studies using graph theory and other new mathematical approaches to model the brain as a complex network of interconnected processing nodes. Using such measures it is possible to detect not only focal, but also subtle, widely distributed drug effects on functional network topology. Most important, graph theoretical measures also quantify whether drug-induced changes in topology or network organization facilitate or hinder information processing. Several studies could show that functional brain integration is highly correlated with behavioral performance suggesting that cholinergic and noradrenergic drugs which improve measures of cognitive performance should increase functional network integration. The purpose of this paper is to show that graph theory provides a mathematical tool to develop theory-driven biomarkers of pro-cognitive drug effects, and also to discuss how these approaches can contribute to the understanding of the role of cholinergic and noradrenergic modulation in the human brain. Finally we discuss the “global workspace” theory as a theoretical framework of pro-cognitive drug effects and argue that pro-cognitive effects of cholinergic and noradrenergic drugs
DEFF Research Database (Denmark)
Garrido, Pablo; Sørensen, Chres Wiant; Roetter, Daniel Enrique Lucani
2016-01-01
Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages...... a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and computational cost. In addition, it would be difficult to implement, due to the feedback delay. In this work...
An editor for the maintenance and use of a bank of contracted Gaussian basis set functions
International Nuclear Information System (INIS)
Taurian, O.E.
1984-01-01
A bank of basis sets to be used in ab-initio calculations has been created. The bases are sets of contracted Gaussian type orbitals to be used as input to any molecular integral package. In this communication we shall describe the organization of the bank and a portable editor program which was designed for its maintenance and use. This program is operated by commands and it may be used to obtain any kind of information about the bases in the bank as well as to produce output to be directly used as input for different integral programs. The editor may also be used to format basis sets in the conventional way utilized in publications, as well as to generate a complete, or partial, manual of the contents of the bank if so desired. (orig.)
Zhang, Jie; Cheng, Wei; Liu, Zhaowen; Zhang, Kai; Lei, Xu; Yao, Ye; Becker, Benjamin; Liu, Yicen; Kendrick, Keith M; Lu, Guangming; Feng, Jianfeng
2016-08-01
SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the
Cellular and molecular basis of chronic constipation: Taking the functional/idiopathic label out
Bassotti, Gabrio; Villanacci, Vincenzo; Creƫoiu, Dragos; Creƫoiu, Sanda Maria; Becheanu, Gabriel
2013-01-01
In recent years, the improvement of technology and the increase in knowledge have shifted several strongly held paradigms. This is particularly true in gastroenterology, and specifically in the field of the so-called “functional” or “idiopathic” disease, where conditions thought for decades to be based mainly on alterations of visceral perception or aberrant psychosomatic mechanisms have, in fact, be reconducted to an organic basis (or, at the very least, have shown one or more demonstrable a...
Kernel Function Tuning for Single-Layer Neural Networks
Czech Academy of Sciences Publication Activity Database
Vidnerová, Petra; Neruda, Roman
-, accepted 28.11. 2017 (2018) ISSN 2278-0149 R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : single-layer neural networks * kernel methods * kernel function * optimisation Subject RIV: IN - Informatics, Computer Science http://www.ijmerr.com/
Functionality for learning networks: lessons learned from social web applications
Berlanga, Adriana; Sloep, Peter; Brouns, Francis; Van Rosmalen, Peter; Bitter-Rijpkema, Marlies; Koper, Rob
2007-01-01
Berlanga, A. J., Sloep, P., Brouns, F., Van Rosmalen, P., Bitter-Rijpkema, M., & Koper, R. (2007). Functionality for learning networks: lessons learned from social web applications. Proceedings of the ePortfolio 2007 Conference. October, 18-19, 2007, Maastricht, The Netherlands. [See also
Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function.
Directory of Open Access Journals (Sweden)
Tim P Vogels
2013-07-01
Full Text Available While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical □ndings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012.
Virtual Networking Performance in OpenStack Platform for Network Function Virtualization
Directory of Open Access Journals (Sweden)
Franco Callegati
2016-01-01
Full Text Available The emerging Network Function Virtualization (NFV paradigm, coupled with the highly flexible and programmatic control of network devices offered by Software Defined Networking solutions, enables unprecedented levels of network virtualization that will definitely change the shape of future network architectures, where legacy telco central offices will be replaced by cloud data centers located at the edge. On the one hand, this software-centric evolution of telecommunications will allow network operators to take advantage of the increased flexibility and reduced deployment costs typical of cloud computing. On the other hand, it will pose a number of challenges in terms of virtual network performance and customer isolation. This paper intends to provide some insights on how an open-source cloud computing platform such as OpenStack implements multitenant network virtualization and how it can be used to deploy NFV, focusing in particular on packet forwarding performance issues. To this purpose, a set of experiments is presented that refer to a number of scenarios inspired by the cloud computing and NFV paradigms, considering both single tenant and multitenant scenarios. From the results of the evaluation it is possible to highlight potentials and limitations of running NFV on OpenStack.
Topology-function conservation in protein-protein interaction networks.
Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša
2015-05-15
Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.
Intrinsic network activity in tinnitus investigated using functional MRI
Leaver, Amber M.; Turesky, Ted K.; Seydell-Greenwald, Anna; Morgan, Susan; Kim, Hung J.; Rauschecker, Josef P.
2016-01-01
Tinnitus is an increasingly common disorder in which patients experience phantom auditory sensations, usually ringing or buzzing in the ear. Tinnitus pathophysiology has been repeatedly shown to involve both auditory and non-auditory brain structures, making network-level studies of tinnitus critical. In this magnetic resonance imaging (MRI) study, we used two resting-state functional connectivity (RSFC) approaches to better understand functional network disturbances in tinnitus. First, we demonstrated tinnitus-related reductions in RSFC between specific brain regions and resting-state networks (RSNs), defined by independent components analysis (ICA) and chosen for their overlap with structures known to be affected in tinnitus. Then, we restricted ICA to data from tinnitus patients, and identified one RSN not apparent in control data. This tinnitus RSN included auditory-sensory regions like inferior colliculus and medial Heschl’s gyrus, as well as classically non-auditory regions like the mediodorsal nucleus of the thalamus, striatum, lateral prefrontal and orbitofrontal cortex. Notably, patients’ reported tinnitus loudness was positively correlated with RSFC between the mediodorsal nucleus and the tinnitus RSN, indicating that this network may underlie the auditory-sensory experience of tinnitus. These data support the idea that tinnitus involves network dysfunction, and further stress the importance of communication between auditory-sensory and fronto-striatal circuits in tinnitus pathophysiology. PMID:27091485
Naltrexone ameliorates functional network abnormalities in alcohol‐dependent individuals
Baek, Kwangyeol; Tait, Roger; Elliott, Rebecca; Ersche, Karen D.; Flechais, Remy; McGonigle, John; Murphy, Anna; Nestor, Liam J.; Orban, Csaba; Passetti, Filippo; Paterson, Louise M.; Rabiner, Ilan; Reed, Laurence; Smith, Dana; Suckling, John; Taylor, Eleanor M.; Bullmore, Edward T.; Lingford‐Hughes, Anne R.; Deakin, Bill; Nutt, David J.; Sahakian, Barbara J.; Robbins, Trevor W.; Voon, Valerie
2017-01-01
Abstract Naltrexone, an opioid receptor antagonist, is commonly used as a relapse prevention medication in alcohol and opiate addiction, but its efficacy and the mechanisms underpinning its clinical usefulness are not well characterized. In the current study, we examined the effects of 50‐mg naltrexone compared with placebo on neural network changes associated with substance dependence in 21 alcohol and 36 poly‐drug‐dependent individuals compared with 36 healthy volunteers. Graph theoretic and network‐based statistical analysis of resting‐state functional magnetic resonance imaging (MRI) data revealed that alcohol‐dependent subjects had reduced functional connectivity of a dispersed network compared with both poly‐drug‐dependent and healthy subjects. Higher local efficiency was observed in both patient groups, indicating clustered and segregated network topology and information processing. Naltrexone normalized heightened local efficiency of the neural network in alcohol‐dependent individuals, to the same levels as healthy volunteers. Naltrexone failed to have an effect on the local efficiency in abstinent poly‐substance‐dependent individuals. Across groups, local efficiency was associated with substance, but no alcohol exposure implicating local efficiency as a potential premorbid risk factor in alcohol use disorders that can be ameliorated by naltrexone. These findings suggest one possible mechanism for the clinical effects of naltrexone, namely, the amelioration of disrupted network topology. PMID:28247526
Reproducibility of graph metrics of human brain functional networks.
Deuker, Lorena; Bullmore, Edward T; Smith, Marie; Christensen, Soren; Nathan, Pradeep J; Rockstroh, Brigitte; Bassett, Danielle S
2009-10-01
Graph theory provides many metrics of complex network organization that can be applied to analysis of brain networks derived from neuroimaging data. Here we investigated the test-retest reliability of graph metrics of functional networks derived from magnetoencephalography (MEG) data recorded in two sessions from 16 healthy volunteers who were studied at rest and during performance of the n-back working memory task in each session. For each subject's data at each session, we used a wavelet filter to estimate the mutual information (MI) between each pair of MEG sensors in each of the classical frequency intervals from gamma to low delta in the overall range 1-60 Hz. Undirected binary graphs were generated by thresholding the MI matrix and 8 global network metrics were estimated: the clustering coefficient, path length, small-worldness, efficiency, cost-efficiency, assortativity, hierarchy, and synchronizability. Reliability of each graph metric was assessed using the intraclass correlation (ICC). Good reliability was demonstrated for most metrics applied to the n-back data (mean ICC=0.62). Reliability was greater for metrics in lower frequency networks. Higher frequency gamma- and beta-band networks were less reliable at a global level but demonstrated high reliability of nodal metrics in frontal and parietal regions. Performance of the n-back task was associated with greater reliability than measurements on resting state data. Task practice was also associated with greater reliability. Collectively these results suggest that graph metrics are sufficiently reliable to be considered for future longitudinal studies of functional brain network changes.
Bubin, Sergiy; Adamowicz, Ludwik
2008-03-01
In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L =1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.
Bubin, Sergiy; Adamowicz, Ludwik
2008-03-21
In this work we consider explicitly correlated complex Gaussian basis functions for expanding the wave function of an N-particle system with the L=1 total orbital angular momentum. We derive analytical expressions for various matrix elements with these basis functions including the overlap, kinetic energy, and potential energy (Coulomb interaction) matrix elements, as well as matrix elements of other quantities. The derivatives of the overlap, kinetic, and potential energy integrals with respect to the Gaussian exponential parameters are also derived and used to calculate the energy gradient. All the derivations are performed using the formalism of the matrix differential calculus that facilitates a way of expressing the integrals in an elegant matrix form, which is convenient for the theoretical analysis and the computer implementation. The new method is tested in calculations of two systems: the lowest P state of the beryllium atom and the bound P state of the positronium molecule (with the negative parity). Both calculations yielded new, lowest-to-date, variational upper bounds, while the number of basis functions used was significantly smaller than in previous studies. It was possible to accomplish this due to the use of the analytic energy gradient in the minimization of the variational energy.
Leclerc, Arnaud; Thomas, Phillip S.; Carrington, Tucker
2017-08-01
Vibrational spectra and wavefunctions of polyatomic molecules can be calculated at low memory cost using low-rank sum-of-product (SOP) decompositions to represent basis functions generated using an iterative eigensolver. Using a SOP tensor format does not determine the iterative eigensolver. The choice of the interative eigensolver is limited by the need to restrict the rank of the SOP basis functions at every stage of the calculation. We have adapted, implemented and compared different reduced-rank algorithms based on standard iterative methods (block-Davidson algorithm, Chebyshev iteration) to calculate vibrational energy levels and wavefunctions of the 12-dimensional acetonitrile molecule. The effect of using low-rank SOP basis functions on the different methods is analysed and the numerical results are compared with those obtained with the reduced rank block power method. Relative merits of the different algorithms are presented, showing that the advantage of using a more sophisticated method, although mitigated by the use of reduced-rank SOP functions, is noticeable in terms of CPU time.
Niu, Haijing; Wang, Jinhui; Zhao, Tengda; Shu, Ni; He, Yong
2012-01-01
The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
Tansig activation function (of MLP network) for cardiac abnormality detection
Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd
2018-02-01
Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.
DEFF Research Database (Denmark)
Xu, Chunsheng; Zhang, Dongfeng; Tian, Xiaocao
2017-01-01
for cognition with handgrip strength, FTSST, near visual acuity, and number of teeth lost. Cognitive function was genetically related to pulmonary function. The FTSST and cognition shared almost the same common environmental factors but only part of the unique environmental factors, both with negative......Although the correlation between cognition and physical function has been well studied in the general population, the genetic and environmental nature of the correlation has been rarely investigated. We conducted a classical twin analysis on cognitive and physical function, including forced...... and cognitive function. Bivariate analysis showed mildly positively genetic correlations between cognition and FEV1, r G = 0.23 [95% CI: 0.03, 0.62], as well as FVC, r G = 0.35 [95% CI: 0.06, 1.00]. We found that FTSST and cognition presented very high common environmental correlation, r C = -1.00 [95% CI: -1...
The genetic basis for altered blood vessel function in disease: large artery stiffening
Directory of Open Access Journals (Sweden)
Alex Agrotis
2005-12-01
Full Text Available Alex AgrotisThe Cell Biology Laboratory, Baker Heart Research Institute, Melbourne, Victoria, AustraliaAbstract: The progressive stiffening of the large arteries in humans that occurs during aging constitutes a potential risk factor for increased cardiovascular morbidity and mortality, and is accompanied by an elevation in systolic blood pressure and pulse pressure. While the underlying basis for these changes remains to be fully elucidated, factors that are able to influence the structure and composition of the extracellular matrix and the way it interacts with arterial smooth muscle cells could profoundly affect the properties of the large arteries. Thus, while age and sex represent important factors contributing to large artery stiffening, the variation in growth-stimulating factors and those that modulate extracellular production and homeostasis are also being increasingly recognized to play a key role in the process. Therefore, elucidating the contribution that genetic variation makes to large artery stiffening could ultimately provide the basis for clinical strategies designed to regulate the process for therapeutic benefit.Keywords: arterial stiffness, genes, polymorphism, extracellular matrix proteins
The Functional Basis of Wing Patterning in Heliconius Butterflies: The Molecules Behind Mimicry
Kronforst, Marcus R.; Papa, Riccardo
2015-01-01
Wing-pattern mimicry in butterflies has provided an important example of adaptation since Charles Darwin and Alfred Russell Wallace proposed evolution by natural selection >150 years ago. The neotropical butterfly genus Heliconius played a central role in the development of mimicry theory and has since been studied extensively in the context of ecology and population biology, behavior, and mimicry genetics. Heliconius species are notable for their diverse color patterns, and previous crossing experiments revealed that much of this variation is controlled by a small number of large-effect, Mendelian switch loci. Recent comparative analyses have shown that the same switch loci control wing-pattern diversity throughout the genus, and a number of these have now been positionally cloned. Using a combination of comparative genetic mapping, association tests, and gene expression analyses, variation in red wing patterning throughout Heliconius has been traced back to the action of the transcription factor optix. Similarly, the signaling ligand WntA has been shown to control variation in melanin patterning across Heliconius and other butterflies. Our understanding of the molecular basis of Heliconius mimicry is now providing important insights into a variety of additional evolutionary phenomena, including the origin of supergenes, the interplay between constraint and evolvability, the genetic basis of convergence, the potential for introgression to facilitate adaptation, the mechanisms of hybrid speciation in animals, and the process of ecological speciation. PMID:25953905
Forging the Basis for Developing Protein-Ligand Interaction Scoring Functions.
Liu, Zhihai; Su, Minyi; Han, Li; Liu, Jie; Yang, Qifan; Li, Yan; Wang, Renxiao
2017-02-21
In structure-based drug design, scoring functions are widely used for fast evaluation of protein-ligand interactions. They are often applied in combination with molecular docking and de novo design methods. Since the early 1990s, a whole spectrum of protein-ligand interaction scoring functions have been developed. Regardless of their technical difference, scoring functions all need data sets combining protein-ligand complex structures and binding affinity data for parametrization and validation. However, data sets of this kind used to be rather limited in terms of size and quality. On the other hand, standard metrics for evaluating scoring function used to be ambiguous. Scoring functions are often tested in molecular docking or even virtual screening trials, which do not directly reflect the genuine quality of scoring functions. Collectively, these underlying obstacles have impeded the invention of more advanced scoring functions. In this Account, we describe our long-lasting efforts to overcome these obstacles, which involve two related projects. On the first project, we have created the PDBbind database. It is the first database that systematically annotates the protein-ligand complexes in the Protein Data Bank (PDB) with experimental binding data. This database has been updated annually since its first public release in 2004. The latest release (version 2016) provides binding data for 16 179 biomolecular complexes in PDB. Data sets provided by PDBbind have been applied to many computational and statistical studies on protein-ligand interaction and various subjects. In particular, it has become a major data resource for scoring function development. On the second project, we have established the Comparative Assessment of Scoring Functions (CASF) benchmark for scoring function evaluation. Our key idea is to decouple the "scoring" process from the "sampling" process, so scoring functions can be tested in a relatively pure context to reflect their quality. In our
The effects of music on brain functional networks: a network analysis.
Wu, J; Zhang, J; Ding, X; Li, R; Zhou, C
2013-10-10
The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Yu, Shiwei; Wang, Ke; Wei, Yi-Ming
2015-01-01
Highlights: • A hybrid self-adaptive PSO–GA-RBF model is proposed for electricity demand prediction. • Each mixed-coding particle is composed by two coding parts of binary and real. • Five independent variables have been selected to predict future electricity consumption in Wuhan. • The proposed model has a simpler structure or higher estimating precision than other ANN models. • No matter what the scenario, the electricity consumption of Wuhan will grow rapidly. - Abstract: The present study proposes a hybrid Particle Swarm Optimization and Genetic Algorithm optimized Radial Basis Function (PSO–GA-RBF) neural network for prediction of annual electricity demand. In the model, each mixed-coding particle (or chromosome) is composed of two coding parts, binary and real, which optimizes the structure of the RBF by GA operation and the parameters of the basis and weights by a PSO–GA implementation. Five independent variables have been selected to predict future electricity consumption in Wuhan by using optimized networks. The results shows that (1) the proposed PSO–GA-RBF model has a simpler network structure (fewer hidden neurons) or higher estimation precision than other selected ANN models; and (2) no matter what the scenario, the electricity consumption of Wuhan will grow rapidly at average annual growth rates of about 9.7–11.5%. By 2020, the electricity demand in the planning scenario, the highest among the scenarios, will be 95.85 billion kW h. The lowest demand is estimated for the business-as-usual scenario, and will be 88.45 billion kW h
Development of a Technical Basis and Guidance for Advanced SMR Function Allocation
Energy Technology Data Exchange (ETDEWEB)
Jacques Hugo; David Gertman; Jeffrey Joe; Ronal Farris; April Whaley; Heather Medema
2013-09-01
This report presents the results from three key activities for FY13 that influence the definition of new concepts of operations for advanced Small Modular Reactors (AdvSMR: a) the development of a framework for the analysis of the functional environmental, and structural attributes, b) the effect that new technologies and operational concepts would have on the way functions are allocated to humans or machines or combinations of the two, and c) the relationship between new concepts of operations, new function allocations, and human performance requirements.
International Nuclear Information System (INIS)
Child, M S; Hiyama, M
2007-01-01
It is shown that the inherent arbitrariness in the construction of basis functions in quantum defect theory allows a choice that eliminates the occurrence of false roots of the quantization condition, with energies below the minimum of the channel potential. Comparisons are given with the well-known Ham procedure and with the more recent generalization to arbitrary fields by Jungen and Texier. The significance of the results for ab initio R matrix/MQDT studies is also discussed
Molecular Basis of Cardiac Delayed Rectifier Potassium Channel Function and Pharmacology.
Wu, Wei; Sanguinetti, Michael C
2016-06-01
Human cardiomyocytes express 3 distinct types of delayed rectifier potassium channels. Human ether-a-go-go-related gene (hERG) channels conduct the rapidly activating current IKr; KCNQ1/KCNE1 channels conduct the slowly activating current IKs; and Kv1.5 channels conduct an ultrarapid activating current IKur. Here the authors provide a general overview of the mechanistic and structural basis of ion selectivity, gating, and pharmacology of the 3 types of cardiac delayed rectifier potassium ion channels. Most blockers bind to S6 residues that line the central cavity of the channel, whereas activators interact with the channel at 4 symmetric binding sites outside the cavity. Copyright © 2016 Elsevier Inc. All rights reserved.
Lee, Dongha; Pae, Chongwon; Lee, Jong Doo; Park, Eun Sook; Cho, Sung-Rae; Um, Min-Hee; Lee, Seung-Koo; Oh, Maeng-Keun; Park, Hae-Jeong
2017-10-01
Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure-function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural-functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure-function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure-function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp 38:5292-5306, 2017. © 2017 Wiley Periodicals
Kolmann, Stephen J.; Jordan, Meredith J. T.
2010-02-01
One of the largest remaining errors in thermochemical calculations is the determination of the zero-point energy (ZPE). The fully coupled, anharmonic ZPE and ground state nuclear wave function of the SSSH radical are calculated using quantum diffusion Monte Carlo on interpolated potential energy surfaces (PESs) constructed using a variety of method and basis set combinations. The ZPE of SSSH, which is approximately 29 kJ mol-1 at the CCSD(T)/6-31G∗ level of theory, has a 4 kJ mol-1 dependence on the treatment of electron correlation. The anharmonic ZPEs are consistently 0.3 kJ mol-1 lower in energy than the harmonic ZPEs calculated at the Hartree-Fock and MP2 levels of theory, and 0.7 kJ mol-1 lower in energy at the CCSD(T)/6-31G∗ level of theory. Ideally, for sub-kJ mol-1 thermochemical accuracy, ZPEs should be calculated using correlated methods with as big a basis set as practicable. The ground state nuclear wave function of SSSH also has significant method and basis set dependence. The analysis of the nuclear wave function indicates that SSSH is localized to a single symmetry equivalent global minimum, despite having sufficient ZPE to be delocalized over both minima. As part of this work, modifications to the interpolated PES construction scheme of Collins and co-workers are presented.
Kolmann, Stephen J; Jordan, Meredith J T
2010-02-07
One of the largest remaining errors in thermochemical calculations is the determination of the zero-point energy (ZPE). The fully coupled, anharmonic ZPE and ground state nuclear wave function of the SSSH radical are calculated using quantum diffusion Monte Carlo on interpolated potential energy surfaces (PESs) constructed using a variety of method and basis set combinations. The ZPE of SSSH, which is approximately 29 kJ mol(-1) at the CCSD(T)/6-31G* level of theory, has a 4 kJ mol(-1) dependence on the treatment of electron correlation. The anharmonic ZPEs are consistently 0.3 kJ mol(-1) lower in energy than the harmonic ZPEs calculated at the Hartree-Fock and MP2 levels of theory, and 0.7 kJ mol(-1) lower in energy at the CCSD(T)/6-31G* level of theory. Ideally, for sub-kJ mol(-1) thermochemical accuracy, ZPEs should be calculated using correlated methods with as big a basis set as practicable. The ground state nuclear wave function of SSSH also has significant method and basis set dependence. The analysis of the nuclear wave function indicates that SSSH is localized to a single symmetry equivalent global minimum, despite having sufficient ZPE to be delocalized over both minima. As part of this work, modifications to the interpolated PES construction scheme of Collins and co-workers are presented.
van Veenendaal, T.M.; IJff, D.M.; Aldenkamp, A.P.; Lazeron, R.H.C.; Hofman, P.A.M.; de Louw, A.J.A.; Backes, W.H.; Jansen, J.F.A.
2017-01-01
AIM: To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment. METHODS: The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients
Directory of Open Access Journals (Sweden)
M. B. Guzairov
2011-06-01
Full Text Available The threats matrix construction on the basis of the access matrixes is discussed. Development of threats model on the basis of fuzzy cognitive maps displaying the threats spreading pathways from attack sources to objects is described.
multi scale analysis of a function by neural networks elementary derivatives functions
International Nuclear Information System (INIS)
Chikhi, A.; Gougam, A.; Chafa, F.
2006-01-01
Recently, the wavelet network has been introduced as a special neural network supported by the wavelet theory . Such networks constitute a tool for function approximation problems as it has been already proved in reference . Our present work deals with this model, treating a multi scale analysis of a function. We have then used a linear expansion of a given function in wavelets, neglecting the usual translation parameters. We investigate two training operations. The first one consists on an optimization of the output synaptic layer, the second one, optimizing the output function with respect to scale parameters. We notice a temporary merging of the scale parameters leading to some interesting results : new elementary derivatives units emerge, representing a new elementary task, which is the derivative of the output task
Population spikes in cortical networks during different functional states.
Directory of Open Access Journals (Sweden)
Shirley eMark
2012-07-01
Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.
Structure, function, and control of the human musculoskeletal network.
Directory of Open Access Journals (Sweden)
Andrew C Murphy
2018-01-01
Full Text Available The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle's role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.
High frequency of functional extinctions in ecological networks.
Säterberg, Torbjörn; Sellman, Stefan; Ebenman, Bo
2013-07-25
Intensified exploitation of natural populations and habitats has led to increased mortality rates and decreased abundances of many species. There is a growing concern that this might cause critical abundance thresholds of species to be crossed, with extinction cascades and state shifts in ecosystems as a consequence. When increased mortality rate and decreased abundance of a given species lead to extinction of other species, this species can be characterized as functionally extinct even though it still exists. Although such functional extinctions have been observed in some ecosystems, their frequency is largely unknown. Here we use a new modelling approach to explore the frequency and pattern of functional extinctions in ecological networks. Specifically, we analytically derive critical abundance thresholds of species by increasing their mortality rates until an extinction occurs in the network. Applying this approach on natural and theoretical food webs, we show that the species most likely to go extinct first is not the one whose mortality rate is increased but instead another species. Indeed, up to 80% of all first extinctions are of another species, suggesting that a species' ecological functionality is often lost before its own existence is threatened. Furthermore, we find that large-bodied species at the top of the food chains can only be exposed to small increases in mortality rate and small decreases in abundance before going functionally extinct compared to small-bodied species lower in the food chains. These results illustrate the potential importance of functional extinctions in ecological networks and lend strong support to arguments advocating a more community-oriented approach in conservation biology, with target levels for populations based on ecological functionality rather than on mere persistence.
Decidable and undecidable arithmetic functions in actin filament networks
Schumann, Andrew
2018-01-01
The plasmodium of Physarum polycephalum is very sensitive to its environment, and reacts to stimuli with appropriate motions. Both the sensory and motor stages of these reactions are explained by hydrodynamic processes, based on fluid dynamics, with the participation of actin filament networks. This paper is devoted to actin filament networks as a computational medium. The point is that actin filaments, with contributions from many other proteins like myosin, are sensitive to extracellular stimuli (attractants as well as repellents), and appear and disappear at different places in the cell to change aspects of the cell structure—e.g. its shape. By assembling and disassembling actin filaments, some unicellular organisms, like Amoeba proteus, can move in response to various stimuli. As a result, these organisms can be considered a simple reversible logic gate—extracellular signals being its inputs and motions its outputs. In this way, we can implement various logic gates on amoeboid behaviours. These networks can embody arithmetic functions within p-adic valued logic. Furthermore, within these networks we can define the so-called diagonalization for deducing undecidable arithmetic functions.
Pupil filter design by using a Bessel functions basis at the image plane.
Canales, Vidal F; Cagigal, Manuel P
2006-10-30
Many applications can benefit from the use of pupil filters for controlling the light intensity distribution near the focus of an optical system. Most of the design methods for such filters are based on a second-order expansion of the Point Spread Function (PSF). Here, we present a new procedure for designing radially-symmetric pupil filters. It is more precise than previous procedures as it considers the exact expression of the PSF, expanded as a function of first-order Bessel functions. Furthermore, this new method presents other advantages: the height of the side lobes can be easily controlled, it allows the design of amplitude-only, phase-only or hybrid filters, and the coefficients of the PSF expansion can be directly related to filter parameters. Finally, our procedure allows the design of filters with very different behaviours and optimal performance.
Concepts of soil mapping as a basis for the assessment of soil functions
Baumgarten, Andreas
2014-05-01
Soil mapping systems in Europe have been designed mainly as a tool for the description of soil characteristics from a morphogenetic viewpoint. Contrasting to the American or FAO system, the soil development has been in the main focus of European systems. Nevertheless , recent developments in soil science stress the importance of the functions of soils with respect to the ecosystems. As soil mapping systems usually offer a sound and extensive database, the deduction of soil functions from "classic" mapping parameters can be used for local and regional assessments. According to the used pedo-transfer functions and mapping systems, tailored approaches can be chosen for different applications. In Austria, a system mainly for spatial planning purposes has been developed that will be presented and illustrated by means of best practice examples.
Comparison of Feedforward Network and Radial Basis Function to Detect Leukemia
Directory of Open Access Journals (Sweden)
Pragya Bagwari
2017-08-01
Full Text Available Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.
Identification of Functional Information Subgraphs in Complex Networks
International Nuclear Information System (INIS)
Bettencourt, Luis M. A.; Gintautas, Vadas; Ham, Michael I.
2008-01-01
We present a general information theoretic approach for identifying functional subgraphs in complex networks. We show that the uncertainty in a variable can be written as a sum of information quantities, where each term is generated by successively conditioning mutual informations on new measured variables in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient optimization algorithms for determining the state of a target variable in terms of functional groups of other nodes. We apply this methodology to electrophysiological recordings of cortical neuronal networks grown in vitro. Each cell's firing is generally explained by the activity of a few neurons. We identify these neuronal subgraphs in terms of their redundant or synergetic character and reconstruct neuronal circuits that account for the state of target cells
Santra, Biswajit; Michaelides, Angelos; Scheffler, Matthias
2007-11-01
The ability of several density-functional theory (DFT) exchange-correlation functionals to describe hydrogen bonds in small water clusters (dimer to pentamer) in their global minimum energy structures is evaluated with reference to second order Møller-Plesset perturbation theory (MP2). Errors from basis set incompleteness have been minimized in both the MP2 reference data and the DFT calculations, thus enabling a consistent systematic evaluation of the true performance of the tested functionals. Among all the functionals considered, the hybrid X3LYP and PBE0 functionals offer the best performance and among the nonhybrid generalized gradient approximation functionals, mPWLYP and PBE1W perform best. The popular BLYP and B3LYP functionals consistently underbind and PBE and PW91 display rather variable performance with cluster size.
The molecular basis of convergence in hemoglobin function in high-altitude Andean birds
DEFF Research Database (Denmark)
Storz, Jay; Natarajan, Chandrasekhar; Witt, Christopher C.
2016-01-01
Max Perutz predicted that adaptive changes in functional properties of vertebrate hemoglobin (Hb) would typically be attributable to a small number of substitutions at key residue positions (1983, Mol. Biol. Evol.). According to Perutz, amino acid substitutions that can be expected to make especi...
Modeling of the Gross Regional Product on the Basis of Production Functions
Sadovin, Nikolay S.; Kokotkina, Tatiana N.; Barkalova, Tatiana G.; Tsaregorodsev, Evgeny I.
2016-01-01
The article is devoted to elaboration and construction of a static model of macroeconomics in which economics is considered as an unstructured holistic unit, the input of which receives the resources, and the output is the result of the functioning of economics in the form of gross domestic product or gross regional product. Resources are…
Kryven, I.; Röblitz, S; Schütte, C.
2015-01-01
Background: The chemical master equation is the fundamental equation of stochastic chemical kinetics. This differential-difference equation describes temporal evolution of the probability density function for states of a chemical system. A state of the system, usually encoded as a vector, represents
Extracting functionally feedforward networks from a population of spiking neurons.
Vincent, Kathleen; Tauskela, Joseph S; Thivierge, Jean-Philippe
2012-01-01
Neuronal avalanches are a ubiquitous form of activity characterized by spontaneous bursts whose size distribution follows a power-law. Recent theoretical models have replicated power-law avalanches by assuming the presence of functionally feedforward connections (FFCs) in the underlying dynamics of the system. Accordingly, avalanches are generated by a feedforward chain of activation that persists despite being embedded in a larger, massively recurrent circuit. However, it is unclear to what extent networks of living neurons that exhibit power-law avalanches rely on FFCs. Here, we employed a computational approach to reconstruct the functional connectivity of cultured cortical neurons plated on multielectrode arrays (MEAs) and investigated whether pharmacologically induced alterations in avalanche dynamics are accompanied by changes in FFCs. This approach begins by extracting a functional network of directed links between pairs of neurons, and then evaluates the strength of FFCs using Schur decomposition. In a first step, we examined the ability of this approach to extract FFCs from simulated spiking neurons. The strength of FFCs obtained in strictly feedforward networks diminished monotonically as links were gradually rewired at random. Next, we estimated the FFCs of spontaneously active cortical neuron cultures in the presence of either a control medium, a GABA(A) receptor antagonist (PTX), or an AMPA receptor antagonist combined with an NMDA receptor antagonist (APV/DNQX). The distribution of avalanche sizes in these cultures was modulated by this pharmacology, with a shallower power-law under PTX (due to the prominence of larger avalanches) and a steeper power-law under APV/DNQX (due to avalanches recruiting fewer neurons) relative to control cultures. The strength of FFCs increased in networks after application of PTX, consistent with an amplification of feedforward activity during avalanches. Conversely, FFCs decreased after application of APV
Methylphenidate Modulates Functional Network Connectivity to Enhance Attention
Rosenberg, Monica D.; Zhang, Sheng; Hsu, Wei-Ting; Scheinost, Dustin; Finn, Emily S.; Shen, Xilin; Constable, R. Todd; Li, Chiang-Shan R.; Chun, Marvin M.
2016-01-01
Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained...
Model Complexities of Shallow Networks Representing Highly Varying Functions
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Sanguineti, M.
2016-01-01
Roč. 171, 1 January (2016), s. 598-604 ISSN 0925-2312 R&D Projects: GA MŠk(CZ) LD13002 Grant - others:grant for Visiting Professors(IT) GNAMPA-INdAM Institutional support: RVO:67985807 Keywords : shallow networks * model complexity * highly varying functions * Chernoff bound * perceptrons * Gaussian kernel units Subject RIV: IN - Informatics, Computer Science Impact factor: 3.317, year: 2016
International Nuclear Information System (INIS)
Keppler, Jan Horst; Meunier, William; Coquentin, Alexandre
2017-01-01
Interconnections for cross-border electricity flows are at the heart of the project to create a common European electricity market. At the time, increase in production from variable renewables clustered during a limited numbers of hours reduces the availability of existing transport infrastructures. This calls for higher levels of optimal interconnection capacity than in the past. In complement to existing scenario-building exercises such as the TYNDP that respond to the challenge of determining optimal levels of infrastructure provision, the present paper proposes a new empirically-based methodology to perform Cost-Benefit analysis for the determination of optimal interconnection capacity, using as an example the French-German cross-border trade. Using a very fine dataset of hourly supply and demand curves (aggregated auction curves) for the year 2014 from the EPEX Spot market, it constructs linearized net export (NEC) and net import demand curves (NIDC) for both countries. This allows assessing hour by hour the welfare impacts for incremental increases in interconnection capacity. Summing these welfare increases over the 8 760 hours of the year, this provides the annual total for each step increase of interconnection capacity. Confronting welfare benefits with the annual cost of augmenting interconnection capacity indicated the socially optimal increase in interconnection capacity between France and Germany on the basis of empirical market micro-data. (authors)
Experimental demonstrations of all-optical networking functions for WDM optical networks
Gurkan, Deniz
The deployment of optical networks will enable high capacity links between users but will introduce the problems associated with transporting and managing more channels. Many network functions should be implemented in optical domain; main reasons are: to avoid electronic processing bottlenecks, to achieve data-format and data-rate independence, to provide reliable and cost efficient control and management information, to simultaneously process multiple wavelength channel operation for wavelength division multiplexed (WDM) optical networks. The following novel experimental demonstrations of network functions in the optical domain are presented: Variable-bit-rate recognition of the header information in a data packet. The technique is reconfigurable for different header sequences and uses optical correlators as look-up tables. The header is processed and a signal is sent to the switch for a series of incoming data packets at 155 Mb/s, 622 Mb/s, and 2.5 Gb/s in a reconfigurable network. Simultaneous optical time-slot-interchange and wavelength conversion of the bits in a 2.5-Gb/s data stream to achieve a reconfigurable time/wavelength switch. The technique uses difference-frequency-generation (DFG) for wavelength conversion and fiber Bragg gratings (FBG) as wavelength-dependent optical time buffers. The WDM header recognition module simultaneously recognizing two header bits on each of two 2.5-Gbit/s WDM packet streams. The module is tunable to enable reconfigurable look-up tables. Simultaneous and independent label swapping and wavelength conversion of two WDM channels for a multi-protocol label switching (MPLS) network. Demonstration of label swapping of distinct 8-bit-long labels for two WDM data channels is presented. Two-dimensional code conversion module for an optical code-division multiple-access (O-CDMA) local area network (LAN) system. Simultaneous wavelength conversion and time shifting is achieved to enable flexible code conversion and increase code re
Sripada, Rebecca K; Swain, James E; Evans, Gary W; Welsh, Robert C; Liberzon, Israel
2014-08-01
Convergent research suggests that childhood poverty is associated with perturbation in the stress response system. This might extend to aberrations in the connectivity of large-scale brain networks, which subserve key cognitive and emotional functions. Resting-state brain activity was measured in adults with a documented history of childhood poverty (n=26) and matched controls from middle-income families (n=26). Participants also underwent a standard laboratory social stress test and provided saliva samples for cortisol assay. Childhood poverty was associated with reduced default mode network (DMN) connectivity. This, in turn, was associated with higher cortisol levels in anticipation of social stress. These results suggest a possible brain basis for exaggerated stress sensitivity in low-income individuals. Alterations in DMN may be associated with less efficient cognitive processing or greater risk for development of stress-related psychopathology among individuals who experienced the adversity of chronic childhood poverty.
Extensible framework for elastic orchestration of service function chains in 5G networks
Medhat, Ahmed M.; Carella, Guiseppe Antonio; Pauls, Michael; Magedanz, Thomas
2017-01-01
With the evolution towards the Fifth Generation of Mobile Communications (5G), Software-based Networks are paving the way to a radical transformation of Network Operators infrastructures. Novel technologies like Network Function Virtualisation (NFV) and Software Defined Network (SDN) are enabling new way of managing on-demand network resources. Focusing on the Mobile Core Network architecture, Service Function Chaining (SFC) is foreseen to be the solution for dynamically routing traffic acros...
The Neural Basis of Mark Making: A Functional MRI Study of Drawing
Yuan, Ye; Brown, Steven
2014-01-01
Compared to most other forms of visually-guided motor activity, drawing is unique in that it “leaves a trail behind” in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet's surface. The critical comparison was between when visual feedback was displayed (image generation) versus when it was not (no image generation). This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time. PMID:25271440
The neural basis of mark making: a functional MRI study of drawing.
Directory of Open Access Journals (Sweden)
Ye Yuan
Full Text Available Compared to most other forms of visually-guided motor activity, drawing is unique in that it "leaves a trail behind" in the form of the emanating image. We took advantage of an MRI-compatible drawing tablet in order to examine both the motor production and perceptual emanation of images. Subjects participated in a series of mark making tasks in which they were cued to draw geometric patterns on the tablet's surface. The critical comparison was between when visual feedback was displayed (image generation versus when it was not (no image generation. This contrast revealed an occipito-parietal stream involved in motion-based perception of the emerging image, including areas V5/MT+, LO, V3A, and the posterior part of the intraparietal sulcus. Interestingly, when subjects passively viewed animations of visual patterns emerging on the projected surface, all of the sensorimotor network involved in drawing was strongly activated, with the exception of the primary motor cortex. These results argue that the origin of the human capacity to draw and write involves not only motor skills for tool use but also motor-sensory links between drawing movements and the visual images that emanate from them in real time.
SoyFN: a knowledge database of soybean functional networks.
Xu, Yungang; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Liu, Yang
2014-01-01
Many databases for soybean genomic analysis have been built and made publicly available, but few of them contain knowledge specifically targeting the omics-level gene-gene, gene-microRNA (miRNA) and miRNA-miRNA interactions. Here, we present SoyFN, a knowledge database of soybean functional gene networks and miRNA functional networks. SoyFN provides user-friendly interfaces to retrieve, visualize, analyze and download the functional networks of soybean genes and miRNAs. In addition, it incorporates much information about KEGG pathways, gene ontology annotations and 3'-UTR sequences as well as many useful tools including SoySearch, ID mapping, Genome Browser, eFP Browser and promoter motif scan. SoyFN is a schema-free database that can be accessed as a Web service from any modern programming language using a simple Hypertext Transfer Protocol call. The Web site is implemented in Java, JavaScript, PHP, HTML and Apache, with all major browsers supported. We anticipate that this database will be useful for members of research communities both in soybean experimental science and bioinformatics. Database URL: http://nclab.hit.edu.cn/SoyFN.
The neuropathological basis to the functional role of microglia/macrophages in gliomas.
Schiffer, Davide; Mellai, Marta; Bovio, Enrica; Annovazzi, Laura
2017-09-01
The paper wants to be a tracking shot of the main recent acquisitions on the function and significance of microglia/macrophages in gliomas. The observations have been principally carried out on in vitro cultures and on tumor transplants in animals. Contrary to what is deduced from microglia in non-neoplastic pathologic conditions of central nervous system (CNS), most conclusions indicate that microglia acts favoring tumor proliferation through an immunosuppression induced by glioma cells. By immunohistochemistry, different microglia phenotypes are recognized in gliomas, from ramified microglia to frank macrophagic aspect. One wonders whether the functional conclusions drawn from many microglia studies, but not in conditions of human pathology, apply to all the phenotypes recognizable in them. It is difficult to verify in human pathology a prognostic significance of microglia. Only CD163-positive microglia/macrophages inversely correlate with glioma patients' survival, whereas the total number of microglia does not change with the malignancy grade.
Modularity-like objective function in annotated networks
Xie, Jia-Rong; Wang, Bing-Hong
2017-12-01
We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.
Plant functional modelling as a basis for assessing the impact of management on plant safety
International Nuclear Information System (INIS)
Rasmussen, Birgitte; Petersen, Kurt E.
1999-01-01
A major objective of the present work is to provide means for representing a chemical process plant as a socio-technical system, so as to allow hazard identification at a high level in order to identify major targets for safety development. The main phases of the methodology are: (1) preparation of a plant functional model where a set of plant functions describes coherently hardware, software, operations, work organization and other safety related aspects. The basic principle is that any aspect of the plant can be represented by an object based upon an Intent and associated with each Intent are Methods, by which the Intent is realized, and Constraints, which limit the Intent. (2) Plant level hazard identification based on keywords/checklists and the functional model. (3) Development of incident scenarios and selection of hazardous situation with different safety characteristics. (4) Evaluation of the impact of management on plant safety through interviews. (5) Identification of safety critical ways of action in the management system, i.e. identification of possible error- and violation-producing conditions
Energy Technology Data Exchange (ETDEWEB)
Jacques Hugo; John Forester; David Gertman; Jeffrey Joe; Heather Medema; Julius Persensky; April Whaley
2013-04-01
This report presents preliminary research results from the investigation in to the development of new models and guidance for concepts of operations (ConOps) in advanced small modular reactor (aSMR) designs. In support of this objective, three important research areas were included: operating principles of multi-modular plants, functional allocation models and strategies that would affect the development of new, non-traditional concept of operations, and the requiremetns for human performance, based upon work domain analysis and current regulatory requirements. As part of the approach for this report, we outline potential functions, including the theoretical and operational foundations for the development of a new functional allocation model and the identification of specific regulatory requirements that will influence the development of future concept of operations. The report also highlights changes in research strategy prompted by confirmationof the importance of applying the work domain analysis methodology to a reference aSMR design. It is described how this methodology will enrich the findings from this phase of the project in the subsequent phases and help in identification of metrics and focused studies for the determination of human performance criteria that can be used to support the design process.
Zhong, Jidan; Rifkin-Graboi, Anne; Ta, Anh Tuan; Yap, Kar Lai; Chuang, Kai-Hsiang; Meaney, Michael J; Qiu, Anqi
2014-07-01
Children begin performing similarly to adults on tasks requiring executive functions in late childhood, a transition that is probably due to neuroanatomical fine-tuning processes, including myelination and synaptic pruning. In parallel to such structural changes in neuroanatomical organization, development of functional organization may also be associated with cognitive behaviors in children. We examined 6- to 10-year-old children's cortical thickness, functional organization, and cognitive performance. We used structural magnetic resonance imaging (MRI) to identify areas with cortical thinning, resting-state fMRI to identify functional organization in parallel to cortical development, and working memory/response inhibition tasks to assess executive functioning. We found that neuroanatomical changes in the form of cortical thinning spread over bilateral frontal, parietal, and occipital regions. These regions were engaged in 3 functional networks: sensorimotor and auditory, executive control, and default mode network. Furthermore, we found that working memory and response inhibition only associated with regional functional connectivity, but not topological organization (i.e., local and global efficiency of information transfer) of these functional networks. Interestingly, functional connections associated with "bottom-up" as opposed to "top-down" processing were more clearly related to children's performance on working memory and response inhibition, implying an important role for brain systems involved in late childhood. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning
Directory of Open Access Journals (Sweden)
Menizibeya O. Welcome
2015-01-01
Full Text Available Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning.
Virtual Routing Function Allocation Method for Minimizing Total Network Power Consumption
Kenichiro Hida; Shin-Ichi Kuribayashi
2016-01-01
In a conventional network, most network devices, such as routers, are dedicated devices that do not have much variation in capacity. In recent years, a new concept of network functions virtualisation (NFV) has come into use. The intention is to implement a variety of network functions with software on general-purpose servers and this allows the network operator to select their capacities and locations without any constraints. This paper focuses on the allocation of NFV-based routing functions...
Directory of Open Access Journals (Sweden)
Young-Bo Sim
2017-11-01
Full Text Available In this paper, we proposed and developed Function-Oriented Networking (FON, a platform for network users. It has a different philosophy as opposed to technologies for network managers of Software-Defined Networking technology, OpenFlow. It is a technology that can immediately reflect the demands of the network users in the network, unlike the existing OpenFlow and Network Functions Virtualization (NFV, which do not reflect directly the needs of the network users. It allows the network user to determine the policy of the direct network, so it can be applied more precisely than the policy applied by the network manager. This is expected to increase the satisfaction of the service users when the network users try to provide new services. We developed FON function that performs on-demand routing for Low-Delay Required service. We analyzed the characteristics of the Ant Colony Optimization (ACO algorithm and found that the algorithm is suitable for low-delay required services. It was also the first in the world to implement the routing software using ACO Algorithm in the real Ethernet network. In order to improve the routing performance, several algorithms of the ACO Algorithm have been developed to enable faster path search-routing and path recovery. The relationship between the network performance index and the ACO routing parameters is derived, and the results are compared and analyzed. Through this, it was possible to develop the ACO algorithm.
A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions
Pan, Guang; Ye, Pengcheng; Yang, Zhidong
2014-01-01
Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples. PMID:25133206
Quetiapine modulates functional connectivity in brain aggression networks.
Klasen, Martin; Zvyagintsev, Mikhail; Schwenzer, Michael; Mathiak, Krystyna A; Sarkheil, Pegah; Weber, René; Mathiak, Klaus
2013-07-15
Aggressive behavior is associated with dysfunctions in an affective regulation network encompassing amygdala and prefrontal areas such as orbitofrontal (OFC), anterior cingulate (ACC), and dorsolateral prefrontal cortex (DLPFC). In particular, prefrontal regions have been postulated to control amygdala activity by inhibitory projections, and this process may be disrupted in aggressive individuals. The atypical antipsychotic quetiapine successfully attenuates aggressive behavior in various disorders; the underlying neural processes, however, are unknown. A strengthened functional coupling in the prefrontal-amygdala system may account for these anti-aggressive effects. An inhibition of this network has been reported for virtual aggression in violent video games as well. However, there have been so far no in-vivo observations of pharmacological influences on corticolimbic projections during human aggressive behavior. In a double-blind, placebo-controlled study, quetiapine and placebo were administered for three successive days prior to an fMRI experiment. In this experiment, functional brain connectivity was assessed during virtual aggressive behavior in a violent video game and an aggression-free control task in a non-violent modification. Quetiapine increased the functional connectivity of ACC and DLPFC with the amygdala during virtual aggression, whereas OFC-amygdala coupling was attenuated. These effects were observed neither for placebo nor for the non-violent control. These results demonstrate for the first time a pharmacological modification of aggression-related human brain networks in a naturalistic setting. The violence-specific modulation of prefrontal-amygdala networks appears to control aggressive behavior and provides a neurobiological model for the anti-aggressive effects of quetiapine. Copyright © 2013 Elsevier Inc. All rights reserved.
Brandenburg, Jan Gerit; Alessio, Maristella; Civalleri, Bartolomeo; Peintinger, Michael F; Bredow, Thomas; Grimme, Stefan
2013-09-26
We extend the previously developed geometrical correction for the inter- and intramolecular basis set superposition error (gCP) to periodic density functional theory (DFT) calculations. We report gCP results compared to those from the standard Boys-Bernardi counterpoise correction scheme and large basis set calculations. The applicability of the method to molecular crystals as the main target is tested for the benchmark set X23. It consists of 23 noncovalently bound crystals as introduced by Johnson et al. (J. Chem. Phys. 2012, 137, 054103) and refined by Tkatchenko et al. (J. Chem. Phys. 2013, 139, 024705). In order to accurately describe long-range electron correlation effects, we use the standard atom-pairwise dispersion correction scheme DFT-D3. We show that a combination of DFT energies with small atom-centered basis sets, the D3 dispersion correction, and the gCP correction can accurately describe van der Waals and hydrogen-bonded crystals. Mean absolute deviations of the X23 sublimation energies can be reduced by more than 70% and 80% for the standard functionals PBE and B3LYP, respectively, to small residual mean absolute deviations of about 2 kcal/mol (corresponding to 13% of the average sublimation energy). As a further test, we compute the interlayer interaction of graphite for varying distances and obtain a good equilibrium distance and interaction energy of 6.75 Å and -43.0 meV/atom at the PBE-D3-gCP/SVP level. We fit the gCP scheme for a recently developed pob-TZVP solid-state basis set and obtain reasonable results for the X23 benchmark set and the potential energy curve for water adsorption on a nickel (110) surface.
Thermal Remote Sensing: A Powerful Tool in the Characterization of Landscapes on a Functional Basis
Jeffrey, Luvall C.; Kay, James; Fraser, Roydon
1999-01-01
Thermal remote sensing instruments can function as environmental measuring tools, with capabilities leading toward new directions in functional landscape ecology. Theoretical deduction and phenomenological observation leads us to believe that the second law of thermodynamics requires that all dynamically systems develop in a manner which dissipates gradients as rapidly as possible within the constraints of the system at hand. The ramification of this requirement is that dynamical systems will evolve dissipative structures which grow and complexify over time. This perspective has allowed us to develop a framework for discussing ecosystem development and integrity. In the context of this framework we have developed measures of development and integrity for ecosystems. One set of these measures is based on destruction of the exergy content of incoming solar energy. More developed ecosystems will be more effective at dissipating the solar gradient (destroying its exergy content). This can be measured by the effective surface temperature of the ecosystem on a landscape scale. These surface temperatures are measured using airborne thermal scanners such as the Thermal Infrared Multispectral Scanner (TIMS) and the Airborne Thermal/Visible Land Application Sensor(ATLAS) sensors. An analysis of agriculture and forest ecosystems will be used to illustrate the concept of ecological thermodynamics and the development of ecosystems.
Functional Cortical Network in Alpha Band Correlates with Social Bargaining
Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco
2014-01-01
Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240
International Nuclear Information System (INIS)
Oda, Ryuichi; Ishida, Shin; Wada, Hiroaki; Yamada, Kenji; Sekiguchi, Motoo
1999-01-01
We examine mass spectra and wave functions of the nn-bar, cc-bar and bb-bar meson systems within the framework of the covariant oscillator quark model with the boosted LS-coupling scheme. We solve nonperturbatively an eigenvalue problem for the squared-mass operator, which incorporates the four-dimensional color-Coulomb-type interaction, by taking a set of covariant oscillator wave functions as an expansion basis. We obtain mass spectra of these meson systems, which reproduce quite well their experimental behavior. The resultant manifestly covariant wave functions, which are applicable to analyses of various reaction phenomena, are given. Our results seem to suggest that the present model may be considered effectively as a covariant version of the nonrelativistic linear-plus-Coulomb potential quark model. (author)
Protein Tyrosine Nitration: Biochemical Mechanisms and Structural Basis of its Functional Effects
Radi, Rafael
2012-01-01
CONSPECTUS The nitration of protein tyrosine residues to 3-nitrotyrosine represents an oxidative postranslational modification that unveils the disruption of nitric oxide (•NO) signaling and metabolism towards pro-oxidant processes. Indeed, excess levels of reactive oxygen species in the presence of •NO or •NO-derived metabolites lead to the formation of nitrating species such as peroxynitrite. Thus, protein 3-nitrotyrosine has been established as a biomarker of cell, tissue and systemic “nitroxidative stress”. Moreover, tyrosine nitration modifies key properties of the amino acid (i.e. phenol group pKa, redox potential, hydrophobicity and volume). Thus, the incorporation of a nitro group (−NO2) to protein tyrosines can lead to profound structural and functional changes, some of which contribute to altered cell and tissue homeostasis. In this Account, I describe our current efforts to define 1) biologically-relevant mechanisms of protein tyrosine nitration and 2) how this modification can cause changes in protein structure and function at the molecular level. First, the relevance of protein tyrosine nitration via free radical-mediated reactions (in both peroxynitrite-dependent or independent pathways) involving the intermediacy of tyrosyl radical (Tyr•) will be underscored. This feature of the nitration process becomes critical as Tyr• can take variable fates, including the formation of 3-nitrotyrosine. Fast kinetic techniques, electron paramagnetic resonance (EPR) studies, bioanalytical methods and kinetic simulations have altogether assisted to characterize and fingerprint the reactions of tyrosine with peroxynitrite and one-electron oxidants and its further evolution to 3-nitrotyrosine. Recent findings show that nitration of tyrosines in proteins associated to biomembranes is linked to the lipid peroxidation process via a connecting reaction that involves the one-electron oxidation of tyrosine by lipid peroxyl radicals (LOO•). Second
Neural Networks For Electrohydrodynamic Effect Modelling
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Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark Functions
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DENIZ ULKER, E.
2013-05-01
Full Text Available In real-world optimization problems, even though the solution quality is of great importance, the robustness of the solution is also an important aspect. This paper investigates how the optimization algorithms are sensitive to the variations of control parameters and to the random initialization of the solution set for fixed control parameters. The comparison is performed of three well-known evolutionary algorithms which are Particle Swarm Optimization (PSO algorithm, Differential Evolution (DE algorithm and the Harmony Search (HS algorithm. Various benchmark functions with different characteristics are used for the evaluation of these algorithms. The experimental results show that the solution quality of the algorithms is not directly related to their robustness. In particular, the algorithm that is highly robust can have a low solution quality, or the algorithm that has a high quality of solution can be quite sensitive to the parameter variations.
Nanocomposites based on thermoplastic elastomers with functional basis of nano titanium dioxide
Energy Technology Data Exchange (ETDEWEB)
Yulovskaya, V. D.; Kuz’micheva, G. M., E-mail: galina-kuzmicheva@list.ru [Federal State Budget Educational Institution of Higher Education “Moscow Technological University” (Russian Federation); Klechkovskaya, V. V. [Russian Academy of Sciences, Shubnikov Institute of Crystallography (Russian Federation); Orekhov, A. S.; Zubavichus, Ya. V. [National Research Centre “Kurchatov Institute” (Russian Federation); Domoroshchina, E. N.; Shegay, A. V. [Federal State Budget Educational Institution of Higher Education “Moscow Technological University” (Russian Federation)
2016-03-15
Nanocomposites based on a thermoplastic elastomer (TPE) (low-density polyethylene (LDPE) and 1,2-polybutadiene in a ratio of 60/40) with functional titanium dioxide nanoparticles of different nature, TiO{sub 2}/TPE, have been prepared and investigated by a complex of methods (X-ray diffraction analysis using X-ray and synchrotron radiation beams, scanning electron microscopy, transmission electron microscopy, and X-ray energy-dispersive spectroscopy). The morphology of the composites is found to be somewhat different, depending on the TiO{sub 2} characteristics. It is revealed that nanocomposites with cellular or porous structures containing nano-TiO{sub 2} aggregates with a large specific surface and large sizes of crystallites and nanoparticles exhibit the best deformation‒strength and fatigue properties and stability to the effect of active media under conditions of ozone and vapor‒air aging.
Chalvet, Fabienne; Netter, Sophie; Dos Santos, Nicolas; Poisot, Emilie; Paces-Fessy, Mélanie; Cumenal, Delphine; Peronnet, Frédérique; Pret, Anne-Marie; Théodore, Laurent
2012-01-01
The potential to produce new cells during adult life depends on the number of stem cell niches and the capacity of stem cells to divide, and is therefore under the control of programs ensuring developmental homeostasis. However, it remains generally unknown how the number of stem cell niches is controlled. In the insect ovary, each germline stem cell (GSC) niche is embedded in a functional unit called an ovariole. The number of ovarioles, and thus the number of GSC niches, varies widely among species. In Drosophila, morphogenesis of ovarioles starts in larvae with the formation of terminal filaments (TFs), each made of 8–10 cells that pile up and sort in stacks. TFs constitute organizers of individual germline stem cell niches during larval and early pupal development. In the Drosophila melanogaster subgroup, the number of ovarioles varies interspecifically from 8 to 20. Here we show that pipsqueak, Trithorax-like, batman and the bric-à-brac (bab) locus, all encoding nuclear BTB/POZ factors of the Tramtrack Group, are involved in limiting the number of ovarioles in D. melanogaster. At least two different processes are differentially perturbed by reducing the function of these genes. We found that when the bab dose is reduced, sorting of TF cells into TFs was affected such that each TF contains fewer cells and more TFs are formed. In contrast, psq mutants exhibited a greater number of TF cells per ovary, with a normal number of cells per TF, thereby leading to formation of more TFs per ovary than in the wild type. Our results indicate that two parallel genetic pathways under the control of a network of nuclear BTB factors are combined in order to negatively control the number of germline stem cell niches. PMID:23185495
Zhang, Gaoyan; Yao, Li; Shen, Jiahui; Yang, Yihong; Zhao, Xiaojie
2015-05-01
Working memory (WM) is essential for individuals' cognitive functions. Neuroimaging studies indicated that WM fundamentally relied on a frontoparietal working memory network (WMN) and a cinguloparietal default mode network (DMN). Behavioral training studies demonstrated that the two networks can be modulated by WM training. Different from the behavioral training, our recent study used a real-time functional MRI (rtfMRI)-based neurofeedback method to conduct WM training, demonstrating that WM performance can be significantly improved after successfully upregulating the activity of the target region of interest (ROI) in the left dorsolateral prefrontal cortex (Zhang et al., [2013]: PloS One 8:e73735); however, the neural substrate of rtfMRI-based WM training remains unclear. In this work, we assessed the intranetwork and internetwork connectivity changes of WMN and DMN during the training, and their correlations with the change of brain activity in the target ROI as well as with the improvement of post-training behavior. Our analysis revealed an "ROI-network-behavior" correlation relationship underlying the rtfMRI training. Further mediation analysis indicated that the reorganization of functional brain networks mediated the effect of self-regulation of the target brain activity on the improvement of cognitive performance following the neurofeedback training. The results of this study enhance our understanding of the neural basis of real-time neurofeedback and suggest a new direction to improve WM performance by regulating the functional connectivity in the WM related networks. © 2014 Wiley Periodicals, Inc.
Chaudhary, Nitika; Sandhu, Padmani; Ahmed, Mushtaq; Akhter, Yusuf
2017-02-01
Trichothecenes are the sesquiterpenes secreted by Trichoderma spp. residing in the rhizosphere. These compounds have been reported to act as plant growth promoters and bio-control agents. The structural knowledge for the transporter proteins of their efflux remained limited. In this study, three-dimensional structure of Thmfs1 protein, a trichothecene transporter from Trichoderma harzianum, was homology modelled and further Molecular Dynamics (MD) simulations were used to decipher its mechanism. Fourteen transmembrane helices of Thmfs1 protein are observed contributing to an inward-open conformation. The transport channel and ligand binding sites in Thmfs1 are identified based on heuristic, iterative algorithm and structural alignment with homologous proteins. MD simulations were performed to reveal the differential structural behaviour occurring in the ligand free and ligand bound forms. We found that two discrete trichothecene binding sites are located on either side of the central transport tunnel running from the cytoplasmic side to the extracellular side across the Thmfs1 protein. Detailed analysis of the MD trajectories showed an alternative access mechanism between N and C-terminal domains contributing to its function. These results also demonstrate that the transport of trichodermin occurs via hopping mechanism in which the substrate molecule jumps from one binding site to another lining the transport tunnel. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Lars H. Wegner
2017-03-01
Full Text Available Current concepts of plant membrane transport are based on the assumption that water and solutes move across membranes via separate pathways. According to this view, coupling between the fluxes is more or less exclusively constituted via the osmotic force that solutes exert on water transport. This view is questioned here, and experimental evidence for a cotransport of water and solutes is reviewed. The overview starts with ion channels that provide pathways for both ion and water transport, as exemplified for maxi K+ channels from cytoplasmic droplets of Chara corallina. Aquaporins are usually considered to be selective for water (just allowing for slippage of some other small, neutral molecules. Recently, however, a “dual function” aquaporin has been characterized from Arabidopsis thaliana (AtPIP2.1 that translocates water and at the same time conducts cations, preferentially Na+. By analogy with mammalian physiology, other candidates for solute-water flux coupling are cation-chloride cotransporters of the CCC type, and transporters of sugars and amino acids. The last part is dedicated to possible physiological functions that could rely on solute-water cotransport. Among these are the generation of root pressure, refilling of embolized xylem vessels, fast turgor-driven movements of leaves, cell elongation (growth, osmoregulation and adjustment of buoyancy in marine algae. This review will hopefully initiate further research in the field.
Discerning Thermodynamic Basis of Self-Organization in Critical Zone Structure and Function
Richardson, M.; Kumar, P.
2017-12-01
Self-organization characterizes the spontaneous emergence of order. Self-organization in the Critical Zone, the region of Earth's skin from below the groundwater table to the top of the vegetation canopy, involves the interaction of biotic and abiotic processes occurring through a hierarchy of temporal and spatial scales. The self-organization is sustained through input of energy and material in an open system framework, and the resulting formations are called dissipative structures. Why do these local states of organization form and how are they thermodynamically favorable? We hypothesize that structure formation is linked to energy conversion and matter throughput rates across driving gradients. Furthermore, we predict that structures in the Critical Zone evolve based on local availability of nutrients, water, and energy. By considering ecosystems as open thermodynamic systems, we model and study the throughput signatures on short times scales to determine origins and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of matter and energy to understand the thermodynamic drivers of the system. By classifying the fluxes and dynamics in a system, we can identify patterns to determine the thermodynamic drivers for organized states. Additionally, studying the partitioning of nutrients, water, and energy throughout ecosystems through dissipative structures will help identify reasons for structure shapes and how these shapes impact major Critical Zone functions.
Gronnier, Julien; Crowet, Jean-Marc; Habenstein, Birgit; Nasir, Mehmet Nail; Bayle, Vincent; Hosy, Eric; Platre, Matthieu Pierre; Gouguet, Paul; Raffaele, Sylvain; Martinez, Denis; Grelard, Axelle; Loquet, Antoine; Simon-Plas, Françoise; Gerbeau-Pissot, Patricia; Der, Christophe; Bayer, Emmanuelle M; Jaillais, Yvon; Deleu, Magali; Germain, Véronique; Lins, Laurence; Mongrand, Sébastien
2017-07-31
Plasma Membrane is the primary structure for adjusting to ever changing conditions. PM sub-compartmentalization in domains is thought to orchestrate signaling. Yet, mechanisms governing membrane organization are mostly uncharacterized. The plant-specific REMORINs are proteins regulating hormonal crosstalk and host invasion. REMs are the best-characterized nanodomain markers via an uncharacterized moiety called REMORIN C-terminal Anchor. By coupling biophysical methods, super-resolution microscopy and physiology, we decipher an original mechanism regulating the dynamic and organization of nanodomains. We showed that targeting of REMORIN is independent of the COP-II-dependent secretory pathway and mediated by PI4P and sterol. REM-CA is an unconventional lipid-binding motif that confers nanodomain organization. Analyses of REM-CA mutants by single particle tracking demonstrate that mobility and supramolecular organization are critical for immunity. This study provides a unique mechanistic insight into how the tight control of spatial segregation is critical in the definition of PM domain necessary to support biological function.
International Nuclear Information System (INIS)
Dalmasse, Kevin; Nychka, Douglas W.; Gibson, Sarah E.; Fan, Yuhong; Flyer, Natasha
2016-01-01
The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 and 10798 lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analog. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.
The conceptual basis of mathematics in cardiology: (I) algebra, functions and graphs.
Bates, Jason H T; Sobel, Burton E
2003-02-01
This is the first in a series of four articles developed for the readers of. Without language ideas cannot be articulated. What may not be so immediately obvious is that they cannot be formulated either. One of the essential languages of cardiology is mathematics. Unfortunately, medical education does not emphasize, and in fact, often neglects empowering physicians to think mathematically. Reference to statistics, conditional probability, multicompartmental modeling, algebra, calculus and transforms is common but often without provision of genuine conceptual understanding. At the University of Vermont College of Medicine, Professor Bates developed a course designed to address these deficiencies. The course covered mathematical principles pertinent to clinical cardiovascular and pulmonary medicine and research. It focused on fundamental concepts to facilitate formulation and grasp of ideas. This series of four articles was developed to make the material available for a wider audience. The articles will be published sequentially in Coronary Artery Disease. Beginning with fundamental axioms and basic algebraic manipulations they address algebra, function and graph theory, real and complex numbers, calculus and differential equations, mathematical modeling, linear system theory and integral transforms and statistical theory. The principles and concepts they address provide the foundation needed for in-depth study of any of these topics. Perhaps of even more importance, they should empower cardiologists and cardiovascular researchers to utilize the language of mathematics in assessing the phenomena of immediate pertinence to diagnosis, pathophysiology and therapeutics. The presentations are interposed with queries (by Coronary Artery Disease, abbreviated as CAD) simulating the nature of interactions that occurred during the course itself. Each article concludes with one or more examples illustrating application of the concepts covered to cardiovascular medicine and
Disrupting Cocaine Trafficking Networks: Interdicting a Combined Social-Functional Network Model
2016-03-01
BENEFITS OF STUDY ....................................14 E. SOCIAL-FUNCTIONAL NETWORK DESCRIPTION .....................16 1. A Representative...data to maintain appropriate classification levels) of cocaine produced each month by the Colombian sources to the U.S. homeland, netting the...Tactical interdiction-centric operational approaches have improved over the years due to previous studies and research, but these approaches rely upon one
Reagh, Zachariah M; Ranganath, Charan
2018-04-25
Historically, research on the cognitive processes that support human memory proceeded, to a large extent, independently of research on the neural basis of memory. Accumulating evidence from neuroimaging, however, has enabled the field to develop a broader and more integrative perspective. Here, we briefly outline how advances in cognitive neuroscience can potentially shed light on concepts and controversies in human memory research. We argue that research on the functional properties of cortico-hippocampal networks informs us about how memories might be organized in the brain, which, in turn, helps to reconcile seemingly disparate perspectives in cognitive psychology. Finally, we discuss several open questions and directions for future research. Copyright © 2018. Published by Elsevier B.V.
Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise
2016-01-01
The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...
van Veenendaal, Tamar M; IJff, Dominique M; Aldenkamp, Albert P; Lazeron, Richard H C; Hofman, Paul A M; de Louw, Anton J A; Backes, Walter H; Jansen, Jacobus F A
2017-06-28
To increase our insight in the neuronal mechanisms underlying cognitive side-effects of antiepileptic drug (AED) treatment. The relation between functional magnetic resonance-acquired brain network measures, AED use, and cognitive function was investigated. Three groups of patients with epilepsy with a different risk profile for developing cognitive side effects were included: A "low risk" category (lamotrigine or levetiracetam, n = 16), an "intermediate risk" category (carbamazepine, oxcarbazepine, phenytoin, or valproate, n = 34) and a "high risk" category (topiramate, n = 5). Brain connectivity was assessed using resting state functional magnetic resonance imaging and graph theoretical network analysis. The Computerized Visual Searching Task was used to measure central information processing speed, a common cognitive side effect of AED treatment. Central information processing speed was lower in patients taking AEDs from the intermediate and high risk categories, compared with patients from the low risk category. The effect of risk category on global efficiency was significant ( P effect on the clustering coefficient (ANCOVA, P > 0.2). Also no significant associations between information processing speed and global efficiency or the clustering coefficient (linear regression analysis, P > 0.15) were observed. Only the four patients taking topiramate show aberrant network measures, suggesting that alterations in functional brain network organization may be only subtle and measureable in patients with more severe cognitive side effects.
Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.
Directory of Open Access Journals (Sweden)
Maria Simak
Full Text Available The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN. For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN model based on techniques of hidden Markov model (HMM, likelihood ratio test and Boolean logic functions. BFN consists of two consecutive tests to establish links between pairs of genes and check their directness. We evaluate the performance of BFN through the application to S. cerevisiae time course data. BFN produces regulatory relations which show consistency with succession of cell cycle phases. Furthermore, it also improves sensitivity and specificity when compared with alternative methods of genetic network reverse engineering. Moreover, we demonstrate that BFN can provide proper resolution for GO enrichment of gene sets. Finally, the Boolean functions discovered by BFN can provide useful insights for the identification of control mechanisms of regulatory processes, which is the special advantage of the proposed approach. In combination with low computational complexity, BFN can serve as an efficient screening tool to reconstruct genes relations on the whole genome level. In addition, the BFN approach is also feasible to a wide range of time course datasets.
Bruinsma, Gerben; Bernasco, Wim
In the study of organised crime, the traditional view of criminal groups as centrally controlled organisations has been replaced by the notion of criminal networks. However, little use has been made of concepts and theories of social networks that have developed in other social sciences. This paper
Network inference from functional experimental data (Conference Presentation)
Desrosiers, Patrick; Labrecque, Simon; Tremblay, Maxime; Bélanger, Mathieu; De Dorlodot, Bertrand; Côté, Daniel C.
2016-03-01
Functional connectivity maps of neuronal networks are critical tools to understand how neurons form circuits, how information is encoded and processed by neurons, how memory is shaped, and how these basic processes are altered under pathological conditions. Current light microscopy allows to observe calcium or electrical activity of thousands of neurons simultaneously, yet assessing comprehensive connectivity maps directly from such data remains a non-trivial analytical task. There exist simple statistical methods, such as cross-correlation and Granger causality, but they only detect linear interactions between neurons. Other more involved inference methods inspired by information theory, such as mutual information and transfer entropy, identify more accurately connections between neurons but also require more computational resources. We carried out a comparative study of common connectivity inference methods. The relative accuracy and computational cost of each method was determined via simulated fluorescence traces generated with realistic computational models of interacting neurons in networks of different topologies (clustered or non-clustered) and sizes (10-1000 neurons). To bridge the computational and experimental works, we observed the intracellular calcium activity of live hippocampal neuronal cultures infected with the fluorescent calcium marker GCaMP6f. The spontaneous activity of the networks, consisting of 50-100 neurons per field of view, was recorded from 20 to 50 Hz on a microscope controlled by a homemade software. We implemented all connectivity inference methods in the software, which rapidly loads calcium fluorescence movies, segments the images, extracts the fluorescence traces, and assesses the functional connections (with strengths and directions) between each pair of neurons. We used this software to assess, in real time, the functional connectivity from real calcium imaging data in basal conditions, under plasticity protocols, and epileptic
Directory of Open Access Journals (Sweden)
Yu Bai
2011-01-01
Full Text Available The anatomical basis for the concept of meridians in traditional Chinese medicine (TCM has not been resolved. This paper reviews the evidence supporting a relationship between acupuncture points/meridians and fascia. The reviewed evidence supports the view that the human body's fascia network may be the physical substrate represented by the meridians of TCM. Specifically, this hypothesis is supported by anatomical observations of body scan data demonstrating that the fascia network resembles the theoretical meridian system in salient ways, as well as physiological, histological, and clinical observations. This view represents a theoretical basis and means for applying modern biomedical research to examining TCM principles and therapies, and it favors a holistic approach to diagnosis and treatment.
Visual Network Asymmetry and Default Mode Network Function in ADHD: An fMRI Study
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T. Sigi eHale
2014-07-01
Full Text Available Background: A growing body of research has identified abnormal visual information processing in ADHD. In particular, slow processing speed and increased reliance on visuo-perceptual strategies have become evident. Objective: The current study used recently developed fMRI methods to replicate and further examine abnormal rightward biased visual information processing in ADHD and to further characterize the nature of this effect; we tested its association to several large-scale distributed network systems. Method: We examined fMRI BOLD response during letter and location judgment tasks, and directly assessed visual network asymmetry and its association to large-scale networks using both a voxelwise and an averaged signal approach. Results: Initial within-group analyses revealed a pattern of left lateralized visual cortical activity in controls but right lateralized visual cortical activity in ADHD children. Direct analyses of visual network asymmetry confirmed atypical rightward bias in ADHD children compared to controls. This ADHD characteristic was atypically associated with reduced activation across several extra-visual networks, including the default mode network (DMN. We also found atypical associations between DMN activation and ADHD subjects’ inattentive symptoms and task performance. Conclusion: The current study demonstrated rightward VNA in ADHD during a simple letter discrimination task. This result adds an important novel consideration to the growing literature identifying abnormal visual processing in ADHD. We postulate that this characteristic reflects greater perceptual engagement of task-extraneous content, and that it may be a basic feature of less efficient top-down task-directed control over visual processing. We additionally argue that abnormal DMN function may contribute to this characteristic.
Genes2FANs: connecting genes through functional association networks
2012-01-01
Background Protein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user’s PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories. Conclusions Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in
Sturmian functions in a L{sup 2} basis: Critical nuclear charge for N-electron atoms
Energy Technology Data Exchange (ETDEWEB)
Frapiccini, A.L. [Departamento de Fisica, Universidad Nacional del Sur, Bahia Blanca (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina)], E-mail: afrapic@uns.edu.ar; Gasaneo, G. [Departamento de Fisica, Universidad Nacional del Sur, Bahia Blanca (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina); Colavecchia, F.D. [Centro Atomico Bariloche, San Carlos de Bariloche, Rio Negro (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina); Mitnik, D. [Instituto de Astronomia y Fisica del Espacio and, Departamento de Fisica, Universidad de Buenos Aires (Argentina); Consejo Nacional de Investigaciones Cientificas y Tecnicas (Argentina)
2007-10-15
Two particle Sturmian functions [M. Rotenberg, Ann. Phys., NY 19 (1962) 262; S.V. Khristenko, Theor. Math. Fiz. 22 (1975) 31 (Engl. Transl. Theor. Math. Phys. 22, 21)] for a short range potentials are obtained by expanding the solution of the Schroedinger equation in a finite L{sup 2}Laguerre-type basis. These functions are chosen to satisfy certain boundary conditions, such as regularity at the origin and the correct asymptotic behavior according to the energy domain: exponential decay for negative energy and outgoing (incoming or standing wave) for positive energy. The set of eigenvalues obtained is discrete for both positive and negative energies. This Sturmian basis is used to solve the Schroedinger equation for a one-particle model potential [A.V. Sergeev, S. Kais, J. Quant. Chem. 75 (1999) 533] to describe the motion of a loosely bound electron in a multielectron atom. Values of the two parameters of the potential are computed to represent the Helium isoelectronic series and the critical nuclear charge Z{sub c} is found, in good agreement with previous calculations.
Drescher, A. C.; Gadgil, A. J.; Price, P. N.; Nazaroff, W. W.
Optical remote sensing and iterative computed tomography (CT) can be applied to measure the spatial distribution of gaseous pollutant concentrations. We conducted chamber experiments to test this combination of techniques using an open path Fourier transform infrared spectrometer (OP-FTIR) and a standard algebraic reconstruction technique (ART). Although ART converged to solutions that showed excellent agreement with the measured ray-integral concentrations, the solutions were inconsistent with simultaneously gathered point-sample concentration measurements. A new CT method was developed that combines (1) the superposition of bivariate Gaussians to represent the concentration distribution and (2) a simulated annealing minimization routine to find the parameters of the Gaussian basis functions that result in the best fit to the ray-integral concentration data. This method, named smooth basis function minimization (SBFM), generated reconstructions that agreed well, both qualitatively and quantitatively, with the concentration profiles generated from point sampling. We present an analysis of two sets of experimental data that compares the performance of ART and SBFM. We conclude that SBFM is a superior CT reconstruction method for practical indoor and outdoor air monitoring applications.
International Nuclear Information System (INIS)
Vrankar, L.; Turk, G.; Runovc, F.; Kansa, E.J.
2006-01-01
Many heat-transfer problems involve a change of phase of material due to solidification or melting. Applications include: the safety studies of nuclear reactors (molten core concrete interaction), the drilling of high ice-content soil, the storage of thermal energy, etc. These problems are often called Stefan's or moving boundary value problems. Mathematically, the interface motion is expressed implicitly in an equation for the conservation of thermal energy at the interface (Stefan's conditions). This introduces a non-linear character to the system which treats each problem somewhat uniquely. The exact solution of phase change problems is limited exclusively to the cases in which e.g. the heat transfer regions are infinite or semi-infinite one dimensional-space. Therefore, solution is obtained either by approximate analytical solution or by numerical methods. Finite-difference methods and finite-element techniques have been used extensively for numerical solution of moving boundary problems. Recently, the numerical methods have focused on the idea of using a mesh-free methodology for the numerical solution of partial differential equations based on radial basis functions. In our case we will study solid-solid transformation. The numerical solutions will be compared with analytical solutions. Actually, in our work we will examine usefulness of radial basis functions (especially multiquadric-MQ) for one-dimensional Stefan's problems. The position of the moving boundary will be simulated by moving grid method. The resultant system of RBF-PDE will be solved by affine space decomposition. (author)
Learning related modulation of functional retrieval networks in man.
Petersson, K M; Sandblom, J; Gisselgård, J; Ingvar, M
2001-07-01
The medial temporal lobe has been implicated in studies of episodic memory tasks involving spatio-temporal context and object-location conjunctions. We have previously demonstrated that an increased level of practice in a free-recall task parallels a decrease in the functional activity of several brain regions, including the medial temporal lobe, the prefrontal, the anterior cingulate, the anterior insular, and the posterior parietal cortices, that in concert demonstrate a move from elaborate controlled processing towards a higher degree of automaticity. Here we report data from two experiments that extend these initial observations. We used a similar experimental approach but probed for effects of retrieval paradigms and stimulus material. In the first experiment we investigated practice related changes during recognition of object-location conjunctions and in the second during free-recall of pseudo-words. Learning in a neural network is a dynamic consequence of information processing and network plasticity. The present and previous PET results indicate that practice can induce a learning related functional restructuring of information processing. Different adaptive processes likely subserve the functional re-organisation observed. These may in part be related to different demands for attentional and working memory processing. It appears that the role(s) of the prefrontal cortex and the medial temporal lobe in memory retrieval are complex, perhaps reflecting several different interacting processes or cognitive components. We suggest that an integrative interactive perspective on the role of the prefrontal and medial temporal lobe is necessary for an understanding of the processing significance of these regions in learning and memory. It appears necessary to develop elaborated and explicit computational models for prefrontal and medial temporal functions in order to derive detailed empirical predictions, and in combination with an efficient use and development of
Directory of Open Access Journals (Sweden)
Marek eMutwil
2014-08-01
Full Text Available The analysis of gene expression data has shown that transcriptionally coordinated (co-expressed genes are often functionally related, enabling scientists to use expression data in gene function prediction. This Focused Review discusses our original paper (Large-scale co-expression approach to dissect secondary cell wall formation across plant species, Frontiers in Plant Science 2:23. In this paper we applied cross-species analysis to co-expression networks of genes involved in cellulose biosynthesis. We show that the co-expression networks from different species are highly similar, indicating that whole biological pathways are conserved across species. This finding has two important implications. First, the analysis can transfer gene function annotation from well-studied plants, such as Arabidopsis, to other, uncharacterized plant species. As the analysis finds genes that have similar sequence and similar expression pattern across different organisms, functionally equivalent genes can be identified. Second, since co-expression analyses are often noisy, a comparative analysis should have higher performance, as parts of co-expression networks that are conserved are more likely to be functionally relevant. In this Focused Review, we outline the comparative analysis done in the original paper and comment on the recent advances and approaches that allow comparative analyses of co-function networks. We hypothesize that, in comparison to simple co-expression analysis, comparative analysis would yield more accurate gene function predictions. Finally, by combining comparative analysis with genomic information of green plants, we propose a possible composition of cellulose biosynthesis machinery during earlier stages of plant evolution.
Analysis on Influential Functions in the Weighted Software Network
Directory of Open Access Journals (Sweden)
Haitao He
2018-01-01
Full Text Available Identifying influential nodes is important for software in terms of understanding the design patterns and controlling the development and the maintenance process. However, there are no efficient methods to discover them so far. Based on the invoking dependency relationships between the nodes, this paper proposes a novel approach to define the node importance for mining the influential software nodes. First, according to the multiple execution information, we construct a weighted software network (WSN to denote the software execution dependency structure. Second, considering the invoking times and outdegree about software nodes, we improve the method PageRank and put forward the targeted algorithm FunctionRank to evaluate the node importance (NI in weighted software network. It has higher influence when the node has lager value of NI. Finally, comparing the NI of nodes, we can obtain the most influential nodes in the software network. In addition, the experimental results show that the proposed approach has good performance in identifying the influential nodes.
Directory of Open Access Journals (Sweden)
Azam Farmani
2014-07-01
Full Text Available Objectives: The aim of the present study is to examine the prediction of the reminiscence functions in older adults on the basis of the five personality factor model. Methods & Materials: 242 elderly adults older than 60 were recruited from retirement clubs of the city of Shiraz via available sampling method. The participants completed the Reminiscence Functions Scale and Goldberg's International Personality Item Pool. Forty participants were deleted from the sample because they did not complete the questionnaires fully. All the participants took part in the study with their conscious consent. To conduct the necessary descriptive and inferential statistical operations, SPSS (Version 16 was used. Mean, standard deviation and Pearson correlation coefficient were utilized to analyze the data in the descriptive statistics section, And in inferential statistics section, simultaneous multiple regression was used to predict reminiscence functions. Results: According to the results of the multiple regression analysis, Neuroticism predicted the reminiscence functions of Bitterness Revival (β=0.28, P≤0.001 and Intimacy Maintenance (β=0.25, P≤0.001 and Extraversion predicted the reminiscence functions of Teach/Inform (β=0.18, P<0.05. Conclusion: The results indicated that people with higher levels of psychological distress tend to rehash and ruminate on bitter memories and hold onto memories of intimate social relations who are no longer part of their lives. Moreover, extravert people tend to share memories to transmit a lesson of life and share personal ideologies and experiences. Clinicians should focus on more adaptive functions of reminiscence (e.g., identity, problem solving and teach/inform and teach such functions.
Jockwitz, Christiane
2016-01-01
The aim of this work was to investigate the structure-function relationship in cognitive resting state networks in a large population-based elderly sample. The first study characterized the functional connectivity in four cognitive resting state networks with respect to age, gender and cognitive performance: Default Mode Network (DMN), executive, and left and right frontoparietal resting state networks. The second study assessed the structural correlates of the functional reorganization of th...
D’Ostilio, Kevin; Garraux, Gaëtan
2016-01-01
The high prevalence of major depressive disorder in people with Parkinson’s disease (PD), its negative impact on health-related quality of life and the low response rate to conventional pharmacological therapies call to seek innovative treatments. Here, we review the new approaches for treating major depressive disorder in patients with PD within the framework of the network model of depression. According to this model, major depressive disorder reflects maladaptive neuronal plasticity. Non-invasive brain stimulation (NIBS) using high frequency repetitive transcranial magnetic stimulation (rTMS) over the prefrontal cortex has been proposed as a feasible and effective strategy with minimal risk. The neurobiological basis of its therapeutic effect may involve neuroplastic modifications in limbic and cognitive networks. However, the way this networks reorganize might be strongly influenced by the environment. To address this issue, we propose a combined strategy that includes NIBS together with cognitive and behavioral interventions. PMID:27148016
Hot Spots in a Network of Functional Sites
Ozbek, Pemra; Soner, Seren; Haliloglu, Turkan
2013-01-01
It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8–58%, C 84–95%, P 5–19% and A 81–92% on unbound structures and S 8–51%, C 97–99%, P 14–50%, A 94–97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation. PMID:24023934
Optical Network as a Service for Service Function Chaining across Datacenters
DEFF Research Database (Denmark)
Mehmeri, Victor; Wang, Xi; Zhang, Qiong
2017-01-01
We present the SPN OS, a Network-as-a-Service orchestration platform for NFV/SDN integrated service provisioning across multiple datacenters over packet/optical networks. Our prototype showcases template-driven service function chaining and high-level network programming-based optical networking....
Lewis, Brian A
2010-01-15
The regulation of transcription and of many other cellular processes involves large multi-subunit protein complexes. In the context of transcription, it is known that these complexes serve as regulatory platforms that connect activator DNA-binding proteins to a target promoter. However, there is still a lack of understanding regarding the function of these complexes. Why do multi-subunit complexes exist? What is the molecular basis of the function of their constituent subunits, and how are these subunits organized within a complex? What is the reason for physical connections between certain subunits and not others? In this article, I address these issues through a model of network allostery and its application to the eukaryotic RNA polymerase II Mediator transcription complex. The multiple allosteric networks model (MANM) suggests that protein complexes such as Mediator exist not only as physical but also as functional networks of interconnected proteins through which information is transferred from subunit to subunit by the propagation of an allosteric state known as conformational spread. Additionally, there are multiple distinct sub-networks within the Mediator complex that can be defined by their connections to different subunits; these sub-networks have discrete functions that are activated when specific subunits interact with other activator proteins.
Directory of Open Access Journals (Sweden)
Byron eBernal
2015-05-01
Full Text Available Background and Objective. Modern neuroimaging developments have demonstrated that cognitive functions correlate with brain networks rather than specific areas. The purpose of this paper was to analyze the connectivity of Broca's area based on language tasks. Methods. A connectivity modeling study was performed by pooling data of Broca's activation in language tasks. Fifty-seven papers that included 883 subjects in 84 experiments were analyzed. Analysis of Likelihood Estimates of pooled data was utilized to generate the map; thresholds at p < 0.01 were corrected for multiple comparisons and false discovery rate. Resulting images were co-registered into MNI standard space. Results. A network consisting of 16 clusters of activation was obtained. Main clusters were located in the frontal operculum, left posterior temporal region, supplementary motor area, and the parietal lobe. Less common clusters were seen in the sub-cortical structures including the left thalamus, left putamen, secondary visual areas and the right cerebellum. Conclusions. BA44-related networks involved in language processing were demonstrated utilizing a pooling-data connectivity study. Significance, interpretation and limitations of the results are discussed.
Integration of relational and hierarchical network information for protein function prediction
Directory of Open Access Journals (Sweden)
Jiang Xiaoyu
2008-08-01
Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased
Energy Technology Data Exchange (ETDEWEB)
Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu
2017-04-15
In a previous study of seismic modeling with radial basis function-generated finite differences (RBF-FD), we outlined a numerical method for solving 2-D wave equations in domains with material interfaces between different regions. The method was applicable on a mesh-free set of data nodes. It included all information about interfaces within the weights of the stencils (allowing the use of traditional time integrators), and was shown to solve problems of the 2-D elastic wave equation to 3rd-order accuracy. In the present paper, we discuss a refinement of that method that makes it simpler to implement. It can also improve accuracy for the case of smoothly-variable model parameter values near interfaces. We give several test cases that demonstrate the method solving 2-D elastic wave equation problems to 4th-order accuracy, even in the presence of smoothly-curved interfaces with jump discontinuities in the model parameters.
Piret, Cécile
2012-05-01
Much work has been done on reconstructing arbitrary surfaces using the radial basis function (RBF) method, but one can hardly find any work done on the use of RBFs to solve partial differential equations (PDEs) on arbitrary surfaces. In this paper, we investigate methods to solve PDEs on arbitrary stationary surfaces embedded in . R3 using the RBF method. We present three RBF-based methods that easily discretize surface differential operators. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent the most complex geometries in any dimension. Two out of the three methods, which we call the orthogonal gradients (OGr) methods are the result of our work and are hereby presented for the first time. © 2012 Elsevier Inc.
Energy Technology Data Exchange (ETDEWEB)
Permoon, M. R.; Haddadpour, H. [Sharif University of Tech, Tehran (Iran, Islamic Republic of); Rashidinia, J.; Parsa, A.; Salehi, R. [Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)
2016-07-15
In this paper, the forced vibrations of the fractional viscoelastic beam with the Kelvin-Voigt fractional order constitutive relationship is studied. The equation of motion is derived from Newton's second law and the Galerkin method is used to discretize the equation of motion in to a set of linear ordinary differential equations. For solving the discretized equations, the radial basis functions and Sinc quadrature rule are used. In order to show the effectiveness and accuracy of this method, some test problem are considered, and it is shown that the obtained results are in very good agreement with exact solution. In the following, the proposed numerical solution is applied to exploring the effects of fractional parameters on the response of the beam and finally some conclusions are outlined.
Lima, C S; Barbosa, D; Ramos, J; Tavares, A; Monteiro, L; Carvalho, L
2008-01-01
This paper presents a system to support medical diagnosis and detection of abnormal lesions by processing capsule endoscopic images. Endoscopic images possess rich information expressed by texture. Texture information can be efficiently extracted from medium scales of the wavelet transform. The set of features proposed in this paper to code textural information is named color wavelet covariance (CWC). CWC coefficients are based on the covariances of second order textural measures, an optimum subset of them is proposed. Third and forth order moments are added to cope with distributions that tend to become non-Gaussian, especially in some pathological cases. The proposed approach is supported by a classifier based on radial basis functions procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data containing 6 full endoscopic exams and reached 95% specificity and 93% sensitivity.
Vavalle, Nicholas A; Schoell, Samantha L; Weaver, Ashley A; Stitzel, Joel D; Gayzik, F Scott
2014-11-01
Human body finite element models (FEMs) are a valuable tool in the study of injury biomechanics. However, the traditional model development process can be time-consuming. Scaling and morphing an existing FEM is an attractive alternative for generating morphologically distinct models for further study. The objective of this work is to use a radial basis function to morph the Global Human Body Models Consortium (GHBMC) average male model (M50) to the body habitus of a 95th percentile male (M95) and to perform validation tests on the resulting model. The GHBMC M50 model (v. 4.3) was created using anthropometric and imaging data from a living subject representing a 50th percentile male. A similar dataset was collected from a 95th percentile male (22,067 total images) and was used in the morphing process. Homologous landmarks on the reference (M50) and target (M95) geometries, with the existing FE node locations (M50 model), were inputs to the morphing algorithm. The radial basis function was applied to morph the FE model. The model represented a mass of 103.3 kg and contained 2.2 million elements with 1.3 million nodes. Simulations of the M95 in seven loading scenarios were presented ranging from a chest pendulum impact to a lateral sled test. The morphed model matched anthropometric data to within a rootmean square difference of 4.4% while maintaining element quality commensurate to the M50 model and matching other anatomical ranges and targets. The simulation validation data matched experimental data well in most cases.
Modular structure of functional networks in olfactory memory.
Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre
2014-07-15
Graph theory enables the study of systems by describing those systems as a set of nodes and edges. Graph theory has been widely applied to characterize the overall structure of data sets in the social, technological, and biological sciences, including neuroscience. Modular structure decomposition enables the definition of sub-networks whose components are gathered in the same module and work together closely, while working weakly with components from other modules. This processing is of interest for studying memory, a cognitive process that is widely distributed. We propose a new method to identify modular structure in task-related functional magnetic resonance imaging (fMRI) networks. The modular structure was obtained directly from correlation coefficients and thus retained information about both signs and weights. The method was applied to functional data acquired during a yes-no odor recognition memory task performed by young and elderly adults. Four response categories were explored: correct (Hit) and incorrect (False alarm, FA) recognition and correct and incorrect rejection. We extracted time series data for 36 areas as a function of response categories and age groups and calculated condition-based weighted correlation matrices. Overall, condition-based modular partitions were more homogeneous in young than elderly subjects. Using partition similarity-based statistics and a posteriori statistical analyses, we demonstrated that several areas, including the hippocampus, caudate nucleus, and anterior cingulate gyrus, belonged to the same module more frequently during Hit than during all other conditions. Modularity values were negatively correlated with memory scores in the Hit condition and positively correlated with bias scores (liberal/conservative attitude) in the Hit and FA conditions. We further demonstrated that the proportion of positive and negative links between areas of different modules (i.e., the proportion of correlated and anti-correlated areas
Identification of a supplier network through Quality Function Deployment
DEFF Research Database (Denmark)
Kristensen, Preben Sander; Holmen, Elsebeth
1994-01-01
and characteristics constituting this fan were based on the company's knowledge before entering into development interaction with suppliers. If these characteristics are used to express the company's demand in the subsequent interaction process, much information may be lost. We suggest that in development interacton...... supplier a company has to cancel the initial transformation processes that caused it to start interaction with the supplier in question, and start interaction on the basis of end-user attributes in their original forms, making new transofrmation processes into characteristics based on the joint knowledge......During 1993-94 the authors followed a product development process in a Danish Butter Cookie company. The process was structured according to the Quality Function Deployment technique House of Quality. Customer attributes were derived from a discus a diabetics end-user focus group. During a series...
Jalili, Mahdi; Gebhardt, Tom; Wolkenhauer, Olaf; Salehzadeh-Yazdi, Ali
2018-06-01
Decoding health and disease phenotypes is one of the fundamental objectives in biomedicine. Whereas high-throughput omics approaches are available, it is evident that any single omics approach might not be adequate to capture the complexity of phenotypes. Therefore, integrated multi-omics approaches have been used to unravel genotype-phenotype relationships such as global regulatory mechanisms and complex metabolic networks in different eukaryotic organisms. Some of the progress and challenges associated with integrated omics studies have been reviewed previously in comprehensive studies. In this work, we highlight and review the progress, challenges and advantages associated with emerging approaches, integrating gene expression and protein-protein interaction networks to unravel network-based functional features. This includes identifying disease related genes, gene prioritization, clustering protein interactions, developing the modules, extract active subnetworks and static protein complexes or dynamic/temporal protein complexes. We also discuss how these approaches contribute to our understanding of the biology of complex traits and diseases. This article is part of a Special Issue entitled: Cardiac adaptations to obesity, diabetes and insulin resistance, edited by Professors Jan F.C. Glatz, Jason R.B. Dyck and Christine Des Rosiers. Copyright © 2018 Elsevier B.V. All rights reserved.
Lv, Han; Zhao, Pengfei; Liu, Zhaohui; Liu, Xuehuan; Ding, Heyu; Liu, Liheng; Wang, Guopeng; Xie, Jing; Zeng, Rong; Chen, Yuchen; Yang, Zhenghan; Gong, Shusheng; Wang, Zhenchang
2018-02-02
Unilateral pulsatile tinnitus (PT) was proved to be a kind of disease with brain functional abnormalities within and beyond the auditory network (AN). However, changes in patterns of the lateralization effects of PT are yet to be established. Relationship between the AN and other brain networks in PT patients is also a scientific question need to be answered. In this study, we recruited 23 left-sided, 23 right-sided PT (LSPT, RSPT) patients and 23 normal controls (NC). We combined applied independent component analysis and seed-based functional connectivity (FC) analysis to investigate alteration feature of the FC of the AN by using resting-state functional magnetic resonance imaging (rs-fMRI). Compared with NC, LSPT patients demonstrated disconnected FC within the AN on both sides. Disrupted network integrity between AN and several brain functional networks, including executive control network, self-perceptual network and the limbic network, was also demonstrated in LSPT patient group bilaterally. In contrast, compared with NC, RSPT demonstrated decreased FC within the AN on the left side, but significant increased FC within the AN on the right side (symptomatic side). Enhanced FC between AN and executive control network, self-perceptual network and limbic network was also found mainly on the right side in patients with RSPT. Positive FC between the auditory network and the limbic network may be a reason to explain why RSPT patients are willing to be in the clinic. Briefly, LSPT exhibit disrupted network integrity in brain functional networks. But RSPT is featured by enhanced FC within AN and between networks, especially on the right (symptomatic) side. Corroboration of featured FC helps to reveal the pathophysiological changing process of the brain in patients with PT, providing imaging-based biomarker to distinguish PT from other kind of tinnitus. Copyright © 2017 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Kotasidis, Fotis A.; Zaidi, Habib
2014-01-01
Purpose: The Ingenuity time-of-flight (TF) PET/MR is a recently developed hybrid scanner combining the molecular imaging capabilities of PET with the excellent soft tissue contrast of MRI. It is becoming common practice to characterize the system's point spread function (PSF) and understand its variation under spatial transformations to guide clinical studies and potentially use it within resolution recovery image reconstruction algorithms. Furthermore, due to the system's utilization of overlapping and spherical symmetric Kaiser-Bessel basis functions during image reconstruction, its image space PSF and reconstructed spatial resolution could be affected by the selection of the basis function parameters. Hence, a detailed investigation into the multidimensional basis function parameter space is needed to evaluate the impact of these parameters on spatial resolution. Methods: Using an array of 12 × 7 printed point sources, along with a custom made phantom, and with the MR magnet on, the system's spatially variant image-based PSF was characterized in detail. Moreover, basis function parameters were systematically varied during reconstruction (list-mode TF OSEM) to evaluate their impact on the reconstructed resolution and the image space PSF. Following the spatial resolution optimization, phantom, a