Selection of neural network structure for system error correction of electro-optical tracker system with horizontal gimbal

NASA Astrophysics Data System (ADS)

Liu, Xing-fa; Cen, Ming

2007-12-01

Neural Network system error correction method is more precise than lest square system error correction method and spheric harmonics function system error correction method. The accuracy of neural network system error correction method is mainly related to the frame of Neural Network. Analysis and simulation prove that both BP neural network system error correction method and RBF neural network system error correction method have high correction accuracy; it is better to use RBF Network system error correction method than BP Network system error correction method for little studying stylebook considering training rate and neural network scale.

Convergence and objective functions of some fault/noise-injection-based online learning algorithms for RBF networks.

PubMed

Ho, Kevin I-J; Leung, Chi-Sing; Sum, John

2010-06-01

In the last two decades, many online fault/noise injection algorithms have been developed to attain a fault tolerant neural network. However, not much theoretical works related to their convergence and objective functions have been reported. This paper studies six common fault/noise-injection-based online learning algorithms for radial basis function (RBF) networks, namely 1) injecting additive input noise, 2) injecting additive/multiplicative weight noise, 3) injecting multiplicative node noise, 4) injecting multiweight fault (random disconnection of weights), 5) injecting multinode fault during training, and 6) weight decay with injecting multinode fault. Based on the Gladyshev theorem, we show that the convergence of these six online algorithms is almost sure. Moreover, their true objective functions being minimized are derived. For injecting additive input noise during training, the objective function is identical to that of the Tikhonov regularizer approach. For injecting additive/multiplicative weight noise during training, the objective function is the simple mean square training error. Thus, injecting additive/multiplicative weight noise during training cannot improve the fault tolerance of an RBF network. Similar to injective additive input noise, the objective functions of other fault/noise-injection-based online algorithms contain a mean square error term and a specialized regularization term.

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

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 (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic 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.

A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function.

PubMed

Roshani, G H; Nazemi, E; Roshani, M M

2017-05-01

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%. Copyright © 2017 Elsevier Ltd. All rights reserved.

Forecasting daily source air quality using multivariate statistical analysis and radial basis function networks.

PubMed

Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A

2008-12-01

It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.

Decoupling control of a five-phase fault-tolerant permanent magnet motor by radial basis function neural network inverse

NASA Astrophysics Data System (ADS)

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.

Mineral content prediction for unconventional oil and gas reservoirs based on logging data

NASA Astrophysics Data System (ADS)

Maojin, Tan; Youlong, Zou; Guoyue

2012-09-01

Coal bed methane and shale oil &gas are both important unconventional oil and gas resources, whose reservoirs are typical non-linear with complex and various mineral components, and the logging data interpretation model are difficult to establish for calculate the mineral contents, and the empirical formula cannot be constructed due to various mineral. The radial basis function (RBF) network analysis is a new method developed in recent years; the technique can generate smooth continuous function of several variables to approximate the unknown forward model. Firstly, the basic principles of the RBF is discussed including net construct and base function, and the network training is given in detail the adjacent clustering algorithm specific process. Multi-mineral content for coal bed methane and shale oil &gas, using the RBF interpolation method to achieve a number of well logging data to predict the mineral component contents; then, for coal-bed methane reservoir parameters prediction, the RBF method is used to realized some mineral contents calculation such as ash, volatile matter, carbon content, which achieves a mapping from various logging data to multimineral. To shale gas reservoirs, the RBF method can be used to predict the clay content, quartz content, feldspar content, carbonate content and pyrite content. Various tests in coalbed and gas shale show the method is effective and applicable for mineral component contents prediction

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

PubMed

Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

2012-09-01

In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer

PubMed Central

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy. PMID:29768463

Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer.

PubMed

Rani R, Hannah Jessie; Victoire T, Aruldoss Albert

2018-01-01

This paper presents an integrated hybrid optimization algorithm for training the radial basis function neural network (RBF NN). Training of neural networks is still a challenging exercise in machine learning domain. Traditional training algorithms in general suffer and trap in local optima and lead to premature convergence, which makes them ineffective when applied for datasets with diverse features. Training algorithms based on evolutionary computations are becoming popular due to their robust nature in overcoming the drawbacks of the traditional algorithms. Accordingly, this paper proposes a hybrid training procedure with differential search (DS) algorithm functionally integrated with the particle swarm optimization (PSO). To surmount the local trapping of the search procedure, a new population initialization scheme is proposed using Logistic chaotic sequence, which enhances the population diversity and aid the search capability. To demonstrate the effectiveness of the proposed RBF hybrid training algorithm, experimental analysis on publicly available 7 benchmark datasets are performed. Subsequently, experiments were conducted on a practical application case for wind speed prediction to expound the superiority of the proposed RBF training algorithm in terms of prediction accuracy.

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

PubMed

Abbasi, Elham; Ghatee, Mehdi; Shiri, M E

2013-09-01

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle. Copyright © 2013 Elsevier Ltd. All rights reserved.

Predicting the random drift of MEMS gyroscope based on K-means clustering and OLS RBF Neural Network

NASA Astrophysics Data System (ADS)

Wang, Zhen-yu; Zhang, Li-jie

2017-10-01

Measure error of the sensor can be effectively compensated with prediction. Aiming at large random drift error of MEMS(Micro Electro Mechanical System))gyroscope, an improved learning algorithm of Radial Basis Function(RBF) Neural Network(NN) based on K-means clustering and Orthogonal Least-Squares (OLS) is proposed in this paper. The algorithm selects the typical samples as the initial cluster centers of RBF NN firstly, candidates centers with K-means algorithm secondly, and optimizes the candidate centers with OLS algorithm thirdly, which makes the network structure simpler and makes the prediction performance better. Experimental results show that the proposed K-means clustering OLS learning algorithm can predict the random drift of MEMS gyroscope effectively, the prediction error of which is 9.8019e-007°/s and the prediction time of which is 2.4169e-006s

Nonlinear Adaptive PID Control for Greenhouse Environment Based on RBF Network

PubMed Central

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture.

PubMed

Chen, Yingyi; Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies.

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

NASA Astrophysics Data System (ADS)

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.

Detection of micro gap weld joint by using magneto-optical imaging and Kalman filtering compensated with RBF neural network

NASA Astrophysics Data System (ADS)

Gao, Xiangdong; Chen, Yuquan; You, Deyong; Xiao, Zhenlin; Chen, Xiaohui

2017-02-01

An approach for seam tracking of micro gap weld whose width is less than 0.1 mm based on magneto optical (MO) imaging technique during butt-joint laser welding of steel plates is investigated. Kalman filtering(KF) technology with radial basis function(RBF) neural network for weld detection by an MO sensor was applied to track the weld center position. Because the laser welding system process noises and the MO sensor measurement noises were colored noises, the estimation accuracy of traditional KF for seam tracking was degraded by the system model with extreme nonlinearities and could not be solved by the linear state-space model. Also, the statistics characteristics of noises could not be accurately obtained in actual welding. Thus, a RBF neural network was applied to the KF technique to compensate for the weld tracking errors. The neural network can restrain divergence filter and improve the system robustness. In comparison of traditional KF algorithm, the RBF with KF was not only more effectively in improving the weld tracking accuracy but also reduced noise disturbance. Experimental results showed that magneto optical imaging technique could be applied to detect micro gap weld accurately, which provides a novel approach for micro gap seam tracking.

Application of artificial neural network in precise prediction of cement elements percentages based on the neutron activation analysis

NASA Astrophysics Data System (ADS)

Eftekhari Zadeh, E.; Feghhi, S. A. H.; Roshani, G. H.; Rezaei, A.

2016-05-01

Due to variation of neutron energy spectrum in the target sample during the activation process and to peak overlapping caused by the Compton effect with gamma radiations emitted from activated elements, which results in background changes and consequently complex gamma spectrum during the measurement process, quantitative analysis will ultimately be problematic. Since there is no simple analytical correlation between peaks' counts with elements' concentrations, an artificial neural network for analyzing spectra can be a helpful tool. This work describes a study on the application of a neural network to determine the percentages of cement elements (mainly Ca, Si, Al, and Fe) using the neutron capture delayed gamma-ray spectra of the substance emitted by the activated nuclei as patterns which were simulated via the Monte Carlo N-particle transport code, version 2.7. The Radial Basis Function (RBF) network is developed with four specific peaks related to Ca, Si, Al and Fe, which were extracted as inputs. The proposed RBF model is developed and trained with MATLAB 7.8 software. To obtain the optimal RBF model, several structures have been constructed and tested. The comparison between simulated and predicted values using the proposed RBF model shows that there is a good agreement between them.

Soft Computing Application in Fault Detection of Induction Motor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

Macrocell path loss prediction using artificial intelligence techniques

NASA Astrophysics Data System (ADS)

Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

2014-04-01

The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals

NASA Astrophysics Data System (ADS)

Johnsson, Roger

2006-11-01

Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.

Two fast and accurate heuristic RBF learning rules for data classification.

PubMed

Rouhani, Modjtaba; Javan, Dawood S

2016-03-01

This paper presents new Radial Basis Function (RBF) learning methods for classification problems. The proposed methods use some heuristics to determine the spreads, the centers and the number of hidden neurons of network in such a way that the higher efficiency is achieved by fewer numbers of neurons, while the learning algorithm remains fast and simple. To retain network size limited, neurons are added to network recursively until termination condition is met. Each neuron covers some of train data. The termination condition is to cover all training data or to reach the maximum number of neurons. In each step, the center and spread of the new neuron are selected based on maximization of its coverage. Maximization of coverage of the neurons leads to a network with fewer neurons and indeed lower VC dimension and better generalization property. Using power exponential distribution function as the activation function of hidden neurons, and in the light of new learning approaches, it is proved that all data became linearly separable in the space of hidden layer outputs which implies that there exist linear output layer weights with zero training error. The proposed methods are applied to some well-known datasets and the simulation results, compared with SVM and some other leading RBF learning methods, show their satisfactory and comparable performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights.

PubMed

Luo, Shaohua; Wu, Songli; Gao, Ruizhen

2015-07-01

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Shaohua; Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021; Wu, Songli

2015-07-15

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in themore » closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.« less

A hybrid intelligent method for three-dimensional short-term prediction of dissolved oxygen content in aquaculture

PubMed Central

Yu, Huihui; Cheng, Yanjun; Cheng, Qianqian; Li, Daoliang

2018-01-01

A precise predictive model is important for obtaining a clear understanding of the changes in dissolved oxygen content in crab ponds. Highly accurate interval forecasting of dissolved oxygen content is fundamental to reduce risk, and three-dimensional prediction can provide more accurate results and overall guidance. In this study, a hybrid three-dimensional (3D) dissolved oxygen content prediction model based on a radial basis function (RBF) neural network, K-means and subtractive clustering was developed and named the subtractive clustering (SC)-K-means-RBF model. In this modeling process, K-means and subtractive clustering methods were employed to enhance the hyperparameters required in the RBF neural network model. The comparison of the predicted results of different traditional models validated the effectiveness and accuracy of the proposed hybrid SC-K-means-RBF model for three-dimensional prediction of dissolved oxygen content. Consequently, the proposed model can effectively display the three-dimensional distribution of dissolved oxygen content and serve as a guide for feeding and future studies. PMID:29466394

RBF neural network prediction on weak electrical signals in Aloe vera var. chinensis

NASA Astrophysics Data System (ADS)

Wang, Lanzhou; Zhao, Jiayin; Wang, Miao

2008-10-01

A Gaussian radial base function (RBF) neural network forecast on signals in the Aloe vera var. chinensis by the wavelet soft-threshold denoised as the time series and using the delayed input window chosen at 50, is set up to forecast backward. There was the maximum amplitude at 310.45μV, minimum -75.15μV, average value -2.69μV and <1.5Hz at frequency in Aloe vera var. chinensis respectively. The electrical signal in Aloe vera var. chinensis is a sort of weak, unstable and low frequency signals. A result showed that it is feasible to forecast plant electrical signals for the timing by the RBF. The forecast data can be used as the preferences for the intelligent autocontrol system based on the adaptive characteristic of plants to achieve the energy saving on the agricultural production in the plastic lookum or greenhouse.

Neural networks with local receptive fields and superlinear VC dimension.

PubMed

Schmitt, Michael

2002-04-01

Local receptive field neurons comprise such well-known and widely used unit types as radial basis function (RBF) neurons and neurons with center-surround receptive field. We study the Vapnik-Chervonenkis (VC) dimension of feedforward neural networks with one hidden layer of these units. For several variants of local receptive field neurons, we show that the VC dimension of these networks is superlinear. In particular, we establish the bound Omega(W log k) for any reasonably sized network with W parameters and k hidden nodes. This bound is shown to hold for discrete center-surround receptive field neurons, which are physiologically relevant models of cells in the mammalian visual system, for neurons computing a difference of gaussians, which are popular in computational vision, and for standard RBF neurons, a major alternative to sigmoidal neurons in artificial neural networks. The result for RBF neural networks is of particular interest since it answers a question that has been open for several years. The results also give rise to lower bounds for networks with fixed input dimension. Regarding constants, all bounds are larger than those known thus far for similar architectures with sigmoidal neurons. The superlinear lower bounds contrast with linear upper bounds for single local receptive field neurons also derived here.

Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method

NASA Astrophysics Data System (ADS)

Salajegheh, Eysa; Gholizadeh, Saeed; Khatibinia, Mohsen

2008-03-01

The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.

Study of CNG/diesel dual fuel engine's emissions by means of RBF neural network.

PubMed

Liu, Zhen-tao; Fei, Shao-mei

2004-08-01

Great efforts have been made to resolve the serious environmental pollution and inevitable declining of energy resources. A review of Chinese fuel reserves and engine technology showed that compressed natural gas (CNG)/diesel dual fuel engine (DFE) was one of the best solutions for the above problems at present. In order to study and improve the emission performance of CNG/diesel DFE, an emission model for DFE based on radial basis function (RBF) neural network was developed which was a black-box input-output training data model not require priori knowledge. The RBF centers and the connected weights could be selected automatically according to the distribution of the training data in input-output space and the given approximating error. Studies showed that the predicted results accorded well with the experimental data over a large range of operating conditions from low load to high load. The developed emissions model based on the RBF neural network could be used to successfully predict and optimize the emissions performance of DFE. And the effect of the DFEmain performance parameters, such as rotation speed, load, pilot quantity and injection timing, were also predicted by means of this model. In resumé, an emission prediction model for CNG/diesel DFE based on RBF neural network was built for analyzing the effect of the main performance parameters on the CO, NOx, emissions of DFE. The predicted results agreed quite well with the traditional emissions model, which indicated that the model had certain application value, although it still has some limitations, because of its high dependence on the quantity of the experimental sample data.

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

NASA Astrophysics Data System (ADS)

Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

2018-03-01

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.

[Spectral quantitative analysis by nonlinear partial least squares based on neural network internal model for flue gas of thermal power plant].

PubMed

Cao, Hui; Li, Yao-Jiang; Zhou, Yan; Wang, Yan-Xia

2014-11-01

To deal with nonlinear characteristics of spectra data for the thermal power plant flue, a nonlinear partial least square (PLS) analysis method with internal model based on neural network is adopted in the paper. The latent variables of the independent variables and the dependent variables are extracted by PLS regression firstly, and then they are used as the inputs and outputs of neural network respectively to build the nonlinear internal model by train process. For spectra data of flue gases of the thermal power plant, PLS, the nonlinear PLS with the internal model of back propagation neural network (BP-NPLS), the non-linear PLS with the internal model of radial basis function neural network (RBF-NPLS) and the nonlinear PLS with the internal model of adaptive fuzzy inference system (ANFIS-NPLS) are compared. The root mean square error of prediction (RMSEP) of sulfur dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 16.96%, 16.60% and 19.55% than that of PLS, respectively. The RMSEP of nitric oxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 8.60%, 8.47% and 10.09% than that of PLS, respectively. The RMSEP of nitrogen dioxide of BP-NPLS, RBF-NPLS and ANFIS-NPLS are reduced by 2.11%, 3.91% and 3.97% than that of PLS, respectively. Experimental results show that the nonlinear PLS is more suitable for the quantitative analysis of glue gas than PLS. Moreover, by using neural network function which can realize high approximation of nonlinear characteristics, the nonlinear partial least squares method with internal model mentioned in this paper have well predictive capabilities and robustness, and could deal with the limitations of nonlinear partial least squares method with other internal model such as polynomial and spline functions themselves under a certain extent. ANFIS-NPLS has the best performance with the internal model of adaptive fuzzy inference system having ability to learn more and reduce the residuals effectively. Hence, ANFIS-NPLS is an accurate and useful quantitative thermal power plant flue gas analysis method.

A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation.

PubMed

Chen, Xin; Wang, Ding; Yin, Jiexin; Wu, Ying

2018-06-13

The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.

Centralized Networks to Generate Human Body Motions

PubMed Central

Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan

2017-01-01

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings. PMID:29240694

Centralized Networks to Generate Human Body Motions.

PubMed

Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan; Weber, Andres

2017-12-14

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons' states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers' trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

PubMed

Du, Jialu; Hu, Xin; Liu, Hongbo; Chen, C L Philip

2015-11-01

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship's position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Identification of vegetable diseases using neural network

NASA Astrophysics Data System (ADS)

Zhang, Jiacai; Tang, Jianjun; Li, Yao

2007-02-01

Vegetables are widely planted all over China, but they often suffer from the some diseases. A method of major technical and economical importance is introduced in this paper, which explores the feasibility of implementing fast and reliable automatic identification of vegetable diseases and their infection grades from color and morphological features of leaves. Firstly, leaves are plucked from clustered plant and pictures of the leaves are taken with a CCD digital color camera. Secondly, color and morphological characteristics are obtained by standard image processing techniques, for examples, Otsu thresholding method segments the region of interest, image opening following closing algorithm removes noise, Principal Components Analysis reduces the dimension of the original features. Then, a recently proposed boosting algorithm AdaBoost. M2 is applied to RBF networks for diseases classification based on the above features, where the kernel function of RBF networks is Gaussian form with argument taking Euclidean distance of the input vector from a center. Our experiment performs on the database collected by Chinese Academy of Agricultural Sciences, and result shows that Boosting RBF Networks classifies the 230 cucumber leaves into 2 different diseases (downy-mildew and angular-leaf-spot), and identifies the infection grades of each disease according to the infection degrees.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

A research using hybrid RBF/Elman neural networks for intrusion detection system secure model

NASA Astrophysics Data System (ADS)

Tong, Xiaojun; Wang, Zhu; Yu, Haining

2009-10-01

A hybrid RBF/Elman neural network model that can be employed for both anomaly detection and misuse detection is presented in this paper. The IDSs using the hybrid neural network can detect temporally dispersed and collaborative attacks effectively because of its memory of past events. The RBF network is employed as a real-time pattern classification and the Elman network is employed to restore the memory of past events. The IDSs using the hybrid neural network are evaluated against the intrusion detection evaluation data sponsored by U.S. Defense Advanced Research Projects Agency (DARPA). Experimental results are presented in ROC curves. Experiments show that the IDSs using this hybrid neural network improve the detection rate and decrease the false positive rate effectively.

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE PAGES

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon; ...

2017-05-19

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Artificial Neural Networks: A Novel Approach to Analysing the Nutritional Ecology of a Blowfly Species, Chrysomya megacephala

PubMed Central

Bianconi, André; Zuben, Cláudio J. Von; Serapião, Adriane B. de S.; Govone, José S.

2010-01-01

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R2) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies. PMID:20569135

Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian

2005-04-01

Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model.

Artificial neural networks applied to forecasting time series.

PubMed

Montaño Moreno, Juan J; Palmer Pol, Alfonso; Muñoz Gracia, Pilar

2011-04-01

This study offers a description and comparison of the main models of Artificial Neural Networks (ANN) which have proved to be useful in time series forecasting, and also a standard procedure for the practical application of ANN in this type of task. The Multilayer Perceptron (MLP), Radial Base Function (RBF), Generalized Regression Neural Network (GRNN), and Recurrent Neural Network (RNN) models are analyzed. With this aim in mind, we use a time series made up of 244 time points. A comparative study establishes that the error made by the four neural network models analyzed is less than 10%. In accordance with the interpretation criteria of this performance, it can be concluded that the neural network models show a close fit regarding their forecasting capacity. The model with the best performance is the RBF, followed by the RNN and MLP. The GRNN model is the one with the worst performance. Finally, we analyze the advantages and limitations of ANN, the possible solutions to these limitations, and provide an orientation towards future research.

An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks

ERIC Educational Resources Information Center

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…

Radial basis function neural networks applied to NASA SSME data

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Dhawan, Atam P.

1993-01-01

This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

PubMed

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

Simulation and prediction of the thuringiensin abiotic degradation processes in aqueous solution by a radius basis function neural network model.

PubMed

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.

Design of cognitive engine for cognitive radio based on the rough sets and radial basis function neural network

NASA Astrophysics Data System (ADS)

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.

Reconfigurable Control Design with Neural Network Augmentation for a Modified F-15 Aircraft

NASA Technical Reports Server (NTRS)

Burken, John J.

2007-01-01

The viewgraphs present background information about reconfiguration control design, design methods used for paper, control failure survivability results, and results and time histories of tests. Topics examined include control reconfiguration, general information about adaptive controllers, model reference adaptive control (MRAC), the utility of neural networks, radial basis functions (RBF) neural network outputs, neurons, and results of investigations of failures.

Evolutionary optimization of radial basis function classifiers for data mining applications.

PubMed

Buchtala, Oliver; Klimek, Manuel; Sick, Bernhard

2005-10-01

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Short-term PV/T module temperature prediction based on PCA-RBF neural network

NASA Astrophysics Data System (ADS)

Li, Jiyong; Zhao, Zhendong; Li, Yisheng; Xiao, Jing; Tang, Yunfeng

2018-02-01

Aiming at the non-linearity and large inertia of temperature control in PV/T system, short-term temperature prediction of PV/T module is proposed, to make the PV/T system controller run forward according to the short-term forecasting situation to optimize control effect. Based on the analysis of the correlation between PV/T module temperature and meteorological factors, and the temperature of adjacent time series, the principal component analysis (PCA) method is used to pre-process the original input sample data. Combined with the RBF neural network theory, the simulation results show that the PCA method makes the prediction accuracy of the network model higher and the generalization performance stronger than that of the RBF neural network without the main component extraction.

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Distributed formation control of nonholonomic autonomous vehicle via RBF neural network

NASA Astrophysics Data System (ADS)

Yang, Shichun; Cao, Yaoguang; Peng, Zhaoxia; Wen, Guoguang; Guo, Konghui

2017-03-01

In this paper, RBF neural network consensus-based distributed control scheme is proposed for nonholonomic autonomous vehicles in a pre-defined formation along the specified reference trajectory. A variable transformation is first designed to convert the formation control problem into a state consensus problem. Then, the complete dynamics of the vehicles including inertia, Coriolis, friction model and unmodeled bounded disturbances are considered, which lead to the formation unstable when the distributed kinematic controllers are proposed based on the kinematics. RBF neural network torque controllers are derived to compensate for them. Some sufficient conditions are derived to accomplish the asymptotically stability of the systems based on algebraic graph theory, matrix theory, and Lyapunov theory. Finally, simulation examples illustrate the effectiveness of the proposed controllers.

Gas Chromatography Data Classification Based on Complex Coefficients of an Autoregressive Model

DOE PAGES

Zhao, Weixiang; Morgan, Joshua T.; Davis, Cristina E.

2008-01-01

This paper introduces autoregressive (AR) modeling as a novel method to classify outputs from gas chromatography (GC). The inverse Fourier transformation was applied to the original sensor data, and then an AR model was applied to transform data to generate AR model complex coefficients. This series of coefficients effectively contains a compressed version of all of the information in the original GC signal output. We applied this method to chromatograms resulting from proliferating bacteria species grown in culture. Three types of neural networks were used to classify the AR coefficients: backward propagating neural network (BPNN), radial basis function-principal component analysismore » (RBF-PCA) approach, and radial basis function-partial least squares regression (RBF-PLSR) approach. This exploratory study demonstrates the feasibility of using complex root coefficient patterns to distinguish various classes of experimental data, such as those from the different bacteria species. This cognition approach also proved to be robust and potentially useful for freeing us from time alignment of GC signals.« less

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization.

PubMed

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

PubMed Central

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method. PMID:27127499

A Novel Higher Order Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Xu, Shuxiang

2010-05-01

In this paper a new Higher Order Neural Network (HONN) model is introduced and applied in several data mining tasks. Data Mining extracts hidden patterns and valuable information from large databases. A hyperbolic tangent function is used as the neuron activation function for the new HONN model. Experiments are conducted to demonstrate the advantages and disadvantages of the new HONN model, when compared with several conventional Artificial Neural Network (ANN) models: Feedforward ANN with the sigmoid activation function; Feedforward ANN with the hyperbolic tangent activation function; and Radial Basis Function (RBF) ANN with the Gaussian activation function. The experimental results seem to suggest that the new HONN holds higher generalization capability as well as abilities in handling missing data.

Modelling and prediction for chaotic fir laser attractor using rational function neural network.

PubMed

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.

Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

Designing an artificial neural network using radial basis function to model exergetic efficiency of nanofluids in mini double pipe heat exchanger

NASA Astrophysics Data System (ADS)

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.

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation.

PubMed

Adetiba, Emmanuel; Olugbara, Oludayo O

2015-01-01

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square error.

Motion planning for autonomous vehicle based on radial basis function neural network in unstructured environment.

PubMed

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.

Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment

PubMed Central

Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao

2014-01-01

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. PMID:25237902

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

PubMed

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

2012-04-01

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

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Forecasting ozone concentrations in the east of Croatia using nonparametric Neural Network Models

NASA Astrophysics Data System (ADS)

Kovač-Andrić, Elvira; Sheta, Alaa; Faris, Hossam; Gajdošik, Martina Šrajer

2016-07-01

Ozone is one of the most significant secondary pollutants with numerous negative effects on human health and environment including plants and vegetation. Therefore, more effort is made recently by governments and associations to predict ozone concentrations which could help in establishing better plans and regulation for environment protection. In this study, we use two Artificial Neural Network based approaches (MPL and RBF) to develop, for the first time, accurate ozone prediction models, one for urban and another one for rural area in the eastern part of Croatia. The evaluation of actual against the predicted ozone concentrations revealed that MLP and RBF models are very competitive for the training and testing data in the case of Kopački Rit area whereas in the case of Osijek city, MLP shows better evaluation results with 9% improvement in the correlation coefficient. Furthermore, subsequent feature selection process has improved the prediction power of RBF network.

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

PubMed

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Prospects of second generation artificial intelligence tools in calibration of chemical sensors.

PubMed

Braibanti, Antonio; Rao, Rupenaguntla Sambasiva; Ramam, Veluri Anantha; Rao, Gollapalli Nageswara; Rao, Vaddadi Venkata Panakala

2005-05-01

Multivariate data driven calibration models with neural networks (NNs) are developed for binary (Cu++ and Ca++) and quaternary (K+, Ca++, NO3- and Cl-) ion-selective electrode (ISE) data. The response profiles of ISEs with concentrations are non-linear and sub-Nernstian. This task represents function approximation of multi-variate, multi-response, correlated, non-linear data with unknown noise structure i.e. multi-component calibration/prediction in chemometric parlance. Radial distribution function (RBF) and Fuzzy-ARTMAP-NN models implemented in the software packages, TRAJAN and Professional II, are employed for the calibration. The optimum NN models reported are based on residuals in concentration space. Being a data driven information technology, NN does not require a model, prior- or posterior- distribution of data or noise structure. Missing information, spikes or newer trends in different concentration ranges can be modeled through novelty detection. Two simulated data sets generated from mathematical functions are modeled as a function of number of data points and network parameters like number of neurons and nearest neighbors. The success of RBF and Fuzzy-ARTMAP-NNs to develop adequate calibration models for experimental data and function approximation models for more complex simulated data sets ensures AI2 (artificial intelligence, 2nd generation) as a promising technology in quantitation.

Stable computations with flat radial basis functions using vector-valued rational approximations

NASA Astrophysics Data System (ADS)

Wright, Grady B.; Fornberg, Bengt

2017-02-01

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are 'flat' leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vector-valued analytic functions with the property that all components of the vector share the same singularities. This new algorithm (RBF-RA) is more accurate, robust, and easier to implement than the Contour-Padé method, which is similarly based on vector-valued rational approximation. In contrast to the stable RBF-QR and RBF-GA algorithms, which are based on finding a better conditioned base in the same RBF-space, the new algorithm can be used with any type of smooth radial kernel, and it is also applicable to a wider range of tasks (including calculating Hermite type implicit RBF-FD stencils). We present a series of numerical experiments demonstrating the effectiveness of this new method for computing RBF interpolants in the flat regime. We also demonstrate the flexibility of the method by using it to compute implicit RBF-FD formulas in the flat regime and then using these for solving Poisson's equation in a 3-D spherical shell.

Conic section function neural network circuitry for offline signature recognition.

PubMed

Erkmen, Burcu; Kahraman, Nihan; Vural, Revna A; Yildirim, Tulay

2010-04-01

In this brief, conic section function neural network (CSFNN) circuitry was designed for offline signature recognition. CSFNN is a unified framework for multilayer perceptron (MLP) and radial basis function (RBF) networks to make simultaneous use of advantages of both. The CSFNN circuitry architecture was developed using a mixed mode circuit implementation. The designed circuit system is problem independent. Hence, the general purpose neural network circuit system could be applied to various pattern recognition problems with different network sizes on condition with the maximum network size of 16-16-8. In this brief, CSFNN circuitry system has been applied to two different signature recognition problems. CSFNN circuitry was trained with chip-in-the-loop learning technique in order to compensate typical analog process variations. CSFNN hardware achieved highly comparable computational performances with CSFNN software for nonlinear signature recognition problems.

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids

PubMed Central

Zhao, Ningbo; Li, Zhiming

2017-01-01

To effectively predict the thermal conductivity and viscosity of alumina (Al2O3)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al2O3-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al2O3-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al2O3-water nanofluids. However, the viscosity only depended strongly on Al2O3 nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al2O3-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data. PMID:28772913

Experiment and Artificial Neural Network Prediction of Thermal Conductivity and Viscosity for Alumina-Water Nanofluids.

PubMed

Zhao, Ningbo; Li, Zhiming

2017-05-19

To effectively predict the thermal conductivity and viscosity of alumina (Al₂O₃)-water nanofluids, an artificial neural network (ANN) approach was investigated in the present study. Firstly, using a two-step method, four Al₂O₃-water nanofluids were prepared respectively by dispersing different volume fractions (1.31%, 2.72%, 4.25%, and 5.92%) of nanoparticles with the average diameter of 30 nm. On this basis, the thermal conductivity and viscosity of the above nanofluids were analyzed experimentally under various temperatures ranging from 296 to 313 K. Then a radial basis function (RBF) neural network was constructed to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids as a function of nanoparticle volume fraction and temperature. The experimental results showed that both nanoparticle volume fraction and temperature could enhance the thermal conductivity of Al₂O₃-water nanofluids. However, the viscosity only depended strongly on Al₂O₃ nanoparticle volume fraction and was increased slightly by changing temperature. In addition, the comparative analysis revealed that the RBF neural network had an excellent ability to predict the thermal conductivity and viscosity of Al₂O₃-water nanofluids with the mean absolute percent errors of 0.5177% and 0.5618%, respectively. This demonstrated that the ANN provided an effective way to predict the thermophysical properties of nanofluids with limited experimental data.

Correlation of Visually Evoked Functional and Blood Flow Changes in the Rat Retina Measured With a Combined OCT+ERG System.

PubMed

Tan, Bingyao; Mason, Erik; MacLellan, Benjamin; Bizheva, Kostadinka K

2017-03-01

To correlate visually evoked functional and blood flow changes in the rat retina measured simultaneously with a combined optical coherence tomography and electroretinography system (OCT+ERG). Male Brown Norway (n = 6) rats were dark adapted and anesthetized with ketamine/xylazine. Visually evoked changes in the retinal blood flow (RBF) and functional response were measured simultaneously with an OCT+ERG system with 3-μm axial resolution in retinal tissue and 47-kHz image acquisition rate. Both single flash (10 and 200 ms) and flicker (10 Hz, 20% duty cycle, 1- and 2-second duration) stimuli were projected onto the retina with a custom visual stimulator, integrated into the OCT imaging probe. Total axial RBF was calculated from circular Doppler OCT scans by integrating over the arterial and venal flow. Temporary increase in the RBF was observed with the 10- and 200-ms continuous stimuli (∼1% and ∼4% maximum RBF change, respectively) and the 10-Hz flicker stimuli (∼8% for 1-second duration and ∼10% for 2-second duration). Doubling the flicker stimulus duration resulted in ∼25% increase in the RBF peak magnitude with no significant change in the peak latency. Single flash (200 ms) and flicker (10 Hz, 1 second) stimuli of the same illumination intensity and photon flux resulted in ∼2× larger peak RBF magnitude and ∼25% larger RBF peak latency for the flicker stimulus. Short, single flash and flicker stimuli evoked measureable RBF changes with larger RBF magnitude and peak latency observed for the flicker stimuli.

Multicomponent kinetic spectrophotometric determination of pefloxacin and norfloxacin in pharmaceutical preparations and human plasma samples with the aid of chemometrics

NASA Astrophysics Data System (ADS)

Ni, Yongnian; Wang, Yong; Kokot, Serge

2008-10-01

A spectrophotometric method for the simultaneous determination of the important pharmaceuticals, pefloxacin and its structurally similar metabolite, norfloxacin, is described for the first time. The analysis is based on the monitoring of a kinetic spectrophotometric reaction of the two analytes with potassium permanganate as the oxidant. The measurement of the reaction process followed the absorbance decrease of potassium permanganate at 526 nm, and the accompanying increase of the product, potassium manganate, at 608 nm. It was essential to use multivariate calibrations to overcome severe spectral overlaps and similarities in reaction kinetics. Calibration curves for the individual analytes showed linear relationships over the concentration ranges of 1.0-11.5 mg L -1 at 526 and 608 nm for pefloxacin, and 0.15-1.8 mg L -1 at 526 and 608 nm for norfloxacin. Various multivariate calibration models were applied, at the two analytical wavelengths, for the simultaneous prediction of the two analytes including classical least squares (CLS), principal component regression (PCR), partial least squares (PLS), radial basis function-artificial neural network (RBF-ANN) and principal component-radial basis function-artificial neural network (PC-RBF-ANN). PLS and PC-RBF-ANN calibrations with the data collected at 526 nm, were the preferred methods—%RPE T ˜ 5, and LODs for pefloxacin and norfloxacin of 0.36 and 0.06 mg L -1, respectively. Then, the proposed method was applied successfully for the simultaneous determination of pefloxacin and norfloxacin present in pharmaceutical and human plasma samples. The results compared well with those from the alternative analysis by HPLC.

Neural network modeling for surgical decisions on traumatic brain injury patients.

PubMed

Li, Y C; Liu, L; Chiu, W T; Jian, W S

2000-01-01

Computerized medical decision support systems have been a major research topic in recent years. Intelligent computer programs were implemented to aid physicians and other medical professionals in making difficult medical decisions. This report compares three different mathematical models for building a traumatic brain injury (TBI) medical decision support system (MDSS). These models were developed based on a large TBI patient database. This MDSS accepts a set of patient data such as the types of skull fracture, Glasgow Coma Scale (GCS), episode of convulsion and return the chance that a neurosurgeon would recommend an open-skull surgery for this patient. The three mathematical models described in this report including a logistic regression model, a multi-layer perceptron (MLP) neural network and a radial-basis-function (RBF) neural network. From the 12,640 patients selected from the database. A randomly drawn 9480 cases were used as the training group to develop/train our models. The other 3160 cases were in the validation group which we used to evaluate the performance of these models. We used sensitivity, specificity, areas under receiver-operating characteristics (ROC) curve and calibration curves as the indicator of how accurate these models are in predicting a neurosurgeon's decision on open-skull surgery. The results showed that, assuming equal importance of sensitivity and specificity, the logistic regression model had a (sensitivity, specificity) of (73%, 68%), compared to (80%, 80%) from the RBF model and (88%, 80%) from the MLP model. The resultant areas under ROC curve for logistic regression, RBF and MLP neural networks are 0.761, 0.880 and 0.897, respectively (P < 0.05). Among these models, the logistic regression has noticeably poorer calibration. This study demonstrated the feasibility of applying neural networks as the mechanism for TBI decision support systems based on clinical databases. The results also suggest that neural networks may be a better solution for complex, non-linear medical decision support systems than conventional statistical techniques such as logistic regression.

Japanese Alzheimer's Disease and Other Complex Disorders Diagnosis Based on Mitochondrial SNP Haplogroups

PubMed Central

Takasaki, Shigeru

2012-01-01

This paper first explains how the relations between Japanese Alzheimer's disease (AD) patients and their mitochondrial SNP frequencies at individual mtDNA positions examined using the radial basis function (RBF) network and a method based on RBF network predictions and that Japanese AD patients are associated with the haplogroups G2a and N9b1. It then describes a method for the initial diagnosis of Alzheimer's disease that is based on the mtSNP haplogroups of the AD patients. The method examines the relations between someone's mtDNA mutations and the mtSNPs of AD patients. As the mtSNP haplogroups thus obtained indicate which nucleotides of mtDNA loci are changed in the Alzheimer's patients, a person's probability of becoming an AD patient can be predicted by comparing those mtDNA mutations with that person's mtDNA mutations. The proposed method can also be used to diagnose diseases such as Parkinson's disease and type 2 diabetes and to identify people likely to become centenarians. PMID:22848858

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA

PubMed Central

de Souza, Alisson C. D.; Fernandes, Marcelo A. C.

2014-01-01

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. PMID:25268918

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

PubMed

Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

2016-04-01

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mechanisms for renal blood flow control early in diabetes as revealed by chronic flow measurement and transfer function analysis.

PubMed

Bell, Tracy D; DiBona, Gerald F; Wang, Ying; Brands, Michael W

2006-08-01

The purpose of this study was to establish the roles of the myogenic response and the TGF mechanism in renal blood flow (RBF) control at the very earliest stages of diabetes. Mean arterial pressure (MAP) and RBF were measured continuously, 18 h/d, in uninephrectomized control and diabetic rats, and transfer function analysis was used to determine the dynamic autoregulatory efficiency of the renal vasculature. During the control period, MAP averaged 91 +/- 0.5 and 89 +/- 0.4 mmHg, and RBF averaged 8.0 +/- 0.1 and 7.8 +/- 0.1 ml/min in the control and diabetic groups, respectively. Induction of diabetes with streptozotocin caused a marked and progressive increase in RBF in the diabetic rats, averaging 10 +/- 6% above control on day 1 of diabetes and 22 +/- 3 and 34 +/- 1% above control by the end of diabetes weeks 1 and 2. MAP increased approximately 9 mmHg during the 2 wk in the diabetic rats, and renal vascular resistance decreased. Transfer function analysis revealed significant increases in gain to positive values over the frequency ranges of both the TGF and myogenic mechanisms, beginning on day 1 of diabetes and continuing through day 14. These very rapid increases in RBF and transfer function gain suggest that autoregulation is impaired at the very onset of hyperglycemia in streptozotocin-induced type 1 diabetes and may play an important role in the increase in RBF and GFR in diabetes. Together with previous reports of decreases in chronically measured cardiac output and hindquarter blood flow, this suggests that there may be differential effects of diabetes on RBF versus nonrenal BF control.

A new adaptive control strategy for a class of nonlinear system using RBF neuro-sliding-mode technique: application to SEIG wind turbine control system

NASA Astrophysics Data System (ADS)

Kenné, Godpromesse; Fotso, Armel Simo; Lamnabhi-Lagarrigue, Françoise

2017-04-01

In this paper, a new hybrid method which combines radial basis function (RBF) neural network with a sliding-mode technique to take advantage of their common features is used to control a class of nonlinear systems. A real-time dynamic nonlinear learning law of the weight vector is synthesized and the closed-loop stability has been demonstrated using Lyapunov theory. The solution presented in this work does not need the knowledge of the perturbation bounds, neither the knowledge of the full state of the nonlinear system. In addition, the bounds of the nonlinear functions are assumed to be unknown and the proposed RBF structure uses reduced number of hidden units. This hybrid control strategy is applied to extract the maximum available energy from a stand-alone self-excited variable low-wind speed energy conversion system and design the dc-voltage and rotor flux controllers as well as the load-side frequency and voltage regulators assuming that the measured outputs are the rotor speed, stator currents, load-side currents and voltages despite large variation of the rotor resistance and uncertainties on the inductances. Finally, simulation results compared with those obtained using the well-known second-order sliding-mode controller are given to show the effectiveness and feasibility of the proposed approach.

Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification.

PubMed

Yang, Fan; Paindavoine, M

2003-01-01

This paper describes a real time vision system that allows us to localize faces in video sequences and verify their identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and identity verification are 92% (FPGA), 85% (ZISC), and 98.2% (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

PubMed

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Fast RBF OGr for solving PDEs on arbitrary surfaces

NASA Astrophysics Data System (ADS)

Piret, Cécile; Dunn, Jarrett

2016-10-01

The Radial Basis Functions Orthogonal Gradients method (RBF-OGr) was introduced in [1] to discretize differential operators defined on arbitrary manifolds defined only by a point cloud. We take advantage of the meshfree character of RBFs, which give us a high accuracy and the flexibility to represent complex geometries in any spatial dimension. A large limitation of the RBF-OGr method was its large computational complexity, which greatly restricted the size of the point cloud. In this paper, we apply the RBF-Finite Difference (RBF-FD) technique to the RBF-OGr method for building sparse differentiation matrices discretizing continuous differential operators such as the Laplace-Beltrami operator. This method can be applied to solving PDEs on arbitrary surfaces embedded in ℛ3. We illustrate the accuracy of our new method by solving the heat equation on the unit sphere.

Self-organizing radial basis function networks for adaptive flight control and aircraft engine state estimation

NASA Astrophysics Data System (ADS)

Shankar, Praveen

The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network based adaptive controller. While the fixed RBF network based controller which is tuned to compensate for control surface failures fails to achieve the same performance under modeling uncertainty and disturbances, the SORBFN is able to achieve good tracking convergence under all error conditions.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

NASA Astrophysics Data System (ADS)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg/day) in Jenderan catchment area.

A hybrid linear/nonlinear training algorithm for feedforward neural networks.

PubMed

McLoone, S; Brown, M D; Irwin, G; Lightbody, A

1998-01-01

This paper presents a new hybrid optimization strategy for training feedforward neural networks. The algorithm combines gradient-based optimization of nonlinear weights with singular value decomposition (SVD) computation of linear weights in one integrated routine. It is described for the multilayer perceptron (MLP) and radial basis function (RBF) networks and then extended to the local model network (LMN), a new feedforward structure in which a global nonlinear model is constructed from a set of locally valid submodels. Simulation results are presented demonstrating the superiority of the new hybrid training scheme compared to second-order gradient methods. It is particularly effective for the LMN architecture where the linear to nonlinear parameter ratio is large.

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics.

PubMed

Ni, Yongnian; Wang, Lin; Kokot, Serge

2011-01-01

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D - first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.

Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

PubMed Central

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops. PMID:22629171

Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops.

PubMed

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

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.

Random Bits Forest: a Strong Classifier/Regressor for Big Data

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Yi; Pu, Weilin; Wen, Kathryn; Shugart, Yin Yao; Xiong, Momiao; Jin, Li

2016-07-01

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ~10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

The Technology of Suppressing Harmonics with Complex Neural Network is Applied to Microgrid

NASA Astrophysics Data System (ADS)

Zhang, Jing; Li, Zhan-Ying; Wang, Yan-ping; Li, Yang; Zong, Ke-yong

2018-03-01

According to the traits of harmonics in microgrid, a new CANN controller which combines BP and RBF neural network is proposed to control APF to detect and suppress harmonics. This controller has the function of current prediction. By simulation in Matlab / Simulink, this design can shorten the delay time nearly 0.02s (a power supply current cycle) in comparison with the traditional controller based on ip-iq method. The new controller also has higher compensation accuracy and better dynamic tracking traits, it can greatly suppress the harmonics and improve the power quality.

Magnaporthe oryzae Glycine-Rich Secretion Protein, Rbf1 Critically Participates in Pathogenicity through the Focal Formation of the Biotrophic Interfacial Complex

PubMed Central

Ishii-Minami, Naoko; Kawahara, Yoshihiro; Yoshida, Yuri; Okada, Kazunori; Ando, Sugihiro; Matsumura, Hideo; Terauchi, Ryohei; Minami, Eiichi; Nishizawa, Yoko

2016-01-01

Magnaporthe oryzae, the fungus causing rice blast disease, should contend with host innate immunity to develop invasive hyphae (IH) within living host cells. However, molecular strategies to establish the biotrophic interactions are largely unknown. Here, we report the biological function of a M. oryzae-specific gene, R equired-for-Focal- B IC- F ormation 1 (RBF1). RBF1 expression was induced in appressoria and IH only when the fungus was inoculated to living plant tissues. Long-term successive imaging of live cell fluorescence revealed that the expression of RBF1 was upregulated each time the fungus crossed a host cell wall. Like other symplastic effector proteins of the rice blast fungus, Rbf1 accumulated in the biotrophic interfacial complex (BIC) and was translocated into the rice cytoplasm. RBF1-knockout mutants (Δrbf1) were severely deficient in their virulence to rice leaves, but were capable of proliferating in abscisic acid-treated or salicylic acid-deficient rice plants. In rice leaves, Δrbf1 inoculation caused necrosis and induced defense-related gene expression, which led to a higher level of diterpenoid phytoalexin accumulation than the wild-type fungus did. Δrbf1 showed unusual differentiation of IH and dispersal of the normally BIC-focused effectors around the short primary hypha and the first bulbous cell. In the Δrbf1-invaded cells, symplastic effectors were still translocated into rice cells but with a lower efficiency. These data indicate that RBF1 is a virulence gene essential for the focal BIC formation, which is critical for the rice blast fungus to suppress host immune responses. PMID:27711180

A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant

NASA Astrophysics Data System (ADS)

Hemmat Esfe, Mohammad; Tatar, Afshin; Ahangar, Mohammad Reza Hassani; Rostamian, Hossein

2018-02-01

Since the conventional thermal fluids such as water, oil, and ethylene glycol have poor thermal properties, the tiny solid particles are added to these fluids to increase their heat transfer improvement. As viscosity determines the rheological behavior of a fluid, studying the parameters affecting the viscosity is crucial. Since the experimental measurement of viscosity is expensive and time consuming, predicting this parameter is the apt method. In this work, three artificial intelligence methods containing Genetic Algorithm-Radial Basis Function Neural Networks (GA-RBF), Least Square Support Vector Machine (LS-SVM) and Gene Expression Programming (GEP) were applied to predict the viscosity of TiO2/SAE 50 nano-lubricant with Non-Newtonian power-law behavior using experimental data. The correlation factor (R2), Average Absolute Relative Deviation (AARD), Root Mean Square Error (RMSE), and Margin of Deviation were employed to investigate the accuracy of the proposed models. RMSE values of 0.58, 1.28, and 6.59 and R2 values of 0.99998, 0.99991, and 0.99777 reveal the accuracy of the proposed models for respective GA-RBF, CSA-LSSVM, and GEP methods. Among the developed models, the GA-RBF shows the best accuracy.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

PubMed

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

Comparing fixed and variable-width Gaussian networks.

PubMed

Kůrková, Věra; Kainen, Paul C

2014-09-01

The role of width of Gaussians in two types of computational models is investigated: Gaussian radial-basis-functions (RBFs) where both widths and centers vary and Gaussian kernel networks which have fixed widths but varying centers. The effect of width on functional equivalence, universal approximation property, and form of norms in reproducing kernel Hilbert spaces (RKHS) is explored. It is proven that if two Gaussian RBF networks have the same input-output functions, then they must have the same numbers of units with the same centers and widths. Further, it is shown that while sets of input-output functions of Gaussian kernel networks with two different widths are disjoint, each such set is large enough to be a universal approximator. Embedding of RKHSs induced by "flatter" Gaussians into RKHSs induced by "sharper" Gaussians is described and growth of the ratios of norms on these spaces with increasing input dimension is estimated. Finally, large sets of argminima of error functionals in sets of input-output functions of Gaussian RBFs are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multiwavelet packet entropy and its application in transmission line fault recognition and classification.

PubMed

Liu, Zhigang; Han, Zhiwei; Zhang, Yang; Zhang, Qiaoge

2014-11-01

Multiwavelets possess better properties than traditional wavelets. Multiwavelet packet transformation has more high-frequency information. Spectral entropy can be applied as an analysis index to the complexity or uncertainty of a signal. This paper tries to define four multiwavelet packet entropies to extract the features of different transmission line faults, and uses a radial basis function (RBF) neural network to recognize and classify 10 fault types of power transmission lines. First, the preprocessing and postprocessing problems of multiwavelets are presented. Shannon entropy and Tsallis entropy are introduced, and their difference is discussed. Second, multiwavelet packet energy entropy, time entropy, Shannon singular entropy, and Tsallis singular entropy are defined as the feature extraction methods of transmission line fault signals. Third, the plan of transmission line fault recognition using multiwavelet packet entropies and an RBF neural network is proposed. Finally, the experimental results show that the plan with the four multiwavelet packet energy entropies defined in this paper achieves better performance in fault recognition. The performance with SA4 (symmetric antisymmetric) multiwavelet packet Tsallis singular entropy is the best among the combinations of different multiwavelet packets and the four multiwavelet packet entropies.

A novel method for improving the accuracy of coordinate transformation in multiple measurement systems

NASA Astrophysics Data System (ADS)

Liu, W. L.; Li, Y. W.

2017-09-01

Large-scale dimensional metrology usually requires a combination of multiple measurement systems, such as laser tracking, total station, laser scanning, coordinate measuring arm and video photogrammetry, etc. Often, the results from different measurement systems must be combined to provide useful results. The coordinate transformation is used to unify coordinate frames in combination; however, coordinate transformation uncertainties directly affect the accuracy of the final measurement results. In this paper, a novel method is proposed for improving the accuracy of coordinate transformation, combining the advantages of the best-fit least-square and radial basis function (RBF) neural networks. First of all, the configuration of coordinate transformation is introduced and a transformation matrix containing seven variables is obtained. Second, the 3D uncertainty of the transformation model and the residual error variable vector are established based on the best-fit least-square. Finally, in order to optimize the uncertainty of the developed seven-variable transformation model, we used the RBF neural network to identify the uncertainty of the dynamic, and unstructured, owing to its great ability to approximate any nonlinear function to the designed accuracy. Intensive experimental studies were conducted to check the validity of the theoretical results. The results show that the mean error of coordinate transformation decreased from 0.078 mm to 0.054 mm after using this method in contrast with the GUM method.

A practical radial basis function equalizer.

PubMed

Lee, J; Beach, C; Tepedelenlioglu, N

1999-01-01

A radial basis function (RBF) equalizer design process has been developed in which the number of basis function centers used is substantially fewer than conventionally required. The reduction of centers is accomplished in two-steps. First an algorithm is used to select a reduced set of centers that lie close to the decision boundary. Then the centers in this reduced set are grouped, and an average position is chosen to represent each group. Channel order and delay, which are determining factors in setting the initial number of centers, are estimated from regression analysis. In simulation studies, an RBF equalizer with more than 2000-to-1 reduction in centers performed as well as the RBF equalizer without reduction in centers, and better than a conventional linear equalizer.

Temperature-based estimation of global solar radiation using soft computing methodologies

NASA Astrophysics Data System (ADS)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Danesh, Amir Seyed; Abdullah, Mohd Shahidan; Zamani, Mazdak

2016-07-01

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures ( T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max- T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

Evaluation of Particle Size Influence on Proximate Composition, Physicochemical, Techno-Functional and Physio-Functional Properties of Flours Obtained from Persimmon (Diospyros kaki Trumb.) Coproducts.

PubMed

Lucas-González, Raquel; Viuda-Martos, Manuel; Pérez-Álvarez, José Ángel; Fernández-López, Juana

2017-03-01

The aim of the work was to study the influence of particle size in the composition, physicochemical, techno-functional and physio-functional properties of two flours obtained from persimmon (Diospyros kaki Trumb. cvs. 'Rojo Brillante' (RBF) and 'Triump' (THF) coproducts. The cultivar (RBF and THF) and particle size significantly affected all parameters under study, although depending on the evaluated property, only one of these effects predominated. Carbohydrates (38.07-46.98 g/100 g) and total dietary fiber (32.07-43.57 g/100 g) were the main components in both flours (RBF and THF). Furthermore, insoluble dietary fiber represented more than 68% of total dietary fiber content. All color properties studied were influenced by cultivar and particle size. For both cultivars, the lower particle size, the higher lightness and hue values. RBF flours showed high values for emulsifying activity (69.33-74.00 mL/mL), while THF presented high values for water holding capacity (WHC: 9.47-12.19 g water/g sample). The bile holding capacity (BHC) and fat/oil binding values were, in general, higher in RBF (19.61-12.19 g bile/g sample and 11.98-9.07, respectively) than THF (16.12-12.40 g bile/g sample and 9.78-7.96, respectively). The effect of particle size was really evident in both WHC and BHC. Due to their dietary fiber content, techno-functional and physio-functional properties, persimmon flours seem to have a good profile to be used as potential functional ingredient.

Association between exercise intensity and renal blood flow evaluated using ultrasound echo.

PubMed

Kawakami, Shotaro; Yasuno, Tetsuhiko; Matsuda, Takuro; Fujimi, Kanta; Ito, Ai; Yoshimura, Saki; Uehara, Yoshinari; Tanaka, Hiroaki; Saito, Takao; Higaki, Yasuki

2018-03-10

High-intensity exercise reduces renal blood flow (RBF) and may transiently exacerbate renal dysfunction. RBF has previously been measured invasively by administration of an indicator material; however, non-invasive measurement is now possible with technological innovations. This study examined variations in RBF at different exercise intensities using ultrasound echo. Eight healthy men with normal renal function (eGFR cys 114 ± 19 mL/min/1.73 m 2 ) participated in this study. Using a bicycle ergometer, participants underwent an incremental exercise test using a ramp protocol (20 W/min) until exhaustion in Study 1 and the lactate acid breaking point (LaBP) was calculated. Participants underwent a multi-stage test at exercise intensities of 60, 80, 100, 120, and 140% LaBP in Study 2. RBF was measured by ultrasound echo at rest and 5 min after exercise in Study 1 and at rest and immediately after each exercise in Study 2. To determine the mechanisms behind RBF decline, a catheter was placed into the antecubital vein to study vasoconstriction dynamics. RBF after maximum exercise decreased by 51% in Study 1. In Study 2, RBF showed no significant decrease until 80% LaBP, and showed a significant decrease (31%) at 100% LaBP compared with at rest (p < 0.01). The sympathetic nervous system may be involved in this reduction in RBF. RBF showed no significant decrease until 80% LaBP, and decreased with an increase in blood lactate. Reduction in RBF with exercise above the intensity at LaBP was due to decreased cross-sectional area rather than time-averaged flow velocity.

Insulin induces the correlation between renal blood flow and glomerular filtration rate in diabetes: implications for mechanisms causing hyperfiltration.

PubMed

Pihl, Liselotte; Persson, Patrik; Fasching, Angelica; Hansell, Peter; DiBona, Gerald F; Palm, Fredrik

2012-07-01

Glomerular filtration rate (GFR) and renal blood flow (RBF) are normally kept constant via renal autoregulation. However, early diabetes results in increased GFR and the potential mechanisms are debated. Tubuloglomerular feedback (TGF) inactivation, with concomitantly increased RBF, is proposed but challenged by the finding of glomerular hyperfiltration in diabetic adenosine A(1) receptor-deficient mice, which lack TGF. Furthermore, we consistently find elevated GFR in diabetes with only minor changes in RBF. This may relate to the use of a lower streptozotocin dose, which produces a degree of hyperglycemia, which is manageable without supplemental suboptimal insulin administration, as has been used by other investigators. Therefore, we examined the relationship between RBF and GFR in diabetic rats with (diabetes + insulin) and without suboptimal insulin administration (untreated diabetes). As insulin can affect nitric oxide (NO) release, the role of NO was also investigated. GFR, RBF, and glomerular filtration pressures were measured. Dynamic RBF autoregulation was examined by transfer function analysis between arterial pressure and RBF. Both diabetic groups had increased GFR (+60-67%) and RBF (+20-23%) compared with controls. However, only the diabetes + insulin group displayed a correlation between GFR and RBF (R(2) = 0.81, P < 0.0001). Net filtration pressure was increased in untreated diabetes compared with both other groups. The difference between untreated and insulin-treated diabetic rats disappeared after administering N(ω)-nitro-l-arginine methyl ester to inhibit NO synthase and subsequent NO release. In conclusion, mechanisms causing diabetes-induced glomerular hyperfiltration are animal model-dependent. Supplemental insulin administration results in a RBF-dependent mechanism, whereas elevated GFR in untreated diabetes is mediated primarily by a tubular event. Insulin-induced NO release partially contributes to these differences.

Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

PubMed

Wu, Yixiao; Yang, Ran; Jia, Sen; Li, Zhanjun; Zhou, Zhiyang; Lou, Ting

2014-01-01

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

PubMed

Amozegar, M; Khorasani, K

2016-04-01

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

CONORBIT: constrained optimization by radial basis function interpolation in trust regions

DOE PAGES

Regis, Rommel G.; Wild, Stefan M.

2016-09-26

Here, this paper presents CONORBIT (CONstrained Optimization by Radial Basis function Interpolation in Trust regions), a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions, and is an extension of the ORBIT algorithm. It uses a small margin for the RBF constraint models to facilitate the generation of feasible iterates, and extensive numerical tests confirm that such a margin is helpful in improving performance. CONORBIT is compared with other algorithms on 27 test problems, amore » chemical process optimization problem, and an automotive application. Numerical results show that CONORBIT performs better than COBYLA, a sequential penalty derivative-free method, an augmented Lagrangian method, a direct search method, and another RBF-based algorithm on the test problems and on the automotive application.« less

A forskolin derivative, colforsin daropate hydrochloride, inhibits the decrease in cortical renal blood flow induced by noradrenaline or angiotensin II in anesthetized rats.

PubMed

Ogata, Junichi; Minami, Kouichiro; Segawa, Kayoko; Uezono, Yasuhito; Shiraishi, Munehiro; Yamamoto, Chikako; Sata, Takeyoshi; Sung-Teh, Kim; Shigematsu, Akio

2004-01-01

A forskolin derivative, colforsin daropate hydrochloride (CDH), acts directly on adenylate cyclase to increase the intracellular cyclic adenosine monophosphate levels which produce a positive inotropic effect and a lower blood pressure. However, little is known about the effects of CDH on the renal function. We used laser Doppler flowmetry to measure the cortical renal blood flow (RBF) in male Wistar rats given a continuous intravenous infusion of CDH and evaluated the effects of CDH on the noradrenaline (NA) and angiotensin II (AngII) induced increases in blood pressure and reductions in RBF. Continuous intravenous administration of CDH at 0.25 microg/kg/min did not affect the mean arterial pressure (MAP), but increased heart rate and RBF. Continuous intravenous administration of CDH at high doses (0.5-0.75 microg/kg/min) decreased the MAP, with little effect on the RBF. The administration of exogenous NA (1.7 microg/kg) increased the MAP and decreased the RBF. However, a bolus injection of NA did not decrease the RBF during continuous intravenous administration of CDH, and CDH did not affect the NA-induced increase in MAP. The administration of exogenous AngII (100 ng/kg) increased MAP and decreased RBF and heart rate, but a bolus injection of AngII did not decrease RBF during continuous intravenous administration of CDH. These results suggest that CDH plays a protective role against the pressor effects and the decrease in RBF induced by NA or AngII. Copyright 2004 S. Karger AG, Basel

Towards a Highly Efficient Meshfree Simulation of Non-Newtonian Free Surface Ice Flow: Application to the Haut Glacier d'Arolla

NASA Astrophysics Data System (ADS)

Shcherbakov, V.; Ahlkrona, J.

2016-12-01

In this work we develop a highly efficient meshfree approach to ice sheet modeling. Traditionally mesh based methods such as finite element methods are employed to simulate glacier and ice sheet dynamics. These methods are mature and well developed. However, despite of numerous advantages these methods suffer from some drawbacks such as necessity to remesh the computational domain every time it changes its shape, which significantly complicates the implementation on moving domains, or a costly assembly procedure for nonlinear problems. We introduce a novel meshfree approach that frees us from all these issues. The approach is built upon a radial basis function (RBF) method that, thanks to its meshfree nature, allows for an efficient handling of moving margins and free ice surface. RBF methods are also accurate and easy to implement. Since the formulation is stated in strong form it allows for a substantial reduction of the computational cost associated with the linear system assembly inside the nonlinear solver. We implement a global RBF method that defines an approximation on the entire computational domain. This method exhibits high accuracy properties. However, it suffers from a disadvantage that the coefficient matrix is dense, and therefore the computational efficiency decreases. In order to overcome this issue we also implement a localized RBF method that rests upon a partition of unity approach to subdivide the domain into several smaller subdomains. The radial basis function partition of unity method (RBF-PUM) inherits high approximation characteristics form the global RBF method while resulting in a sparse system of equations, which essentially increases the computational efficiency. To demonstrate the usefulness of the RBF methods we model the velocity field of ice flow in the Haut Glacier d'Arolla. We assume that the flow is governed by the nonlinear Blatter-Pattyn equations. We test the methods for different basal conditions and for a free moving surface. Both RBF methods are compared with a classical finite element method in terms of accuracy and efficiency. We find that the RBF methods are more efficient than the finite element method and well suited for ice dynamics modeling, especially the partition of unity approach.

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

PubMed

Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E

2017-11-01

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Zhang, Huaguang; Lin, Chong

2016-01-01

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

A novel vibration measurement and active control method for a hinged flexible two-connected piezoelectric plate

NASA Astrophysics Data System (ADS)

Qiu, Zhi-cheng; Wang, Xian-feng; Zhang, Xian-Min; Liu, Jin-guo

2018-07-01

A novel non-contact vibration measurement method using binocular vision sensors is proposed for piezoelectric flexible hinged plate. Decoupling methods of the bending and torsional low frequency vibration on measurement and driving control are investigated, using binocular vision sensors and piezoelectric actuators. A radial basis function neural network controller (RBFNNC) is designed to suppress both the larger and the smaller amplitude vibrations. To verify the non-contact measurement method and the designed controller, an experimental setup of the flexible hinged plate with binocular vision is constructed. Experiments on vibration measurement and control are conducted by using binocular vision sensors and the designed RBFNNC controllers, compared with the classical proportional and derivative (PD) control algorithm. The experimental measurement results demonstrate that the binocular vision sensors can detect the low-frequency bending and torsional vibration effectively. Furthermore, the designed RBF can suppress the bending vibration more quickly than the designed PD controller owing to the adjustment of the RBF control, especially for the small amplitude residual vibrations.

Universal approximators for multi-objective direct policy search in water reservoir management problems: a comparative analysis

NASA Astrophysics Data System (ADS)

Giuliani, Matteo; Mason, Emanuele; Castelletti, Andrea; Pianosi, Francesca

2014-05-01

The optimal operation of water resources systems is a wide and challenging problem due to non-linearities in the model and the objectives, high dimensional state-control space, and strong uncertainties in the hydroclimatic regimes. The application of classical optimization techniques (e.g., SDP, Q-learning, gradient descent-based algorithms) is strongly limited by the dimensionality of the system and by the presence of multiple, conflicting objectives. This study presents a novel approach which combines Direct Policy Search (DPS) and Multi-Objective Evolutionary Algorithms (MOEAs) to solve high-dimensional state and control space problems involving multiple objectives. DPS, also known as parameterization-simulation-optimization in the water resources literature, is a simulation-based approach where the reservoir operating policy is first parameterized within a given family of functions and, then, the parameters optimized with respect to the objectives of the management problem. The selection of a suitable class of functions to which the operating policy belong to is a key step, as it might restrict the search for the optimal policy to a subspace of the decision space that does not include the optimal solution. In the water reservoir literature, a number of classes have been proposed. However, many of these rules are based largely on empirical or experimental successes and they were designed mostly via simulation and for single-purpose reservoirs. In a multi-objective context similar rules can not easily inferred from the experience and the use of universal function approximators is generally preferred. In this work, we comparatively analyze two among the most common universal approximators: artificial neural networks (ANN) and radial basis functions (RBF) under different problem settings to estimate their scalability and flexibility in dealing with more and more complex problems. The multi-purpose HoaBinh water reservoir in Vietnam, accounting for hydropower production and flood control, is used as a case study. Preliminary results show that the RBF policy parametrization is more effective than the ANN one. In particular, the approximated Pareto front obtained with RBF control policies successfully explores the full tradeoff space between the two conflicting objectives, while most of the ANN solutions results to be Pareto-dominated by the RBF ones.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty.

PubMed

Zhen, Hong-tao; Qi, Xiao-hui; Li, Jie; Tian, Qing-min

2014-01-01

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.

Use of artificial neural networks to identify the origin of green macroalgae

NASA Astrophysics Data System (ADS)

Żbikowski, Radosław

2011-08-01

This study demonstrates application of artificial neural networks (ANNs) for identifying the origin of green macroalgae ( Enteromorpha sp. and Cladophora sp.) according to their concentrations of Cd, Cu, Ni, Zn, Mn, Pb, Na, Ca, K and Mg. Earlier studies confirmed that algae can be used for biomonitoring surveys of metal contaminants in coastal areas of the Southern Baltic. The same data sets were classified with the use of different structures of radial basis function (RBF) and multilayer perceptron (MLP) networks. The selected networks were able to classify the samples according to their geographical origin, i.e. Southern Baltic, Gulf of Gdańsk and Vistula Lagoon. Additionally in the case of macroalgae from the Gulf of Gdańsk, the networks enabled the discrimination of samples according to areas of contrasting levels of pollution. Hence this study shows that artificial neural networks can be a valuable tool in biomonitoring studies.

Simultaneous determination of penicillin G salts by infrared spectroscopy: Evaluation of combining orthogonal signal correction with radial basis function-partial least squares regression

NASA Astrophysics Data System (ADS)

Talebpour, Zahra; Tavallaie, Roya; Ahmadi, Seyyed Hamid; Abdollahpour, Assem

2010-09-01

In this study, a new method for the simultaneous determination of penicillin G salts in pharmaceutical mixture via FT-IR spectroscopy combined with chemometrics was investigated. The mixture of penicillin G salts is a complex system due to similar analytical characteristics of components. Partial least squares (PLS) and radial basis function-partial least squares (RBF-PLS) were used to develop the linear and nonlinear relation between spectra and components, respectively. The orthogonal signal correction (OSC) preprocessing method was used to correct unexpected information, such as spectral overlapping and scattering effects. In order to compare the influence of OSC on PLS and RBF-PLS models, the optimal linear (PLS) and nonlinear (RBF-PLS) models based on conventional and OSC preprocessed spectra were established and compared. The obtained results demonstrated that OSC clearly enhanced the performance of both RBF-PLS and PLS calibration models. Also in the case of some nonlinear relation between spectra and component, OSC-RBF-PLS gave satisfactory results than OSC-PLS model which indicated that the OSC was helpful to remove extrinsic deviations from linearity without elimination of nonlinear information related to component. The chemometric models were tested on an external dataset and finally applied to the analysis commercialized injection product of penicillin G salts.

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks

PubMed Central

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600

A Novel RSSI Prediction Using Imperialist Competition Algorithm (ICA), Radial Basis Function (RBF) and Firefly Algorithm (FFA) in Wireless Networks.

PubMed

Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M

2016-01-01

This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF-FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model's performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF-FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF-FFA model can be applied as an efficient technique for the accurate prediction of vertical handover.

Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

PubMed

Paschalidou, Anastasia K; Karakitsios, Spyridon; Kleanthous, Savvas; Kassomenos, Pavlos A

2011-02-01

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

Linear genetic programming application for successive-station monthly streamflow prediction

NASA Astrophysics Data System (ADS)

Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

2014-09-01

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

Radial basis function and its application in tourism management

NASA Astrophysics Data System (ADS)

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.

Continuously measured renal blood flow does not increase in diabetes if nitric oxide synthesis is blocked.

PubMed

Bell, Tracy D; DiBona, Gerald F; Biemiller, Rachel; Brands, Michael W

2008-11-01

This study used 16 h/day measurement of renal blood flow (RBF) and arterial pressure (AP) to determine the role of nitric oxide (NO) in mediating the renal vasodilation caused by onset of type 1 diabetes. The AP and RBF power spectra were used to determine the autoregulatory efficiency of the renal vasculature. Rats were instrumented with artery and vein catheters and a Transonic flow probe on the left renal artery and were divided randomly into four groups: control (C), diabetes (D), control plus nitro-L-arginine methyl ester (L-NAME; CL), and diabetes plus L-NAME (DL). Mean AP averaged 90 +/- 1 and 121 +/- 1 mmHg in the D and DL groups, respectively, during the control period, and RBF averaged 5.9 +/- 1.2 and 5.7 +/- 0.7 ml/min, respectively. Respective C and CL groups were not different. Onset of diabetes (streptozotocin 40 mg/kg iv) in D rats increased RBF gradually, but it averaged 55% above control by day 14. In DL rats, on the other hand, RBF remained essentially constant, tracking with RBF in the nondiabetic C and CL groups for the 2-wk period. Diabetes did not change mean AP in any group. Transfer function analysis revealed impaired dynamic autoregulation of RBF overall, including the frequency range of tubuloglomerular feedback (TGF), and L-NAME completely prevented those changes as well. These data strongly support a role for NO in causing renal vasodilation in diabetes and suggest that an effect of NO to blunt RBF autoregulation may play an important role.

Continuously measured renal blood flow does not increase in diabetes if nitric oxide synthesis is blocked

PubMed Central

Bell, Tracy D.; DiBona, Gerald F.; Biemiller, Rachel; Brands, Michael W.

2008-01-01

This study used 16 h/day measurement of renal blood flow (RBF) and arterial pressure (AP) to determine the role of nitric oxide (NO) in mediating the renal vasodilation caused by onset of type 1 diabetes. The AP and RBF power spectra were used to determine the autoregulatory efficiency of the renal vasculature. Rats were instrumented with artery and vein catheters and a Transonic flow probe on the left renal artery and were divided randomly into four groups: control (C), diabetes (D), control plus nitro-l-arginine methyl ester (l-NAME; CL), and diabetes plus l-NAME (DL). Mean AP averaged 90 ± 1 and 121 ± 1 mmHg in the D and DL groups, respectively, during the control period, and RBF averaged 5.9 ± 1.2 and 5.7 ± 0.7 ml/min, respectively. Respective C and CL groups were not different. Onset of diabetes (streptozotocin 40 mg/kg iv) in D rats increased RBF gradually, but it averaged 55% above control by day 14. In DL rats, on the other hand, RBF remained essentially constant, tracking with RBF in the nondiabetic C and CL groups for the 2-wk period. Diabetes did not change mean AP in any group. Transfer function analysis revealed impaired dynamic autoregulation of RBF overall, including the frequency range of tubuloglomerular feedback (TGF), and l-NAME completely prevented those changes as well. These data strongly support a role for NO in causing renal vasodilation in diabetes and suggest that an effect of NO to blunt RBF autoregulation may play an important role. PMID:18753304

Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.

PubMed

Zangooei, Hossein; Delnavaz, Mohammad; Asadollahfardi, Gholamreza

2016-09-01

Coagulation and flocculation are two main processes used to integrate colloidal particles into larger particles and are two main stages of primary water treatment. Coagulation and flocculation processes are only needed when colloidal particles are a significant part of the total suspended solid fraction. Our objective was to predict turbidity of water after the coagulation and flocculation process while other parameters such as types and concentrations of coagulants, pH, and influent turbidity of raw water were known. We used a multilayer perceptron (MLP), a radial basis function (RBF) of artificial neural networks (ANNs) and various kinds of fuzzy regression analysis to predict turbidity after the coagulation and flocculation processes. The coagulant used in the pilot plant, which was located in water treatment plant, was poly aluminum chloride. We used existing data, including the type and concentrations of coagulant, pH and influent turbidity, of the raw water because these types of data were available from the pilot plant for simulation and data was collected by the Tehran water authority. The results indicated that ANNs had more ability in simulating the coagulation and flocculation process and predicting turbidity removal with different experimental data than did the fuzzy regression analysis, and may have the ability to reduce the number of jar tests, which are time-consuming and expensive. The MLP neural network proved to be the best network compared to the RBF neural network and fuzzy regression analysis in this study. The MLP neural network can predict the effluent turbidity of the coagulation and the flocculation process with a coefficient of determination (R 2 ) of 0.96 and root mean square error of 0.0106.

Supervised Machine Learning for Regionalization of Environmental Data: Distribution of Uranium in Groundwater in Ukraine

NASA Astrophysics Data System (ADS)

Govorov, Michael; Gienko, Gennady; Putrenko, Viktor

2018-05-01

In this paper, several supervised machine learning algorithms were explored to define homogeneous regions of con-centration of uranium in surface waters in Ukraine using multiple environmental parameters. The previous study was focused on finding the primary environmental parameters related to uranium in ground waters using several methods of spatial statistics and unsupervised classification. At this step, we refined the regionalization using Artifi-cial Neural Networks (ANN) techniques including Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Convolutional Neural Network (CNN). The study is focused on building local ANN models which may significantly improve the prediction results of machine learning algorithms by taking into considerations non-stationarity and autocorrelation in spatial data.

Big geo data surface approximation using radial basis functions: A comparative study

NASA Astrophysics Data System (ADS)

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.

Diagnosis of edge condition based on force measurement during milling of composites

NASA Astrophysics Data System (ADS)

Felusiak, Agata; Twardowski, Paweł

2018-04-01

The present paper presents comparative results of the forecasting of a cutting tool wear with the application of different methods of diagnostic deduction based on the measurement of cutting force components. The research was carried out during the milling of the Duralcan F3S.10S aluminum-ceramic composite. Prediction of the toolwear was based on one variable, two variables regression Multilayer Perceptron(MLP)and Radial Basis Function(RBF)neural networks. Forecasting the condition of the cutting tool on the basis of cutting forces has yielded very satisfactory results.

A Hierarchical Learning Control Framework for an Aerial Manipulation System

NASA Astrophysics Data System (ADS)

Ma, Le; Chi, yanxun; Li, Jiapeng; Li, Zhongsheng; Ding, Yalei; Liu, Lixing

2017-07-01

A hierarchical learning control framework for an aerial manipulation system is proposed. Firstly, the mechanical design of aerial manipulation system is introduced and analyzed, and the kinematics and the dynamics based on Newton-Euler equation are modeled. Secondly, the framework of hierarchical learning for this system is presented, in which flight platform and manipulator are controlled by different controller respectively. The RBF (Radial Basis Function) neural networks are employed to estimate parameters and control. The Simulation and experiment demonstrate that the methods proposed effective and advanced.

Neural image analysis for estimating aerobic and anaerobic decomposition of organic matter based on the example of straw decomposition

NASA Astrophysics Data System (ADS)

Boniecki, P.; Nowakowski, K.; Slosarz, P.; Dach, J.; Pilarski, K.

2012-04-01

The purpose of the project was to identify the degree of organic matter decomposition by means of a neural model based on graphical information derived from image analysis. Empirical data (photographs of compost content at various stages of maturation) were used to generate an optimal neural classifier (Boniecki et al. 2009, Nowakowski et al. 2009). The best classification properties were found in an RBF (Radial Basis Function) artificial neural network, which demonstrates that the process is non-linear.

Evaluation of renal allografts function early after transplantation using intravoxel incoherent motion and arterial spin labeling MRI.

PubMed

Ren, Tao; Wen, Cheng-Long; Chen, Li-Hua; Xie, Shuang-Shuang; Cheng, Yue; Fu, Ying-Xin; Oesingmann, Niels; de Oliveira, Andre; Zuo, Pan-Li; Yin, Jian-Zhong; Xia, Shuang; Shen, Wen

2016-09-01

To evaluate renal allografts function early after transplantation using intravoxel incoherent motion (IVIM) and arterial spin labeling (ASL) MRI. This prospective study was approved by the local ethics committee, and written informed consent was obtained from all participants. A total of 82 participants with 62 renal allograft recipients (2-4weeks after kidney transplantation) and 20 volunteers were enrolled to be scanned using IVIM and ASL MRI on a 3.0T MR scanner. Recipients were divided into two groups with either normal or impaired function according to the estimated glomerular filtration rate (eGFR) with a threshold of 60ml/min/1.73m(2). The apparent diffusion coefficient (ADC) of pure diffusion (ADCslow), the ADC of pseudodiffusion (ADCfast), perfusion fraction (PF), and renal blood flow (RBF) of cortex were compared among three groups. The correlation of ADCslow, ADCfast, PF and RBF with eGFR was evaluated. The receiver operating characteristic (ROC) curve and binary logistic regression analyses were performed to assess the diagnostic efficiency of using IVIM and ASL parameters to discriminate allografts with impaired function from normal function. P<0.05 was considered statistically significant. In allografts with normal function, no significant difference of mean cortical ADCslow, ADCfast, and PF was found compared with healthy controls (P>0.05). Cortical RBF in allografts with normal function was statistically lower than that of healthy controls (P<0.001). Mean cortical ADCslow, ADCfast, PF and RBF were lower for allografts with impaired function than that with normal function (P<0.05). Mean cortical ADCslow, ADCfast, PF and RBF showed a positive correlation with eGFR (all P<0.01) for recipients. The combination of IVIM and ASL MRI showed a higher area under the ROC curve (AUC) (0.865) than that of ASL MRI alone (P=0.02). Combined IVIM and ASL MRI can better evaluate the diffusion and perfusion properties for allografts early after kidney transplantation. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Blood pressure-renal blood flow relationships in conscious angiotensin II- and phenylephrine-infused rats.

PubMed

Polichnowski, Aaron J; Griffin, Karen A; Long, Jianrui; Williamson, Geoffrey A; Bidani, Anil K

2013-10-01

Chronic ANG II infusion in rodents is widely used as an experimental model of hypertension, yet very limited data are available describing the resulting blood pressure-renal blood flow (BP-RBF) relationships in conscious rats. Accordingly, male Sprague-Dawley rats (n = 19) were instrumented for chronic measurements of BP (radiotelemetry) and RBF (Transonic Systems, Ithaca, NY). One week later, two or three separate 2-h recordings of BP and RBF were obtained in conscious rats at 24-h intervals, in addition to separate 24-h BP recordings. Rats were then administered either ANG II (n = 11, 125 ng·kg(-1)·min(-1)) or phenylephrine (PE; n = 8, 50 mg·kg(-1)·day(-1)) as a control, ANG II-independent, pressor agent. Three days later the BP-RBF and 24-h BP recordings were repeated over several days. Despite similar increases in BP, PE led to significantly greater BP lability at the heart beat and very low frequency bandwidths. Conversely, ANG II, but not PE, caused significant renal vasoconstriction (a 62% increase in renal vascular resistance and a 21% decrease in RBF) and increased variability in BP-RBF relationships. Transfer function analysis of BP (input) and RBF (output) were consistent with a significant potentiation of the renal myogenic mechanism during ANG II administration, likely contributing, in part, to the exaggerated reductions in RBF during periods of BP elevations. We conclude that relatively equipressor doses of ANG II and PE lead to greatly different ambient BP profiles and effects on the renal vasculature when assessed in conscious rats. These data may have important implications regarding the pathogenesis of hypertension-induced injury in these models of hypertension.

Spatiotemporal groundwater level modeling using hybrid artificial intelligence-meshless method

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Mousavi, Shahram

2016-05-01

Uncertainties of the field parameters, noise of the observed data and unknown boundary conditions are the main factors involved in the groundwater level (GL) time series which limit the modeling and simulation of GL. This paper presents a hybrid artificial intelligence-meshless model for spatiotemporal GL modeling. In this way firstly time series of GL observed in different piezometers were de-noised using threshold-based wavelet method and the impact of de-noised and noisy data was compared in temporal GL modeling by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). In the second step, both ANN and ANFIS models were calibrated and verified using GL data of each piezometer, rainfall and runoff considering various input scenarios to predict the GL at one month ahead. In the final step, the simulated GLs in the second step of modeling were considered as interior conditions for the multiquadric radial basis function (RBF) based solve of governing partial differential equation of groundwater flow to estimate GL at any desired point within the plain where there is not any observation. In order to evaluate and compare the GL pattern at different time scales, the cross-wavelet coherence was also applied to GL time series of piezometers. The results showed that the threshold-based wavelet de-noising approach can enhance the performance of the modeling up to 13.4%. Also it was found that the accuracy of ANFIS-RBF model is more reliable than ANN-RBF model in both calibration and validation steps.

Contour interpolated radial basis functions with spline boundary correction for fast 3D reconstruction of the human articular cartilage from MR images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Javaid, Zarrar; Unsworth, Charles P., E-mail: c.unsworth@auckland.ac.nz; Boocock, Mark G.

2016-03-15

Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhancesmore » volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume estimation of a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Furthermore, the performance of the segmentation method used for the extraction of the femoral, tibial, and patellar cartilages is assessed with a Dice similarity coefficient, sensitivity, and specificity measure providing high agreement to manual segmentation. Conclusions: The CI-RBF method provides a fast, accurate, and robust 3D model reconstruction that matches Carr’s RBF method, 3D DOCTOR, and a manual benchmark method in accuracy and significantly improves upon Carr’s RBF method in data requirement and computational speed. In addition, the visualization tool has been designed to quickly segment MR images requiring only four mouse clicks per MR image slice.« less

Unimolecular reactivity of organotrifluoroborate anions, RBF3- , and their alkali metal cluster ions, M(RBF3 )2- (M = Na, K; R = CH3 , CH3 CH2 , CH3 (CH2 )3 , CH3 (CH2 )5 , c-C3 H5 , C6 H5 , C6 H5 CH2 , CH2 CHCH2 , CH2 CH, C6 H5 CO).

PubMed

Bathie, Fiona L B; Bowen, Chris J; Hutton, Craig A; O'Hair, Richard A J

2018-07-15

Potassium organotrifluoroborates (RBF 3 K) are important reagents used in organic synthesis. Although mass spectrometry is commonly used to confirm their molecular formulae, the gas-phase fragmentation reactions of organotrifluoroborates and their alkali metal cluster ions have not been previously reported. Negative-ion mode electrospray ionization (ESI) together with collision-induced dissociation (CID) using a triple quadrupole mass spectrometer were used to examine the fragmentation pathways for RBF 3 - (where R = CH 3 , CH 3 CH 2 , CH 3 (CH 2 ) 3 , CH 3 (CH 2 ) 5 , c-C 3 H 5 , C 6 H 5 , C 6 H 5 CH 2 , CH 2 CHCH 2 , CH 2 CH, C 6 H 5 CO) and M(RBF 3 ) 2 - (M = Na, K), while density functional theory (DFT) calculations at the M06/def2-TZVP level were used to examine the structures and energies associated with fragmentation reactions for R = Me and Ph. Upon CID, preferentially elimination of HF occurs for RBF 3 - ions for systems where R = an alkyl anion, whereas R - formation is favoured when R = a stabilized anion. At higher collision energies loss of F - and additional HF losses are sometimes observed. Upon CID of M(RBF 3 ) 2 - , formation of RBF 3 - is the preferred pathway with some fluoride transfer observed only when M = Na. The DFT-calculated relative thermochemistry for competing fragmentation pathways is consistent with the experiments. The main fragmentation pathways of RBF 3 - are HF elimination and/or R - loss. This contrasts with the fragmentation reactions of other organometallate anions, where reductive elimination, beta hydride transfer and bond homolysis are often observed. The presence of fluoride transfer upon CID of Na(RBF 3 ) 2 - but not K(RBF 3 ) 2 - is in agreement with the known fluoride affinities of Na + and K + and can be rationalized by Pearson's HSAB theory. Copyright © 2018 John Wiley & Sons, Ltd.

Evaluation of modulation transfer function of optical lens system by support vector regression methodologies - A comparative study

NASA Astrophysics Data System (ADS)

Petković, Dalibor; Shamshirband, Shahaboddin; Saboohi, Hadi; Ang, Tan Fong; Anuar, Nor Badrul; Rahman, Zulkanain Abdul; Pavlović, Nenad T.

2014-07-01

The quantitative assessment of image quality is an important consideration in any type of imaging system. The modulation transfer function (MTF) is a graphical description of the sharpness and contrast of an imaging system or of its individual components. The MTF is also known and spatial frequency response. The MTF curve has different meanings according to the corresponding frequency. The MTF of an optical system specifies the contrast transmitted by the system as a function of image size, and is determined by the inherent optical properties of the system. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) to estimate and predict estimate MTF value of the actual optical system according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR_rbf approach in compare to SVR_poly soft computing methodology.

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation

PubMed Central

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-01-01

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects. PMID:28773606

An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation.

PubMed

Zhang, Wei; Bao, Zhangmin; Jiang, Shan; He, Jingjing

2016-06-17

In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc. , it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.

Estimating monthly temperature using point based interpolation techniques

NASA Astrophysics Data System (ADS)

Saaban, Azizan; Mah Hashim, Noridayu; Murat, Rusdi Indra Zuhdi

2013-04-01

This paper discusses the use of point based interpolation to estimate the value of temperature at an unallocated meteorology stations in Peninsular Malaysia using data of year 2010 collected from the Malaysian Meteorology Department. Two point based interpolation methods which are Inverse Distance Weighted (IDW) and Radial Basis Function (RBF) are considered. The accuracy of the methods is evaluated using Root Mean Square Error (RMSE). The results show that RBF with thin plate spline model is suitable to be used as temperature estimator for the months of January and December, while RBF with multiquadric model is suitable to estimate the temperature for the rest of the months.

Renal nerves dynamically regulate renal blood flow in conscious, healthy rabbits.

PubMed

Schiller, Alicia M; Pellegrino, Peter R; Zucker, Irving H

2016-01-15

Despite significant clinical interest in renal denervation as a therapy, the role of the renal nerves in the physiological regulation of renal blood flow (RBF) remains debated. We hypothesized that the renal nerves physiologically regulate beat-to-beat RBF variability (RBFV). This was tested in chronically instrumented, healthy rabbits that underwent either bilateral surgical renal denervation (DDNx) or a sham denervation procedure (INV). Artifact-free segments of RBF and arterial pressure (AP) from calmly resting, conscious rabbits were used to extract RBFV and AP variability for time-domain, frequency-domain, and nonlinear analysis. Whereas steady-state measures of RBF, AP, and heart rate did not statistically differ between groups, DDNx rabbits had greater RBFV than INV rabbits. AP-RBF transfer function analysis showed greater admittance gain in DDNx rabbits than in INV rabbits, particularly in the low-frequency (LF) range where systemic sympathetic vasomotion gives rise to AP oscillations. In the LF range, INV rabbits exhibited a negative AP-RBF phase shift and low coherence, consistent with the presence of an active control system. Neither of these features were present in the LF range of DDNx rabbits, which showed no phase shift and high coherence, consistent with a passive, Ohm's law pressure-flow relationship. Renal denervation did not significantly affect nonlinear RBFV measures of chaos, self-affinity, or complexity, nor did it significantly affect glomerular filtration rate or extracellular fluid volume. Cumulatively, these data suggest that the renal nerves mediate LF renal sympathetic vasomotion, which buffers RBF from LF AP oscillations in conscious, healthy rabbits. Copyright © 2016 the American Physiological Society.

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

PubMed Central

Shi, Tiezhu; Liu, Huizeng; Chen, Yiyun; Fei, Teng; Wang, Junjie; Wu, Guofeng

2017-01-01

This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies. PMID:28471412

The Transcription Factor Rbf1 Is the Master Regulator for b-Mating Type Controlled Pathogenic Development in Ustilago maydis

PubMed Central

Vranes, Miroslav; Wahl, Ramon; Pothiratana, Chetsada; Schuler, David; Vincon, Volker; Finkernagel, Florian; Flor-Parra, Ignacio; Kämper, Jörg

2010-01-01

In the phytopathogenic basidiomycete Ustilago maydis, sexual and pathogenic development are tightly connected and controlled by the heterodimeric bE/bW transcription factor complex encoded by the b-mating type locus. The formation of the active bE/bW heterodimer leads to the formation of filaments, induces a G2 cell cycle arrest, and triggers pathogenicity. Here, we identify a set of 345 bE/bW responsive genes which show altered expression during these developmental changes; several of these genes are associated with cell cycle coordination, morphogenesis and pathogenicity. 90% of the genes that show altered expression upon bE/bW-activation require the zinc finger transcription factor Rbf1, one of the few factors directly regulated by the bE/bW heterodimer. Rbf1 is a novel master regulator in a multilayered network of transcription factors that facilitates the complex regulatory traits of sexual and pathogenic development. PMID:20700446

[Capacity of response of the health system to the expectations of populations in zones exposed to results-based financing in Benin in 2015].

PubMed

Salami, Lamidhi; Dona Ouendo, Edgard-Marius; Fayomi, Benjamin

2017-07-10

Introduction: The increased use of results-based financing (RBF) services was the basis for this study designed to evaluate the contribution of RBF to the capacity of response of the health system to the population’s expectations. Methods: This study, conducted in six Benin health zones randomly selected in two strata exposed to RBF (FBR_PRPSS and FBR_PASS) and one zone not exposed to RBF (Non_FBR), examined the seven dimensions of reactivity. A score, followed by weighting of their attributes, was used to calculate the index of reactivity (IR). Results: Sixty-seven health care units and 653 people were observed and interviewed. The FBR_PRPSS and FBR_PASS strata, managed by the new provisions of RBF, displayed good performances for the “rapidity of management” (70% and 80%) and “quality of the health care environment” dimensions, with a more marked improvement for the PRPSS model, which provides greater resources. Poor access to social welfare networks in the three strata led to renouncing of health care. The capacity of response to expectations was moderate and similar in the Non_FBR (IR = 0.53), FBR_PASS (IR = 0.62) and FBR_PRPSS (IR = 0.61) strata (p > 0.05). Conclusion: The FBR_PRPSS and FBR_PASS models have a non-significant effect on the capacity of response. Their success probably depends on the health system context, the combination of targeted interventions, such as universal health insurance, but also the importance and the use of the new resources that they provide.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization design of LED heat dissipation structure based on strip fins

NASA Astrophysics Data System (ADS)

Xue, Lingyun; Wan, Wenbin; Chen, Qingguang; Rao, Huanle; Xu, Ping

2018-03-01

To solve the heat dissipation problem of LED, a radiator structure based on strip fins is designed and the method to optimize the structure parameters of strip fins is proposed in this paper. The combination of RBF neural networks and particle swarm optimization (PSO) algorithm is used for modeling and optimization respectively. During the experiment, the 150 datasets of LED junction temperature when structure parameters of number of strip fins, length, width and height of the fins have different values are obtained by ANSYS software. Then RBF neural network is applied to build the non-linear regression model and the parameters optimization of structure based on particle swarm optimization algorithm is performed with this model. The experimental results show that the lowest LED junction temperature reaches 43.88 degrees when the number of hidden layer nodes in RBF neural network is 10, the two learning factors in particle swarm optimization algorithm are 0.5, 0.5 respectively, the inertia factor is 1 and the maximum number of iterations is 100, and now the number of fins is 64, the distribution structure is 8*8, and the length, width and height of fins are 4.3mm, 4.48mm and 55.3mm respectively. To compare the modeling and optimization results, LED junction temperature at the optimized structure parameters was simulated and the result is 43.592°C which approximately equals to the optimal result. Compared with the ordinary plate-fin-type radiator structure whose temperature is 56.38°C, the structure greatly enhances heat dissipation performance of the structure.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

PubMed

Kashyap, Kanchan L; Bajpai, Manish K; Khanna, Pritee; Giakos, George

2018-01-01

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function. Copyright © 2017 John Wiley & Sons, Ltd.

Comparison of Response Surface Construction Methods for Derivative Estimation Using Moving Least Squares, Kriging and Radial Basis Functions

NASA Technical Reports Server (NTRS)

Krishnamurthy, Thiagarajan

2005-01-01

Response construction methods using Moving Least Squares (MLS), Kriging and Radial Basis Functions (RBF) are compared with the Global Least Squares (GLS) method in three numerical examples for derivative generation capability. Also, a new Interpolating Moving Least Squares (IMLS) method adopted from the meshless method is presented. It is found that the response surface construction methods using the Kriging and RBF interpolation yields more accurate results compared with MLS and GLS methods. Several computational aspects of the response surface construction methods also discussed.

Discrimination of raw and processed Dipsacus asperoides by near infrared spectroscopy combined with least squares-support vector machine and random forests

NASA Astrophysics Data System (ADS)

Xin, Ni; Gu, Xiao-Feng; Wu, Hao; Hu, Yu-Zhu; Yang, Zhong-Lin

2012-04-01

Most herbal medicines could be processed to fulfill the different requirements of therapy. The purpose of this study was to discriminate between raw and processed Dipsacus asperoides, a common traditional Chinese medicine, based on their near infrared (NIR) spectra. Least squares-support vector machine (LS-SVM) and random forests (RF) were employed for full-spectrum classification. Three types of kernels, including linear kernel, polynomial kernel and radial basis function kernel (RBF), were checked for optimization of LS-SVM model. For comparison, a linear discriminant analysis (LDA) model was performed for classification, and the successive projections algorithm (SPA) was executed prior to building an LDA model to choose an appropriate subset of wavelengths. The three methods were applied to a dataset containing 40 raw herbs and 40 corresponding processed herbs. We ran 50 runs of 10-fold cross validation to evaluate the model's efficiency. The performance of the LS-SVM with RBF kernel (RBF LS-SVM) was better than the other two kernels. The RF, RBF LS-SVM and SPA-LDA successfully classified all test samples. The mean error rates for the 50 runs of 10-fold cross validation were 1.35% for RBF LS-SVM, 2.87% for RF, and 2.50% for SPA-LDA. The best classification results were obtained by using LS-SVM with RBF kernel, while RF was fast in the training and making predictions.

Crack orientation and depth estimation in a low-pressure turbine disc using a phased array ultrasonic transducer and an artificial neural network.

PubMed

Yang, Xiaoxia; Chen, Shili; Jin, Shijiu; Chang, Wenshuang

2013-09-13

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks.

Crack Orientation and Depth Estimation in a Low-Pressure Turbine Disc Using a Phased Array Ultrasonic Transducer and an Artificial Neural Network

PubMed Central

Yang, Xiaoxia; Chen, Shili; Jin, Shijiu; Chang, Wenshuang

2013-01-01

Stress corrosion cracks (SCC) in low-pressure steam turbine discs are serious hidden dangers to production safety in the power plants, and knowing the orientation and depth of the initial cracks is essential for the evaluation of the crack growth rate, propagation direction and working life of the turbine disc. In this paper, a method based on phased array ultrasonic transducer and artificial neural network (ANN), is proposed to estimate both the depth and orientation of initial cracks in the turbine discs. Echo signals from cracks with different depths and orientations were collected by a phased array ultrasonic transducer, and the feature vectors were extracted by wavelet packet, fractal technology and peak amplitude methods. The radial basis function (RBF) neural network was investigated and used in this application. The final results demonstrated that the method presented was efficient in crack estimation tasks. PMID:24064602

Functional dilatation and medial remodeling of the renal artery in response to chronic increased blood flow.

PubMed

Roan, Jun-Neng; Yeh, Chin-Yi; Chiu, Wen-Cheng; Lee, Chou-Hwei; Chang, Shih-Wei; Jiangshieh, Ya-Fen; Tsai, Yu-Chuan; Lam, Chen-Fuh

2011-01-01

Renal blood flow (RBF) is tightly regulated by several intrinsic pathways in maintaining optimal kidney blood supply. Using a rat model of aortocaval (AC) fistula, we investigated remodeling of the renal artery following prolonged increased blood flow. An AC fistula was created in the infrarenal aorta of anesthetized rats, and changes of blood flow in the renal artery were assessed using an ultrasonic flow probe. Morphological changes and expression of endothelial nitric oxide synthase and matrix metalloproteinase-2 in the remodeled renal artery were analyzed. Blood flow in the renal artery increased immediately after creation of AC fistula, but normal RBF was restored 8 weeks later. The renal artery dilated significantly 8 weeks after operation. Expression of endothelial nitric oxide synthase and matrix metalloproteinase-2 was upregulated shortly after blood flow increase, and returned to baseline levels after 3 weeks. Histological sections showed luminal dilatation with medial thickening and endothelial cell-to-smooth muscle cell attachments in the remodeled renal artery. Increased RBF was accommodated by functional dilatation and remodeling in the medial layer of the renal artery in order to restore normal blood flow. Our results provide important mechanistic insight into the intrinsic regulation of the renal artery in response to increased RBF. Copyright © 2011 S. Karger AG, Basel.

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

PubMed

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

2018-04-01

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

Automated radial basis function neural network based image classification system for diabetic retinopathy detection in retinal images

NASA Astrophysics Data System (ADS)

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.

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

PubMed

Anam, Khairul; Al-Jumaily, Adel

2017-01-01

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and non-amputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels. Copyright © 2016 Elsevier Ltd. All rights reserved.

Bioremediation of petroleum hydrocarbon contaminated soil by Rhodobacter sphaeroides biofertilizer and plants.

PubMed

Jiao, Haihua; Luo, Jinxue; Zhang, Yiming; Xu, Shengjun; Bai, Zhihui; Huang, Zhanbin

2015-09-01

Bio-augmentation is a promising technique for remediation of polluted soils. This study aimed to evaluate the bio-augmentation effect of Rhodobacter sphaeroides biofertilizer (RBF) on the bioremediation of total petroleum hydrocarbons (TPH) contaminated soil. A greenhouse pot experiment was conducted over a period of 120 days, three methods for enhancing bio-augmentation were tested on TPH contaminated soils, including single addition RBF, planting, and combining of RBF and three crop species, such as wheat (W), cabbage (C) and spinach (S), respectively. The results demonstrated that the best removal of TPH from contaminated soil in the RBF bio-augmentation rhizosphere soils was found to be 46.2%, 65.4%, 67.5% for W+RBF, C+RBF, S+RBF rhizosphere soils respectively. RBF supply impacted on the microbial community diversity (phospholipid fatty acids, PLFA) and the activity of soil enzymes, such as dehydrogenase (DH), alkaline phosphatase (AP) and urease (UR). There were significant difference among the soil only containing crude oil (CK), W, C and S rhizosphere soils and RBF bio-augmentation soils. Moreover, the changes were significantly distinct depended on crops species. It was concluded that the RBF is a valuable material for improving effect of remediation of TPH polluted soils.

Using Radial Basis Functions in Airborne Gravimetry for Local Geoid Improvement

NASA Astrophysics Data System (ADS)

Li, Xiaopeng

2017-04-01

Radial basis functions (RBF, Schmidt et al 2007, Klees and Wittwer 2007, Klees et al 2008) have been extensively used in satellite geodetic applications (Eicker 2008, Wittwer 2009, Naeimi 2013, among others). However, to date, to the author's knowledge, their roles in processing and modeling airborne gravity data have not been fully advocated or extensively investigated in detail, though compared with satellite missions, the airborne data is more suitable for this kind of localized basis functions especially considering the following facts: (1) Unlike the satellite missions that can provide global or near global data coverage, airborne gravity data is usually geographically limited. (2) It is also band limited in the frequency domain, considering that various filter banks and/or de-noising techniques (Li 2007) have to be applied to overcome the low signal-to-noise ratio problem that is present in airborne gravimetric systems. This is mainly due to the mechanical and mathematical limitations in computing the accelerations (both the kinematic and dynamic accelerations, Jekeli 2000). (3) It is much easier to formulate the RBF observation equations from an airborne gravimetric system (either a scalar one (Forsberg and Olesen 2010) or a vector one (Kwon and Jekeli 2001)) than from any satellite mission, especially compared with Gravity Recovery and Climate Experiment satellites (GRACE, Tapley et al. 2004) where many accurate background environmental models have to be used in order to separate out the gravity related functionals. As a result, in this study, a set of band-limited RBF is developed to model and downward continue the airborne gravity data for local geoid improvement. First, the algorithm is tested with synthesized data from global coefficient models such as EIGEN6c4 (Försteet al. 2014), during which the RBF not only successfully recovers a harmonic field but also presents filtering properties due to its particular design in the frequency domain. Then, the software is tested for the GSVS14 (Geoid Slope Validation Survey 2014) area as well as for the area around Puerto Rico and the U.S. Virgin Islands by using the real airborne gravity data from the Gravity for the Redefinition of the American Vertical Datum (GRAV-D, Smith 2007) project. The newly acquired cm-level accurate GPS/Leveling bench marks prove the RBF airborne enhanced geoid models are not inferior to other models computed by conventional approaches. By fully utilizing the three dimensional correlation information among the flight tracks, the RBF can also be used as a data editing tool for airborne data adjustment and cleaning.

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

PubMed Central

Ramirez-Villegas, Juan F.; Lam-Espinosa, Eric; Ramirez-Moreno, David F.; Calvo-Echeverry, Paulo C.; Agredo-Rodriguez, Wilfredo

2011-01-01

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis. PMID:21386966

Riverbank filtration in China: A review and perspective

NASA Astrophysics Data System (ADS)

Hu, Bin; Teng, Yanguo; Zhai, Yuanzheng; Zuo, Rui; Li, Jiao; Chen, Haiyang

2016-10-01

Riverbank filtration (RBF) for water supplies is used widely throughout the world because it guarantees a sustainable quantity and improves water quality. In this study, the development history and the technical overview of RBF in China are reviewed and summarized. Most RBF systems in China were constructed using vertical wells, horizontal wells, and infiltration galleries in flood plains, alluvial fans, and intermountain basins. Typical pollutants such as NH4+, pathogens, metals, and organic materials were removed or diluted by most RBF investigated. There have recently been many investigations of the interaction between groundwater and surface water and biogeochemical processes in RBF. Comprehensive RBF applications should include not only the positive but also negative effects. Based on a discussion of the advantages and disadvantages, the perspectives of China's RBF technology development were proposed. To protect the security of water supply, China's RBF systems should establish a management system, monitoring system and forecasting system of risk. Guidelines of RBF construction and management should also be issued on the basic of relevant fundamental investigations such as climate influence, clogging, and purification mechanism of water-quality improvement.

Prediction of dissolved oxygen in the Mediterranean Sea along Gaza, Palestine - an artificial neural network approach.

PubMed

Zaqoot, Hossam Adel; Ansari, Abdul Khalique; Unar, Mukhtiar Ali; Khan, Shaukat Hyat

2009-01-01

Artificial Neural Networks (ANNs) are flexible tools which are being used increasingly to predict and forecast water resources variables. The human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. The presence of dissolved oxygen is essential for the survival of most organisms in the water bodies. This paper is concerned with the use of ANNs - Multilayer Perceptron (MLP) and Radial Basis Function neural networks for predicting the next fortnight's dissolved oxygen concentrations in the Mediterranean Sea water along Gaza. MLP and Radial Basis Function (RBF) neural networks are trained and developed with reference to five important oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. These variables are considered as inputs of the network. The data sets used in this study consist of four years and collected from nine locations along Gaza coast. The network performance has been tested with different data sets and the results show satisfactory performance. Prediction results prove that neural network approach has good adaptability and extensive applicability for modelling the dissolved oxygen in the Mediterranean Sea along Gaza. We hope that the established model will help in assisting the local authorities in developing plans and policies to reduce the pollution along Gaza coastal waters to acceptable levels.

Can paying for results help to achieve the Millennium Development Goals? A critical review of selected evaluations of results-based financing.

PubMed

Oxman, Andrew D; Fretheim, Atle

2009-08-01

Results-based financing (RBF) refers to the transfer of money or material goods conditional on taking a measurable action or achieving a predetermined performance target. RBF is being promoted for helping to achieve the Millennium Development Goals (MDGs). We undertook a critical appraisal of selected evaluations of RBF schemes in the health sector in low and middle-income countries (LMIC). In addition, key informants were interviewed to identify literature relevant to the use of RBF in the health sector in LMIC, key examples, evaluations, and other key informants. The use of RBF in LMIC has commonly been a part of a package that may include increased funding, technical support, training, changes in management, and new information systems. It is not possible to disentangle the effects of financial incentives as one element of RBF schemes, and there is very limited evidence of RBF per se having an effect. RBF schemes can have unintended effects. When RBF schemes are used, they should be designed carefully, including the level at which they are targeted, the choice of targets and indicators, the type and magnitude of incentives, the proportion of financing that is paid based on results, and the ancillary components of the scheme. For RBF to be effective, it must be part of an appropriate package of interventions, and technical capacity or support must be available. RBF schemes should be monitored for possible unintended effects and evaluated using rigorous study designs. © 2009 Blackwell Publishing Asia Pty Ltd and Chinese Cochrane Center, West China Hospital of Sichuan University.

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

PubMed

El-Sayed, Hesham; Sankar, Sharmi; Daraghmi, Yousef-Awwad; Tiwari, Prayag; Rattagan, Ekarat; Mohanty, Manoranjan; Puthal, Deepak; Prasad, Mukesh

2018-05-24

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

A fuzzy neural network sliding mode controller for vibration suppression in robotically assisted minimally invasive surgery.

PubMed

Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang

2016-12-01

It is very important for robotically assisted minimally invasive surgery to achieve a high-precision and smooth motion control. However, the surgical instrument tip will exhibit vibration caused by nonlinear friction and unmodeled dynamics, especially when the surgical robot system is attempting low-speed, fine motion. A fuzzy neural network sliding mode controller (FNNSMC) is proposed to suppress vibration of the surgical robotic system. Nonlinear friction and modeling uncertainties are compensated by a Stribeck model, a radial basis function (RBF) neural network and a fuzzy system, respectively. Simulations and experiments were performed on a 3 degree-of-freedom (DOF) minimally invasive surgical robot. The results demonstrate that the FNNSMC is effective and can suppress vibrations at the surgical instrument tip. The proposed FNNSMC can provide a robust performance and suppress the vibrations at the surgical instrument tip, which can enhance the quality and security of surgical procedures. Copyright © 2016 John Wiley & Sons, Ltd.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

PubMed

Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

2018-03-05

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

An RBF-FD closest point method for solving PDEs on surfaces

NASA Astrophysics Data System (ADS)

Petras, A.; Ling, L.; Ruuth, S. J.

2018-10-01

Partial differential equations (PDEs) on surfaces appear in many applications throughout the natural and applied sciences. The classical closest point method (Ruuth and Merriman (2008) [17]) is an embedding method for solving PDEs on surfaces using standard finite difference schemes. In this paper, we formulate an explicit closest point method using finite difference schemes derived from radial basis functions (RBF-FD). Unlike the orthogonal gradients method (Piret (2012) [22]), our proposed method uses RBF centers on regular grid nodes. This formulation not only reduces the computational cost but also avoids the ill-conditioning from point clustering on the surface and is more natural to couple with a grid based manifold evolution algorithm (Leung and Zhao (2009) [26]). When compared to the standard finite difference discretization of the closest point method, the proposed method requires a smaller computational domain surrounding the surface, resulting in a decrease in the number of sampling points on the surface. In addition, higher-order schemes can easily be constructed by increasing the number of points in the RBF-FD stencil. Applications to a variety of examples are provided to illustrate the numerical convergence of the method.

Free variable selection QSPR study to predict 19F chemical shifts of some fluorinated organic compounds using Random Forest and RBF-PLS methods

NASA Astrophysics Data System (ADS)

Goudarzi, Nasser

2016-04-01

In this work, two new and powerful chemometrics methods are applied for the modeling and prediction of the 19F chemical shift values of some fluorinated organic compounds. The radial basis function-partial least square (RBF-PLS) and random forest (RF) are employed to construct the models to predict the 19F chemical shifts. In this study, we didn't used from any variable selection method and RF method can be used as variable selection and modeling technique. Effects of the important parameters affecting the ability of the RF prediction power such as the number of trees (nt) and the number of randomly selected variables to split each node (m) were investigated. The root-mean-square errors of prediction (RMSEP) for the training set and the prediction set for the RBF-PLS and RF models were 44.70, 23.86, 29.77, and 23.69, respectively. Also, the correlation coefficients of the prediction set for the RBF-PLS and RF models were 0.8684 and 0.9313, respectively. The results obtained reveal that the RF model can be used as a powerful chemometrics tool for the quantitative structure-property relationship (QSPR) studies.

Fate and transport of TNT, RDX, and HMX in streambed sediments: Implications for riverbank filtration.

PubMed

Zheng, Weixi; Lichwa, Joseph; D'Alessio, Matteo; Ray, Chittaranjan

2009-08-01

Riverbank filtration (RBF) refers to the process of capturing surface water passing through the river-sediment-aquifer system by using a collection technique such as a well or an infiltration gallery. RBF removes nearly all suspended and a large number of dissolved contaminants from the surface water. Therefore, it can function as an effective pretreatment process in drinking-water production. TNT (2,4,6-trinitrotoluene), RDX (1,3,5-trinitro-1,3,5-triazacyclohexane), and HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) are three military explosive chemicals that are considered of concern to human health when present in source waters. This study is to evaluate the ability of the filtration media in RBF systems to remove these chemicals. The results from an anoxic batch test showed that all three chemicals will degrade while passing through streambed sediments. The pseudo first-order degradation-rate constants for TNT, RDX, and HMX were measured to be 0.33, 0.055, and 0.033d(-1), respectively. Under aerobic conditions only TNT showed significant degradation. Results from a model RBF system showed that the mobility of the three chemical contaminants in streambed sediments was in the order: HMX>RDX>TNT. The results suggest that RBF is capable of removing TNT and RDX but HMX levels may continue to be of concern-especially when collector wells use laterals running directly beneath the stream or riverbed.

Measurement of total and unilateral renal blood flow by oblique-angle velocity-encoded 2D-cine magnetic resonance angiography.

PubMed

Lundin, B; Cooper, T G; Meyer, R A; Potchen, E J

1993-01-01

Two independent measurements of total renal blood flow (RBF) were made in healthy human subjects (n = 14, mean age 30 yr) by CINE phase-contrast magnetic resonance angiography. RBF, measured by summing the flows measured in the right and left renal arteries, was 1152 +/- 44 ml/min (mean +/- SE). RBF, measured from the difference between supra- and infrarenal abdominal aorta flow, was 1109 +/- 68 ml/min. Regression analysis of the comparison of these two different RBF calculations yielded a correlation coefficient of 0.72 at a p < .05 level of significance. Based on other studies of RBF in normal subjects by para-aminohippuric acid (PAH) clearance, the expected RBF in this subject group was 1211 +/- 62 ml/min. The results indicate that noninvasive measurement of RBF is possible using phase-contrast magnetic resonance methods.

Investigation of Large Scale Cortical Models on Clustered Multi-Core Processors

DTIC Science & Technology

2013-02-01

with the bias node ( gray ) denoted as ww and the weights associated with the remaining first layer nodes (black) denoted as W. In forming the overall...Implementation of RBF network on GPU Platform 3.5.1 The Cholesky decomposition algorithm We need to invert the matrix multiplication GTG to

Modification of Hazen's equation in coarse grained soils by soft computing techniques

NASA Astrophysics Data System (ADS)

Kaynar, Oguz; Yilmaz, Isik; Marschalko, Marian; Bednarik, Martin; Fojtova, Lucie

2013-04-01

Hazen first proposed a Relationship between coefficient of permeability (k) and effective grain size (d10) was first proposed by Hazen, and it was then extended by some other researchers. However many attempts were done for estimation of k, correlation coefficients (R2) of the models were generally lower than ~0.80 and whole grain size distribution curves were not included in the assessments. Soft computing techniques such as; artificial neural networks, fuzzy inference systems, genetic algorithms, etc. and their hybrids are now being successfully used as an alternative tool. In this study, use of some soft computing techniques such as Artificial Neural Networks (ANNs) (MLP, RBF, etc.) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for prediction of permeability of coarse grained soils was described, and Hazen's equation was then modificated. It was found that the soft computing models exhibited high performance in prediction of permeability coefficient. However four different kinds of ANN algorithms showed similar prediction performance, results of MLP was found to be relatively more accurate than RBF models. The most reliable prediction was obtained from ANFIS model.

Hemodynamic responses to acute and gradual renal artery stenosis in pigs.

PubMed

Rognant, Nicolas; Rouvière, Olivier; Janier, Marc; Lê, Quoc Hung; Barthez, Paul; Laville, Maurice; Juillard, Laurent

2010-11-01

Reduction of renal blood flow (RBF) due to a renal artery stenosis (RAS) can lead to renal ischemia and atrophy. However in pigs, there are no data describing the relationship between the degree of RAS, the reduction of RBF, and the increase of systemic plasma renin activity (PRA). Therefore, we conducted a study in order to measure the effect of acute and gradual RAS on RBF, mean arterial pressure (MAP), and systemic PRA in pigs. RAS was induced experimentally in six pigs using an occluder placed around the renal artery downstream of an ultrasound flow probe. The vascular occluder was inflated gradually to reduce RBF. At each inflation step, percentage of RAS was measured by digital subtraction angiography (DSA) with simultaneous measurements of RBF, MAP, and PRA. Data were normalized to baseline values obtained before RAS induction. Piecewise regression analysis was performed between percentage of RAS and relative RBF, MAP, and PRA, respectively. In all pigs, the relationship between the degree of RAS and RBF was similar. RBF decreased over a threshold of 42% of RAS, with a rapid drop in RBF when RAS reached 70%. PRA increased dramatically over a threshold of 58% of RAS (+1,300% before occlusion). MAP increased slightly (+15% before occlusion) without identifiable threshold. This study emphasizes that the relation between the degree of RAS and RBF and systemic PRA is not linear and that a high degree of RAS must be reached before the occurrence of significant hemodynamic and humoral effects.

High-NaCl intake impairs dynamic autoregulation of renal blood flow in ANG II-infused rats.

PubMed

Saeed, Aso; Dibona, Gerald F; Marcussen, Niels; Guron, Gregor

2010-11-01

The aim of this study was to investigate dynamic autoregulation of renal blood flow (RBF) in ANG II-infused rats and the influence of high-NaCl intake. Sprague-Dawley rats received ANG II (250 ng·kg(-1)·min(-1) sc) or saline vehicle (sham) for 14 days after which acute renal clearance experiments were performed during thiobutabarbital anesthesia. Rats (n = 8-10 per group) were either on a normal (NNa; 0.4% NaCl)- or high (HNa; 8% NaCl)-NaCl diet. Separate groups were treated with 4-hydroxy-2,2,6,6-tetramethylpiperidine-1-oxyl (tempol; 1 M in drinking water). Transfer function analysis from arterial pressure to RBF in the frequency domain was used to examine the myogenic response (MR; 0.06-0.09 Hz) and the tubuloglomerular feedback mechanism (TGF; 0.03-0.06 Hz). MAP was elevated in ANG II-infused rats compared with sham groups (P < 0.05). RBF in ANG II HNa was reduced vs. sham NNa and sham HNa (6.0 ± 0.3 vs. 7.9 ± 0.3 and 9.1 ± 0.3 ml·min(-1)·g kidney wt(-1), P < 0.05). transfer function gain in ANG II HNa was significantly elevated in the frequency range of the MR (1.26 ± 0.50 dB, P < 0.05 vs. all other groups) and in the frequency range of the TGF (-0.02 ± 0.50 dB, P < 0.05 vs. sham NNa and sham HNa). Gain values in the frequency range of the MR and TGF were significantly reduced by tempol in ANG II-infused rats on HNa diet. In summary, the MR and TGF components of RBF autoregulation were impaired in ANG II HNa, and these abnormalities were attenuated by tempol, suggesting a pathogenetic role for superoxide in the impaired RBF autoregulatory response.

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform.

PubMed

Zhang, Xiaolei; Zhao, Yan; Guo, Kai; Li, Gaoliang; Deng, Nianmao

2017-04-28

The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

An RBF-based compression method for image-based relighting.

PubMed

Leung, Chi-Sing; Wong, Tien-Tsin; Lam, Ping-Man; Choy, Kwok-Hung

2006-04-01

In image-based relighting, a pixel is associated with a number of sampled radiance values. This paper presents a two-level compression method. In the first level, the plenoptic property of a pixel is approximated by a spherical radial basis function (SRBF) network. That means that the spherical plenoptic function of each pixel is represented by a number of SRBF weights. In the second level, we apply a wavelet-based method to compress these SRBF weights. To reduce the visual artifact due to quantization noise, we develop a constrained method for estimating the SRBF weights. Our proposed approach is superior to JPEG, JPEG2000, and MPEG. Compared with the spherical harmonics approach, our approach has a lower complexity, while the visual quality is comparable. The real-time rendering method for our SRBF representation is also discussed.

Surrogate models for sheet metal stamping problem based on the combination of proper orthogonal decomposition and radial basis function

NASA Astrophysics Data System (ADS)

Dang, Van Tuan; Lafon, Pascal; Labergere, Carl

2017-10-01

In this work, a combination of Proper Orthogonal Decomposition (POD) and Radial Basis Function (RBF) is proposed to build a surrogate model based on the Benchmark Springback 3D bending from the Numisheet2011 congress. The influence of the two design parameters, the geometrical parameter of the die radius and the process parameter of the blank holder force, on the springback of the sheet after a stamping operation is analyzed. The classical Design of Experience (DoE) uses Full Factorial to design the parameter space with sample points as input data for finite element method (FEM) numerical simulation of the sheet metal stamping process. The basic idea is to consider the design parameters as additional dimensions for the solution of the displacement fields. The order of the resultant high-fidelity model is reduced through the use of POD method which performs model space reduction and results in the basis functions of the low order model. Specifically, the snapshot method is used in our work, in which the basis functions is derived from snapshot deviation of the matrix of the final displacements fields of the FEM numerical simulation. The obtained basis functions are then used to determine the POD coefficients and RBF is used for the interpolation of these POD coefficients over the parameter space. Finally, the presented POD-RBF approach which is used for shape optimization can be performed with high accuracy.

The effect of intravenous insulin infusion on renal blood flow in conscious sheep is partially mediated by nitric oxide but not by prostaglandins.

PubMed

Tebot, I; Bonnet, J-M; Paquet, C; Ayoub, J-Y; Da Silva, S M; Louzier, V; Cirio, A

2012-04-01

To test the effect of insulin on renal perfusion and the participation of NO and PG as mediators of this response, renal blood flow (RBF) was measured in sheep (n = 8) implanted with ultrasonic flow probes around renal arteries and with a systemic arterial pressure (SAP, n = 4) telemetry device. Three protocols were performed: 1) RBF and SAP were recorded (0800 to 1800 h) in fed and fasted sheep, with the latter receiving intravenous (i.v.) infusions (0.5 mL/min) of insulin at 2 or 6 mU/(kg·min); 2) fasted sheep received i.v. infusions of either an inhibitor of NO synthesis (N(G)-nitro-L-arginine methyl ester, L-NAME) alone [0.22 mg/(kg·min), 1000 to 1200 h] or L-NAME (1000 to 1200 h) + insulin during the second hour (6 mU/(kg·min), 1100 to 1200 h); and 3) the same protocol was followed as in protocol 2, substituting L-NAME with ketoprofen [0.2 mg/(kg·min)], a cyclooxygenase inhibitor. In all protocols, plasma insulin and glucose were determined. During insulin administration, euglycemia was maintained and hypokalemia was prevented by infusing glucose and KCl solutions. After the onset of meals, a long-lasting 18% increase in RBF and a 48% insulin increase were observed (P < 0.05), without changes in SAP. Low- and high-dose insulin infusions increased RBF by 19 and 40%, respectively (P < 0.05). As after meals, the increases in RBF lasted longer than the insulin increase (P < 0.05). The L-NAME infusion decreased RBF by 15% (P < 0.05); when insulin was added, RBF increased to preinfusion values. Ketoprofen decreased RBF by 9% (P < 0.05); when insulin was added, RBF increased to 13% above preinfusion values (P < 0.05). In no case was a modification in SAP or glucose noted during the RBF changes. In conclusion, insulin infusion mimics the meal-dependent increase in RBF, independent of SAP, and lasts longer than the blood insulin plateau. The RBF increase induced by insulin was only partially prevented by L-NAME. Ketoprofen failed to prevent the insulin-dependent RBF increase. Both facts suggested that complementary vasodilatatory agents accounted for the insulin effect on sheep renal hemodynamics.

Impact of the NO-Sensitive Guanylyl Cyclase 1 and 2 on Renal Blood Flow and Systemic Blood Pressure in Mice.

PubMed

Mergia, Evanthia; Thieme, Manuel; Hoch, Henning; Daniil, Georgios; Hering, Lydia; Yakoub, Mina; Scherbaum, Christina Rebecca; Rump, Lars Christian; Koesling, Doris; Stegbauer, Johannes

2018-03-23

Nitric oxide (NO) modulates renal blood flow (RBF) and kidney function and is involved in blood pressure (BP) regulation predominantly via stimulation of the NO-sensitive guanylyl cyclase (NO-GC), existing in two isoforms, NO-GC1 and NO-GC2. Here, we used isoform-specific knockout (KO) mice and investigated their contribution to renal hemodynamics under normotensive and angiotensin II-induced hypertensive conditions. Stimulation of the NO-GCs by S -nitrosoglutathione (GSNO) reduced BP in normotensive and hypertensive wildtype (WT) and NO-GC2-KO mice more efficiently than in NO-GC1-KO. NO-induced increase of RBF in normotensive mice did not differ between the genotypes, but the respective increase under hypertensive conditions was impaired in NO-GC1-KO. Similarly, inhibition of endogenous NO increased BP and reduced RBF to a lesser extent in NO-GC1-KO than in NO-GC2-KO. These findings indicate NO-GC1 as a target of NO to normalize RBF in hypertension. As these effects were not completely abolished in NO-GC1-KO and renal cyclic guanosine monophosphate (cGMP) levels were decreased in both NO-GC1-KO and NO-GC2-KO, the results suggest an additional contribution of NO-GC2. Hence, NO-GC1 plays a predominant role in the regulation of BP and RBF, especially in hypertension. However, renal NO-GC2 appears to compensate the loss of NO-GC1, and is able to regulate renal hemodynamics under physiological conditions.

Arterial spin labeling blood flow magnetic resonance imaging for evaluation of renal injury.

PubMed

Liu, Yupin P; Song, Rui; Liang, Chang hong; Chen, Xin; Liu, Bo

2012-08-15

A multitude of evidence suggests that iodinated contrast material causes nephrotoxicity; however, there have been no previous studies that use arterial spin labeling (ASL) blood flow functional magnetic resonance imaging (fMRI) to investigate the alterations in effective renal plasma flow between normointensive and hypertensive rats following injection of contrast media. We hypothesized that FAIR-SSFSE arterial spin labeling MRI may enable noninvasive and quantitative assessment of regional renal blood flow abnormalities and correlate with disease severity as assessed by histological methods. Renal blood flow (RBF) values of the cortex and medulla of rat kidneys were obtained from ASL images postprocessed at ADW4.3 workstation 0.3, 24, 48, and 72 h before and after injection of iodinated contrast media (6 ml/kg). The H&E method for morphometric measurements was used to confirm the MRI findings. The RBF values of the outer medulla were lower than those of the cortex and the inner medulla as reported previously. Iodinated contrast media treatment resulted in decreases in RBF in the outer medulla and cortex in spontaneously hypertensive rats (SHR), but only in the outer medulla in normotensive rats. The iodinated contrast agent significantly decreased the RBF value in the outer medulla and the cortex in SHR compared with normotensive rats after injection of the iodinated contrast media. Histological observations of kidney morphology were also consistent with ASL perfusion changes. These results demonstrate that the RBF value can reflect changes of renal perfusion in the cortex and medulla. ASL-MRI is a feasible and accurate method for evaluating nephrotoxic drugs-induced kidney damage.

In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

PubMed Central

Lu, Peng; Chen, Jianxin; Zhao, Huihui; Gao, Yibo; Luo, Liangtao; Zuo, Xiaohan; Shi, Qi; Yang, Yiping; Yi, Jianqiang; Wang, Wei

2012-01-01

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM. PMID:22567030

Arterial Transit Time-corrected Renal Blood Flow Measurement with Pulsed Continuous Arterial Spin Labeling MR Imaging.

PubMed

Shimizu, Kazuhiro; Kosaka, Nobuyuki; Fujiwara, Yasuhiro; Matsuda, Tsuyoshi; Yamamoto, Tatsuya; Tsuchida, Tatsuro; Tsuchiyama, Katsuki; Oyama, Nobuyuki; Kimura, Hirohiko

2017-01-10

The importance of arterial transit time (ATT) correction for arterial spin labeling MRI has been well debated in neuroimaging, but it has not been well evaluated in renal imaging. The purpose of this study was to evaluate the feasibility of pulsed continuous arterial spin labeling (pcASL) MRI with multiple post-labeling delay (PLD) acquisition for measuring ATT-corrected renal blood flow (ATC-RBF). A total of 14 volunteers were categorized into younger (n = 8; mean age, 27.0 years) and older groups (n = 6; 64.8 years). Images of pcASL were obtained at three different PLDs (0.5, 1.0, and 1.5 s), and ATC-RBF and ATT were calculated using a single-compartment model. To validate ATC-RBF, a comparative study of effective renal plasma flow (ERPF) measured by 99m Tc-MAG3 scintigraphy was performed. ATC-RBF was corrected by kidney volume (ATC-cRBF) for comparison with ERPF. The younger group showed significantly higher ATC-RBF (157.68 ± 38.37 mL/min/100 g) and shorter ATT (961.33 ± 260.87 ms) than the older group (117.42 ± 24.03 mL/min/100 g and 1227.94 ± 226.51 ms, respectively; P < 0.05). A significant correlation was evident between ATC-cRBF and ERPF (P < 0.05, r = 0.47). With suboptimal single PLD (1.5 s) settings, there was no significant correlation between ERPF and kidney volume-corrected RBF calculated from single PLD data. Calculation of ATT and ATC-RBF by pcASL with multiple PLD was feasible in healthy volunteers, and differences in ATT and ATC-RBF were seen between the younger and older groups. Although ATT correction by multiple PLD acquisitions may not always be necessary for RBF quantification in the healthy subjects, the effect of ATT should be taken into account in renal ASL-MRI as debated in brain imaging.

Household costs and time to seek care for pregnancy related complications: The role of results-based financing.

PubMed

Chinkhumba, Jobiba; De Allegri, Manuela; Mazalale, Jacob; Brenner, Stephan; Mathanga, Don; Muula, Adamson S; Robberstad, Bjarne

2017-01-01

Results-based financing (RBF) schemes-including performance based financing (PBF) and conditional cash transfers (CCT)-are increasingly being used to encourage use and improve quality of institutional health care for pregnant women in order to reduce maternal and neonatal mortality in low-income countries. While there is emerging evidence that RBF can increase service use and quality, little is known on the impact of RBF on costs and time to seek care for obstetric complications, although the two represent important dimensions of access. We conducted this study to fill the existing gap in knowledge by investigating the impact of RBF (PBF+CCT) on household costs and time to seek care for obstetric complications in four districts in Malawi. The analysis included data on 2,219 women with obstetric complications from three waves of a population-based survey conducted at baseline in 2013 and repeated in 2014(midline) and 2015(endline). Using a before and after approach with controls, we applied generalized linear models to study the association between RBF and household costs and time to seek care. Results indicated that receipt of RBF was associated with a significant reduction in the expected mean time to seek care for women experiencing an obstetric complication. Relative to non-RBF, time to seek care in RBF areas decreased by 27.3% (95%CI: 28.4-25.9) at midline and 34.2% (95%CI: 37.8-30.4) at endline. No substantial change in household costs was observed. We conclude that the reduced time to seek care is a manifestation of RBF induced quality improvements, prompting faster decisions on care seeking at household level. Our results suggest RBF may contribute to timely emergency care seeking and thus ultimately reduce maternal and neonatal mortality in beneficiary populations.

Control of Glucose- and NaCl-Induced Biofilm Formation by rbf in Staphylococcus aureus

PubMed Central

Lim, Yong; Jana, Malabendu; Luong, Thanh T.; Lee, Chia Y.

2004-01-01

Both Staphylococcus aureus and S. epidermidis are capable of forming biofilm on biomaterials. We used Tn917 mutagenesis to identify a gene, rbf, affecting biofilm formation in S. aureus NCTC8325-4. Sequencing revealed that Rbf contained a consensus region signature of the AraC/XylS family of regulators, suggesting that Rbf is a transcriptional regulator. Insertional duplication inactivation of the rbf gene confirmed that the gene was involved in biofilm formation on polystyrene and glass. Phenotypic analysis of the wild type and the mutant suggested that the rbf gene mediates the biofilm formation of S. aureus at the multicellular aggregation stage rather than at initial attachment. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis demonstrated that the mutation resulted in the loss of an ∼190-kDa protein. Biofilm production by the mutant could be restored by complementation with a 2.5-kb DNA fragment containing the rbf gene. The rbf-specific mutation affected the induction of biofilm formation by glucose and a high concentration of NaCl but not by ethanol. The mutation did not affect the transcription of the ica genes previously shown to be required for biofilm formation. Taken together, our results suggest that the rbf gene is involved in the regulation of the multicellular aggregation step of S. aureus biofilm formation in response to glucose and salt and that this regulation may be mediated through the 190-kDa protein. PMID:14729698

Exploring the potential for using results-based financing to address non-communicable diseases in low- and middle-income countries

PubMed Central

2013-01-01

Background The burden of disease due to non-communicable diseases (NCDs) is rising in low- and middle-income countries (LMICs) and funding for global health is increasingly limited. As a large contributor of development assistance for health, the US government has the potential to influence overall trends in NCDs. Results-based financing (RBF) has been proposed as a strategy to increase aid effectiveness and efficiency through incentives for positive performance and results in health programs, but its potential for addressing NCDs has not been explored. Methods Qualitative methods including literature review and key informant interviews were used to identify promising RBF mechanisms for addressing NCDs in resource-limited settings. Eight key informants identified by area of expertise participated in semi-structured interviews. Results The majority of RBF schemes to date have been applied to maternal and child health. Evidence from existing RBF programs suggests that RBF principles can be applied to health programs for NCDs. Several options were identified for US involvement with RBF for NCDs. Conclusion There is potential for the US to have a significant impact on NCDs in LMICs through a comprehensive RBF strategy for global health. RBF mechanisms should be tested for use in NCD programs through pilot programs incorporating robust impact evaluations. PMID:23368959

Exploring the potential for using results-based financing to address non-communicable diseases in low- and middle-income countries.

PubMed

Beane, Chelsey R; Hobbs, Suzanne Havala; Thirumurthy, Harsha

2013-02-01

The burden of disease due to non-communicable diseases (NCDs) is rising in low- and middle-income countries (LMICs) and funding for global health is increasingly limited. As a large contributor of development assistance for health, the US government has the potential to influence overall trends in NCDs. Results-based financing (RBF) has been proposed as a strategy to increase aid effectiveness and efficiency through incentives for positive performance and results in health programs, but its potential for addressing NCDs has not been explored. Qualitative methods including literature review and key informant interviews were used to identify promising RBF mechanisms for addressing NCDs in resource-limited settings. Eight key informants identified by area of expertise participated in semi-structured interviews. The majority of RBF schemes to date have been applied to maternal and child health. Evidence from existing RBF programs suggests that RBF principles can be applied to health programs for NCDs. Several options were identified for US involvement with RBF for NCDs. There is potential for the US to have a significant impact on NCDs in LMICs through a comprehensive RBF strategy for global health. RBF mechanisms should be tested for use in NCD programs through pilot programs incorporating robust impact evaluations.

The rise of the rats: A growing paediatric issue

PubMed Central

Khatchadourian, Karine; Ovetchkine, Philippe; Minodier, Philippe; Lamarre, Valérie; Lebel, Marc H; Tapiéro, Bruce

2010-01-01

Rat bite fever (RBF), a systemic infection of Streptobacillus moniliformis or Spirillum minus characterized by fever, arthralgias and petechial-purpuric rash on the extremities, carries a mortality rate of 7% to 10% if untreated. In Canada, one adult and two paediatric cases of RBF have been reported since 2000. In recent years, pet rats have become quite popular among children, placing them at an increased risk for RBF. Thus, paediatricians need to be more wary of the potential for RBF in their patients. In the present report, a culture-confirmed case of RBF and two additional cases of suspected infection are described. PMID:21358889

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

A modified artificial immune system based pattern recognition approach -- an application to clinic diagnostics

PubMed Central

Zhao, Weixiang; Davis, Cristina E.

2011-01-01

Objective This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis. Methods and materials Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function – partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network). Results The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of “replacement threshold = 0.3”, the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network. Conclusion In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems. PMID:21515033

A qualitative study assessing the acceptability and adoption of implementing a results based financing intervention to improve maternal and neonatal health in Malawi.

PubMed

Wilhelm, Danielle J; Brenner, Stephan; Muula, Adamson S; De Allegri, Manuela

2016-08-17

Results Based Financing (RBF) interventions have recently gained significant momentum, especially in sub-Saharan Africa. However, most of the research has focused on the evaluation of the impacts of this approach, providing little insight into how the contextual circumstances surrounding the implementation have contributed to its success or failure. This study aims to fill a void in the current literature on RBF by focusing explicitly on the process of implementing a RBF intervention rather than on its impact. Specifically, this study focuses on the acceptability and adoption of the RBF intervention's implementation among local and international key stakeholders with the aim to inform further implementation. The Results Based Financing for Maternal and Neonatal Health (RBF4MNH) Initiative is currently being implemented in Malawi. Our study employed an exploratory cross-sectional qualitative design to explore the factors affecting the acceptability and adoption of the intervention's implementation. Purposeful sampling techniques were used to identify each key stakeholder who participated in all or parts of the implementation process. In-depth interviews were conducted and analyzed using a deductive open coding approach. The final interpretation of the findings emerged through active discussion among the co-authors. Despite encountering several challenges, such as delay in procurement of equipment and difficulties in arranging local bank accounts, all stakeholders responded positively to the RBF4MNH Initiative. Stakeholders' acceptance of the RBF4MNH Initiative grew stronger over time as understanding of the intervention improved and was supported by early inclusion during the design and implementation process. In addition, stakeholders took on functions not directly incentivized by the intervention, suggesting that they turned adoption into actual ownership. All stakeholders raised concerns that the intervention may not be sustainable after its initial program phase would end, which contributed to hesitancy in fully accepting the intervention. Based on the results of this study, we recommend the inclusion of local stakeholders into the intervention's implementation process at the earliest stages. We also recommend setting up continuous feedback mechanisms to tackle challenges encountered during the implementation process. The sustainability of the intervention and its incorporation into national budgets should be addressed from the earliest stages.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Neural computing thermal comfort index PMV for the indoor environment intelligent control system

NASA Astrophysics Data System (ADS)

Liu, Chang; Chen, Yifei

2013-03-01

Providing indoor thermal comfort and saving energy are two main goals of indoor environmental control system. An intelligent comfort control system by combining the intelligent control and minimum power control strategies for the indoor environment is presented in this paper. In the system, for realizing the comfort control, the predicted mean vote (PMV) is designed as the control goal, and with chastening formulas of PMV, it is controlled to optimize for improving indoor comfort lever by considering six comfort related variables. On the other hand, a RBF neural network based on genetic algorithm is designed to calculate PMV for better performance and overcoming the nonlinear feature of the PMV calculation better. The formulas given in the paper are presented for calculating the expected output values basing on the input samples, and the RBF network model is trained depending on input samples and the expected output values. The simulation result is proved that the design of the intelligent calculation method is valid. Moreover, this method has a lot of advancements such as high precision, fast dynamic response and good system performance are reached, it can be used in practice with requested calculating error.

Visually evoked changes in the rat retinal blood flow measured with Doppler optical coherence tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Tan, Bingyao; Mason, Erik; MacLellan, Ben; Bizheva, Kostadinka

2017-02-01

Visually evoked changes of retinal blood flow can serve as an important research tool to investigate eye disease such as glaucoma and diabetic retinopathy. In this study we used a combined, research-grade, high-resolution Doppler OCT+ERG system to study changes in the retinal blood flow (RBF) and retinal neuronal activity in response to visual stimuli of different intensities, durations and type (flicker vs single flash). Specifically, we used white light stimuli of 10 ms and 200 ms single flash, 1s and 2s for flickers stimuli of 20% duty cycle. The study was conducted in-vivo in pigmented rats. Both single flash (SF) and flicker stimuli caused increase in the RBF. The 10 ms SF stimulus did not generate any consistent measurable response, while the 200 ms SF of the same intensity generated 4% change in the RBF peaking at 1.5 s after the stimulus onset. Single flash stimuli introduced 2x smaller change in RBF and 30% earlier RBF peak response compared to flicker stimuli of the same intensity and duration. Doubling the intensity of SF or flicker stimuli increased the RBF peak magnitude by 1.5x. Shortening the flicker stimulus duration by 2x increased the RBF recovery rate by 2x, however, had no effect on the rate of RBF change from baseline to peak.

Effect of renal nerve stimulation on responsiveness of the rat renal vasculature.

PubMed

DiBona, Gerald F; Sawin, Linda L

2002-11-01

When the renal nerves are stimulated with sinusoidal stimuli over the frequency range 0.04-0.8 Hz, low (< or =0.4 Hz)- but not high (> or =0.4 Hz)-frequency oscillations appear in renal blood flow (RBF) and are proposed to increase responsiveness of the renal vasculature to stimuli. This hypothesis was tested in anesthetized rats in which RBF responses to intrarenal injection of norepinephrine and angiotensin and to reductions in renal arterial pressure (RAP) were determined during conventional rectangular pulse and sinusoidal renal nerve stimulation. Conventional rectangular pulse renal nerve stimulation decreased RBF at 2 Hz but not at 0.2 or 1.0 Hz. Sinusoidal renal nerve stimulation elicited low-frequency oscillations (< or =0.4 Hz) in RBF only when the basal carrier signal frequency produced renal vasoconstriction, i.e., at 5 Hz but not at 1 Hz. Regardless of whether renal vasoconstriction occurred, neither conventional rectangular pulse nor sinusoidal renal nerve stimulation altered renal vasoconstrictor responses to norepinephrine and angiotensin. The RBF response to reduction in RAP was altered by both conventional rectangular pulse and sinusoidal renal nerve stimulation only when renal vasoconstriction occurred: the decrease in RBF during reduced RAP was greater. Sinusoidal renal nerve stimulation with a renal vasoconstrictor carrier frequency results in a decrease in RBF with superimposed low-frequency oscillations. However, these low-frequency RBF oscillations do not alter renal vascular responsiveness to vasoconstrictor stimuli.

Investigating the management performance of disinfection analysis of water distribution networks using data mining approaches.

PubMed

Zounemat-Kermani, Mohammad; Ramezani-Charmahineh, Abdollah; Adamowski, Jan; Kisi, Ozgur

2018-06-13

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.

[Rbf1 (RPG-box binding factor), a transcription factor involved in yeast-hyphal transition of Candida albicans].

PubMed

Aoki, Y; Ishii, N; Watanabe, M; Yoshihara, F; Arisawa, M

1998-01-01

The major fungal pathogen for fungal diseases which have become a major medical problem in the last few years is Candida albicans, which can grow both in yeast and hyphae forms. This ability of C. albicans is thought to contribute to its colonization and dissemination within host tissues. In a recent few years, accompanying the introduction of molecular biological tools into C. albicans organism, several factors involved in the signal transduction pathway for yeast-hyphal transition have been identified. One MAP kinase pathway in C. albicans, similar to that leading to STE12 activation in Saccharomyces cerevisiae, has been reported. C. albicans strains mutant in these genes show retarded filamentous growth on a solid media but no impairment of filamentous growth in mice. These results suggest two scenarios that a kinase signaling cascade plays a part in stimulating the morphological transition in C. albicans, and that there would be another signaling pathway effective in animals. In this latter true hyphal pathway, although some candidate proteins, such as Efg1 (transcription factor), Int1 (integrin-like membrane protein), or Phr1 (pH-regulated membrane protein), have been identified, it is still too early to say that we understand the whole picture of that cascade. We have cloned a C. albicans gene encoding a novel DNA binding protein, Rbf1, that predominantly localizes in the nucleus, and shows transcriptional activation capability. Disruption of the functional RBF1 genes of C. albicans induced the filamentous growth on all solid and liquid media tested, suggesting that Rbf1 might be another candidate for the true hyphal pathway. Relationships with other factors described above, and the target (regulated) genes of Rbf1 is under investigation.

Polyuria and impaired renal blood flow after asphyxia in preterm fetal sheep.

PubMed

Quaedackers, J S; Roelfsema, V; Hunter, C J; Heineman, E; Gunn, A J; Bennet, L

2004-03-01

Renal impairment is common in preterm infants, often after exposure to hypoxia/asphyxia or other circulatory disturbances. We examined the hypothesis that this association is mediated by reduced renal blood flow (RBF), using a model of asphyxia induced by complete umbilical cord occlusion for 25 min (n = 13) or sham occlusion (n = 6) in chronically instrumented preterm fetal sheep (104 days, term is 147 days). During asphyxia there was a significant fall in RBF and urine output (UO). After asphyxia, RBF transiently recovered, followed within 30 min by a secondary period of hypoperfusion (P < 0.05). This was mediated by increased renal vascular resistance (RVR, P < 0.05); arterial blood pressure was mildly increased in the first 24 h (P < 0.05). RBF relatively normalized between 3 and 24 h, but hypoperfusion developed again from 24 to 60 h (P < 0.05, analysis of covariance). UO significantly increased to a peak of 249% of baseline between 3 and 12 h (P < 0.05), with increased fractional excretion of sodium, peak 10.5 +/- 1.4 vs. 2.6 +/- 0.6% (P < 0.001). Creatinine clearance returned to normal after 2 h; there was a transient reduction at 48 h to 0.32 +/- 0.02 ml.min(-1).g(-1) (vs. 0.45 +/- 0.04, P < 0.05) corresponding with the time of maximal depression of RBF. No renal injury was seen on histological examination at 72 h. In conclusion, severe asphyxia in the preterm fetus was associated with evolving renal tubular dysfunction, as shown by transient polyuria and natriuresis. Despite a prolonged increase in RVR, there was only a modest effect on glomerular function.

Exercise training normalizes renal blood flow responses to acute hypoxia in experimental heart failure: role of the α1-adrenergic receptor.

PubMed

Pügge, Carolin; Mediratta, Jai; Marcus, Noah J; Schultz, Harold D; Schiller, Alicia M; Zucker, Irving H

2016-02-01

Recent data suggest that exercise training (ExT) is beneficial in chronic heart failure (CHF) because it improves autonomic and peripheral vascular function. In this study, we hypothesized that ExT in the CHF state ameliorates the renal vasoconstrictor responses to hypoxia and that this beneficial effect is mediated by changes in α1-adrenergic receptor activation. CHF was induced in rabbits. Renal blood flow (RBF) and renal vascular conductance (RVC) responses to 6 min of 5% isocapnic hypoxia were assessed in the conscious state in sedentary (SED) and ExT rabbits with CHF with and without α1-adrenergic blockade. α1-adrenergic receptor expression in the kidney cortex was also evaluated. A significant decline in baseline RBF and RVC and an exaggerated renal vasoconstriction during acute hypoxia occurred in CHF-SED rabbits compared with the prepaced state (P < 0.05). ExT diminished the decline in baseline RBF and RVC and restored changes during hypoxia to those of the prepaced state. α1-adrenergic blockade partially prevented the decline in RBF and RVC in CHF-SED rabbits and eliminated the differences in hypoxia responses between SED and ExT animals. Unilateral renal denervation (DnX) blocked the hypoxia-induced renal vasoconstriction in CHF-SED rabbits. α1-adrenergic protein in the renal cortex of animals with CHF was increased in SED animals and normalized after ExT. These data provide evidence that the acute decline in RBF during hypoxia is caused entirely by the renal nerves but is only partially mediated by α1-adrenergic receptors. Nonetheless, α1-adrenergic receptors play an important role in the beneficial effects of ExT in the kidney. Copyright © 2016 the American Physiological Society.

Angiotensin II Type 1 Receptor Antagonism Attenuates Abnormalities in Dynamic Renal Blood Flow Autoregulation in Rats with Endotoxin-Induced Acute Kidney Injury.

PubMed

Nitescu, Nicoletta; DiBona, Gerald F; Grimberg, Elisabeth; Guron, Gregor

2010-01-01

The aim was to examine the role of angiotensin II type 1 receptors in dynamic autoregulation of renal blood flow (RBF) in endotoxemia. Experiments were performed on anesthetized rats 16 h after intraperitoneal lipopolysaccharide (LPS) or vehicle administration. After baseline measurements, groups Sham-Saline, LPS-Saline and LPS-Candesartan received isotonic saline or candesartan (10 μg kg(-1) i.v.). Data were collected during eight consecutive 20-min clearance periods (C1-8). Transfer function (TF) analysis in the frequency domain was used to examine dynamic autoregulation of RBF. Endotoxemic rats showed an approximate 50% reduction in glomerular filtration rate (GFR) and RBF (p < 0.05 vs. Sham-Saline). Candesartan significantly increased RBF (+40 ± 6% vs. baseline; p < 0.05) but did not significantly influence GFR. Endotoxemic animals showed a normal myogenic response but had elevated TF gain values in the frequency range of the tubuloglomerular feedback mechanism (TGF; 0.01-0.03 Hz) reflecting impaired autoregulation (periods C3-4, 2.2 ± 1.6 vs. -2.6 ± 0.6 dB, p < 0.05, and C7-8, -0.4 ± 1.3 vs. -4.0 ± 0.8 dB, p < 0.05; in groups LPS-Saline and Sham-Saline, respectively). Candesartan normalized TF gain in this frequency range (periods C7-8, -6.1 ± 2.3 dB in group LPS-Candesartan, p < 0.05 vs. LPS-Saline). Candesartan ameliorates the adverse effect of endotoxin on the TGF component of dynamic autoregulation of RBF. Copyright © 2010 S. Karger AG, Basel.

Design and Validation of a Ten-Port Waveguide Reflectometer Sensor: Application to Efficiency Measurement and Optimization of Microwave-Heating Ovens

PubMed Central

Pedreño-Molina, Juan L.; Monzó-Cabrera, Juan; Lozano-Guerrero, Antonio; Toledo-Moreo, Ana

2008-01-01

This work presents the design, manufacturing process, calibration and validation of a new microwave ten-port waveguide reflectometer based on the use of neural networks. This low-cost novel device solves some of the shortcomings of previous reflectometers such as non-linear behavior of power sensors, noise presence and the complexity of the calibration procedure, which is often based on complex mathematical equations. These problems, which imply the reduction of the reflection coefficient measurement accuracy, have been overcome by using a higher number of probes than usual six-port configurations and by means of the use of Radial Basis Function (RBF) neural networks in order to reduce the influence of noise and non-linear processes over the measurements. Additionally, this sensor can be reconfigured whenever some of the eight coaxial power detectors fail, still providing accurate values in real time. The ten-port performance has been compared against a high-cost measurement instrument such as a vector network analyzer and applied to the measurement and optimization of energy efficiency of microwave ovens, with good results. PMID:27873961

Weather forecasting based on hybrid neural model

NASA Astrophysics Data System (ADS)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Household costs and time to seek care for pregnancy related complications: The role of results-based financing

PubMed Central

Chinkhumba, Jobiba; De Allegri, Manuela; Mazalale, Jacob; Brenner, Stephan; Mathanga, Don; Muula, Adamson S.; Robberstad, Bjarne

2017-01-01

Results-based financing (RBF) schemes–including performance based financing (PBF) and conditional cash transfers (CCT)-are increasingly being used to encourage use and improve quality of institutional health care for pregnant women in order to reduce maternal and neonatal mortality in low-income countries. While there is emerging evidence that RBF can increase service use and quality, little is known on the impact of RBF on costs and time to seek care for obstetric complications, although the two represent important dimensions of access. We conducted this study to fill the existing gap in knowledge by investigating the impact of RBF (PBF+CCT) on household costs and time to seek care for obstetric complications in four districts in Malawi. The analysis included data on 2,219 women with obstetric complications from three waves of a population-based survey conducted at baseline in 2013 and repeated in 2014(midline) and 2015(endline). Using a before and after approach with controls, we applied generalized linear models to study the association between RBF and household costs and time to seek care. Results indicated that receipt of RBF was associated with a significant reduction in the expected mean time to seek care for women experiencing an obstetric complication. Relative to non-RBF, time to seek care in RBF areas decreased by 27.3% (95%CI: 28.4–25.9) at midline and 34.2% (95%CI: 37.8–30.4) at endline. No substantial change in household costs was observed. We conclude that the reduced time to seek care is a manifestation of RBF induced quality improvements, prompting faster decisions on care seeking at household level. Our results suggest RBF may contribute to timely emergency care seeking and thus ultimately reduce maternal and neonatal mortality in beneficiary populations. PMID:28934320

Relative performance of selected detectors

NASA Astrophysics Data System (ADS)

Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.; Sichina, Jeffrey

2000-08-01

The quadratic polynomial detector (QPD) and the radial basis function (RBF) family of detectors -- including the Bayesian neural network (BNN) -- might well be considered workhorses within the field of automatic target detection (ATD). The QPD works reasonably well when the data is unimodal, and it also achieves the best possible performance if the underlying data follow a Gaussian distribution. The BNN, on the other hand, has been applied successfully in cases where the underlying data are assumed to follow a multimodal distribution. We compare the performance of a BNN detector and a QPD for various scenarios synthesized from a set of Gaussian probability density functions (pdfs). This data synthesis allows us to control parameters such as modality and correlation, which, in turn, enables us to create data sets that can probe the weaknesses of the detectors. We present results for different data scenarios and different detector architectures.

The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach.

PubMed

Taha, Zahari; Musa, Rabiu Muazu; P P Abdul Majeed, Anwar; Alim, Muhammad Muaz; Abdullah, Mohamad Razali

2018-02-01

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme. Copyright © 2017 Elsevier B.V. All rights reserved.

Exercise training attenuates chemoreflex-mediated reductions of renal blood flow in heart failure

PubMed Central

Pügge, Carolin; Mediratta, Jai; Schiller, Alicia M.; Del Rio, Rodrigo; Zucker, Irving H.; Schultz, Harold D.

2015-01-01

In chronic heart failure (CHF), carotid body chemoreceptor (CBC) activity is increased and contributes to increased tonic and hypoxia-evoked elevation in renal sympathetic nerve activity (RSNA). Elevated RSNA and reduced renal perfusion may contribute to development of the cardio-renal syndrome in CHF. Exercise training (EXT) has been shown to abrogate CBC-mediated increases in RSNA in experimental heart failure; however, the effect of EXT on CBC control of renal blood flow (RBF) is undetermined. We hypothesized that CBCs contribute to tonic reductions in RBF in CHF, that stimulation of the CBC with hypoxia would result in exaggerated reductions in RBF, and that these responses would be attenuated with EXT. RBF was measured in CHF-sedentary (SED), CHF-EXT, CHF-carotid body denervation (CBD), and CHF-renal denervation (RDNX) groups. We measured RBF at rest and in response to hypoxia (FiO2 10%). All animals exhibited similar reductions in ejection fraction and fractional shortening as well as increases in ventricular systolic and diastolic volumes. Resting RBF was lower in CHF-SED (29 ± 2 ml/min) than in CHF-EXT animals (46 ± 2 ml/min, P < 0.05) or in CHF-CBD animals (42 ± 6 ml/min, P < 0.05). In CHF-SED, RBF decreased during hypoxia, and this was prevented in CHF-EXT animals. Both CBD and RDNX abolished the RBF response to hypoxia in CHF. Mean arterial pressure increased in response to hypoxia in CHF-SED, but was prevented by EXT, CBD, and RDNX. EXT is effective in attenuating chemoreflex-mediated tonic and hypoxia-evoked reductions in RBF in CHF. PMID:26001414

Exercise training attenuates chemoreflex-mediated reductions of renal blood flow in heart failure.

PubMed

Marcus, Noah J; Pügge, Carolin; Mediratta, Jai; Schiller, Alicia M; Del Rio, Rodrigo; Zucker, Irving H; Schultz, Harold D

2015-07-15

In chronic heart failure (CHF), carotid body chemoreceptor (CBC) activity is increased and contributes to increased tonic and hypoxia-evoked elevation in renal sympathetic nerve activity (RSNA). Elevated RSNA and reduced renal perfusion may contribute to development of the cardio-renal syndrome in CHF. Exercise training (EXT) has been shown to abrogate CBC-mediated increases in RSNA in experimental heart failure; however, the effect of EXT on CBC control of renal blood flow (RBF) is undetermined. We hypothesized that CBCs contribute to tonic reductions in RBF in CHF, that stimulation of the CBC with hypoxia would result in exaggerated reductions in RBF, and that these responses would be attenuated with EXT. RBF was measured in CHF-sedentary (SED), CHF-EXT, CHF-carotid body denervation (CBD), and CHF-renal denervation (RDNX) groups. We measured RBF at rest and in response to hypoxia (FiO2 10%). All animals exhibited similar reductions in ejection fraction and fractional shortening as well as increases in ventricular systolic and diastolic volumes. Resting RBF was lower in CHF-SED (29 ± 2 ml/min) than in CHF-EXT animals (46 ± 2 ml/min, P < 0.05) or in CHF-CBD animals (42 ± 6 ml/min, P < 0.05). In CHF-SED, RBF decreased during hypoxia, and this was prevented in CHF-EXT animals. Both CBD and RDNX abolished the RBF response to hypoxia in CHF. Mean arterial pressure increased in response to hypoxia in CHF-SED, but was prevented by EXT, CBD, and RDNX. EXT is effective in attenuating chemoreflex-mediated tonic and hypoxia-evoked reductions in RBF in CHF. Copyright © 2015 the American Physiological Society.

Stable modeling based control methods using a new RBF network.

PubMed

Beyhan, Selami; Alci, Musa

2010-10-01

This paper presents a novel model with radial basis functions (RBFs), which is applied successively for online stable identification and control of nonlinear discrete-time systems. First, the proposed model is utilized for direct inverse modeling of the plant to generate the control input where it is assumed that inverse plant dynamics exist. Second, it is employed for system identification to generate a sliding-mode control input. Finally, the network is employed to tune PID (proportional + integrative + derivative) controller parameters automatically. The adaptive learning rate (ALR), which is employed in the gradient descent (GD) method, provides the global convergence of the modeling errors. Using the Lyapunov stability approach, the boundedness of the tracking errors and the system parameters are shown both theoretically and in real time. To show the superiority of the new model with RBFs, its tracking results are compared with the results of a conventional sigmoidal multi-layer perceptron (MLP) neural network and the new model with sigmoid activation functions. To see the real-time capability of the new model, the proposed network is employed for online identification and control of a cascaded parallel two-tank liquid-level system. Even though there exist large disturbances, the proposed model with RBFs generates a suitable control input to track the reference signal better than other methods in both simulations and real time. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Role of Mas receptor in renal blood flow response to angiotensin-(1-7) in ovariectomized estradiol treated rats.

PubMed

Saberi, Shadan; Dehghani, Aghdas; Nematbakhsh, Mehdi

2016-01-01

The angiotensin 1-7 (Ang 1-7), is abundantly produced in kidneys and antagonizes the function of angiotensin II through Mas receptor (MasR) or other unknown mechanisms. In the current study, the role of MasR and steroid hormone estrogen on renal blood flow response to Ang 1-7 administration was investigated in ovariectomized (OV) female rats. OV female Wistar-rats received estradiol (500 μg/kg/week) or vehicle for two weeks. In the day of the experiment, the animals were anesthetized, cannulated, and the responses including mean arterial pressure, renal blood flow (RBF), and renal vascular resistance at the constant level of renal perfusion pressure to graded infusion of Ang 1-7 at 0, 100 and 300 ng/kg/min were determined in OV and OV estradiol-treated (OVE) rats, treated with vehicle or MasR antagonist; A779. RBF response to Ang 1-7 infusion increased dose-dependently in vehicle (Pdose <0.001) and A779-treated (Pdose <0.01) animals. However, when MasR was blocked, the RBF response to Ang 1-7 significantly increased in OV animals compared with OVE rats (P<0.05). When estradiol was limited by ovariectomy, A779 increased RBF response to Ang 1-7 administration, while this response was attenuated in OVE animals.

RBF kernel based support vector regression to estimate the blood volume and heart rate responses during hemodialysis.

PubMed

Javed, Faizan; Chan, Gregory S H; Savkin, Andrey V; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-01-01

This paper uses non-linear support vector regression (SVR) to model the blood volume and heart rate (HR) responses in 9 hemodynamically stable kidney failure patients during hemodialysis. Using radial bias function (RBF) kernels the non-parametric models of relative blood volume (RBV) change with time as well as percentage change in HR with respect to RBV were obtained. The e-insensitivity based loss function was used for SVR modeling. Selection of the design parameters which includes capacity (C), insensitivity region (e) and the RBF kernel parameter (sigma) was made based on a grid search approach and the selected models were cross-validated using the average mean square error (AMSE) calculated from testing data based on a k-fold cross-validation technique. Linear regression was also applied to fit the curves and the AMSE was calculated for comparison with SVR. For the model based on RBV with time, SVR gave a lower AMSE for both training (AMSE=1.5) as well as testing data (AMSE=1.4) compared to linear regression (AMSE=1.8 and 1.5). SVR also provided a better fit for HR with RBV for both training as well as testing data (AMSE=15.8 and 16.4) compared to linear regression (AMSE=25.2 and 20.1).

Renal hemodynamic response to galanin: importance of elevated plasma glucose.

PubMed

Premen, A J

1989-12-01

Although recent data point to a possible indirect role for galanin in modulating renal blood flow (RBF) and fluid homeostasis in experimental animals, there have been no systematic studies exploring the possible direct effects of the peptide on the mammalian kidney. We ascertained the RBF, glomerular filtration rate (GFR) and plasma glucose responses to direct intrarenal infusion of three progressively increasing doses of synthetic galanin in anesthetized dogs. A 50 ng/kg per min dose (n = 6) failed to affect RBF, GFR or arterial plasma glucose (APG). Yet, a 100 ng/kg per min dose elevated RBF and GFR by 13 and 14%, respectively, while concomitantly increasing APG by 38%. At 200 ng/kg per min, galanin elevated RBF and GFR by 32 and 33%, respectively, while elevating APG by 57%. Intrarenal infusion of glucose (12.5 mg/kg per min; n = 6), reproducing the percentage rise in glucose (62%) elicited by the highest dose of galanin, elevated RBF and GFR by 20 and 23%, respectively. These data indicate that the elevated plasma glucose level, stimulated by galanin infusion, may account for about 63 and 70% of the RBF and GFR responses, respectively, elicited by galanin infusion at the 200 ng dose. The factors mediating the remaining renal hyperemia and hyperfiltration await resolution.

Feasibility of measuring renal blood flow by phase-contrast magnetic resonance imaging in patients with autosomal dominant polycystic kidney disease.

PubMed

Spithoven, E M; Meijer, E; Borns, C; Boertien, W E; Gaillard, C A J M; Kappert, P; Greuter, M J W; van der Jagt, E; Vart, P; de Jong, P E; Gansevoort, R T

2016-03-01

Renal blood flow (RBF) has been shown to predict disease progression in autosomal dominant polycystic kidney disease (ADPKD). We investigated the feasibility and accuracy of phase-contrast RBF by MRI (RBFMRI) in ADPKD patients with a wide range of estimated glomerular filtration rate (eGFR) values. First, we validated RBFMRI measurement using phantoms simulating renal artery hemodynamics. Thereafter, we investigated in a test-set of 21 patients intra- and inter-observer coefficient of variation of RBFMRI. After validation, we measured RBFMRI in a cohort of 91 patients and compared the variability explained by characteristics indicative for disease severity for RBFMRI and RBF measured by continuous hippuran infusion. The correlation in flow measurement using phantoms by phase-contrast MRI was high and fluid collection was high (CCC=0.969). Technical problems that precluded RBFMRI measurement occurred predominantly in patients with a lower eGFR (34% vs. 16%). In subjects with higher eGFRs, variability in RBF explained by disease characteristics was similar for RBFMRI compared to RBFHip, whereas in subjects with lower eGFRs, this was significantly less for RBFMRI. Our study shows that RBF can be measured accurately in ADPKD patients by phase-contrast, but this technique may be less feasible in subjects with a lower eGFR. Renal blood flow (RBF) can be accurately measured by phase-contrast MRI in ADPKD patients. RBF measured by phase-contrast is associated with ADPKD disease severity. RBF measurement by phase-contrast MRI may be less feasible in patients with an impaired eGFR.

Exploring implementation practices in results-based financing: the case of the verification in Benin.

PubMed

Antony, Matthieu; Bertone, Maria Paola; Barthes, Olivier

2017-03-14

Results-based financing (RBF) has been introduced in many countries across Africa and a growing literature is building around the assessment of their impact. These studies are usually quantitative and often silent on the paths and processes through which results are achieved and on the wider health system effects of RBF. To address this gap, our study aims at exploring the implementation of an RBF pilot in Benin, focusing on the verification of results. The study is based on action research carried out by authors involved in the pilot as part of the agency supporting the RBF implementation in Benin. While our participant observation and operational collaboration with project's stakeholders informed the study, the analysis is mostly based on quantitative and qualitative secondary data, collected throughout the project's implementation and documentation processes. Data include project documents, reports and budgets, RBF data on service outputs and on the outcome of the verification, daily activity timesheets of the technical assistants in the districts, as well as focus groups with Community-based Organizations and informal interviews with technical assistants and district medical officers. Our analysis focuses on the actual practices of quantitative, qualitative and community verification. Results show that the verification processes are complex, costly and time-consuming, and in practice they end up differing from what designed originally. We explore the consequences of this on the operation of the scheme, on its potential to generate the envisaged change. We find, for example, that the time taken up by verification procedures limits the time available for data analysis and feedback to facility staff, thus limiting the potential to improve service delivery. Verification challenges also result in delays in bonus payment, which delink effort and reward. Additionally, the limited integration of the verification activities of district teams with their routine tasks causes a further verticalization of the health system. Our results highlight the potential disconnect between the theory of change behind RBF and the actual scheme's implementation. The implications are relevant at methodological level, stressing the importance of analyzing implementation processes to fully understand results, as well as at operational level, pointing to the need to carefully adapt the design of RBF schemes (including verification and other key functions) to the context and to allow room to iteratively modify it during implementation. They also question whether the rationale for thorough and costly verification is justified, or rather adaptations are possible.

Noninvasive measurement of renal blood flow by magnetic resonance imaging in rats.

PubMed

Romero, Cesar A; Cabral, Glauber; Knight, Robert A; Ding, Guangliang; Peterson, Edward L; Carretero, Oscar A

2018-01-01

Renal blood flow (RBF) provides important information regarding renal physiology and nephropathies. Arterial spin labeling-magnetic resonance imaging (ASL-MRI) is a noninvasive method of measuring blood flow without exogenous contrast media. However, low signal-to-noise ratio and respiratory motion artifacts are challenges for RBF measurements in small animals. Our objective was to evaluate the feasibility and reproducibility of RBF measurements by ASL-MRI using respiratory-gating and navigator correction methods to reduce motion artifacts. ASL-MRI images were obtained from the kidneys of Sprague-Dawley (SD) rats on a 7-Tesla Varian MRI system with a spin-echo imaging sequence. After 4 days, the study was repeated to evaluate its reproducibility. RBF was also measured in animals under unilateral nephrectomy and in renal artery stenosis (RST) to evaluate the sensitivity in high and low RBF models, respectively. RBF was also evaluated in Dahl salt-sensitive (SS) rats and spontaneous hypertensive rats (SHR). In SD rats, the cortical RBFs (cRBF) were 305 ± 59 and 271.8 ± 39 ml·min -1 ·100 g tissue -1 in the right and left kidneys, respectively. Retest analysis revealed no differences ( P = 0.2). The test-retest reliability coefficient was 92 ± 5%. The cRBFs before and after the nephrectomy were 296.8 ± 30 and 428.2 ± 45 ml·min -1 ·100 g tissue -1 ( P = 0.02), respectively. The kidneys with RST exhibited a cRBF decrease compared with sham animals (86 ± 17.6 vs. 198 ± 33.7 ml·min -1 ·100 g tissue -1 ; P < 0.01). The cRBFs in SD, Dahl-SS, and SHR rats were not different ( P = 0.35). We conclude that ASL-MRI performed with navigator correction and respiratory gating is a feasible and reliable noninvasive method for measuring RBF in rats.

Modulation of radial blood flow during Braille character discrimination task.

PubMed

Murata, Jun; Matsukawa, K; Komine, H; Tsuchimochi, H

2012-03-01

Human hands are excellent in performing sensory and motor function. We have hypothesized that blood flow of the hand is dynamically regulated by sympathetic outflow during concentrated finger perception. To identify this hypothesis, we measured radial blood flow (RBF), radial vascular conductance (RVC), heart rate (HR), and arterial blood pressure (AP) during Braille reading performed under the blind condition in nine healthy subjects. The subjects were instructed to read a flat plate with raised letters (Braille reading) for 30 s by the forefinger, and to touch a blank plate as control for the Braille discrimination procedure. HR and AP slightly increased during Braille reading but remained unchanged during the touching of the blank plate. RBF and RVC were reduced during the Braille character discrimination task (decreased by -46% and -49%, respectively). Furthermore, the changes in RBF and RVC were much greater during the Braille character discrimination task than during the touching of the blank plate (decreased by -20% and -20%, respectively). These results have suggested that the distribution of blood flow to the hand is modulated via sympathetic nerve activity during concentrated finger perception.

Adaptive radial basis function mesh deformation using data reduction

NASA Astrophysics Data System (ADS)

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 bandwidth available between CPU and memory. In terms of parallel efficiency/scaling the different studied methods perform similarly, with the greedy algorithm being the bottleneck. In terms of absolute computational work the adaptive methods are better for the cases studied due to their more efficient selection of the control points. By automating most of the RBF mesh deformation, a robust, efficient and almost user-independent mesh deformation method is presented.

Materials Data on RbF (SG:225) by Materials Project

DOE Data Explorer

Kristin Persson

2014-11-02

Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

NASA Astrophysics Data System (ADS)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

Experimental and AI-based numerical modeling of contaminant transport in porous media

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Mousavi, Shahram; Sadikoglu, Fahreddin; Singh, Vijay P.

2017-10-01

This study developed a new hybrid artificial intelligence (AI)-meshless approach for modeling contaminant transport in porous media. The key innovation of the proposed approach is that both black box and physically-based models are combined for modeling contaminant transport. The effectiveness of the approach was evaluated using experimental and real world data. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were calibrated to predict temporal contaminant concentrations (CCs), and the effect of noisy and de-noised data on the model performance was evaluated. Then, considering the predicted CCs at test points (TPs, in experimental study) and piezometers (in Myandoab plain) as interior conditions, the multiquadric radial basis function (MQ-RBF), as a meshless approach which solves partial differential equation (PDE) of contaminant transport in porous media, was employed to estimate the CC values at any point within the study area where there was no TP or piezometer. Optimal values of the dispersion coefficient in the advection-dispersion PDE and shape coefficient of MQ-RBF were determined using the imperialist competitive algorithm. In temporal contaminant transport modeling, de-noised data enhanced the performance of ANN and ANFIS methods in terms of the determination coefficient, up to 6 and 5%, respectively, in the experimental study and up to 39 and 18%, respectively, in the field study. Results showed that the efficiency of ANFIS-meshless model was more than ANN-meshless model up to 2 and 13% in the experimental and field studies, respectively.

Effects of antioxidants (AREDS medication) on ocular blood flow and endothelial function in an endotoxin-induced model of oxidative stress in humans.

PubMed

Pemp, Berthold; Polska, Elzbieta; Karl, Katharina; Lasta, Michael; Minichmayr, Alexander; Garhofer, Gerhard; Wolzt, Michael; Schmetterer, Leopold

2010-01-01

The Age-Related Eye Disease Study (AREDS) has shown that supplementation of antioxidants slows the progression of age-related macular degeneration (AMD). The mechanism underlying this therapeutic effect may be related to a reduction of reactive oxygen species (ROS). The authors have recently introduced a model showing that the response of retinal blood flow (RBF) to hyperoxia is diminished by administration of lipopolysaccharide (LPS). In the present study, the hypothesis was that this response can be restored by the AREDS medication. Twenty-one healthy volunteers were included in this randomized, double-masked, placebo-controlled, parallel group study. On each study day, RBF and the reactivity of RBF to hyperoxia were investigated before and after infusion of 2 ng/kg LPS. Between the two study days, subjects took either the AREDS medication or placebo for 14 days. After administration of LPS reduced retinal arterial vasoconstriction during hyperoxia (AREDS group: 12.5% +/- 4.8% pre-LPS vs. 9.4% +/- 4.6% post-LPS; placebo group: 9.2% +/- 3.3% pre-LPS vs. 7.1% +/- 3.5% post-LPS) and a reduced reactivity of RBF during hyperoxia (AREDS: 50.4% +/- 8.9% vs. 44.9% +/- 11.6%, placebo: 54.2% +/- 8.6% vs. 46.0% +/- 6.9%) was found. The reduced responses were normalized after 2 weeks of AREDS antioxidants but not after placebo (vasoconstriction: 13.1% +/- 4.5% vs. 13.1% +/- 5.0% AREDS, 11.2% +/- 4.2 vs. 7.4% +/- 4.2% placebo; RBF: 52.8% +/- 10.5% vs. 52.4% +/- 10.5% AREDS, 52.4% +/- 9.3% vs. 44.2% +/- 6.3% placebo). The sustained retinal vascular reaction to hyperoxia after LPS in the AREDS group indicates that antioxidants reduce oxidative stress-induced endothelial dysfunction, possibly by eliminating ROS. The model may be an attractive approach to studying the antioxidative capacity of dietary supplements for the treatment of AMD (ClinicalTrials.gov number, NCT00431691).

Use of gene-expression programming to estimate Manning’s roughness coefficient for high gradient streams

USGS Publications Warehouse

Azamathulla, H. Md.; Jarrett, Robert D.

2013-01-01

Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Therefore, the selection of appropriate Manning’s nvalues is of paramount importance for hydraulic engineers and hydrologists and requires considerable experience, although extensive guidelines are available. Generally, the largest source of error in post-flood estimates (termed indirect measurements) is due to estimates of Manning’s n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for estimating n values. The objective of this study was to develop a soft computing model in the estimation of the Manning’s n values using 75 discharge measurements on 21 high gradient streams in Colorado, USA. The data are from high gradient (S > 0.002 m/m), cobble- and boulder-bed streams for within bank flows. This study presents Gene-Expression Programming (GEP), an extension of Genetic Programming (GP), as an improved approach to estimate Manning’s roughness coefficient for high gradient streams. This study uses field data and assessed the potential of gene-expression programming (GEP) to estimate Manning’s n values. GEP is a search technique that automatically simplifies genetic programs during an evolutionary processes (or evolves) to obtain the most robust computer program (e.g., simplify mathematical expressions, decision trees, polynomial constructs, and logical expressions). Field measurements collected by Jarrett (J Hydraulic Eng ASCE 110: 1519–1539, 1984) were used to train the GEP network and evolve programs. The developed network and evolved programs were validated by using observations that were not involved in training. GEP and ANN-RBF (artificial neural network-radial basis function) models were found to be substantially more effective (e.g., R2 for testing/validation of GEP and RBF-ANN is 0.745 and 0.65, respectively) than Jarrett’s (J Hydraulic Eng ASCE 110: 1519–1539, 1984) equation (R2 for testing/validation equals 0.58) in predicting the Manning’s n.

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Perceptions of quality across the maternal care continuum in the context of a health financing intervention: Evidence from a mixed methods study in rural Malawi.

PubMed

Kambala, Christabel; Lohmann, Julia; Mazalale, Jacob; Brenner, Stephan; Sarker, Malabika; Muula, Adamson S; De Allegri, Manuela

2017-06-08

In 2013, Malawi with its development partners introduced a Results-Based Financing for Maternal and Newborn Health (RBF4MNH) intervention to improve the quality of maternal and newborn health-care services. Financial incentives are awarded to health facilities conditional on their performance and to women for delivering in the health facility. We assessed the effect of the RBF4MNH on quality of care from women's perspectives. We used a mixed-method prospective sequential controlled pre- and post-test design. We conducted 3060 structured client exit interviews, 36 in-depth interviews and 29 focus group discussions (FGDs) with women and 24 in-depth interviews with health service providers between 2013 and 2015. We used difference-in-differences regression models to measure the effect of the RBF4MNH on experiences and perceived quality of care. We used qualitative data to explore the matter more in depth. We did not observe a statistically significant effect of the intervention on women's perceptions of technical care, quality of amenities and interpersonal relations. However, in the qualitative interviews, most women reported improved health service provision as a result of the intervention. RBF4MNH increased the proportion of women reporting to have received medications/treatment during childbirth. Participants in interviews expressed that drugs, equipment and supplies were readily available due to the RBF4MNH. However, women also reported instances of neglect, disrespect and verbal abuse during the process of care. Providers attributed these negative instances to an increased workload resulting from an increased number of women seeking services at RBF4MNH facilities. Our qualitative findings suggest improvements in the availability of drugs and supplies due to RBF4MNH. Despite the intervention, challenges in the provision of quality care persisted, especially with regard to interpersonal relations. RBF interventions may need to consider including indicators that specifically target the provision of respectful maternity care as a means to foster providers' positive attitudes towards women in labour. In parallel, governments should consider enhancing staff and infrastructural capacity before implementing RBF.

Arterial spin labeling MRI is able to detect early hemodynamic changes in diabetic nephropathy.

PubMed

Mora-Gutiérrez, José María; Garcia-Fernandez, Nuria; Slon Roblero, M Fernanda; Páramo, Jose A; Escalada, F Javier; Wang, Danny Jj; Benito, Alberto; Fernández-Seara, María A

2017-12-01

To investigate whether arterial spin labeling (ASL) MRI could detect renal hemodynamic impairment in diabetes mellitus (DM) along different stages of chronic kidney disease (CKD). Three Tesla (3T) ASL-MRI was performed to evaluate renal blood flow (RBF) in 91 subjects (46 healthy volunteers and 45 type 2 diabetic patients). Patients were classified according to their estimated glomerular filtration rate (eGFR) as group I (eGFR > 60 mL/min/1.73 m 2 ), group II (60 ≥ eGFR>30 mL/min/1.73 m 2 ), or group III (eGFR ≤ 30 mL/min/1.73 m 2 ), to determine differences depending on renal function. Studies were performed at 3T using a 12-channel flexible body array combined with the spine array coil as receiver. A 28% reduction in cortical RBF was seen in diabetics in comparison with healthy controls (185.79 [54.60] versus 258.83 [37.96] mL/min/100 g, P < 3 × 10 -6 ). Differences were also seen between controls and diabetic patients despite normal eGFR and absence of overt albuminuria (RBF [mL/min/100 g]: controls=258.83 [37.96], group I=208.89 [58.83], P = 0.0018; eGFR [mL/min/1.73 m 2 ]: controls = 95.50 [12.60], group I = 82.00 [20.76], P > 0.05; albumin-creatinine ratio [mg/g]: controls = 3.50 [4.45], group I = 17.50 [21.20], P > 0.05). A marked decrease in RBF was noted a long with progression of diabetic nephropathy (DN) through the five stages of CKD (χ 2  = 43.58; P = 1.85 × 10 -9 ). Strong correlation (r = 0.62; P = 4 × 10 -10 ) was obtained between RBF and GFR estimated by cystatin C. ASL-MRI is able to quantify early renal perfusion impairment in DM, as well as changes according to different CKD stages of DN. In addition, we demonstrated a correlation of RBF quantified by ASL and GFR estimated by cystatin C. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1810-1817. © 2017 International Society for Magnetic Resonance in Medicine.

Selective visual attention in object detection processes

NASA Astrophysics Data System (ADS)

Paletta, Lucas; Goyal, Anurag; Greindl, Christian

2003-03-01

Object detection is an enabling technology that plays a key role in many application areas, such as content based media retrieval. Attentive cognitive vision systems are here proposed where the focus of attention is directed towards the most relevant target. The most promising information is interpreted in a sequential process that dynamically makes use of knowledge and that enables spatial reasoning on the local object information. The presented work proposes an innovative application of attention mechanisms for object detection which is most general in its understanding of information and action selection. The attentive detection system uses a cascade of increasingly complex classifiers for the stepwise identification of regions of interest (ROIs) and recursively refined object hypotheses. While the most coarse classifiers are used to determine first approximations on a region of interest in the input image, more complex classifiers are used for more refined ROIs to give more confident estimates. Objects are modelled by local appearance based representations and in terms of posterior distributions of the object samples in eigenspace. The discrimination function to discern between objects is modeled by a radial basis functions (RBF) network that has been compared with alternative networks and been proved consistent and superior to other artifical neural networks for appearance based object recognition. The experiments were led for the automatic detection of brand objects in Formula One broadcasts within the European Commission's cognitive vision project DETECT.

Reduced kernel recursive least squares algorithm for aero-engine degradation prediction

NASA Astrophysics Data System (ADS)

Zhou, Haowen; Huang, Jinquan; Lu, Feng

2017-10-01

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data directly. Although these methods curb the growth of the network effectively, it should be noted that information conveyed by these redundant samples is omitted, which may lead to accuracy degradation. In this paper, we present a novel online sparsification method which requires much less training time without sacrificing the accuracy performance. Specifically, a reduced kernel recursive least squares (RKRLS) algorithm is developed based on the reduced technique and the linear independency. Unlike conventional methods, our novel methodology employs these redundant data to update the coefficients of the existing network. Due to the effective utilization of the redundant data, the novel algorithm achieves a better accuracy performance, although the network size is significantly reduced. Experiments on time series prediction and online regression demonstrate that RKRLS algorithm requires much less computational consumption and maintains the satisfactory accuracy performance. Finally, we propose an enhanced multi-sensor prognostic model based on RKRLS and Hidden Markov Model (HMM) for remaining useful life (RUL) estimation. A case study in a turbofan degradation dataset is performed to evaluate the performance of the novel prognostic approach.

Factors governing sustainable groundwater pumping near a river.

PubMed

Zhang, Yingqi; Hubbard, Susan; Finsterle, Stefan

2011-01-01

The objective of this paper was to provide new insights into processes affecting riverbank filtration (RBF). We consider a system with an inflatable dam installed for enhancing water production from downstream collector wells. Using a numerical model, we investigate the impact of groundwater pumping and dam operation on the hydrodynamics in the aquifer and water production. We focus our study on two processes that potentially limit water production of an RBF system: the development of an unsaturated zone and riverbed clogging. We quantify river clogging by calibrating a time-dependent riverbed permeability function based on knowledge of pumping rate, river stage, and temperature. The dynamics of the estimated riverbed permeability reflects clogging and scouring mechanisms. Our results indicate that (1) riverbed permeability is the dominant factor affecting infiltration needed for sustainable RBF production; (2) dam operation can influence pumping efficiency and prevent the development of an unsaturated zone beneath the riverbed only under conditions of sufficient riverbed permeability; (3) slow river velocity, caused by dam raising during summer months, may lead to sedimentation and deposition of fine-grained material within the riverbed, which may clog the riverbed, limiting recharge to the collector wells and contributing to the development of an unsaturated zone beneath the riverbed; and (4) higher river flow velocities, caused by dam lowering during winter storms, scour the riverbed and thus increase its permeability. These insights can be used as the basis for developing sustainable water management of a RBF system. Journal compilation © 2010 National Ground Water Association. No claim to original US government works.

Renal blood flow using arterial spin labelling MRI and calculated filtration fraction in healthy adult kidney donors Pre-nephrectomy and post-nephrectomy.

PubMed

Cutajar, Marica; Hilton, Rachel; Olsburgh, Jonathon; Marks, Stephen D; Thomas, David L; Banks, Tina; Clark, Christopher A; Gordon, Isky

2015-08-01

Renal plasma flow (RPF) (derived from renal blood flow, RBF) and glomerular filtration rate (GFR) allow the determination of the filtration fraction (FF), which may have a role as a non-invasive renal biomarker. This is a hypothesis-generating pilot study assessing the effect of nephrectomy on renal function in healthy kidney donors. Eight living kidney donors underwent arterial spin labelling (ASL) magnetic resonance imaging (MRI) and GFR measurement prior to and 1 year after nephrectomy. Chromium-51 labelled ethylenediamine tetraacetic acid ((51)Cr-EDTA) with multi-blood sampling was undertaken and GFR calculated. The RBF and GFR obtained were used to calculate FF. All donors showed an increase in single kidney GFR of 24 - 75 %, and all but two showed an increase in FF (-7 to +52 %) after nephrectomy. The increase in RBF, and hence RPF, post-nephrectomy was not as great as the increase in GFR in seven out of eight donors. As with any pilot study, the small number of donors and their relatively narrow age range are potential limiting factors. The ability to measure RBF, and hence RPF, non-invasively, coupled with GFR measurement, allows calculation of FF, a biomarker that might provide a sensitive indicator of loss of renal reserve in potential donors. • Non-invasive MRI measured renal blood flow and calculated renal plasma flow. • Effect of nephrectomy on blood flow and filtration in donors is presented. • Calculated filtration fraction may be a useful new kidney biomarker.

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

PubMed

Ha, S R; Park, S Y; Park, D H

2003-01-01

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

[Study on application of SVM in prediction of coronary heart disease].

PubMed

Zhu, Yue; Wu, Jianghua; Fang, Ying

2013-12-01

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Regulation of oxygen utilization by angiotensin II in chronic kidney disease

PubMed Central

Deng, Aihua; Tang, Tong; Singh, Prabhleen; Wang, Chen; Satriano, Joe; Thomson, Scott C; Blantz, Roland C

2010-01-01

Angiotensin II (ANG II) blockade delays progression of chronic kidney disease (CKD) by modifying intrarenal hemodynamics, but the effect on metabolic adaptations has not been examined. Using renal ablation/infarction (A/I) model of CKD in rats at one week, the effects of ANG II blockade by captopril (CAP) and losartan (LOS) on renal O2 consumption (QO2), renal nitric oxide (NO) activity and nitric oxide synthase (NOS) protein expression was examined. A/I kidneys exhibited proteinuria, reduced GFR, renal blood flow (RBF) and NOS-1 protein expression, while QO2 factored by sodium reabsorption (QO2/TNa) was markedly increased. CAP + LOS treatment increased GFR, RBF, and TNa, while QO2 remained unchanged, thus normalizing QO2/TNa. NOS-1 expression was normalized with CAP + LOS, as was proteinuria. Triple antihypertensive therapy administered to control for the blood pressure reduction, and lysine administration to increase GFR and RBF, did not normalize QO2/TNa, suggesting a specific effect of ANG II in elevating QO2/TNa. NOS blockade, to test functional NO activity on QO2 and QO2/TNa, increased QO2 in shams, but not in untreated A/I. The increase in QO2 was restored in CAP + LOS treated A/I. CAP + LOS treatment normalized the increased QO2/TNa and functional NO activity in A/I independent of the blood pressure and GFR effects, providing evidence for an additional mechanism underlying the benefits of ANG II inhibition therapy. PMID:18818681

Evaluation of the effect of elevated intraocular pressure and reduced ocular perfusion pressure on retinal capillary bed filling and total retinal blood flow in rats by OMAG/OCT.

PubMed

Zhi, Zhongwei; Cepurna, William; Johnson, Elaine; Jayaram, Hari; Morrison, John; Wang, Ruikang K

2015-09-01

To determine if retinal capillary filling is preserved in the face of acutely elevated intraocular pressure (IOP) in anesthetized rats, despite a reduction in total retinal blood flow (RBF), using optical microangiography/optical coherence tomography (OMAG/OCT). OMAG provided the capability of depth-resolved imaging of the retinal microvasculature down to the capillary level. Doppler OCT was applied to measure the total RBF using an enface integration approach. The microvascular pattern, capillary density, and total RBF were monitored in vivo as the IOP was increased from 10 to 100mmHg in 10mmHg intervals and returned back to 10mmHg. In animals with mean arterial pressure (MAP) of 102±4mmHg (n=10), when IOP was increased from 0 to 100mmHg, the capillary density remained at or above 80% of baseline for the IOP up to 60mmHg [or ocular perfusion pressure (OPP) at 40mmHg]. This was then decreased, achieving 60% of baseline at IOP 70mmHg and OPP of 30mmHg. Total RBF was unaffected by moderate increases in IOP up to 30mmHg, beyond which total RBF decreased linearly, reaching 50% of baseline at IOP 60mmHg and OPP 40mmHg. Both capillary density and total RBF were totally extinguished at 100mmHg, but fully recovered when IOP returned to baseline. By comparison, a separate group of animals with lower MAP (mean=75±6mmHg, n=7) demonstrated comparable decreases in both capillary filling and total RBF at IOPs that were 20mmHg lower than in the initial group. Both were totally extinguished at 80mmHg, but fully recovered when IOP returned to baseline. Relationships of both parameters to OPP were unchanged. Retinal capillary filling and total RBF responses to IOP elevation can be monitored non-invasively by OMAG/OCT and both are influenced by OPP. Retinal capillary filling was relatively preserved down to a perfusion pressure of 40mmHg, despite a linear reduction in total RBF. Copyright © 2015 Elsevier Inc. All rights reserved.

Hydraulic and hydrogeochemical characteristics of a riverbank filtration site in rural India.

PubMed

Boving, T B; Choudri, B S; Cady, P; Cording, A; Patil, K; Reddy, Veerabaswant

2014-07-01

A riverbank filtration (RBF) system was tested along the Kali River in rural part of the state of Karnataka in India. The polluted river and water from open wells served the local population as their principal irrigation water resource and some used it for drinking. Four RBF wells (up to 25 m deep) were installed. The mean hydraulic conductivity of the well field is 6.3 x 10(-3) cm/s and, based on Darcy's law, the water travel time from the river to the principal RBF well (MW3) is 45.2 days. A mixing model based on dissolved silica concentrations indicated that, depending on the distance from the river and closeness to irrigated rice fields, approximately 27 to 73% of the well water originated from groundwater. Stable isotopic data indicates that a fraction of the water was drawn in from the nearby rice fields that were irrigated with river water. Relative to preexisting drinking water sources (Kali River and an open well), RBF well water showed lower concentration of dissolved metals (60.1% zinc, 27.8% cadmium, 83.9% lead, 75.5% copper, 100% chromium). This study demonstrates that RBF technology can produce high-quality water from low-quality surface water sources in a rural, tropical setting typical for many emerging economies. Further, in parts of the world where flood irrigation is common, RBF well water may draw in infiltrated irrigation water, which possibly alters its geochemical composition. A combination of more than one mixing model, silica together with stable isotopes, was shown to be useful explaining the origin of the RBF water at this study site.

Randomized controlled trial of electro-stimulation therapies to modulate retinal blood flow and visual function in retinitis pigmentosa.

PubMed

Bittner, Ava K; Seger, Kenneth; Salveson, Rachel; Kayser, Samantha; Morrison, Natalia; Vargas, Patricia; Mendelsohn, Deborah; Han, Jorge; Bi, Hua; Dagnelie, Gislin; Benavente, Alexandra; Ramella-Roman, Jessica

2018-05-01

We examined changes in visual function and ocular and retinal blood flow (RBF) among retinitis pigmentosa (RP) participants in a randomized controlled trial of electro-stimulation therapies. Twenty-one RP participants were randomized (1:1:1) to transcorneal electrical stimulation (TES) at 6 weekly half-hour sessions, electro-acupuncture or inactive laser acupuncture (sham control) at 10 half-hour sessions over 2 weeks. Early Treatment of Diabetic Retinopathy Study (ETDRS) visual acuity (VA), quick contrast sensitivity function, Goldmann visual fields, AdaptDx scotopic sensitivity, spectral flow and colour Doppler imaging of the central retinal artery (CRA), and RBF in macular capillaries were measured twice pre-treatment, after 2 TES sessions, within a week and a month after intervention completion. We measured a significant improvement in retrobulbar CRA mean flow velocity for both the TES (p = 0.038) and electro-acupuncture groups (p = 0.001) on average after 2 weeks of treatment when compared to sham controls. Transcorneal electrical simulation (TES) and electro-acupuncture subjects had significant 55% and 34% greater increases, respectively, in RBF in the macular vessels when compared to sham controls (p < 0.001; p = 0.008) within a week of completing six TES sessions or a month after electro-acupuncture. There was a significant difference in the proportion of eyes that had improved visual function when comparing the three intervention groups (p = 0.038): four of seven TES subjects (57%), two of seven electro-acupuncture subjects (29%) and none of the seven control subjects (0%) had a significant visual improvement outside of typical test-retest variability at two consecutive post-treatment visits. Increased blood flow following electro-stimulation therapies is an objective, physiological change that occurred in addition to visual function improvements in some RP patients. © 2017 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.

Experimental and AI-based numerical modeling of contaminant transport in porous media.

PubMed

Nourani, Vahid; Mousavi, Shahram; Sadikoglu, Fahreddin; Singh, Vijay P

2017-10-01

This study developed a new hybrid artificial intelligence (AI)-meshless approach for modeling contaminant transport in porous media. The key innovation of the proposed approach is that both black box and physically-based models are combined for modeling contaminant transport. The effectiveness of the approach was evaluated using experimental and real world data. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were calibrated to predict temporal contaminant concentrations (CCs), and the effect of noisy and de-noised data on the model performance was evaluated. Then, considering the predicted CCs at test points (TPs, in experimental study) and piezometers (in Myandoab plain) as interior conditions, the multiquadric radial basis function (MQ-RBF), as a meshless approach which solves partial differential equation (PDE) of contaminant transport in porous media, was employed to estimate the CC values at any point within the study area where there was no TP or piezometer. Optimal values of the dispersion coefficient in the advection-dispersion PDE and shape coefficient of MQ-RBF were determined using the imperialist competitive algorithm. In temporal contaminant transport modeling, de-noised data enhanced the performance of ANN and ANFIS methods in terms of the determination coefficient, up to 6 and 5%, respectively, in the experimental study and up to 39 and 18%, respectively, in the field study. Results showed that the efficiency of ANFIS-meshless model was more than ANN-meshless model up to 2 and 13% in the experimental and field studies, respectively. Copyright © 2017. Published by Elsevier B.V.

Authentication of vegetable oils on the basis of their physico-chemical properties with the aid of chemometrics.

PubMed

Zhang, Guowen; Ni, Yongnian; Churchill, Jane; Kokot, Serge

2006-09-15

In food production, reliable analytical methods for confirmation of purity or degree of spoilage are required by growers, food quality assessors, processors, and consumers. Seven parameters of physico-chemical properties, such as acid number, colority, density, refractive index, moisture and volatility, saponification value and peroxide value, were measured for quality and adulterated soybean, as well as quality and rancid rapeseed oils. Chemometrics methods were then applied for qualitative and quantitative discrimination and prediction of the oils by methods such exploratory principal component analysis (PCA), partial least squares (PLS), radial basis function-artificial neural networks (RBF-ANN), and multi-criteria decision making methods (MCDM), PROMETHEE and GAIA. In general, the soybean and rapeseed oils were discriminated by PCA, and the two spoilt oils behaved differently with the rancid rapeseed samples exhibiting more object scatter on the PC-scores plot, than the adulterated soybean oil. For the PLS and RBF-ANN prediction methods, suitable training models were devised, which were able to predict satisfactorily the category of the four different oil samples in the verification set. Rank ordering with the use of MCDM models indicated that the oil types can be discriminated on the PROMETHEE II scale. For the first time, it was demonstrated how ranking of oil objects with the use of PROMETHEE and GAIA could be utilized as a versatile indicator of quality performance of products on the basis of a standard selected by the stakeholder. In principle, this approach provides a very flexible method for assessment of product quality directly from the measured data.

Endothelin-1 receptor antagonists protect the kidney against the nephrotoxicity induced by cyclosporine-A in normotensive and hypertensive rats

PubMed Central

Caires, A.; Fernandes, G.S.; Leme, A.M.; Castino, B.; Pessoa, E.A.; Fernandes, S.M.; Fonseca, C.D.; Vattimo, M.F.; Schor, N.; Borges, F.T.

2017-01-01

Cyclosporin-A (CsA) is an immunosuppressant associated with acute kidney injury and chronic kidney disease. Nephrotoxicity associated with CsA involves the increase in afferent and efferent arteriole resistance, decreased renal blood flow (RBF) and glomerular filtration. The aim of this study was to evaluate the effect of Endothelin-1 (ET-1) receptor blockade with bosentan (BOS) and macitentan (MAC) antagonists on altered renal function induced by CsA in normotensive and hypertensive animals. Wistar and genetically hypertensive rats (SHR) were separated into control group, CsA group that received intraperitoneal injections of CsA (40 mg/kg) for 15 days, CsA+BOS and CsA+MAC that received CsA and BOS (5 mg/kg) or MAC (25 mg/kg) by gavage for 15 days. Plasma creatinine and urea, mean arterial pressure (MAP), RBF and renal vascular resistance (RVR), and immunohistochemistry for ET-1 in the kidney cortex were measured. CsA decreased renal function, as shown by increased creatinine and urea. There was a decrease in RBF and an increase in MAP and RVR in normotensive and hypertensive animals. These effects were partially reversed by ET-1 antagonists, especially in SHR where increased ET-1 production was observed in the kidney. Most MAC effects were similar to BOS, but BOS seemed to be better at reversing cyclosporine-induced changes in renal function in hypertensive animals. The results of this work suggested the direct participation of ET-1 in renal hemodynamics changes induced by cyclosporin in normotensive and hypertensive rats. The antagonists of ET-1 MAC and BOS reversed part of these effects. PMID:29267497

Endothelin-1 receptor antagonists protect the kidney against the nephrotoxicity induced by cyclosporine-A in normotensive and hypertensive rats.

PubMed

Caires, A; Fernandes, G S; Leme, A M; Castino, B; Pessoa, E A; Fernandes, S M; Fonseca, C D; Vattimo, M F; Schor, N; Borges, F T

2017-12-11

Cyclosporin-A (CsA) is an immunosuppressant associated with acute kidney injury and chronic kidney disease. Nephrotoxicity associated with CsA involves the increase in afferent and efferent arteriole resistance, decreased renal blood flow (RBF) and glomerular filtration. The aim of this study was to evaluate the effect of Endothelin-1 (ET-1) receptor blockade with bosentan (BOS) and macitentan (MAC) antagonists on altered renal function induced by CsA in normotensive and hypertensive animals. Wistar and genetically hypertensive rats (SHR) were separated into control group, CsA group that received intraperitoneal injections of CsA (40 mg/kg) for 15 days, CsA+BOS and CsA+MAC that received CsA and BOS (5 mg/kg) or MAC (25 mg/kg) by gavage for 15 days. Plasma creatinine and urea, mean arterial pressure (MAP), RBF and renal vascular resistance (RVR), and immunohistochemistry for ET-1 in the kidney cortex were measured. CsA decreased renal function, as shown by increased creatinine and urea. There was a decrease in RBF and an increase in MAP and RVR in normotensive and hypertensive animals. These effects were partially reversed by ET-1 antagonists, especially in SHR where increased ET-1 production was observed in the kidney. Most MAC effects were similar to BOS, but BOS seemed to be better at reversing cyclosporine-induced changes in renal function in hypertensive animals. The results of this work suggested the direct participation of ET-1 in renal hemodynamics changes induced by cyclosporin in normotensive and hypertensive rats. The antagonists of ET-1 MAC and BOS reversed part of these effects.

Sorption and biodegradation of organic micropollutants during river bank filtration: a laboratory column study.

PubMed

Bertelkamp, C; Reungoat, J; Cornelissen, E R; Singhal, N; Reynisson, J; Cabo, A J; van der Hoek, J P; Verliefde, A R D

2014-04-01

This study investigated sorption and biodegradation behaviour of 14 organic micropollutants (OMP) in soil columns representative of the first metre (oxic conditions) of the river bank filtration (RBF) process. Breakthrough curves were modelled to differentiate between OMP sorption and biodegradation. The main objective of this study was to investigate if the OMP biodegradation rate could be related to the physico-chemical properties (charge, hydrophobicity and molecular weight) or functional groups of the OMPs. Although trends were observed between charge or hydrophobicity and the biodegradation rate for charged compounds, a statistically significant linear relationship for the complete OMP mixture could not be obtained using these physico-chemical properties. However, a statistically significant relationship was obtained between biological degradation rates and the OMP functional groups. The presence of ethers and carbonyl groups will increase biodegradability, while the presence of amines, ring structures, aliphatic ethers and sulphur will decrease biodegradability. This predictive model based on functional groups can be used by drinking water companies to make a first estimate whether a newly detected compound will be biodegraded during the first metre of RBF or that additional treatment is required. In addition, the influence of active and inactive biomass (biosorption), sand grains and the water matrix on OMP sorption was found to be negligible under the conditions investigated in this study. Retardation factors for most compounds were close to 1, indicating mobile behaviour of these compounds during soil passage. Adaptation of the biomass towards the dosed OMPs was not observed for a 6 month period, implying that new developed RBF sites might not be able to biodegrade compounds such as atrazine and sulfamethoxazole in the first few months of operation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

PubMed

Bayro-Corrochano, Eduardo; Vazquez-Santacruz, Eduardo; Moya-Sanchez, Eduardo; Castillo-Munis, Efrain

2016-10-01

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.

An RBF-based reparameterization method for constrained texture mapping.

PubMed

Yu, Hongchuan; Lee, Tong-Yee; Yeh, I-Cheng; Yang, Xiaosong; Li, Wenxi; Zhang, Jian J

2012-07-01

Texture mapping has long been used in computer graphics to enhance the realism of virtual scenes. However, to match the 3D model feature points with the corresponding pixels in a texture image, surface parameterization must satisfy specific positional constraints. However, despite numerous research efforts, the construction of a mathematically robust, foldover-free parameterization that is subject to positional constraints continues to be a challenge. In the present paper, this foldover problem is addressed by developing radial basis function (RBF)-based reparameterization. Given initial 2D embedding of a 3D surface, the proposed method can reparameterize 2D embedding into a foldover-free 2D mesh, satisfying a set of user-specified constraint points. In addition, this approach is mesh free. Therefore, generating smooth texture mapping results is possible without extra smoothing optimization.

A Simplified Whole-Organ CT Perfusion Technique with Biphasic Acquisition: Preliminary Investigation of Accuracy and Protocol Feasibility in Kidneys.

PubMed

Yuan, XiaoDong; Zhang, Jing; Quan, ChangBin; Tian, Yuan; Li, Hong; Ao, GuoKun

2016-04-01

To determine the feasibility and accuracy of a protocol for calculating whole-organ renal perfusion (renal blood flow [RBF]) and regional perfusion on the basis of biphasic computed tomography (CT), with concurrent dynamic contrast material-enhanced (DCE) CT perfusion serving as the reference standard. This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Biphasic CT of the kidneys, including precontrast and arterial phase imaging, was integrated with a first-pass dynamic volume CT protocol and performed and analyzed in 23 patients suspected of having renal artery stenosis. The perfusion value derived from biphasic CT was calculated as CT number enhancement divided by the area under the arterial input function and compared with the DCE CT perfusion data by using the paired t test, correlation analysis, and Bland-Altman plots. Correlation analysis was made between the RBF and the extent of renal artery stenosis. All postprocessing was independently performed by two observers and then averaged as the final result. Mean ± standard deviation biphasic and DCE CT perfusion data for RBF were 425.62 mL/min ± 124.74 and 419.81 mL/min ± 121.13, respectively (P = .53), and for regional perfusion they were 271.15 mL/min per 100 mL ± 82.21 and 266.33 mL/min per 100 mL ± 74.40, respectively (P = .31). Good correlation and agreement were shown between biphasic and DCE CT perfusion for RBF (r = 0.93; ±10% variation from mean perfusion data [P < .001]) and for regional perfusion (r = 0.90; ±13% variation from mean perfusion data [P < .001]). The extent of renal artery stenosis was negatively correlated with RBF with biphasic CT perfusion (r = -0.81, P = .012). Biphasic CT perfusion is clinically feasible and provides perfusion data comparable to DCE CT perfusion data at both global and regional levels in the kidney. Online supplemental material is available for this article.

Seismic modeling with radial basis function-generated finite differences (RBF-FD) – a simplified treatment of interfaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu

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 formore » 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.« less

An Efficient Radial Basis Function Mesh Deformation Scheme within an Adjoint-Based Aerodynamic Optimization Framework

NASA Astrophysics Data System (ADS)

Poirier, Vincent

Mesh deformation schemes play an important role in numerical aerodynamic optimization. As the aerodynamic shape changes, the computational mesh must adapt to conform to the deformed geometry. In this work, an extension to an existing fast and robust Radial Basis Function (RBF) mesh movement scheme is presented. Using a reduced set of surface points to define the mesh deformation increases the efficiency of the RBF method; however, at the cost of introducing errors into the parameterization by not recovering the exact displacement of all surface points. A secondary mesh movement is implemented, within an adjoint-based optimization framework, to eliminate these errors. The proposed scheme is tested within a 3D Euler flow by reducing the pressure drag while maintaining lift of a wing-body configured Boeing-747 and an Onera-M6 wing. As well, an inverse pressure design is executed on the Onera-M6 wing and an inverse span loading case is presented for a wing-body configured DLR-F6 aircraft.

Seismic modeling with radial basis function-generated finite differences (RBF-FD) - a simplified treatment of interfaces

NASA Astrophysics Data System (ADS)

Martin, Bradley; Fornberg, Bengt

2017-04-01

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.

[Research on hyperspectral remote sensing in monitoring snow contamination concentration].

PubMed

Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan

2011-05-01

Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.

Influence of the renal endothelin system on the autoregulation of renal blood flow in spontaneously hypertensive rats.

PubMed

Braun, C; Lang, C; Hocher, B; Gretz, N; van der Woude, F J; Rohmeiss, P

1997-01-01

The renal endothelin (ET) system has been claimed to play an important role in the regulation of renal blood flow (RBF) and sodium excretion in primary hypertension. The aim of the present study was to investigate the contribution of the endogenous ET system in the autoregulation of total RBF, cortical blood flow (CBF), pressure-dependent plasma renin activity (PRA) and pressure natriuresis in spontaneously hypertensive rats (SHR) by means of the combined (A/B) ET-receptor antagonist, bosentan. In anesthetized rats, RBF was measured by transit-time flow probes and CBF by laser flow probes. During the experiments, the rats received an intrarenal infusion of either bosentan (1 mg/kg/h) or vehicle. Renal perfusion pressure (RPP) was lowered in pressure steps of 5 mm Hg with a servo-controlled electropneumatic device via an inflatable suprarenal cuff. Bosentan had no effect on resting RPP, CBF, PRA and renal sodium excretion, whereas RBF was lowered by 30% (p < 0.05). Furthermore after bosentan the rats revealed a complete loss of RBF autoregulation. In contrast no changes in autoregulation of CBF, pressure-dependent PRA and pressure natriuresis were observed. Our findings demonstrate a significant impairment in total RBF autoregulatory ability during renal ET-receptor blockade which is not confined to the cortical vessels. These data suggest that the renal ET system plays an important role in the dynamic regulation of renal blood flow in SHR.

Diffuse optical characterization of an exercising patient group with peripheral artery disease

PubMed Central

Putt, Mary; Chandra, Malavika; Yu, Guoqiang; Xing, Xiaoman; Han, Sung Wan; Lech, Gwen; Shang, Yu; Durduran, Turgut; Zhou, Chao; Yodh, Arjun G.; Mohler, Emile R.

2013-01-01

Abstract. Peripheral artery disease (PAD) is a common condition with high morbidity. While measurement of tissue oxygen saturation (StO2) has been demonstrated, this is the first study to assess both StO2 and relative blood flow (rBF) in the extremities of PAD patients. Diffuse optics is employed to measure hemodynamic response to treadmill and pedal exercises in 31 healthy controls and 26 patients. For StO2, mild and moderate/severe PAD groups show pronounced differences compared with controls. Pre-exercise mean StO2 is lower in PAD groups by 9.3% to 10.6% compared with means of 63.5% to 66.2% in controls. For pedal, relative rate of return of StO2 to baseline is more rapid in controls (p<0.05). Patterns of rBF also differ among groups. After both exercises, rBF tend to occur at depressed levels among severe PAD patients compared with healthy (p<0.05); post-treadmill, rBF tend to occur at elevated levels among healthy compared with severe PAD patients (p<0.05). Additionally, relative rate of return to baseline StO2 is more rapid among subjects with reduced levels of depression in rBF (p=0.041), even after adjustment for ankle brachial index. This suggests a physiologic connection between rBF and oxygenation that can be measured using diffuse optics, and potentially employed as an evaluative tool in further studies. PMID:23708193

Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong.

PubMed

Lu, Wei-Zhen; Wang, Wen-Jian; Wang, Xie-Kang; Yan, Sui-Hang; Lam, Joseph C

2004-09-01

The forecasting of air pollutant trends has received much attention in recent years. It is an important and popular topic in environmental science, as concerns have been raised about the health impacts caused by unacceptable ambient air pollutant levels. Of greatest concern are metropolitan cities like Hong Kong. In Hong Kong, respirable suspended particulates (RSP), nitrogen oxides (NOx), and nitrogen dioxide (NO2) are major air pollutants due to the dominant usage of diesel fuel by commercial vehicles and buses. Hence, the study of the influence and the trends relating to these pollutants is extremely significant to the public health and the image of the city. The use of neural network techniques to predict trends relating to air pollutants is regarded as a reliable and cost-effective method for the task of prediction. The works reported here involve developing an improved neural network model that combines both the principal component analysis technique and the radial basis function network and forecasts pollutant tendencies based on a recorded database. Compared with general neural network models, the proposed model features a more simple network architecture, a faster training speed, and a more satisfactory prediction performance. The improved model was evaluated with hourly time series of RSP, NOx and NO2 concentrations monitored at the Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000 and proved to be effective. The model developed is a potential tool for forecasting air quality parameters and is superior to traditional neural network methods.

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

PubMed Central

He, Peilin; Jia, Pengfei; Qiao, Siqi; Duan, Shukai

2017-01-01

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy. PMID:28991154

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

A new device for intraoperative renal blood flow measurement during open-heart surgery: an experimental study and the clinical pilot study.

PubMed

Tirilomis, Theodor; Popov, Aron F; Hanekop, Gunnar G; Braeuer, Anselm; Quintel, Michael; Schoendube, Friedrich A; Friedrich, Martin G

2013-10-01

Renal blood flow (RBF) may vary during cardiopulmonary bypass and low flow may cause insufficient blood supply of the kidney triggering renal failure postoperatively. Still, a valid intraoperative method of continuous RBF measurement is not available. A new catheter combining thermodilution and intravascular Doppler was developed, first calibrated in an in vitro model, and the catheter specific constant was determined. Then, application of the device was evaluated in a pilot study in an adult cardiovascular population. The data of the clinical pilot study revealed high correlation between the flow velocities detected by intravascular Doppler and the RBF measured by thermodilution (Pearson's correlation range: 0.78 to 0.97). In conclusion, the RBF can be measured excellently in real time using the new catheter, even under cardiopulmonary bypass. © 2013 Wiley Periodicals, Inc. and International Center for Artificial Organs and Transplantation.

Efficient Vpu-Mediated Tetherin Antagonism by an HIV-1 Group O Strain

PubMed Central

Mack, Katharina; Starz, Kathrin; Sauter, Daniel; Langer, Simon; Bibollet-Ruche, Frederic; Learn, Gerald H.; Stürzel, Christina M.; Leoz, Marie; Plantier, Jean-Christophe; Geyer, Matthias; Hahn, Beatrice H.

2017-01-01

ABSTRACT Simian immunodeficiency viruses (SIVs) use their Nef proteins to counteract the restriction factor tetherin. However, a deletion in human tetherin prevents antagonism by the Nef proteins of SIVcpz and SIVgor, which represent the ape precursors of human immunodeficiency virus type 1 (HIV-1). To promote virus release from infected cells, pandemic HIV-1 group M strains evolved Vpu as a tetherin antagonist, while the Nef protein of less widespread HIV-1 group O strains acquired the ability to target a region adjacent to this deletion. In this study, we identified an unusual HIV-1 group O strain (RBF206) that evolved Vpu as an effective antagonist of human tetherin. While both RBF206 Vpu and Nef exert anti-tetherin activity in transient-transfection assays, mainly Vpu promotes RBF206 release in infected CD4+ T cells. Although mutations distinct from the adaptive changes observed in group M Vpus (M-Vpus) were critical for the acquisition of its anti-tetherin activity, RBF206 O-Vpu potently suppresses NF-κB activation and reduces CD4 cell surface expression. Interestingly, RBF206 Vpu counteracts tetherin in a largely species-independent manner, degrading both the long and short isoforms of human tetherin. Downmodulation of CD4, but not counteraction of tetherin, by RBF206 Vpu was dependent on the cellular ubiquitin ligase machinery. Our data present the first example of an HIV-1 group O Vpu that efficiently antagonizes human tetherin and suggest that counteraction by O-Nefs may be suboptimal. IMPORTANCE Previous studies showed that HIV-1 groups M and O evolved two alternative strategies to counteract the human ortholog of the restriction factor tetherin. While HIV-1 group M switched from Nef to Vpu due to a deletion in the cytoplasmic domain of human tetherin, HIV-1 group O, which lacks Vpu-mediated anti-tetherin activity, acquired a Nef protein that is able to target a region adjacent to the deletion. Here we report an unusual exception, identifying a strain of HIV-1 group O (RBF206) whose Vpu protein evolved an effective antagonism of human tetherin. Interestingly, the adaptive changes in RBF206 Vpu are distinct from those found in M-Vpus and mediate efficient counteraction of both the long and short isoforms of this restriction factor. Our results further illustrate the enormous flexibility of HIV-1 in counteracting human defense mechanisms. PMID:28077643

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

PubMed

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Automated diagnosis of epilepsy using CWT, HOS and texture parameters.

PubMed

Acharya, U Rajendra; Yanti, Ratna; Zheng, Jia Wei; Krishnan, M Muthu Rama; Tan, Jen Hong; Martis, Roshan Joy; Lim, Choo Min

2013-06-01

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Signal processing and neural network toolbox and its application to failure diagnosis and prognosis

NASA Astrophysics Data System (ADS)

Tu, Fang; Wen, Fang; Willett, Peter K.; Pattipati, Krishna R.; Jordan, Eric H.

2001-07-01

Many systems are comprised of components equipped with self-testing capability; however, if the system is complex involving feedback and the self-testing itself may occasionally be faulty, tracing faults to a single or multiple causes is difficult. Moreover, many sensors are incapable of reliable decision-making on their own. In such cases, a signal processing front-end that can match inference needs will be very helpful. The work is concerned with providing an object-oriented simulation environment for signal processing and neural network-based fault diagnosis and prognosis. In the toolbox, we implemented a wide range of spectral and statistical manipulation methods such as filters, harmonic analyzers, transient detectors, and multi-resolution decomposition to extract features for failure events from data collected by data sensors. Then we evaluated multiple learning paradigms for general classification, diagnosis and prognosis. The network models evaluated include Restricted Coulomb Energy (RCE) Neural Network, Learning Vector Quantization (LVQ), Decision Trees (C4.5), Fuzzy Adaptive Resonance Theory (FuzzyArtmap), Linear Discriminant Rule (LDR), Quadratic Discriminant Rule (QDR), Radial Basis Functions (RBF), Multiple Layer Perceptrons (MLP) and Single Layer Perceptrons (SLP). Validation techniques, such as N-fold cross-validation and bootstrap techniques, are employed for evaluating the robustness of network models. The trained networks are evaluated for their performance using test data on the basis of percent error rates obtained via cross-validation, time efficiency, generalization ability to unseen faults. Finally, the usage of neural networks for the prediction of residual life of turbine blades with thermal barrier coatings is described and the results are shown. The neural network toolbox has also been applied to fault diagnosis in mixed-signal circuits.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

NASA Astrophysics Data System (ADS)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

RIVERBANK FILTRATION: FATE OF DBP PRECURSORS AND SELECTED MICROORGANISMS

EPA Science Inventory

The fate of disinfection by-product (DBP) precursors and selected microorganisms during riverbank filtration (RBF) was monitored at three different mid-Western drinking water utilities. At all three sites, filtration (RBF) was monitored at three different mid-Western drinking wa...

A DNA-binding protein from Candida albicans that binds to the RPG box of Saccharomyces cerevisiae and the telomeric repeat sequence of C. albicans.

PubMed

Ishii, N; Yamamoto, M; Lahm, H W; Iizumi, S; Yoshihara, F; Nakayama, H; Arisawa, M; Aoki, Y

1997-02-01

Electromobility shift assays with a DNA probe containing the Saccharomyces cerevisiae ENO1 RPG box identified a specific DNA-binding protein in total protein extracts of Candida albicans. The protein, named Rbf1p (RPG-box-binding protein 1), bound to other S. cerevisiae RPG boxes, although the nucleotide recognition profile was not completely the same as that of S. cerevisiae Rap 1p (repressor-activator protein 1), an RPG-box-binding protein. The repetitive sequence of the C. albicans chromosomal telomere also competed with RPG-box binding to Rbf1p. For further analysis, we purified Rbf1p 57,600-fold from C. albicans total protein extracts, raised mAbs against the purified protein and immunologically cloned the gene, whose ORF specified a protein of 527 aa. The bacterially expressed protein showed RPG-box-binding activity with the same profile as that of the purified one. The Rbf1p, containing two glutamine-rich regions that are found in many transcription factors, showed transcriptional activation capability in S. cerevisiae and was predominantly observed in nuclei. These results suggest that Rbf1p is a transcription factor with telomere-binding activity in C. albicans.

Managing Microbial Risks from Indirect Wastewater Reuse for Irrigation in Urbanizing Watersheds.

PubMed

Verbyla, Matthew E; Symonds, Erin M; Kafle, Ram C; Cairns, Maryann R; Iriarte, Mercedes; Mercado Guzmán, Alvaro; Coronado, Olver; Breitbart, Mya; Ledo, Carmen; Mihelcic, James R

2016-07-05

Limited supply of clean water in urbanizing watersheds creates challenges for safely sustaining irrigated agriculture and global food security. On-farm interventions, such as riverbank filtration (RBF), are used in developing countries to treat irrigation water from rivers with extensive fecal contamination. Using a Bayesian approach incorporating ethnographic data and pathogen measurements, quantitative microbial risk assessment (QMRA) methods were employed to assess the impact of RBF on consumer health burdens for Giardia, Cryptosporidium, rotavirus, norovirus, and adenovirus infections resulting from indirect wastewater reuse, with lettuce irrigation in Bolivia as a model system. Concentrations of the microbial source tracking markers pepper mild mottle virus and HF183 Bacteroides were respectively 2.9 and 5.5 log10 units lower in RBF-treated water than in the river water. Consumption of lettuce irrigated with river water caused an estimated median health burden that represents 37% of Bolivia's overall diarrheal disease burden, but RBF resulted in an estimated health burden that is only 1.1% of this overall diarrheal disease burden. Variability and uncertainty associated with environmental and cultural factors affecting exposure correlated more with QMRA-predicted health outcomes than factors related to disease vulnerability. Policies governing simple on-farm interventions like RBF can be intermediary solutions for communities in urbanizing watersheds that currently lack wastewater treatment.

Apelin impairs myogenic response to induce diabetic nephropathy in mice.

PubMed

Zhang, Jia; Yin, Jiming; Wang, Yangjia; Li, Bin; Zeng, Xiangjun

2018-03-09

The cause of the invalid reaction of smooth muscle cells to mechanical stimulation that results in a dysfunctional myogenic response that mediates the disruption of renal blood flow (RBF) in patients with diabetes is debatable. The present study revealed that increased apelin concentration in serum of diabetic mice neutralized the myogenic response mediated by apelin receptor (APJ) and resulted in increased RBF, which promoted the progression of diabetic nephropathy. The results showed that apelin concentration, RBF, and albuminuria:creatinine ratio were all increased in kkAy mice, and increased RBF correlated positively with serum apelin both in C57 and diabetic mice. The increased RBF was accompanied by decreased phosphorylation of myosin light chain (MLC), β-arrestin, and increased endothelial NOS in glomeruli. Meanwhile, calcium, phosphorylation of MLC, and β-arrestin were decreased by high glucose and apelin treatment in cultured smooth muscle cells, as well. eNOS was increased by high glucose and increased by apelin in cultured endothelial cells (ECs). Knockdown of β-arrestin expression in smooth muscle cells cancelled phosphorylation of MLC induced by apelin. Therefore, apelin may induce the progression of diabetic nephropathy by counteracting the myogenic response in smooth muscle cells.-Zhang, J., Yin, J., Wang, Y., Li, B., Zeng, X. Apelin impairs myogenic response to induce diabetic nephropathy in mice.

Noninvasive prostate cancer screening based on serum surface-enhanced Raman spectroscopy and support vector machine

NASA Astrophysics Data System (ADS)

Li, Shaoxin; Zhang, Yanjiao; Xu, Junfa; Li, Linfang; Zeng, Qiuyao; Lin, Lin; Guo, Zhouyi; Liu, Zhiming; Xiong, Honglian; Liu, Songhao

2014-09-01

This study aims to present a noninvasive prostate cancer screening methods using serum surface-enhanced Raman scattering (SERS) and support vector machine (SVM) techniques through peripheral blood sample. SERS measurements are performed using serum samples from 93 prostate cancer patients and 68 healthy volunteers by silver nanoparticles. Three types of kernel functions including linear, polynomial, and Gaussian radial basis function (RBF) are employed to build SVM diagnostic models for classifying measured SERS spectra. For comparably evaluating the performance of SVM classification models, the standard multivariate statistic analysis method of principal component analysis (PCA) is also applied to classify the same datasets. The study results show that for the RBF kernel SVM diagnostic model, the diagnostic accuracy of 98.1% is acquired, which is superior to the results of 91.3% obtained from PCA methods. The receiver operating characteristic curve of diagnostic models further confirm above research results. This study demonstrates that label-free serum SERS analysis technique combined with SVM diagnostic algorithm has great potential for noninvasive prostate cancer screening.

The numerical study and comparison of radial basis functions in applications of the dual reciprocity boundary element method to convection-diffusion problems

NASA Astrophysics Data System (ADS)

Chanthawara, Krittidej; Kaennakham, Sayan; Toutip, Wattana

2016-02-01

The methodology of Dual Reciprocity Boundary Element Method (DRBEM) is applied to the convection-diffusion problems and investigating its performance is our first objective of the work. Seven types of Radial Basis Functions (RBF); Linear, Thin-plate Spline, Cubic, Compactly Supported, Inverse Multiquadric, Quadratic, and that proposed by [12], were closely investigated in order to numerically compare their effectiveness drawbacks etc. and this is taken as our second objective. A sufficient number of simulations were performed covering as many aspects as possible. Varidated against both exacts and other numerical works, the final results imply strongly that the Thin-Plate Spline and Linear type of RBF are superior to others in terms of both solutions' quality and CPU-time spent while the Inverse Multiquadric seems to poorly yield the results. It is also found that DRBEM can perform relatively well at moderate level of convective force and as anticipated becomes unstable when the problem becomes more convective-dominated, as normally found in all classical mesh-dependence methods.

Mannitol increases renal blood flow and maintains filtration fraction and oxygenation in postoperative acute kidney injury: a prospective interventional study.

PubMed

Bragadottir, Gudrun; Redfors, Bengt; Ricksten, Sven-Erik

2012-08-17

Acute kidney injury (AKI), which is a major complication after cardiovascular surgery, is associated with significant morbidity and mortality. Diuretic agents are frequently used to improve urine output and to facilitate fluid management in these patients. Mannitol, an osmotic diuretic, is used in the perioperative setting in the belief that it exerts reno-protective properties. In a recent study on uncomplicated postcardiac-surgery patients with normal renal function, mannitol increased glomerular filtration rate (GFR), possibly by a deswelling effect on tubular cells. Furthermore, experimental studies have previously shown that renal ischemia causes an endothelial cell injury and dysfunction followed by endothelial cell edema. We studied the effects of mannitol on renal blood flow (RBF), glomerular filtration rate (GFR), renal oxygen consumption (RVO2), and extraction (RO2Ex) in early, ischemic AKI after cardiac surgery. Eleven patients with AKI were studied during propofol sedation and mechanical ventilation 2 to 6 days after complicated cardiac surgery. All patients had severe heart failure treated with one (100%) or two (73%) inotropic agents and intraaortic balloon pump (36%). Systemic hemodynamics were measured with a pulmonary artery catheter. RBF and renal filtration fraction (FF) were measured by the renal vein thermo-dilution technique and by renal extraction of chromium-51-ethylenediaminetetraacetic acid (51Cr-EDTA), respectively. GFR was calculated as the product of FF and renal plasma flow RBF × (1-hematocrit). RVO2 and RO2Ex were calculated from arterial and renal vein blood samples according to standard formulae. After control measurements, a bolus dose of mannitol, 225 mg/kg, was given, followed by an infusion at a rate of 75 mg/kg/h for two 30-minute periods. Mannitol did not affect cardiac index or cardiac filling pressures. Mannitol increased urine flow by 61% (P < 0.001). This was accompanied by a 12% increase in RBF (P < 0.05) and a 13% decrease in renal vascular resistance (P < 0.05). Mannitol increased the RBF/cardiac output (CO) relation (P = 0.040). Mannitol caused no significant changes in RO2Ext or renal FF. Mannitol treatment of postoperative AKI induces a renal vasodilation and redistributes systemic blood flow to the kidneys. Mannitol does not affect filtration fraction or renal oxygenation, suggestive of balanced increases in perfusion/filtration and oxygen demand/supply.

Mannitol increases renal blood flow and maintains filtration fraction and oxygenation in postoperative acute kidney injury: a prospective interventional study

PubMed Central

2012-01-01

Introduction Acute kidney injury (AKI), which is a major complication after cardiovascular surgery, is associated with significant morbidity and mortality. Diuretic agents are frequently used to improve urine output and to facilitate fluid management in these patients. Mannitol, an osmotic diuretic, is used in the perioperative setting in the belief that it exerts reno-protective properties. In a recent study on uncomplicated postcardiac-surgery patients with normal renal function, mannitol increased glomerular filtration rate (GFR), possibly by a deswelling effect on tubular cells. Furthermore, experimental studies have previously shown that renal ischemia causes an endothelial cell injury and dysfunction followed by endothelial cell edema. We studied the effects of mannitol on renal blood flow (RBF), glomerular filtration rate (GFR), renal oxygen consumption (RVO2), and extraction (RO2Ex) in early, ischemic AKI after cardiac surgery. Methods Eleven patients with AKI were studied during propofol sedation and mechanical ventilation 2 to 6 days after complicated cardiac surgery. All patients had severe heart failure treated with one (100%) or two (73%) inotropic agents and intraaortic balloon pump (36%). Systemic hemodynamics were measured with a pulmonary artery catheter. RBF and renal filtration fraction (FF) were measured by the renal vein thermo-dilution technique and by renal extraction of chromium-51-ethylenediaminetetraacetic acid (51Cr-EDTA), respectively. GFR was calculated as the product of FF and renal plasma flow RBF × (1-hematocrit). RVO2 and RO2Ex were calculated from arterial and renal vein blood samples according to standard formulae. After control measurements, a bolus dose of mannitol, 225 mg/kg, was given, followed by an infusion at a rate of 75 mg/kg/h for two 30-minute periods. Results Mannitol did not affect cardiac index or cardiac filling pressures. Mannitol increased urine flow by 61% (P < 0.001). This was accompanied by a 12% increase in RBF (P < 0.05) and a 13% decrease in renal vascular resistance (P < 0.05). Mannitol increased the RBF/cardiac output (CO) relation (P = 0.040). Mannitol caused no significant changes in RO2Ext or renal FF. Conclusions Mannitol treatment of postoperative AKI induces a renal vasodilation and redistributes systemic blood flow to the kidneys. Mannitol does not affect filtration fraction or renal oxygenation, suggestive of balanced increases in perfusion/filtration and oxygen demand/supply. PMID:22901953

COMPARING RBF WITH BENCH-SCALE CONVENTIONAL TREATMENT FOR PRECURSOR REDUCTION

EPA Science Inventory

The reduction of disinfection by-product (DBP) precursors upon riverbank filtration (RBF) at three drinking water utilities in the mid-Western United States was compared with that obtained using a bench-scale conventional treatment train on the corresponding river waters. The riv...

Binding effect of polychlorinated compounds and environmental carcinogens on rice bran fiber.

PubMed

Sera, Nobuyuki; Morita, Kunimasa; Nagasoe, Masami; Tokieda, Hisako; Kitaura, Taeko; Tokiwa, Hiroshi

2005-01-01

To accelerate the fecal excretion of polycyclic biphenyl (PCB), polychlorinated dibenzofurans (PCDFs), polychlorinated-p-dioxines (PCDDs) and various mutagens and carcinogens, their binding effect on rice bran fiber (RBF) was investigated for nine heterocyclic amines, six nitroarenes, 4-nitroquinoline-N-oxide, benzo[a]pyrene, furylfuramide, two kinds of flavonoid compounds and formaldehyde and ascorbic acid. PCBs, PCDFs and PCDDs suspended in nonane were incubated with RBF (10 mg/ml) at 37 degrees C and after centrifugation, unbound chemicals in the supernatant were analyzed by high-performance liquid chromatography (HPLC) and gas chromatography (GC). The binding effects on RBF were enhanced more than other dietary fibers (DFs), which were tested including corn, wheat bran, spinach, Hijiki (a kind of seaweed), sweet potatoes and burdock fibers. It was found that the binding effects were related to lignin contents. Binding of 3-amino-1(or 1,4)-dimethyl-5H-pyrido[4,3-b]indole (Trp-p-1 and Trp-p-2), food-derived carcinogens and 1-nitropyrene (1-NP), suspended in methanol, to RBF occurred within 10 min of incubation at 37 degrees C at pH 5-7, and decreased below pH 4; binding of food-derived carcinogens was pH dependent. The binding effects to RBF and pulp lignin were obtained at ratio of over 90%, while corn fiber and cellulose were at ratios of 4-30%. Polycyclic aromatic compounds were related to the number of rings, showing high binding effects to chemical structures with triple rings. Binding of 1-NP and PCB to RBF was not influenced in any aerobic and anaerobic bacterial cultures. It was also found that RBF was capable of binding even conjugates containing mutagens such as glucuronides and sulfates, as well as metabolites in urine. It was suggested, therefore, that mutagens and carcinogens were available for the fecal excretion of residual chemicals and their metabolites, and also for the fecal excretion of PCBs, PCDFs and related compound residues in patients of Yusho disease, who suffered food poisoning due to rice oil contaminated with PCB in Japan.

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

PubMed

Belciug, Smaranda; Gorunescu, Florin

2018-06-08

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models. Copyright © 2018. Published by Elsevier Inc.

Rat-bite fever in children: case report and review.

PubMed

Ojukwu, Ifeoma C; Christy, Cynthia

2002-01-01

We report 2 cases of rat-bite fever (RBF), a multisystem zoonosis, in children and review the literature. RBF is caused by I of 2 Gram-negative organisms: Streptobacillus moniliformis or, less commonly, Spirillum minus. Both of our cases developed in school-aged girls with a history of rat exposure who presented with a multisystem illness consisting of fever, petechial and purpuric rash, arthralgia and polyarthritis. Both responded promptly to antibiotic treatment. An additional 10 cases from a MEDLINE review (1960-2000) are reviewed. RBF must be included in the differential diagnosis of febrile patients with rashes and a history of exposure to rats.

REDUCTION IN DBP PRECURSORS AND PATHOGENS DURING RIVERBANK FILTRATION AT THREE MIDWESTERN DRINKING WATER UTILITIES

EPA Science Inventory

Riverbank filtrtion (RBF) is a process that subjects river water to ground passage prior to its use as a drinking water supply. European expereince with RBF demonstrate that during infiltration and underground transport, processes such as filtration, sorption, and biodegradation...

Relationship Between Retinal Blood Flow and Serum Adiponectin Concentrations in Patients With Type 2 Diabetes Mellitus.

PubMed

Omae, Tsuneaki; Nagaoka, Taiji; Yoshida, Akitoshi

2015-06-01

To study the relationship between retinal microcirculation and serum adiponectin, an important adipocytokine secreted by adipocytes, concentrations in patients with type 2 diabetes mellitus. Using a laser Doppler velocimetry system, we simultaneously measured the retinal blood flow (RBF) values and retinal vessel diameter and blood velocity in 64 consecutive Japanese patients (mean age ± SD, 59.8 ± 10.4 years) with type 2 diabetes with no or mild nonproliferative diabetic retinopathy. We compared the values with the RBF and serum adiponectin concentrations in these patients. The patients were divided into two groups based on sex (33 males, 31 females). The plasma adiponectin concentrations were correlated positively with the retinal vessel diameter (r = 0.480; P = 0.005), retinal blood velocity (r = 0.399; P = 0.02), and RBF (r = 0.518; P = 0.002) and correlated negatively with the retinal arterial vascular resistance (r = -0.598; P = 0.0002) in males, but not females, with type 2 diabetes with early-stage diabetic retinopathy. Multiple regression analysis showed that the plasma adiponectin level was independently and positively correlated with RBF and negatively correlated with retinal arterial vascular resistance. Our results indicated that a high concentration of serum adiponectin may be associated with increased RBF, probably via the increased blood velocity and dilated vessel diameter in males with type 2 diabetes with early-phase diabetic retinopathy.

A hybrid PSO-SVM-based method for predicting the friction coefficient between aircraft tire and coating

NASA Astrophysics Data System (ADS)

Zhan, Liwei; Li, Chengwei

2017-02-01

A hybrid PSO-SVM-based model is proposed to predict the friction coefficient between aircraft tire and coating. The presented hybrid model combines a support vector machine (SVM) with particle swarm optimization (PSO) technique. SVM has been adopted to solve regression problems successfully. Its regression accuracy is greatly related to optimizing parameters such as the regularization constant C , the parameter gamma γ corresponding to RBF kernel and the epsilon parameter \\varepsilon in the SVM training procedure. However, the friction coefficient which is predicted based on SVM has yet to be explored between aircraft tire and coating. The experiment reveals that drop height and tire rotational speed are the factors affecting friction coefficient. Bearing in mind, the friction coefficient can been predicted using the hybrid PSO-SVM-based model by the measured friction coefficient between aircraft tire and coating. To compare regression accuracy, a grid search (GS) method and a genetic algorithm (GA) are used to optimize the relevant parameters (C , γ and \\varepsilon ), respectively. The regression accuracy could be reflected by the coefficient of determination ({{R}2} ). The result shows that the hybrid PSO-RBF-SVM-based model has better accuracy compared with the GS-RBF-SVM- and GA-RBF-SVM-based models. The agreement of this model (PSO-RBF-SVM) with experiment data confirms its good performance.

Brain-machine interface control of a manipulator using small-world neural network and shared control strategy.

PubMed

Li, Ting; Hong, Jun; Zhang, Jinhua; Guo, Feng

2014-03-15

The improvement of the resolution of brain signal and the ability to control external device has been the most important goal in BMI research field. This paper describes a non-invasive brain-actuated manipulator experiment, which defined a paradigm for the motion control of a serial manipulator based on motor imagery and shared control. The techniques of component selection, spatial filtering and classification of motor imagery were involved. Small-world neural network (SWNN) was used to classify five brain states. To verify the effectiveness of the proposed classifier, we replace the SWNN classifier by a radial basis function (RBF) networks neural network, a standard multi-layered feed-forward backpropagation network (SMN) and a multi-SVM classifier, with the same features for the classification. The results also indicate that the proposed classifier achieves a 3.83% improvement over the best results of other classifiers. We proposed a shared control method consisting of two control patterns to expand the control of BMI from the software angle. The job of path building for reaching the 'end' point was designated as an assessment task. We recorded all paths contributed by subjects and picked up relevant parameters as evaluation coefficients. With the assistance of two control patterns and series of machine learning algorithms, the proposed BMI originally achieved the motion control of a manipulator in the whole workspace. According to experimental results, we confirmed the feasibility of the proposed BMI method for 3D motion control of a manipulator using EEG during motor imagery. Copyright © 2013 Elsevier B.V. All rights reserved.

Using Bacterial Surrogates to Assess Pathogen Transport in the Subsurface: Laboratory and Field Indications of Co-Transport Considerations

NASA Astrophysics Data System (ADS)

Emelko, M.; Stimson, J. R.; McLellan, N. L.; Mesquita, M.

2009-12-01

Prediction of the transport and fate of colloids and nanoparticles in porous media environments remains challenging because factors such as experimental scale, subsurface heterogeneity, and variable flow paths and fluxes have made it difficult to relate laboratory outcomes to field performance. Moreover, field studies have been plagued with inadequate consideration of ground water flow, reliance on unproven “surrogate” parameters, non-detects at the extraction well, and limited sampling. Riverbank filtration (RBF) is an example of an application for which some predictive capacity regarding colloid transport is desirable. RBF is a relatively low-cost, natural water treatment technology in which surface water contaminants are removed or degraded as the infiltrating water flows from a surface source to abstraction wells. RBF has been used for water treatment for at least 200 years and its potential to provide a significant barrier to microorganisms has been demonstrated. Assignment of microbial treatment credits for RBF remains a regulatory challenge because strategies for demonstrating effective subsurface filtration of organisms are not standardized. The potential passage of Giardia lamblia and Cryptosporidium parvum through RBF systems is of particular regulatory concern because these pathogens are known to be resistant to conventional disinfection processes. The transport or relatively small, pathogenic viruses through RBF systems is also a common concern. To comply with the U.S. Long Term 2 Enhanced Surface Water Treatment Rule, utilities with sufficiently high levels of Cryptosporidium oocysts in their source water must amend existing treatment by choosing from a ‘‘toolbox’’ of technologies, including RBF. Aerobic bacterial spores have been evaluated and proposed by some as surrogates for evaluating drinking water treatment plant performance; they also have been proposed as potential surrogates for Cryptosporidium removal during subsurface filtration processes such as RBF. Here, duplicate column studies were conducted to evaluate the transport of nano- and micro-sized polystyrene micropsheres, aerobic spores of Bacillus subtilis, PR772 bacteriophage, and pathogenic Salmonella typhimurium bacteria in a well-sorted fine sand (d 50 = 0.6 mm). A field validation experiment investigating transport of 1.5 µm polystyrene micropsheres and aerobic spores in and RBF system comprised of unconsolidated silty sand, gravel, and boulders was conducted. The column studies demonstrated that the presence of the aerobic spores resulted in increased removal of 4.5 µm microspheres from< 2 log to ~4 log, and 1.5 µm microsphere removal from <0.5 log to ~1 log removal. Microscopic examination of the samples also revealed extensive clumping of microspheres and microorganisms during the experiments conducted with aerobic spores. A field trial during which microspheres and spores of B. subtilis were injected into the subsurface provided corroborating evidence of a co-transport effect of aerobic spores by demonstrating ~1.6 log increase in 1.5 µm microsphere removal in the presence of aerobic spores.

Validating the Kinematic Wave Approach for Rapid Soil Erosion Assessment and Improved BMP Site Selection to Enhance Training Land Sustainability

DTIC Science & Technology

2014-02-01

installation based on a Euclidean distance allocation and assigned that installation’s threshold values. The second approach used a thin - plate spline ...installation critical nLS+ thresholds involved spatial interpolation. A thin - plate spline radial basis functions (RBF) was selected as the...the interpolation of installation results using a thin - plate spline radial basis function technique. 6.5 OBJECTIVE #5: DEVELOP AND

Phenotyping of tumor biology in patients by multimodality multiparametric imaging: relationship of microcirculation, alphavbeta3 expression, and glucose metabolism.

PubMed

Metz, Stephan; Ganter, Carl; Lorenzen, Sylvie; van Marwick, Sandra; Herrmann, Ken; Lordick, Florian; Nekolla, Stephan G; Rummeny, Ernst J; Wester, Hans-Jürgen; Brix, Gunnar; Schwaiger, Markus; Beer, Ambros J

2010-11-01

Both dynamic contrast-enhanced (DCE) MRI and PET provide quantitative information on tumor biology in living organisms. However, imaging biomarkers often neglect tissue heterogeneity by focusing on distributional summary statistics. We analyzed the spatial relationship of α(v)β(3) expression, glucose metabolism, and perfusion by PET and DCE MRI, focusing on tumor heterogeneity. Thirteen patients with primary or metastasized cancer (non-small cell lung cancer, n = 9; others, n = 4) were examined with DCE MRI and with PET using (18)F-galacto-RGD and (18)F-FDG. Twenty-three different regions of interest were defined by cluster analysis based on the heterogeneity of tracer uptake. In these regions, the initial area under the gadopentetate dimeglumine concentration-time curve (IAUGC), as well as the regional blood volume (rBV) and regional blood flow (rBF), were estimated from DCE MRI and correlated with standardized uptake values from PET. Regions with simultaneously high uptake of (18)F-galacto-RGD and (18)F-FDG showed higher functional MRI data (IAUGC, 0.35 ± 0.04 mM·s; rBF, 70.2 ± 12.7 mL/min/100 g; rBV, 23.3 ± 2.7 mL/100 g) than did areas with low uptake of both tracers (IAUGC, 0.15 ± 0.04 mM·s [P < 0.01]; rBF, 28.3 ± 10.8 mL/min/100 g; rBV, 9.9 ± 1.9 mL/100 g [P < 0.01]). There was a weak to moderate correlation between the functional MRI parameters and (18)F-galacto-RGD (r = 0.30-0.62) and also (18)F-FDG (r = 0.44-0.52); these correlations were significant (P < 0.05), except for (18)F-galacto-RGD versus rBF (P = 0.17). These data show that multiparametric assessment of tumor heterogeneity is feasible by combining PET and MRI. Perfusion is highest in tumor areas with simultaneously high α(v)β(3) expression and high glucose metabolism and restricted in areas with both low α(v)β(3) expression and low glucose metabolism. The current limitations resulting from imaging with separate scanners might be overcome by future hybrid PET/MRI scanners.

Artificial Neural Network approach to develop unique Classification and Raga identification tools for Pattern Recognition in Carnatic Music

NASA Astrophysics Data System (ADS)

Srimani, P. K.; Parimala, Y. G.

2011-12-01

A unique approach has been developed to study patterns in ragas of Carnatic Classical music based on artificial neural networks. Ragas in Carnatic music which have found their roots in the Vedic period, have grown on a Scientific foundation over thousands of years. However owing to its vastness and complexities it has always been a challenge for scientists and musicologists to give an all encompassing perspective both qualitatively and quantitatively. Cognition, comprehension and perception of ragas in Indian classical music have always been the subject of intensive research, highly intriguing and many facets of these are hitherto not unravelled. This paper is an attempt to view the melakartha ragas with a cognitive perspective using artificial neural network based approach which has given raise to very interesting results. The 72 ragas of the melakartha system were defined through the combination of frequencies occurring in each of them. The data sets were trained using several neural networks. 100% accurate pattern recognition and classification was obtained using linear regression, TLRN, MLP and RBF networks. Performance of the different network topologies, by varying various network parameters, were compared. Linear regression was found to be the best performing network.

"A Deep Passion for Reading and Writing": An Interview with Lena Townsend

ERIC Educational Resources Information Center

Afterschool Matters, 2015

2015-01-01

The Robert Bowne Foundation (RBF), which published "Afterschool Matters" from 2003 to 2008 and continues to fund the journal and related projects, is closing in December. "Afterschool Matters" sat down with Lena Townsend, executive director, to talk about RBF's legacy and continuing influence on literacy work in afterschool…

The ART of representation: Memory reduction and noise tolerance in a neural network vision system

NASA Astrophysics Data System (ADS)

Langley, Christopher S.

The Feature Cerebellar Model Arithmetic Computer (FCMAC) is a multiple-input-single-output neural network that can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. The FCMAC provides sufficient accuracy to enable a manipulator to grasp an object from an arbitrary pose within its workspace. The network learns an appearance-based representation of an object by storing coarsely quantized feature patterns. As all unique patterns are encoded, the network size grows uncontrollably. A new architecture is introduced herein, which combines the FCMAC with an Adaptive Resonance Theory (ART) network. The ART module categorizes patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART layer tends to discard the least relevant information first. The smaller network performs recall faster, and in some cases is better for generalization, resulting in a reduction of error at recall time. The ART-Under-Constraint (ART-C) algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. The FCMAC is also extended to include real-valued input activations. As a result, the network can be tuned to reject a variety of types of noise in the image feature detection. A quantitative analysis of noise tolerance was performed using four synthetic noise algorithms, and a qualitative investigation was made using noisy real-world image data. In validation experiments, the FCMAC system outperformed Radial Basis Function (RBF) networks for the 3-DOF problem, and had accuracy comparable to that of Principal Component Analysis (PCA) and superior to that of Shape Context Matching (SCM), both of which estimate orientation only.

Clinical Correlates and Prognostic Value of Proenkephalin in Acute and Chronic Heart Failure.

PubMed

Matsue, Yuya; Ter Maaten, Jozine M; Struck, Joachim; Metra, Marco; O'Connor, Christopher M; Ponikowski, Piotr; Teerlink, John R; Cotter, Gad; Davison, Beth; Cleland, John G; Givertz, Michael M; Bloomfield, Daniel M; Dittrich, Howard C; van Veldhuisen, Dirk J; van der Meer, Peter; Damman, Kevin; Voors, Adriaan A

2017-03-01

Proenkephalin (pro-ENK) has emerged as a novel biomarker associated with both renal function and cardiac function. However, its clinical and prognostic value have not been well evaluated in symptomatic patients with heart failure. The association between pro-ENK and markers of renal function was evaluated in 95 patients with chronic heart failure who underwent renal hemodynamic measurements, including renal blood flow (RBF) and glomerular filtration rate (GFR) with the use of 131 I-Hippuran and 125 I-iothalamate clearances, respectively. The association between pro-ENK and clinical outcome in acute heart failure was assessed in another 1589 patients. Pro-ENK was strongly correlated with both RBF (P < .001) and GFR (P < .001), but not with renal tubular markers. In the acute heart failure cohort, pro-ENK was a predictor of death through 180 days, heart failure rehospitalization through 60 days, and death or cardiovascular or renal rehospitalization through day 60 in univariable analyses, but its predictive value was lost in a multivariable model when other renal markers were entered in the model. In patients with chronic and acute heart failure, pro-ENK is strongly associated with glomerular function, but not with tubular damage. Pro-ENK provides limited prognostic information in patients with acute heart failure on top of established renal markers. Copyright © 2016 Elsevier Inc. All rights reserved.

Effects of carprofen on renal function during medetomidine-propofol-isoflurane anesthesia in dogs.

PubMed

Frendin, Jan H M; Boström, Ingrid M; Kampa, Naruepon; Eksell, Per; Häggström, Jens U; Nyman, Görel C

2006-12-01

To investigate effects of carprofen on indices of renal function and results of serum bio-chemical analyses and effects on cardiovascular variables during medetomidine-propofol-isoflurane anesthesia in dogs. 8 healthy male Beagles. A randomized crossover study was conducted with treatments including saline (0.9% NaCl) solution (0.08 mL/kg) and carprofen (4 mg/kg) administered IV. Saline solution or carprofen was administered 30 minutes before induction of anesthesia and immediately before administration of medetomidine (20 microg/kg, IM). Anesthesia was induced with propofol and maintained with inspired isoflurane in oxygen. Blood gas concentrations and ventilation were measured. Cardiovascular variables were continuously monitored via pulse contour cardiac output (CO) measurement. Renal function was assessed via glomerular filtration rate (GFR), renal blood flow (RBF), scintigraphy, serum biochemical analyses, urinalysis, and continuous CO measurements. Hematologic analysis was performed. Values did not differ significantly between the carprofen and saline solution groups. For both treatments, sedation and anesthesia caused changes in results of serum biochemical and hematologic analyses; a transient, significant increase in urine alkaline phosphatase activity; and blood flow diversion to the kidneys. The GFR increased significantly in both groups despite decreased CO, mean arterial pressure, and absolute RBF variables during anesthesia. Carprofen administered IV before anesthesia did not cause detectable, significant adverse effects on renal function during medetomidine-propofol-isoflurane anesthesia in healthy Beagles.

Gender Difference in Renal Blood Flow Response to Angiotensin II Administration after Ischemia/Reperfusion in Rats: The Role of AT2 Receptor.

PubMed

Maleki, Maryam; Nematbakhsh, Mehdi

2016-01-01

Background. Renal ischemia/reperfusion (I/R) is one of the major causes of kidney failure, and it may interact with renin angiotensin system while angiotensin II (Ang II) type 2 receptor (AT2R) expression is gender dependent. We examined the role of AT2R blockade on vascular response to Ang II after I/R in rats. Methods. Male and female rats were subjected to 30 min renal ischemia followed by reperfusion. Two groups of rats received either vehicle or AT2R antagonist, PD123319. Mean arterial pressure (MAP), and renal blood flow (RBF) responses were assessed during graded Ang II (100, 300, and 1000 ng/kg/min, i.v.) infusion at controlled renal perfusion pressure (RPP). Results. Vehicle or antagonist did not alter MAP, RPP, and RBF levels significantly; however, 30 min after reperfusion, RBF decreased insignificantly in female treated with PD123319 (P = 0.07). Ang II reduced RBF and increased renal vascular resistance (RVR) in a dose-related fashion (P dose < 0.0001), and PD123319 intensified the reduction of RBF response in female (P group < 0.005), but not in male rats. Conclusion. The impact of the AT2R on vascular responses to Ang II in renal I/R injury appears to be sexually dimorphic. PD123319 infusion promotes these hemodynamic responses in female more than in male rats.

Role of Mas receptor in renal blood flow response to angiotensin (1-7) in male and female rats.

PubMed

Nematbakhsh, Mehdi; Safari, Tahereh

2014-01-01

Epidemiologic and clinical studies have shown that progression of renal disease in male is faster than that in female. However, the exact mechanisms are not well recognized. Angiotensin (1-7) (Ang 1-7) receptor, called "Mas", is an element in the depressor arm of renin angiotensin system (RAS), and its expression is enhanced in females. We test the hypothesis that Mas receptor (MasR) blockade (A779) attenuates renal blood flow (RBF) in response to infusion of graded doses of Ang 1-7 in female rats. Male and female Wistar rats were anesthetized and catheterized. Then, the mean arterial pressure (MAP), RBF, and controlled renal perfusion pressure (RPP) responses to infusion of graded doses of Ang 1-7 (100-1000 ng/kg/min i.v.) with and without A779 were measured in the animals. Basal MAP, RPP, RBF, and renal vascular resistance (RVR) were not significantly different between the two groups. After Ang 1-7 administration, RPP was controlled at a constant level. However, RBF increased in a dose-related manner in response to Ang 1-7 infusion in both male and female rats (Pdose<0.0001), but masR blockade significantly attenuated this response only in female (Pgroup=0.04) and not male (Pgroup=0.23). In addition, A779 increased the RBF response to Ang 1-7 to a greater extent. This is while the increase in male was not significant when compared with that in female (Pgender=0.08). RVR response to Ang 1-7 was insignificantly attenuated by A779 in both genders. The masR differently regulated RBF response to Ang 1-7 in the two genders, and the effect was greater in female rats. The masR may be a target for improvement of kidney circulation in renal diseases.

Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

NASA Astrophysics Data System (ADS)

Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

2013-10-01

Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

PubMed Central

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

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

PubMed

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.

Fire Risk Assessment of Some Indian Coals Using Radial Basis Function (RBF) Technique

NASA Astrophysics Data System (ADS)

Nimaje, Devidas; Tripathy, Debi Prasad

2017-04-01

Fires, whether surface or underground, pose serious and environmental problems in the global coal mining industry. It is causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution. Most of the instances of coal mine fires happening worldwide are mainly due to the spontaneous combustion. Hence, attention must be paid to take appropriate measures to prevent occurrence and spread of fire. In this paper, to evaluate the different properties of coals for fire risk assessment, forty-nine in situ coal samples were collected from major coalfields of India. Intrinsic properties viz. proximate and ultimate analysis; and susceptibility indices like crossing point temperature, flammability temperature, Olpinski index and wet oxidation potential method of Indian coals were carried out to ascertain the liability of coal to spontaneous combustion. Statistical regression analysis showed that the parameters of ultimate analysis provide significant correlation with all investigated susceptibility indices as compared to the parameters of proximate analysis. Best correlated parameters (ultimate analysis) were used as inputs to the radial basis function network model. The model revealed that Olpinski index can be used as a reliable method to assess the liability of Indian coals to spontaneous combustion.

Bayesian nonparametric adaptive control using Gaussian processes.

PubMed

Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A

2015-03-01

Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

PubMed

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Riverbank filtration for the treatment of highly turbid Colombian rivers

NASA Astrophysics Data System (ADS)

Gutiérrez, Juan Pablo; van Halem, Doris; Rietveld, Luuk

2017-05-01

The poor quality of many Colombian surface waters forces us to seek alternative, sustainable treatment solutions with the ability to manage peak pollution events and to guarantee the uninterrupted provision of safe drinking water to the population. This review assesses the potential of using riverbank filtration (RBF) for the highly turbid and contaminated waters in Colombia, emphasizing water quality improvement and the influence of clogging by suspended solids. The suspended sediments may be favorable for the improvement of the water quality, but they may also reduce the production yield capacity. The cake layer must be balanced by scouring in order for an RBF system to be sustainable. The infiltration rate must remain high enough throughout the river-aquifer interface to provide the water quantity needed, and the residence time of the contaminants must be sufficient to ensure adequate water quality. In general, RBF seems to be a technology appropriate for use in highly turbid and contaminated surface rivers in Colombia, where improvements are expected due to the removal of turbidity, pathogens and to a lesser extent inorganics, organic matter and micro-pollutants. RBF has the potential to mitigate shock loads, thus leading to the prevention of shutdowns of surface water treatment plants. In addition, RBF, as an alternative pretreatment step, may provide an important reduction in chemical consumption, considerably simplifying the operation of the existing treatment processes. However, clogging and self-cleansing issues must be studied deeper in the context of these highly turbid waters to evaluate the potential loss of abstraction capacity yield as well as the development of different redox zones for efficient contaminant removal.

An Inverse Neural Controller Based on the Applicability Domain of RBF Network Models

PubMed Central

Alexandridis, Alex; Stogiannos, Marios; Papaioannou, Nikolaos; Zois, Elias; Sarimveis, Haralambos

2018-01-01

This paper presents a novel methodology of generic nature for controlling nonlinear systems, using inverse radial basis function neural network models, which may combine diverse data originating from various sources. The algorithm starts by applying the particle swarm optimization-based non-symmetric variant of the fuzzy means (PSO-NSFM) algorithm so that an approximation of the inverse system dynamics is obtained. PSO-NSFM offers models of high accuracy combined with small network structures. Next, the applicability domain concept is suitably tailored and embedded into the proposed control structure in order to ensure that extrapolation is avoided in the controller predictions. Finally, an error correction term, estimating the error produced by the unmodeled dynamics and/or unmeasured external disturbances, is included to the control scheme to increase robustness. The resulting controller guarantees bounded input-bounded state (BIBS) stability for the closed loop system when the open loop system is BIBS stable. The proposed methodology is evaluated on two different control problems, namely, the control of an experimental armature-controlled direct current (DC) motor and the stabilization of a highly nonlinear simulated inverted pendulum. For each one of these problems, appropriate case studies are tested, in which a conventional neural controller employing inverse models and a PID controller are also applied. The results reveal the ability of the proposed control scheme to handle and manipulate diverse data through a data fusion approach and illustrate the superiority of the method in terms of faster and less oscillatory responses. PMID:29361781

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

PubMed

Van Looy, Stijn; Verplancke, Thierry; Benoit, Dominique; Hoste, Eric; Van Maele, Georges; De Turck, Filip; Decruyenaere, Johan

2007-01-01

Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies--a comparative study.

PubMed

Zakaria, Rozalina; Sheng, Ong Yong; Wern, Kam; Shamshirband, Shahaboddin; Wahab, Ainuddin Wahid Abdul; Petković, Dalibor; Saboohi, Hadi

2014-05-01

A soft methodology study has been applied on tapered plastic multimode sensors. This study basically used tapered plastic multimode fiber [polymethyl methacrylate (PMMA)] optics as a sensor. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using a calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at high concentrations. As the concentration was varied from 0 to 6 ppm, the output voltage of the sensor increased linearly. The silver film coating increased the sensitivity of the proposed sensor because of the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. In this study, the polynomial and radial basis function (RBF) were applied as the kernel function of the support vector regression (SVR) to estimate and predict the output voltage response of the sensors with and without silver film according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf were used in an attempt to minimize the generalization error bound so as to achieve generalized performance. An adaptive neuro-fuzzy interference system (ANFIS) approach was also investigated for comparison. The experimental results showed that improvements in the predictive accuracy and capacity for generalization can be achieved by the SVR_poly approach in comparison to the SVR_rbf methodology. The same testing errors were found for the SVR_poly approach and the ANFIS approach.

Assessing the blood volume and heart rate responses during haemodialysis in fluid overloaded patients using support vector regression.

PubMed

Javed, Faizan; Savkin, Andrey V; Chan, Gregory S H; Middleton, Paul M; Malouf, Philip; Steel, Elizabeth; Mackie, James; Lovell, Nigel H

2009-11-01

This study aims to assess the blood volume and heart rate (HR) responses during haemodialysis in fluid overloaded patients by a nonparametric nonlinear regression approach based on a support vector machine (SVM). Relative blood volume (RBV) and electrocardiogram (ECG) was recorded from 23 haemodynamically stable renal failure patients during regular haemodialysis. Modelling was performed on 18 fluid overloaded patients (fluid removal of >2 L). SVM-based regression was used to obtain the models of RBV change with time as well as the percentage change in HR with respect to RBV. Mean squared error (MSE) and goodness of fit (R(2)) were used for comparison among different kernel functions. The design parameters were estimated using a grid search approach and the selected models were validated by a k-fold cross-validation technique. For the model of HR versus RBV change, a radial basis function (RBF) kernel (MSE = 17.37 and R(2) = 0.932) gave the least MSE compared to linear (MSE = 25.97 and R(2) = 0.898) and polynomial (MSE = 18.18 and R(2)= 0.929). The MSE was significantly lower for training data set when using RBF kernel compared to other kernels (p < 0.01). The RBF kernel also provided a slightly better fit of RBV change with time (MSE = 1.12 and R(2) = 0.91) compared to a linear kernel (MSE = 1.46 and R(2) = 0.88). The modelled HR response was characterized by an initial drop and a subsequent rise during progressive reduction in RBV, which may be interpreted as the reflex response to a transition from central hypervolaemia to hypovolaemia. These modelled curves can be used as references to a controller that can be designed to regulate the haemodynamic variables to ensure the stability of patients undergoing haemodialysis.

Role of angiotensin II in dynamic renal blood flow autoregulation of the conscious dog

PubMed Central

Just, Armin; Ehmke, Heimo; Wittmann, Uwe; Kirchheim, Hartmut R

2002-01-01

The influence of angiotensin II (ANGII) on the dynamic characteristics of renal blood flow (RBF) was studied in conscious dogs by testing the response to a step increase in renal artery pressure (RAP) after a 60 s period of pressure reduction (to 50 mmHg) and by calculating the transfer function between physiological fluctuations in RAP and RBF. During the RAP reduction, renal vascular resistance (RVR) decreased and upon rapid restoration of RAP, RVR returned to baseline with a characteristic time course: within the first 10 s, RVR rose rapidly by 40 % of the initial change (first response, myogenic response). A second rise began after 20–30 s and reached baseline after an overshoot at 40 s (second response, tubuloglomerular feedback (TGF)). Between both responses, RVR rose very slowly (plateau). The transfer function had a low gain below 0.01 Hz (high autoregulatory efficiency) and two corner frequencies at 0.026 Hz (TGF) and at 0.12 Hz (myogenic response). Inhibition of angiotensin converting enzyme (ACE) lowered baseline RVR, but not the minimum RVR at the end of the RAP reduction (autoregulation-independent RVR). Both the first and second response were reduced, but the normalised level of the plateau (balance between myogenic response, TGF and possible slower mechanisms) and the transfer gain below 0.01 Hz were not affected. Infusion of ANGII after ramipril raised baseline RVR above the control condition. The first and second response and the transfer gain at both corner frequencies were slightly augmented, but the normalised level of the plateau was not affected. It is concluded that alterations of plasma ANGII within a physiological range do not modulate the relative contribution of the myogenic response to the overall short-term autoregulation of RBF. Consequently, it appears that ANGII augments not only TGF, but also the myogenic response. PMID:11773325

Comparison of the common spatial interpolation methods used to analyze potentially toxic elements surrounding mining regions.

PubMed

Ding, Qian; Wang, Yong; Zhuang, Dafang

2018-04-15

The appropriate spatial interpolation methods must be selected to analyze the spatial distributions of Potentially Toxic Elements (PTEs), which is a precondition for evaluating PTE pollution. The accuracy and effect of different spatial interpolation methods, which include inverse distance weighting interpolation (IDW) (power = 1, 2, 3), radial basis function interpolation (RBF) (basis function: thin-plate spline (TPS), spline with tension (ST), completely regularized spline (CRS), multiquadric (MQ) and inverse multiquadric (IMQ)) and ordinary kriging interpolation (OK) (semivariogram model: spherical, exponential, gaussian and linear), were compared using 166 unevenly distributed soil PTE samples (As, Pb, Cu and Zn) in the Suxian District, Chenzhou City, Hunan Province as the study subject. The reasons for the accuracy differences of the interpolation methods and the uncertainties of the interpolation results are discussed, then several suggestions for improving the interpolation accuracy are proposed, and the direction of pollution control is determined. The results of this study are as follows: (i) RBF-ST and OK (exponential) are the optimal interpolation methods for As and Cu, and the optimal interpolation method for Pb and Zn is RBF-IMQ. (ii) The interpolation uncertainty is positively correlated with the PTE concentration, and higher uncertainties are primarily distributed around mines, which is related to the strong spatial variability of PTE concentrations caused by human interference. (iii) The interpolation accuracy can be improved by increasing the sample size around the mines, introducing auxiliary variables in the case of incomplete sampling and adopting the partition prediction method. (iv) It is necessary to strengthen the prevention and control of As and Pb pollution, particularly in the central and northern areas. The results of this study can provide an effective reference for the optimization of interpolation methods and parameters for unevenly distributed soil PTE data in mining areas. Copyright © 2018 Elsevier Ltd. All rights reserved.

SVM and SVM Ensembles in Breast Cancer Prediction.

PubMed

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

SVM and SVM Ensembles in Breast Cancer Prediction

PubMed Central

Huang, Min-Wei; Chen, Chih-Wen; Lin, Wei-Chao; Ke, Shih-Wen; Tsai, Chih-Fong

2017-01-01

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers. PMID:28060807

Disparate effects of single endothelin A and B receptor blocker therapy on the progression of renal injury in advanced renovascular disease

PubMed Central

Chade, Alejandro R.; Stewart, Nicholas J.; Peavy, Patrick R.

2013-01-01

We hypothesized that chronic specific endothelin (ET)-A receptor blockade therapy would reverse renal dysfunction and injury in advanced experimental renovascular disease. To test this, unilateral renovascular disease was induced in 19 pigs and after 6 weeks, single-kidney hemodynamics and function was quantified in vivo using computed-tomography. All pigs with renovascular disease were divided such that 7 were untreated, 7 were treated with ET-A blockers, and 5 were treated with ET-B blockers. Four weeks later, all pigs were re-studied in vivo, then euthanized and ex vivo studies performed on the stenotic kidney to quantify microvascular density, remodeling, renal oxidative stress, inflammation, and fibrosis. RBF, GFR, and redox status were significantly improved in the stenotic kidney after ET-A but not ET-B blockade. Furthermore, only ET-A blockade therapy reversed renal microvascular rarefaction and diminished remodeling, which was accompanied by a marked decreased in renal inflammatory and fibrogenic activity. Thus, ET-A but not ET-B blockade ameliorated renal injury in pigs with advanced renovascular disease by stimulating microvascular proliferation and decreasing the progression of microvascular remodeling, renal inflammation and fibrosis in the stenotic kidney. These effects were functionally consequential since ET-A blockade improved single kidney microvascular endothelial function, RBF, and GFR, and decreased albuminuria. PMID:24352153

Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS

NASA Astrophysics Data System (ADS)

Tehrany, Mahyat Shafapour; Pradhan, Biswajeet; Jebur, Mustafa Neamah

2014-05-01

Flood is one of the most devastating natural disasters that occur frequently in Terengganu, Malaysia. Recently, ensemble based techniques are getting extremely popular in flood modeling. In this paper, weights-of-evidence (WoE) model was utilized first, to assess the impact of classes of each conditioning factor on flooding through bivariate statistical analysis (BSA). Then, these factors were reclassified using the acquired weights and entered into the support vector machine (SVM) model to evaluate the correlation between flood occurrence and each conditioning factor. Through this integration, the weak point of WoE can be solved and the performance of the SVM will be enhanced. The spatial database included flood inventory, slope, stream power index (SPI), topographic wetness index (TWI), altitude, curvature, distance from the river, geology, rainfall, land use/cover (LULC), and soil type. Four kernel types of SVM (linear kernel (LN), polynomial kernel (PL), radial basis function kernel (RBF), and sigmoid kernel (SIG)) were used to investigate the performance of each kernel type. The efficiency of the new ensemble WoE and SVM method was tested using area under curve (AUC) which measured the prediction and success rates. The validation results proved the strength and efficiency of the ensemble method over the individual methods. The best results were obtained from RBF kernel when compared with the other kernel types. Success rate and prediction rate for ensemble WoE and RBF-SVM method were 96.48% and 95.67% respectively. The proposed ensemble flood susceptibility mapping method could assist researchers and local governments in flood mitigation strategies.

A numerical scheme based on radial basis function finite difference (RBF-FD) technique for solving the high-dimensional nonlinear Schrödinger equations using an explicit time discretization: Runge-Kutta method

NASA Astrophysics Data System (ADS)

Dehghan, Mehdi; Mohammadi, Vahid

2017-08-01

In this research, we investigate the numerical solution of nonlinear Schrödinger equations in two and three dimensions. The numerical meshless method which will be used here is RBF-FD technique. The main advantage of this method is the approximation of the required derivatives based on finite difference technique at each local-support domain as Ωi. At each Ωi, we require to solve a small linear system of algebraic equations with a conditionally positive definite matrix of order 1 (interpolation matrix). This scheme is efficient and its computational cost is same as the moving least squares (MLS) approximation. A challengeable issue is choosing suitable shape parameter for interpolation matrix in this way. In order to overcome this matter, an algorithm which was established by Sarra (2012), will be applied. This algorithm computes the condition number of the local interpolation matrix using the singular value decomposition (SVD) for obtaining the smallest and largest singular values of that matrix. Moreover, an explicit method based on Runge-Kutta formula of fourth-order accuracy will be applied for approximating the time variable. It also decreases the computational costs at each time step since we will not solve a nonlinear system. On the other hand, to compare RBF-FD method with another meshless technique, the moving kriging least squares (MKLS) approximation is considered for the studied model. Our results demonstrate the ability of the present approach for solving the applicable model which is investigated in the current research work.

ETMB-RBF: discrimination of metal-binding sites in electron transporters based on RBF networks with PSSM profiles and significant amino acid pairs.

PubMed

Ou, Yu-Yen; Chen, Shu-An; Wu, Sheng-Cheng

2013-01-01

Cellular respiration is the process by which cells obtain energy from glucose and is a very important biological process in living cell. As cells do cellular respiration, they need a pathway to store and transport electrons, the electron transport chain. The function of the electron transport chain is to produce a trans-membrane proton electrochemical gradient as a result of oxidation-reduction reactions. In these oxidation-reduction reactions in electron transport chains, metal ions play very important role as electron donor and acceptor. For example, Fe ions are in complex I and complex II, and Cu ions are in complex IV. Therefore, to identify metal-binding sites in electron transporters is an important issue in helping biologists better understand the workings of the electron transport chain. We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and significant amino acid pairs to identify metal-binding residues in electron transport proteins. We have selected a non-redundant set of 55 metal-binding electron transport proteins as our dataset. The proposed method can predict metal-binding sites in electron transport proteins with an average 10-fold cross-validation accuracy of 93.2% and 93.1% for metal-binding cysteine and histidine, respectively. Compared with the general metal-binding predictor from A. Passerini et al., the proposed method can improve over 9% of sensitivity, and 14% specificity on the independent dataset in identifying metal-binding cysteines. The proposed method can also improve almost 76% sensitivity with same specificity in metal-binding histidine, and MCC is also improved from 0.28 to 0.88. We have developed a novel approach based on PSSM profiles and significant amino acid pairs for identifying metal-binding sites from electron transport proteins. The proposed approach achieved a significant improvement with independent test set of metal-binding electron transport proteins.

ETMB-RBF: Discrimination of Metal-Binding Sites in Electron Transporters Based on RBF Networks with PSSM Profiles and Significant Amino Acid Pairs

PubMed Central

Ou, Yu-Yen; Chen, Shu-An; Wu, Sheng-Cheng

2013-01-01

Background Cellular respiration is the process by which cells obtain energy from glucose and is a very important biological process in living cell. As cells do cellular respiration, they need a pathway to store and transport electrons, the electron transport chain. The function of the electron transport chain is to produce a trans-membrane proton electrochemical gradient as a result of oxidation–reduction reactions. In these oxidation–reduction reactions in electron transport chains, metal ions play very important role as electron donor and acceptor. For example, Fe ions are in complex I and complex II, and Cu ions are in complex IV. Therefore, to identify metal-binding sites in electron transporters is an important issue in helping biologists better understand the workings of the electron transport chain. Methods We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and significant amino acid pairs to identify metal-binding residues in electron transport proteins. Results We have selected a non-redundant set of 55 metal-binding electron transport proteins as our dataset. The proposed method can predict metal-binding sites in electron transport proteins with an average 10-fold cross-validation accuracy of 93.2% and 93.1% for metal-binding cysteine and histidine, respectively. Compared with the general metal-binding predictor from A. Passerini et al., the proposed method can improve over 9% of sensitivity, and 14% specificity on the independent dataset in identifying metal-binding cysteines. The proposed method can also improve almost 76% sensitivity with same specificity in metal-binding histidine, and MCC is also improved from 0.28 to 0.88. Conclusions We have developed a novel approach based on PSSM profiles and significant amino acid pairs for identifying metal-binding sites from electron transport proteins. The proposed approach achieved a significant improvement with independent test set of metal-binding electron transport proteins. PMID:23405059

Real-time measurement of renal blood flow in healthy subjects using contrast-enhanced ultrasound.

PubMed

Kalantarinia, Kambiz; Belcik, J Todd; Patrie, James T; Wei, Kevin

2009-10-01

Current methods for measuring renal blood flow (RBF) are time consuming and not widely available. Contrast-enhanced ultrasound (CEU) is a safe and noninvasive imaging technique suitable for assessment of tissue blood flow, which has been used clinically to assess myocardial blood flow. We tested the utility of CEU in monitoring changes in RBF in healthy volunteers. We utilized CEU to monitor the expected increase in RBF following a high protein meal in healthy adults. Renal cortical perfusion was assessed by CEU using low mechanical index (MI) power modulation Angio during continuous infusions of Definity. Following destruction of tissue microbubbles using ultrasound at a MI of 1.0, the rate of tissue replenishment with microbubbles and the plateau acoustic intensity (AI) were used to estimate the RBF velocity and cortical blood volume, respectively. Healthy adults (n = 19, mean age 26.6 yr) were enrolled. The A.beta parameter of CEU, representing mean RBF increased by 42.8%from a baseline of 17.05 +/- 6.23 to 23.60 +/- 6.76 dB/s 2 h after the ingestion of the high-protein meal (P = 0.002). Similarly, there was a 37.3%increase in the beta parameter, representing the geometric mean of blood velocity after the high protein meal (P < 0.001). The change in cortical blood volume was not significant (P = 0.89). Infusion time of Definity was 6.3 +/- 2.0 min. The ultrasound contrast agent was tolerated well with no serious adverse events. CEU is a fast, noninvasive, and practical imaging technique that may be useful for monitoring renal blood velocity, volume, and flow.

Renal hemodynamic effects of activation of specific renal sympathetic nerve fiber groups.

PubMed

DiBona, G F; Sawin, L L

1999-02-01

To examine the effect of activation of a unique population of renal sympathetic nerve fibers on renal blood flow (RBF) dynamics, anesthetized rats were instrumented with a renal sympathetic nerve activity (RSNA) recording electrode and an electromagnetic flow probe on the ipsilateral renal artery. Peripheral thermal receptor stimulation (external heat) was used to activate a unique population of renal sympathetic nerve fibers and to increase total RSNA. Total RSNA was reflexly increased to the same degree with somatic receptor stimulation (tail compression). Arterial pressure and heart rate were increased by both stimuli. Total RSNA was increased to the same degree by both stimuli but external heat produced a greater renal vasoconstrictor response than tail compression. Whereas both stimuli increased spectral density power of RSNA at both cardiac and respiratory frequencies, modulation of RBF variability by fluctuations of RSNA was small at these frequencies, with values for the normalized transfer gain being approximately 0.1 at >0.5 Hz. During tail compression coherent oscillations of RSNA and RBF were found at 0.3-0.4 Hz with normalized transfer gain of 0.33 +/- 0.02. During external heat coherent oscillations of RSNA and RBF were found at both 0.2 and 0.3-0.4 Hz with normalized transfer gains of 0. 63 +/- 0.05 at 0.2 Hz and 0.53 +/- 0.04 to 0.36 +/- 0.02 at 0.3-0.4 Hz. Renal denervation eliminated the oscillations in RBF at both 0.2 and 0.3-0.4 Hz. These findings indicate that despite similar increases in total RSNA, external heat results in a greater renal vasoconstrictor response than tail compression due to the activation of a unique population of renal sympathetic nerve fibers with different frequency-response characteristics of the renal vasculature.

The Natural Neighbour Radial Point Interpolation Meshless Method Applied to the Non-Linear Analysis

NASA Astrophysics Data System (ADS)

Dinis, L. M. J. S.; Jorge, R. M. Natal; Belinha, J.

2011-05-01

In this work the Natural Neighbour Radial Point Interpolation Method (NNRPIM), is extended to large deformation analysis of elastic and elasto-plastic structures. The NNPRIM uses the Natural Neighbour concept in order to enforce the nodal connectivity and to create a node-depending background mesh, used in the numerical integration of the NNRPIM interpolation functions. Unlike the FEM, where geometrical restrictions on elements are imposed for the convergence of the method, in the NNRPIM there are no such restrictions, which permits a random node distribution for the discretized problem. The NNRPIM interpolation functions, used in the Galerkin weak form, are constructed using the Radial Point Interpolators, with some differences that modify the method performance. In the construction of the NNRPIM interpolation functions no polynomial base is required and the used Radial Basis Function (RBF) is the Multiquadric RBF. The NNRPIM interpolation functions posses the delta Kronecker property, which simplify the imposition of the natural and essential boundary conditions. One of the scopes of this work is to present the validation the NNRPIM in the large-deformation elasto-plastic analysis, thus the used non-linear solution algorithm is the Newton-Rapson initial stiffness method and the efficient "forward-Euler" procedure is used in order to return the stress state to the yield surface. Several non-linear examples, exhibiting elastic and elasto-plastic material properties, are studied to demonstrate the effectiveness of the method. The numerical results indicated that NNRPIM handles large material distortion effectively and provides an accurate solution under large deformation.

A predictive multi-linear regression model for organic micropollutants, based on a laboratory-scale column study simulating the river bank filtration process.

PubMed

Bertelkamp, C; Verliefde, A R D; Reynisson, J; Singhal, N; Cabo, A J; de Jonge, M; van der Hoek, J P

2016-03-05

This study investigated relationships between OMP biodegradation rates and the functional groups present in the chemical structure of a mixture of 31 OMPs. OMP biodegradation rates were determined from lab-scale columns filled with soil from RBF site Engelse Werk of the drinking water company Vitens in The Netherlands. A statistically significant relationship was found between OMP biodegradation rates and the functional groups of the molecular structures of OMPs in the mixture. The OMP biodegradation rate increased in the presence of carboxylic acids, hydroxyl groups, and carbonyl groups, but decreased in the presence of ethers, halogens, aliphatic ethers, methyl groups and ring structures in the chemical structure of the OMPs. The predictive model obtained from the lab-scale soil column experiment gave an accurate qualitative prediction of biodegradability for approximately 70% of the OMPs monitored in the field (80% excluding the glymes). The model was found to be less reliable for the more persistent OMPs (OMPs with predicted biodegradation rates lower or around the standard error=0.77d(-1)) and OMPs containing amide or amine groups. These OMPs should be carefully monitored in the field to determine their removal during RBF. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel optimization algorithm for MIMO Hammerstein model identification under heavy-tailed noise.

PubMed

Jin, Qibing; Wang, Hehe; Su, Qixin; Jiang, Beiyan; Liu, Qie

2018-01-01

In this paper, we study the system identification of multi-input multi-output (MIMO) Hammerstein processes under the typical heavy-tailed noise. To the best of our knowledge, there is no general analytical method to solve this identification problem. Motivated by this, we propose a general identification method to solve this problem based on a Gaussian-Mixture Distribution intelligent optimization algorithm (GMDA). The nonlinear part of Hammerstein process is modeled by a Radial Basis Function (RBF) neural network, and the identification problem is converted to an optimization problem. To overcome the drawbacks of analytical identification method in the presence of heavy-tailed noise, a meta-heuristic optimization algorithm, Cuckoo search (CS) algorithm is used. To improve its performance for this identification problem, the Gaussian-mixture Distribution (GMD) and the GMD sequences are introduced to improve the performance of the standard CS algorithm. Numerical simulations for different MIMO Hammerstein models are carried out, and the simulation results verify the effectiveness of the proposed GMDA. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

a Gsa-Svm Hybrid System for Classification of Binary Problems

NASA Astrophysics Data System (ADS)

Sarafrazi, Soroor; Nezamabadi-pour, Hossein; Barahman, Mojgan

2011-06-01

This paperhybridizesgravitational search algorithm (GSA) with support vector machine (SVM) and made a novel GSA-SVM hybrid system to improve the classification accuracy in binary problems. GSA is an optimization heuristic toolused to optimize the value of SVM kernel parameter (in this paper, radial basis function (RBF) is chosen as the kernel function). The experimental results show that this newapproach can achieve high classification accuracy and is comparable to or better than the particle swarm optimization (PSO)-SVM and genetic algorithm (GA)-SVM, which are two hybrid systems for classification.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

Towards the application of one-dimensional sonomyography for powered upper-limb prosthetic control using machine learning models.

PubMed

Guo, Jing-Yi; Zheng, Yong-Ping; Xie, Hong-Bo; Koo, Terry K

2013-02-01

The inherent properties of surface electromyography limit its potential for multi-degrees of freedom control. Our previous studies demonstrated that wrist angle could be predicted by muscle thickness measured from B-mode ultrasound, and hence, it could be an alternative signal for prosthetic control. However, an ultrasound imaging machine is too bulky and expensive. We aim to utilize a portable A-mode ultrasound system to examine the feasibility of using one-dimensional sonomyography (i.e. muscle thickness signals detected by A-mode ultrasound) to predict wrist angle with three different machine learning models - (1) support vector machine (SVM), (2) radial basis function artificial neural network (RBF ANN), and (3) back-propagation artificial neural network (BP ANN). Feasibility study using nine healthy subjects. Each subject performed wrist extension guided at 15, 22.5, and 30 cycles/minute, respectively. Data obtained from 22.5 cycles/minute trials was used to train the models and the remaining trials were used for cross-validation. Prediction accuracy was quantified by relative root mean square error (RMSE) and correlation coefficients (CC). Excellent prediction was noted using SVM (RMSE = 13%, CC = 0.975), which outperformed the other methods. It appears that one-dimensional sonomyography could be an alternative signal for prosthetic control. Clinical relevance Surface electromyography has inherent limitations that prohibit its full functional use for prosthetic control. Research that explores alternative signals to improve prosthetic control (such as the one-dimensional sonomyography signals evaluated in this study) may revolutionize powered prosthesis design and ultimately benefit amputee patients.

Aerodynamic Shape Optimization Design of Wing-Body Configuration Using a Hybrid FFD-RBF Parameterization Approach

NASA Astrophysics Data System (ADS)

Liu, Yuefeng; Duan, Zhuoyi; Chen, Song

2017-10-01

Aerodynamic shape optimization design aiming at improving the efficiency of an aircraft has always been a challenging task, especially when the configuration is complex. In this paper, a hybrid FFD-RBF surface parameterization approach has been proposed for designing a civil transport wing-body configuration. This approach is simple and efficient, with the FFD technique used for parameterizing the wing shape and the RBF interpolation approach used for handling the wing body junction part updating. Furthermore, combined with Cuckoo Search algorithm and Kriging surrogate model with expected improvement adaptive sampling criterion, an aerodynamic shape optimization design system has been established. Finally, the aerodynamic shape optimization design on DLR F4 wing-body configuration has been carried out as a study case, and the result has shown that the approach proposed in this paper is of good effectiveness.

Removal of pathogens using riverbank filtration

NASA Astrophysics Data System (ADS)

Cote, M. M.; Emelko, M. B.; Thomson, N. R.

2003-04-01

Although more than hundred years old, in situ or Riverbank Filtration (RBF) has undergone a renewed interest in North America because of its potential as a surface water pre-treatment tool for removal of pathogenic microorganisms. A new RBF research field site has been constructed along the banks of the Grand River in Kitchener, Ontario, Canada to assess factors influencing pathogen removal in the subsurface. Implementation of RBF and appropriate design of subsequent treatment (UV, chlorination, etc.) processes requires successful quantification of in situ removals of Cryptosporidium parvum or a reliable surrogate parameter. C.~parvum is often present in surface water at low indigenous concentrations and can be difficult to detect in well effluents. Since releases of inactivated C.~parvum at concentrations high enough for detection in well effluents are cost prohibitive, other approaches for demonstrating effective in situ filtration of C.~parvum must be considered; these include the use of other microbial species or microspheres as indicators of C.~parvum transport in the environment. Spores of Bacillus subtilis may be considered reasonable indicators of C.~parvum removal by in situ filtration because of their size (˜1 μm in diameter), spherical shape, relatively high indigenous concentration is many surface waters, and relative ease of enumeration. Based on conventional particle filtration theory and assuming equivalent chemical interactions for all particle sizes, a 1 μm B.~subtilis spore will be removed less readily than a larger C. parvum oocyst (4-6 μm) in an ideal granular filter. Preliminary full-scale data obtained from a high rate RBF production well near the new RBF test site demonstrated greater than 1 log removal of B.~subtilis spores. This observed spore removal is higher than that prescribed by the proposed U.S. Long Term 2 Enhanced Surface Water Treatment Rule for C.~parvum. To further investigate the removal relationship between C.~parvum, Giardia lamblia and proposed surrogates such as B.~subtilis, detailed characterization of site hydrogeology, geochemistry, and water quality (MPA, particles, TOC, ionic strength) are underway. Particle counts are being measured in the bank filtrate to compare particle breakthrough with breakthrough of B.~subtilis spores. Particle counting has been suggested by some regulatory bodies as a real-time measure of in situ filtration performance; however, particle counting is a limited tool for assessing the efficacy of pathogen removal by in situ filtration because it is incapable of identifying discrete particles and can fail to detect microorganisms with refraction indexes close to that of water. Preliminary B.~subtilis removal data from the full scale RBF well and preliminary site characterization, particle count, and B.~subtilis removal data from the RBF test site are presented.

Study of support vector machine and serum surface-enhanced Raman spectroscopy for noninvasive esophageal cancer detection

NASA Astrophysics Data System (ADS)

Li, Shao-Xin; Zeng, Qiu-Yao; Li, Lin-Fang; Zhang, Yan-Jiao; Wan, Ming-Ming; Liu, Zhi-Ming; Xiong, Hong-Lian; Guo, Zhou-Yi; Liu, Song-Hao

2013-02-01

The ability of combining serum surface-enhanced Raman spectroscopy (SERS) with support vector machine (SVM) for improving classification esophageal cancer patients from normal volunteers is investigated. Two groups of serum SERS spectra based on silver nanoparticles (AgNPs) are obtained: one group from patients with pathologically confirmed esophageal cancer (n=30) and the other group from healthy volunteers (n=31). Principal components analysis (PCA), conventional SVM (C-SVM) and conventional SVM combination with PCA (PCA-SVM) methods are implemented to classify the same spectral dataset. Results show that a diagnostic accuracy of 77.0% is acquired for PCA technique, while diagnostic accuracies of 83.6% and 85.2% are obtained for C-SVM and PCA-SVM methods based on radial basis functions (RBF) models. The results prove that RBF SVM models are superior to PCA algorithm in classification serum SERS spectra. The study demonstrates that serum SERS in combination with SVM technique has great potential to provide an effective and accurate diagnostic schema for noninvasive detection of esophageal cancer.

Sympathetic regulation and anterior cingulate cortex volume are altered in a rat model of chronic back pain.

PubMed

Touj, Sara; Houle, Sébastien; Ramla, Djamel; Jeffrey-Gauthier, Renaud; Hotta, Harumi; Bronchti, Gilles; Martinoli, Maria-Grazia; Piché, Mathieu

2017-06-03

Chronic pain is associated with autonomic disturbance. However, specific effects of chronic back pain on sympathetic regulation remain unknown. Chronic pain is also associated with structural changes in the anterior cingulate cortex (ACC), which may be linked to sympathetic dysregulation. The aim of this study was to determine whether sympathetic regulation and ACC surface and volume are affected in a rat model of chronic back pain, in which complete Freund Adjuvant (CFA) is injected in back muscles. Sympathetic regulation was assessed with renal blood flow (RBF) changes induced by electrical stimulation of a hind paw, while ACC structure was examined by measuring cortical surface and volume. RBF changes and ACC volume were compared between control rats and rats injected with CFA in back muscles segmental (T10) to renal sympathetic innervation or not (T2). In rats with CFA, chronic inflammation was observed in the affected muscles in addition to increased nuclear factor-kappa B (NF-kB) protein expression in corresponding spinal cord segments (p=0.01) as well as decreased ACC volume (p<0.05). In addition, intensity-dependent decreases in RBF during hind paw stimulation were attenuated by chronic pain at T2 (p's<0.05) and T10 (p's<0.05), but less so at T10 compared with T2 (p's<0.05). These results indicate that chronic back pain alters sympathetic functions through non-segmental mechanisms, possibly by altering descending regulatory pathways from ACC. Yet, segmental somato-sympathetic reflexes may compete with non-segmental processes depending on the back region affected by pain and according to the segmental organization of the sympathetic nervous system. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Effect of additive renin inhibition with aliskiren on renal blood flow in patients with Chronic Heart Failure and Renal Dysfunction (Additive Renin Inhibition with Aliskiren on renal blood flow and Neurohormonal Activation in patients with Chronic Heart Failure and Renal Dysfunction).

PubMed

Schroten, Nicolas F; Damman, Kevin; Hemmelder, Marc H; Voors, Adriaan A; Navis, Gerjan; Gaillard, Carlo A J M; van Veldhuisen, Dirk J; Van Gilst, Wiek H; Hillege, Hans L

2015-05-01

We examined the effect of the renin inhibitor, aliskiren, on renal blood flow (RBF) in patients with heart failure with reduced ejection fraction (HFREF) and decreased glomerular filtration rate (GFR). Renal blood flow is the main determinant of GFR in HFREF patients. Both reduced GFR and RBF are associated with increased mortality. Aliskiren can provide additional renin-angiotensin-aldosterone system inhibition and increases RBF in healthy individuals. Patients with left ventricular ejection fraction ≤45% and estimated GFR 30 to 75 mL/min per 1.73 m(2) on optimal medical therapy were randomized 2:1 to receive aliskiren 300 mg once daily or placebo. Renal blood flow and GFR were measured using radioactive-labeled (125)I-iothalamate and (131)I-hippuran at baseline and 26 weeks. After 41 patients were included, the trial was halted based on an interim safety analysis showing futility. Mean age was 68 ± 9 years, 82% male, GFR (49 ± 16 mL/min per 1.73 m(2)), RBF (294 ± 77 mL/min per 1.73 m(2)), and NT-proBNP 999 (435-2040) pg/mL. There was a nonsignificant change in RBF after 26 weeks in the aliskiren group compared with placebo (-7.1 ± 30 vs +14 ± 54 mL/min per 1.73 m(2); P = .16). However, GFR decreased significantly in the aliskiren group compared with placebo (-2.8 ± 6.0 vs +4.4 ± 9.6 mL/min per 1.73 m(2); P = .01) as did filtration fraction (-2.2 ± 3.3 vs +1.1 ± 3.1%; P = .01). There were no significant differences in plasma aldosterone, NT-proBNP, urinary tubular markers, or adverse events. Plasma renin activity was markedly reduced in the aliskiren group versus placebo throughout the treatment phase (P = .007). Adding aliskiren on top of optimal HFREF medical therapy did not improve RBF and was associated with a reduction of GFR and filtration fraction. Copyright © 2015 Mosby, Inc. All rights reserved.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

NASA Astrophysics Data System (ADS)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Urea-Driven Epigallocatechin Gallate (EGCG) Permeation into the Ferritin Cage, an Innovative Method for Fabrication of Protein-Polyphenol Co-assemblies.

PubMed

Yang, Rui; Liu, Yuqian; Meng, Demei; Chen, Zhiyu; Blanchard, Christopher L; Zhou, Zhongkai

2017-02-22

The 8 nm diameter cavity endows the ferritin cage with a natural space to encapsulate food components. In this work, urea was explored as a novel medium to facilitate the formation of ferritin-polyphenol co-assemblies. Results indicated that urea (20 mM) could expand the 4-fold channel size of apo-red bean ferritin (apoRBF) with an increased initial iron release rate υ 0 (0.22 ± 0.02 μM min -1 ) and decreased α-helix content (5.6%). Moreover, urea (20 mM) could facilitate the permeation of EGCG into the apoRBF without destroying the ferritin structure and thus form ferritin-EGCG co-assemblies (FECs) with an encapsulation ratio and loading capacity of 17.6 and 2.1% (w/w), respectively. TEM exhibited that FECs maintained a spherical morphology with a 12 nm diameter in size. Fluorescence analysis showed that urea intervention could improve the binding constant K [(1.22 ± 0.8) × 10 4 M -1 ] of EGCG to apoRBF. Furthermore, the EGCG thermal stability was significantly improved (20-60 °C) compared with free EGCG. Additionally, this urea-involved method was applicable for chlorogenic acid and anthocyanin encapsulation by the apoRBF cage. Thus, urea shows potential as a novel potential medium to encapsulate and stabilize bioactive polyphenols for food usage based on the ferritin protein cage structure.

Relationship between retinal blood flow and arterial oxygen

PubMed Central

Cheng, Richard W.; Yusof, Firdaus; Tsui, Edmund; Jong, Monica; Duffin, James; Flanagan, John G.; Fisher, Joseph A.

2016-01-01

Key points Vascular reactivity, the response of the vessels to a vasoactive stimulus such as hypoxia and hyperoxia, can be used to assess the vascular range of adjustment in which the vessels are able to compensate for changes in PO2.Previous studies in the retina have not accurately quantified retinal vascular responses and precisely targeted multiple PaO2 stimuli at the same time as controlling the level of carbon dioxide, thus precluding them from modelling the relationship between retinal blood flow and oxygen.The present study modelled the relationship between retinal blood flow and PaO2, showing them to be a combined linear and hyperbolic function.This model demonstrates that the resting tonus of the vessels is at the mid‐point and that they have great vascular range of adjustment, compensating for decreases in oxygen above a P ETC O2 of 32–37 mmHg but being limited below this threshold. Abstract Retinal blood flow (RBF) increases in response to a reduction in oxygen (hypoxia) but decreases in response to increased oxygen (hyperoxia). However, the relationship between blood flow and the arterial partial pressure of oxygen has not been quantified and modelled in the retina, particularly in the vascular reserve and resting tonus of the vessels. The present study aimed to determine the limitations of the retinal vasculature by modelling the relationship between RBF and oxygen. Retinal vascular responses were measured in 13 subjects for eight different blood gas conditions, with the end‐tidal partial pressure of oxygen (P ETC O2) ranging from 40–500 mmHg. Retinal vascular response measurements were repeated twice; using the Canon laser blood flowmeter (Canon Inc., Tokyo, Japan) during the first visit and using Doppler spectral domain optical coherence tomography during the second visit. We determined that the relationship between RBF and PaO2 can be modelled as a combination of hyperbolic and linear functions. We concluded that RBF compensated for decreases in arterial oxygen content for all stages of hypoxia used in the present study but can no longer compensate below a P ETC O2 of 32–37 mmHg. These vessels have a great vascular range of adjustment, increasing diameter (8.5% arteriolar and 21% total venous area) with hypoxia (40 mmHg P ETC O2; P < 0.001) and decreasing diameter (6.9% arteriolar and 23% total venous area) with hyperoxia (500 mmHg P ETC O2; P < 0.001) to the same extent. This indicates that the resting tonus is near the mid‐point of the adjustment ranges at resting PaO2 where sensitivity is maximum. PMID:26607393

Relationship between retinal blood flow and arterial oxygen.

PubMed

Cheng, Richard W; Yusof, Firdaus; Tsui, Edmund; Jong, Monica; Duffin, James; Flanagan, John G; Fisher, Joseph A; Hudson, Chris

2016-02-01

Vascular reactivity, the response of the vessels to a vasoactive stimulus such as hypoxia and hyperoxia, can be used to assess the vascular range of adjustment in which the vessels are able to compensate for changes in PO2. Previous studies in the retina have not accurately quantified retinal vascular responses and precisely targeted multiple PaO2 stimuli at the same time as controlling the level of carbon dioxide, thus precluding them from modelling the relationship between retinal blood flow and oxygen. The present study modelled the relationship between retinal blood flow and PaO2, showing them to be a combined linear and hyperbolic function. This model demonstrates that the resting tonus of the vessels is at the mid-point and that they have great vascular range of adjustment, compensating for decreases in oxygen above a PETCO2 of 32-37 mmHg but being limited below this threshold. Retinal blood flow (RBF) increases in response to a reduction in oxygen (hypoxia) but decreases in response to increased oxygen (hyperoxia). However, the relationship between blood flow and the arterial partial pressure of oxygen has not been quantified and modelled in the retina, particularly in the vascular reserve and resting tonus of the vessels. The present study aimed to determine the limitations of the retinal vasculature by modelling the relationship between RBF and oxygen. Retinal vascular responses were measured in 13 subjects for eight different blood gas conditions, with the end-tidal partial pressure of oxygen (PETCO2) ranging from 40-500 mmHg. Retinal vascular response measurements were repeated twice; using the Canon laser blood flowmeter (Canon Inc., Tokyo, Japan) during the first visit and using Doppler spectral domain optical coherence tomography during the second visit. We determined that the relationship between RBF and PaO2 can be modelled as a combination of hyperbolic and linear functions. We concluded that RBF compensated for decreases in arterial oxygen content for all stages of hypoxia used in the present study but can no longer compensate below a PETCO2 of 32-37 mmHg. These vessels have a great vascular range of adjustment, increasing diameter (8.5% arteriolar and 21% total venous area) with hypoxia (40 mmHg P ETC O2; P < 0.001) and decreasing diameter (6.9% arteriolar and 23% total venous area) with hyperoxia (500 mmHg PETCO2; P < 0.001) to the same extent. This indicates that the resting tonus is near the mid-point of the adjustment ranges at resting PaO2 where sensitivity is maximum. © 2015 The Authors. The Journal of Physiology © 2015 The Physiological Society.

Use of neural image analysis methods in the process to determine the dry matter content in the compost

NASA Astrophysics Data System (ADS)

Wojcieszak, D.; Przybył, J.; Lewicki, A.; Ludwiczak, A.; Przybylak, A.; Boniecki, P.; Koszela, K.; Zaborowicz, M.; Przybył, K.; Witaszek, K.

2015-07-01

The aim of this research was investigate the possibility of using methods of computer image analysis and artificial neural networks for to assess the amount of dry matter in the tested compost samples. The research lead to the conclusion that the neural image analysis may be a useful tool in determining the quantity of dry matter in the compost. Generated neural model may be the beginning of research into the use of neural image analysis assess the content of dry matter and other constituents of compost. The presented model RBF 19:19-2-1:1 characterized by test error 0.092189 may be more efficient.

Study of water quality improvements during riverbank filtration at three midwestern United States drinking water utilities

NASA Astrophysics Data System (ADS)

Weiss, W.; Bouwer, E.; Ball, W.; O'Melia, C.; Lechevallier, M.; Arora, H.; Aboytes, R.; Speth, T.

2003-04-01

Riverbank filtration (RBF) is a process during which surface water is subjected to subsurface flow prior to extraction from wells. During infiltration and soil passage, surface water is subjected to a combination of physical, chemical, and biological processes such as filtration, dilution, sorption, and biodegradation that can significantly improve the raw water quality (Tufenkji et al, 2002; Kuehn and Mueller, 2000; Kivimaki et al, 1998; Stuyfzand, 1998). Transport through alluvial aquifers is associated with a number of water quality benefits, including removal of microbes, pesticides, total and dissolved organic carbon (TOC and DOC), nitrate, and other contaminants (Hiscock and Grischek, 2002; Tufenkji et al., 2002; Ray et al, 2002; Kuehn and Mueller, 2000; Doussan et al, 1997; Cosovic et al, 1996; Juttner, 1995; Miettinen et al, 1994). In comparison to most groundwater sources, alluvial aquifers that are hydraulically connected to rivers are typically easier to exploit (shallow) and more highly productive for drinking water supplies (Doussan et al, 1997). Increased applications of RBF are anticipated as drinking water utilities strive to meet increasingly stringent drinking water regulations, especially with regard to the provision of multiple barriers for protection against microbial pathogens, and with regard to tighter regulations for disinfection by-products (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs). In the above context, research was conducted to document the water quality benefits during RBF at three major river sources in the mid-western United States, specifically with regard to DBP precursor organic matter and microbial pathogens. Specific objectives were to: 1. Evaluate the merits of RBF for removing/controlling DBP precursors and certain other drinking water contaminants (e.g. microorganisms). 2. Evaluate whether RBF can improve finished drinking water quality by removing and/or altering natural organic matter (NOM) in a manner that is not otherwise accomplished through conventional processes of drinking water treatment (e.g. coagulation, flocculation, sedimentation). 3. Evaluate changes in the character of NOM upon ground passage from the river to the wells. The experimental approach entailed monitoring the performance of three different RBF systems along the Ohio, Wabash, and Missouri Rivers in the Midwestern United States and involved a cooperative effort between the American Water Works Company, Inc. and Johns Hopkins University. Samples of the river source waters and the bank-filtered well waters were analyzed for a range of water quality parameters including TOC, DOC, UV-absorbance at 254-nm (UV-254), biodegradable dissolved organic carbon (BDOC), biologically assimilable organic carbon (AOC), inorganic species, DBP formation potential, and microorganisms. In the second year of the project, river waters were subjected to a bench-scale conventional treatment train consisting of coagulation, flocculation, sedimentation, glass-fiber filtration, and ozonation. The treated river waters were compared with the bank-filtered waters in terms of TOC, DOC, UV-254, and DBP formation potential. In the third and fourth years of the project, NOM from the river and well waters was characterized using the XAD-8 resin adsorption fractionation method (Leenheer, 1981; Thurman &Malcolm, 1981). XAD-8 adsorbing (hydrophobic) and non-adsorbing (hydrophilic) fractions of the river and well waters were compared with respect to DOC, UV-254, and DBP formation potential to determine whether RBF alters the character of the source water NOM upon ground passage and if so, which fractions are preferentially removed. The results demonstrate the effectiveness of RBF at removing the organic precursors to potentially carcinogenic DBPs. When compared to a bench-scale conventional treatment train optimized for turbidity removal, RBF performed as well as the treatment at one of the sites and significantly better than the treatment at the other two sites in terms of removal of organic carbon and DBP precursor material. Removals of TOC and DOC upon RBF at the three sites generally ranged from 30 to 70% compared to 20 to 50% removals upon bench-scale treatment of the river waters. Reductions in precursor material for a variety of DBP precursors for trihalomethanes, haloacetic acids, haloacetonitriles, haloketones, chloral hydrate, and chloropicrin upon RBF ranged from 50 to 100% using both the formation potential (FP) and the uniform formation conditions (UFC) tests (Standard Methods, 1998; Summers et al., 1996), while reductions upon bench-scale treatment were generally in the range of 40 to 80%. The significantly higher reductions of the DBP precursors relative to those of TOC and DOC indicate a preferential reduction upon ground passage in the NOM that reacts with chlorine to form DBPs. Upon both bench-scale conventional treatment and RBF, a shift was observed in DBP formation from the chlorinated to the more brominated species due to the removal of DOC relative to bromide upon treatment or RBF. As DOC is removed, the bromide:DOC ratio increases, leading to the formation of more brominated DBPs. The shift was more pronounced upon RBF due to the generally higher reductions in DOC. UFC testing with a constant chlorine:DOC:bromide ratio ruled out the possibility of any significant preferential removal of the NOM precursor material for the more chlorinated DBPs. These results highlight the importance of the bromide ion in the formation of DBPs in drinking water, especially in light of the higher theoretical cancer risk associated with the brominated DBPs. Risk calculations demonstrated the ability of RBF to reduce the theoretical excess cancer risk due to THMs formed upon chlorination, in all cases, and with substantially better performance than the bench-scale treatment train. The characterization studies were carried out to evaluate whether the observed removals of DBP precursor material upon RBF reflected a preferential removal of NOM of particular character. The results of this study indicate that RBF appears to be equally capable of removing material of different character. The different removal mechanisms in the subsurface (e.g. sorption, biodegradation, filtration) combine to provide similar removal of the operationally defined hydrophilic and hydrophobic fractions of organic material upon ground passage. Thus, the reductions in DBP formation upon RBF observed during the first two phases of this research are largely the result of a decrease in the NOM concentration rather than a major shift in the NOM character. Preliminary monitoring of a number of microorganisms indicates that RBF may also serve as a significant barrier for the removal of microbial contaminants, including human pathogens. The monitoring data demonstrated >3 log removal of Clostridium spores and >2 log removal of bacteriophage. Assuming that these indicator organisms can be used as surrogates for Giardia cysts and human enteric viruses, RBF at the three study sites surpassed the performance requirements in the United States for conventional coagulation, sedimentation, and filtration (e.g., 2.5 log removal for Giardia cysts and 2.0 log removal of viruses). References Cosovic, D.; Hrsak, V.; Vojvodic, V.; &Krznaric, D., 1996. Transformation of organic matter and bank filtration from a polluted stream. Wat. Res., 30:12:2921. Doussan, C.; Poitevin, G.; Ledoux, E.; &Detay, M., 1997. River bank filtration: Modeling of the changes in water chemistry with emphasis on nitrogen species, J. Contam. Hydrol., 25:129. Hiscock, K.M. &Grischek, T., 2002. Attenuation of Groundwater Pollution by Bank Filtration. Jour. Hydrol., 266:139. Juttner, F., 1995. Elimination of Terpenoid Odorous Compounds by Slow Sand and River Bank Filtration of the Ruhr River, Germany. Wat. Sci. Tech., 31:11:211. Kivimaki, A-L.; Lahti, K.; Hatva, T.; Tuominen, S.M.; &Miettinen, I.T., 1998. Removal of organic matter during bank filtration. Artificial Recharge of Groundwater (J.H. Peters, editor). A.A. Balkema. Rotterdam, Netherlands; Brookfield, VT. Kuehn, W. &Mueller, U., 2000. Riverbank filtration: an overview. Jour. AWWA, 92:12:60. Leenheer, J.A., 1981. Comprehensive Approach to Preparative Isolation and Fractionation of Dissolved Organic Carbon from Natural Waters and Wastewaters. Environ. Sci. Technol., 15:5:578. Miettinen, I.T.; Martikainen, P.J.; &Vartiainen, T., 1994. Humus Transformation at the Bank Filtration Water Plant. Wat. Sci. Tech., 30:10:179. Ray, C.; Grischek, T.; Schubert, J.; Wang, J.Z.; &Speth, T.F., 2002. A perspective of riverbank filtration. Jour. AWWA, 94:4:149. Standard Methods for the Examination of Water and Wastewater, 1998 (20th ed.). APHA, AWWA, and WEF, Washington. Stuyfzand, P.J., 1998. Fate of pollutants during artificial recharge and bank filtration in the Netherlands. Artificial Recharge of Groundwater (J.H. Peters, editor). A.A. Balkema. Rotterdam, Netherlands; Brookfield, Vermont. Summers, R.S.; Hooper, S.M.; Shukairy, H.M.; Solarik, G.; &Owen, D., 1996. Assessing DBP Yield: Uniform Formation Conditions. Jour. AWWA, 88:6:80. Thurman, E.M. &Malcolm, R.L., 1981. Preparative Isolation of Aquatic Humic Substances. Environ. Sci. Technol., 15:4:463. Tufenkji, N.; Ryan, J.N.; &Elimelech, M., 2002. The Promise of Bank Filtration. Envir. Sci. &Technol., 36:21:423A.

Insights into multimodal imaging classification of ADHD

PubMed Central

Colby, John B.; Rudie, Jeffrey D.; Brown, Jesse A.; Douglas, Pamela K.; Cohen, Mark S.; Shehzad, Zarrar

2012-01-01

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD. PMID:22912605

Results-Based Financing in Mozambique's Central Medical Store: A Review After 1 Year.

PubMed

Spisak, Cary; Morgan, Lindsay; Eichler, Rena; Rosen, James; Serumaga, Brian; Wang, Angela

2016-03-01

Public health commodity supply chains are typically weak in low-income countries, partly because they have many disparate yet interdependent functions and components. Approaches to strengthening supply chains in such settings have often fallen short-they address technical weaknesses, but not the incentives that motivate staff to perform better. We reviewed the first year of a results-based financing (RBF) program in Mozambique, which began in January 2013. The program aimed to improve the performance of the central medical store-Central de Medicamentos e Artigos Medicos (CMAM)-by realigning incentives. We completed in-depth interviews and focus group discussions with 33 key informants, including representatives from CMAM and donor agencies, and collected quantitative data on performance measures and use of funds. The RBF agreement linked CMAM performance payments to quarterly results on 5 performance indicators related to supply planning, distribution planning, and warehouse management. RBF is predicated on the theory that a combination of carrot and stick-i.e., shared financial incentives, plus increased accountability for results-will spur changes in behavior. Important design elements: (1) indicators were measured against quarterly targets, and payments were made only for indicators that met those targets; (2) targets were set based on documented performance, at levels that could be reasonably attained, yet pushed for improvement; (3) payment was shared with and dependent on all staff, encouraging teamwork and collaboration; (4) results were validated by verifiable data sources; and (5) CMAM had discretion over how to use the funds. We found that CMAM's performance continually improved over baseline and that CMAM achieved many of its performance targets, for example, timely submission of quarterly supply and distribution planning reports. Warehouse indicators, such as inventory management and order fulfillment, proved more challenging but were nonetheless positive. By linking payments to periodic verified results, and giving CMAM discretion over how to spend the funds, the RBF agreement motivated the workforce; focused attention on results; strengthened data collection; encouraged teamwork and innovation; and ultimately strengthened the central supply chain. Policy makers and program managers can use performance incentives to catalyze and leverage existing investments. To further strengthen the approach, such incentive programs can shift attention from quantity to quality indicators, improve verification processes, and aim to institutionalize the approach. © Mukuria et al.

Results-Based Financing in Mozambique’s Central Medical Store: A Review After 1 Year

PubMed Central

Spisak, Cary; Morgan, Lindsay; Eichler, Rena; Rosen, James; Serumaga, Brian; Wang, Angela

2016-01-01

ABSTRACT Background: Public health commodity supply chains are typically weak in low-income countries, partly because they have many disparate yet interdependent functions and components. Approaches to strengthening supply chains in such settings have often fallen short—they address technical weaknesses, but not the incentives that motivate staff to perform better. Methods: We reviewed the first year of a results-based financing (RBF) program in Mozambique, which began in January 2013. The program aimed to improve the performance of the central medical store—Central de Medicamentos e Artigos Medicos (CMAM)—by realigning incentives. We completed in-depth interviews and focus group discussions with 33 key informants, including representatives from CMAM and donor agencies, and collected quantitative data on performance measures and use of funds. Implementation: The RBF agreement linked CMAM performance payments to quarterly results on 5 performance indicators related to supply planning, distribution planning, and warehouse management. RBF is predicated on the theory that a combination of carrot and stick—i.e., shared financial incentives, plus increased accountability for results—will spur changes in behavior. Important design elements: (1) indicators were measured against quarterly targets, and payments were made only for indicators that met those targets; (2) targets were set based on documented performance, at levels that could be reasonably attained, yet pushed for improvement; (3) payment was shared with and dependent on all staff, encouraging teamwork and collaboration; (4) results were validated by verifiable data sources; and (5) CMAM had discretion over how to use the funds. Findings: We found that CMAM’s performance continually improved over baseline and that CMAM achieved many of its performance targets, for example, timely submission of quarterly supply and distribution planning reports. Warehouse indicators, such as inventory management and order fulfillment, proved more challenging but were nonetheless positive. By linking payments to periodic verified results, and giving CMAM discretion over how to spend the funds, the RBF agreement motivated the workforce; focused attention on results; strengthened data collection; encouraged teamwork and innovation; and ultimately strengthened the central supply chain. Conclusion: Policy makers and program managers can use performance incentives to catalyze and leverage existing investments. To further strengthen the approach, such incentive programs can shift attention from quantity to quality indicators, improve verification processes, and aim to institutionalize the approach. PMID:27016552

Doubly stochastic radial basis function methods

NASA Astrophysics Data System (ADS)

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).

Application and evaluation of the washing effect in the collector well using pilot plant with washing device

NASA Astrophysics Data System (ADS)

Cho, K. H.; Kim, B. J.; Choi, N. C.; Lee, S. J.; Lee, B. H.

2012-04-01

Riverbed/bank filtration (RBF) is a natural process used as a first step in drinking water treatment. RBF systems consist of well fields that draw water from an aquifer that is hydraulically connected to surface waters. The benefits of RBF are multiple and include a reduction of turbidity, total coliform, microbial contaminants natural organic matter, and organic contaminants. Some of the disadvantages of RBF include the difficulty of preventing river water from infiltrating the aquifer in in-stances of severe river contamination, the geochemical reaction of the infiltrate with aquifer materials that may raise the aqueous concentrations of Fe2+, Mn2+, As, NH4+, CH4, Ca2+ and HCO3- , and clogging of the riverbed. For example, has demonstrated that riverbed clogging may decrease the specific capacity of RBF wells (flow reduction in the collector well etc.). The objective of this study is to optimization and evaluation the washing effect on various nozzle type and intervals, soil retention rate in the collector well using pilot plant with washing device for prevention flow reduction in the collector well. The Pilot plant experiments were conducted under various conditions; two kinds nozzle type (spray nozzle of circle type (single - Full Cone, multi - Hollow Cone) and spray nozzle of fan shape type (Veejet)), two different nozzle intervals (200 mm, 400mm) and a various soil retention rate in the collector well (10 ~ 40%). The results of experiment showed that in the nozzle type case, the washing effect of the veeject nozzle was more effective than other (Full Cone, Hollow Cone) nozzle through spray results (range, strength and height). In the nozzle interval conditions, washing effect is 200 mm better than 400 mm through spray distance and soil height. The washing efficiency in the collector well increased on soil retention rate decreased and the nozzle injection pressure increased using washing device

Measurement of arterial transit time and renal blood flow using pseudocontinuous ASL MRI with multiple post-labeling delays: Feasibility, reproducibility, and variation.

PubMed

Kim, Dong Won; Shim, Woo Hyun; Yoon, Seong Kuk; Oh, Jong Yeong; Kim, Jeong Kon; Jung, Hoesu; Matsuda, Tsuyoshi; Kim, Dongeun

2017-09-01

To evaluate the feasibility, reproducibility, and variation of renal perfusion and arterial transit time (ATT) using pseudocontinuous arterial spin labeling magnetic resonance imaging (PCASL MRI) in healthy volunteers. PCASL MRI at 3T was performed in 25 healthy volunteers on two different occasions. The ATT and ATT-corrected renal blood flow (ATT-cRBF) were calculated at four different post-labeling delay points (0.5, 1.0, 1.5, and 2.0 s) and evaluated for each kidney and subject. The intraclass correlation (ICC) and Bland-Altman plot were used to assess the reproducibility of the PCASL MRI technique. The within-subject coefficient of variance was determined. Results were obtained for 46 kidneys of 23 subjects with a mean age of 38.6 ± 9.8 years and estimated glomerular filtration rate (eGFR) of 89.1 ± 21.2 ml/min/1.73 m 2 . Two subjects failed in the ASL MRI examination. The mean cortical and medullary ATT-cRBF for the subjects were 215 ± 65 and 81 ± 21 ml/min/100 g, respectively, and the mean cortical and medullary ATT were 1141 ± 262 and 1123 ± 245 msec, correspondingly. The ICC for the cortical ATT-cRBF was 0.927 and the within-subject coefficient of variance was 14.4%. The ICCs for the medullary ATT-cRBF and the cortical and medullary ATT were poor. The Bland-Altman plot for cortical RBF showed good agreement between the two measurements. PCASL MRI is a feasible and reproducible method for measuring renal cortical perfusion. In contrast, ATT for the renal cortex and medulla has poor reproducibility and high variation. 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:813-819. © 2017 International Society for Magnetic Resonance in Medicine.

Improvement in Renal Hemodynamics following Combined Angiotensin II Infusion and AT1R Blockade in Aged Female Sheep following Fetal Unilateral Nephrectomy

PubMed Central

Singh, Reetu R.; Lankadeva, Yugeesh R.

2013-01-01

Renin-angiotensin system (RAS) is a powerful modulator of renal hemodynamic and fluid homeostasis. Up-regulation in components of intra-renal RAS occurs with ageing. Recently we reported that 2 year old uninephrectomised (uni-x) female sheep have low renin hypertension and reduced renal function. By 5 years of age, these uni-x sheep had augmented decrease in renal blood flow (RBF) compared to sham. We hypothesised that this decrease in RBF in 5 year old uni-x sheep was due to an up-regulation in components of the intra-renal RAS. In this study, renal responses to angiotensin II (AngII) infusion and AngII type 1 receptor (AT1R) blockade were examined in the same 5 year old sheep. We also administered AngII in the presence of losartan to increase AngII bioavailability to the AT2R in order to understand AT2R contribution to renal function in this model. Uni-x animals had significantly lower renal cortical content of renin, AngII (∼40%) and Ang 1–7 (∼60%) and reduced cortical expression of AT1R gene than sham animals. In response to both AngII infusion and AT1R blockade via losartan, renal hemodynamic responses and tubular sodium excretion were significantly attenuated in uni-x animals compared to sham. However, AngII infusion in the presence of losartan caused ∼33% increase in RBF in uni-x sheep compared to ∼14% in sham (P<0.05). This was associated with a significant decrease in renal vascular resistance in the uni-x animals (22% vs 15%, P<0.05) without any changes in systemic blood pressure. The present study shows that majority of the intra-renal RAS components are suppressed in this model of low renin hypertension. However, increasing the availability of AngII to AT2R by AT1R blockade improved renal blood flow in uni-x sheep. This suggests that manipulation of the AT2R maybe a potential therapeutic target for treatment of renal dysfunction associated with a congenital nephron deficit. PMID:23840884

Atherosclerotic renal artery stenosis is associated with elevated cell cycle arrest markers related to reduced renal blood flow and postcontrast hypoxia.

PubMed

Saad, Ahmed; Wang, Wei; Herrmann, Sandra M S; Glockner, James F; Mckusick, Michael A; Misra, Sanjay; Bjarnason, Haraldur; Lerman, Lilach O; Textor, Stephen C

2016-11-01

Atherosclerotic renal artery stenosis (ARAS) reduces renal blood flow (RBF), ultimately leading to kidney hypoxia and inflammation. Insulin-like growth factor binding protein-7 (IGFBP-7) and tissue inhibitor of metalloproteinases-2 (TIMP-2) are biomarkers of cell cycle arrest, often increased in ischemic conditions and predictive of acute kidney injury (AKI). This study sought to examine the relationships between renal vein levels of IGFBP-7, TIMP-2, reductions in RBF and postcontrast hypoxia as measured by blood oxygen level-dependent (BOLD) magnetic resonance imaging. Renal vein levels of IGFBP-7 and TIMP-2 were obtained in an ARAS cohort (n= 29) scheduled for renal artery stenting and essential hypertensive (EH) healthy controls (n = 32). Cortical and medullary RBFs were measured by multidetector computed tomography (CT) immediately before renal artery stenting and 3 months later. BOLD imaging was performed before and 3 months after stenting in all patients, and a subgroup (N = 12) underwent repeat BOLD imaging 24 h after CT/stenting to examine postcontrast/procedure levels of hypoxia. Preintervention IGFBP-7 and TIMP-2 levels were elevated in ARAS compared with EH (18.5 ± 2.0 versus 15.7 ± 1.5 and 97.4 ± 23.1 versus 62.7 ± 9.2 ng/mL, respectively; P< 0.0001); baseline IGFBP-7 correlated inversely with hypoxia developing 24 h after contrast injection (r = -0.73, P< 0.0001) and with prestent cortical blood flow (r = -0.59, P= 0.004). These data demonstrate elevated IGFBP-7 and TIMP-2 levels in ARAS as a function of the degree of reduced RBF. Elevated baseline IGFBP-7 levels were associated with protection against postimaging hypoxia, consistent with 'ischemic preconditioning'. Despite contrast injection and stenting, AKI in these high-risk ARAS subjects with elevated IGFBP-7/TIMP-2 was rare and did not affect long-term kidney function. © The Author 2016. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Simultaneous measurement of deep tissue blood flow and oxygenation using noncontact diffuse correlation spectroscopy flow-oximeter

PubMed Central

Li, Ting; Lin, Yu; Shang, Yu; He, Lian; Huang, Chong; Szabunio, Margaret; Yu, Guoqiang

2013-01-01

We report a novel noncontact diffuse correlation spectroscopy flow-oximeter for simultaneous quantification of relative changes in tissue blood flow (rBF) and oxygenation (Δ[oxygenation]). The noncontact probe was compared against a contact probe in tissue-like phantoms and forearm muscles (n = 10), and the dynamic trends in both rBF and Δ[oxygenation] were found to be highly correlated. However, the magnitudes of Δ[oxygenation] measured by the two probes were significantly different. Monte Carlo simulations and phantom experiments revealed that the arm curvature resulted in a significant underestimation (~−20%) for the noncontact measurements in Δ[oxygenation], but not in rBF. Other factors that may cause the residual discrepancies between the contact and noncontact measurements were discussed, and further comparisons with other established technologies are needed to identify/quantify these factors. Our research paves the way for noncontact and simultaneous monitoring of blood flow and oxygenation in soft and vulnerable tissues without distorting tissue hemodynamics. PMID:23446991

Effects of boundary conditions on the cleaning efficiency of riverbank filtration and artificial groundwater recharge systems regarding bulk parameters and trace pollutants.

PubMed

Storck, Florian R; Schmidt, Carsten K; Wülser, Richard; Brauch, Heinz-Jürgen

2012-01-01

Drinking water is often produced from surface water by riverbank filtration (RBF) or artificial groundwater recharge (AGR). In this study, an AGR system was exemplarily investigated and results were compared with those of RBF systems, in which the effects of redox milieu, temperature and surface water discharge on the cleaning efficiency were evaluated. Besides bulk parameters such as DOC (dissolved organic carbon), organic trace pollutants including iodinated X-ray contrast media, personal care products, complexing agents, and pharmaceuticals were investigated. At all studied sites, levels of TOC (total organic carbon), DOC, AOX (adsorbable organic halides), SAC (spectral absorption coefficient at 254 nm), and turbidity were reduced significantly. DOC removal was stimulated at higher groundwater temperatures during AGR. Several substances were generally easily removable during both AGR and RBF, regardless of the site, season, discharge or redox regime. For some more refractory substances, however, removal efficiency turned out to be significantly influenced by redox conditions.

[Clinical and experimental study of the production of renal hemodynamic effects of IABP-assisted pulsatile flow extracorporeal circulation].

PubMed

Moro, H

1992-01-01

Renal hemodynamics during IABP-assisted pulsatile flow extracorporeal circulation was assessed in terms of measurement values for intraoperative renal blood flow obtained by the local thermodilution method in human clinical patients. In addition, the effect of IABP on renal hemodynamics was investigated in an animal model of renal denervation in a study undertaken to elucidate the action mechanism of IABP. Eighteen patients with acquired heart disease were involved in the study and measured for the renal blood flow (RBF), cardiac output (CO), renal-systemic partition coefficient for blood flow (RBF/CO), renal vascular resistance (RVR) and perfusion pressure. In the pulsatile flow group, the RBF/CO increased as the number of pump runs increased, whole the RVR was conversely reduced with increasing pump runs. The experimental study without extracorporeal circulation was conducted on 19 mongrel dogs. During IABP runs RBF/CO increased, while the RVR decreased. After renal denervation, no noticeable influence of IABP upon renal hemodynamics was observed. Following a loading dose of noradrenaline (Norad), the RVR increased in a Norad concentration-dependent fashion, independently of IABP and renal denervation. These results indicate that IABP reduces the RVR and thereby exerts a favorable action on renal hemodynamics during pump times. The study thus warrants us to surmise that a mechanism involving the renal sympathetic nerves might play an important role in the production of favorable renal hemodynamic effects of IABP-assisted pulsatile flow extracorporeal circulation.

A Novel Therapy to Attenuate Acute Kidney Injury and Ischemic Allograft Damage after Allogenic Kidney Transplantation in Mice

PubMed Central

Gueler, Faikah; Shushakova, Nelli; Mengel, Michael; Hueper, Katja; Chen, Rongjun; Liu, Xiaokun; Park, Joon-Keun; Haller, Hermann

2015-01-01

Ischemia followed by reperfusion contributes to the initial damage to allografts after kidney transplantation (ktx). In this study we tested the hypothesis that a tetrapeptide EA-230 (AQGV), might improve survival and attenuate loss of kidney function in a mouse model of renal ischemia/reperfusion injury (IRI) and ischemia-induced delayed graft function after allogenic kidney transplantation. IRI was induced in male C57Bl/6N mice by transient bilateral renal pedicle clamping for 35 min. Treatment with EA-230 (20–50mg/kg twice daily i.p. for four consecutive days) was initiated 24 hours after IRI when acute kidney injury (AKI) was already established. The treatment resulted in markedly improved survival in a dose dependent manner. Acute tubular injury two days after IRI was diminished and tubular epithelial cell proliferation was significantly enhanced by EA-230 treatment. Furthermore, CTGF up-regulation, a marker of post-ischemic fibrosis, at four weeks after IRI was significantly less in EA-230 treated renal tissue. To learn more about these effects, we measured renal blood flow (RBF) and glomerular filtration rate (GFR) at 28 hours after IRI. EA-230 improved both GFR and RBF significantly. Next, EA-230 treatment was tested in a model of ischemia-induced delayed graft function after allogenic kidney transplantation. The recipients were treated with EA-230 (50 mg/kg) twice daily i.p. which improved renal function and allograft survival by attenuating ischemic allograft damage. In conclusion, EA-230 is a novel and promising therapeutic agent for treating acute kidney injury and preventing IRI-induced post-transplant ischemic allograft injury. Its beneficial effect is associated with improved renal perfusion after IRI and enhanced regeneration of tubular epithelial cells. PMID:25617900

A novel therapy to attenuate acute kidney injury and ischemic allograft damage after allogenic kidney transplantation in mice.

PubMed

Gueler, Faikah; Shushakova, Nelli; Mengel, Michael; Hueper, Katja; Chen, Rongjun; Liu, Xiaokun; Park, Joon-Keun; Haller, Hermann; Wensvoort, Gert; Rong, Song

2015-01-01

Ischemia followed by reperfusion contributes to the initial damage to allografts after kidney transplantation (ktx). In this study we tested the hypothesis that a tetrapeptide EA-230 (AQGV), might improve survival and attenuate loss of kidney function in a mouse model of renal ischemia/reperfusion injury (IRI) and ischemia-induced delayed graft function after allogenic kidney transplantation. IRI was induced in male C57Bl/6N mice by transient bilateral renal pedicle clamping for 35 min. Treatment with EA-230 (20-50mg/kg twice daily i.p. for four consecutive days) was initiated 24 hours after IRI when acute kidney injury (AKI) was already established. The treatment resulted in markedly improved survival in a dose dependent manner. Acute tubular injury two days after IRI was diminished and tubular epithelial cell proliferation was significantly enhanced by EA-230 treatment. Furthermore, CTGF up-regulation, a marker of post-ischemic fibrosis, at four weeks after IRI was significantly less in EA-230 treated renal tissue. To learn more about these effects, we measured renal blood flow (RBF) and glomerular filtration rate (GFR) at 28 hours after IRI. EA-230 improved both GFR and RBF significantly. Next, EA-230 treatment was tested in a model of ischemia-induced delayed graft function after allogenic kidney transplantation. The recipients were treated with EA-230 (50 mg/kg) twice daily i.p. which improved renal function and allograft survival by attenuating ischemic allograft damage. In conclusion, EA-230 is a novel and promising therapeutic agent for treating acute kidney injury and preventing IRI-induced post-transplant ischemic allograft injury. Its beneficial effect is associated with improved renal perfusion after IRI and enhanced regeneration of tubular epithelial cells.

Effect of Rice Bran and Wheat Fibers on Microbiological and Physicochemical Properties of Fermented Sausages during Ripening and Storage

PubMed Central

Jung, Ji-Taek; Lee, Jin-kyu; Choi, Yeong-Seok; Lee, Ju-Ho; Choi, Jung-Seok; Choi, Yang-Il; Chung, Yoon-Kyung

2018-01-01

Abstract This study investigated the effect of rice bran fiber (RBF) and wheat fibers (WF) on microbiological and physicochemical properties of fermented sausages during ripening and storage. The experimental design included three treatments: Control, no addition; RBF, 1.5%; and WF, 1.5%. During the ripening periods, the addition of dietary fibers rapidly decreased pH and maintained high water activity values of fermented sausages (p<0.05). Lactic acid bacteria were more prevalent in fermented sausages with rice bran fiber than control and sausages with added wheat fiber. During cold storage, lower pH was observed in sausages with dietary fibers (p<0.05), and the water activity and color values were reduced as the storage period lengthened. Fermented sausages containing dietary fibers were higher in lactic acid bacteria counts, volatile basic nitrogen and 2-thiobarbituric acid reactive substance values compared to the control (p<0.05). The results indicate that, the addition of dietary fibers in the fermented sausages promotes the growth of lactic bacteria and fermentation, and suggests that development of functional fermented sausages is possible. PMID:29805280

Potential of cancer screening with serum surface-enhanced Raman spectroscopy and a support vector machine

NASA Astrophysics Data System (ADS)

Li, S. X.; Zhang, Y. J.; Zeng, Q. Y.; Li, L. F.; Guo, Z. Y.; Liu, Z. M.; Xiong, H. L.; Liu, S. H.

2014-06-01

Cancer is the most common disease to threaten human health. The ability to screen individuals with malignant tumours with only a blood sample would be greatly advantageous to early diagnosis and intervention. This study explores the possibility of discriminating between cancer patients and normal subjects with serum surface-enhanced Raman spectroscopy (SERS) and a support vector machine (SVM) through a peripheral blood sample. A total of 130 blood samples were obtained from patients with liver cancer, colonic cancer, esophageal cancer, nasopharyngeal cancer, gastric cancer, as well as 113 blood samples from normal volunteers. Several diagnostic models were built with the serum SERS spectra using SVM and principal component analysis (PCA) techniques. The results show that a diagnostic accuracy of 85.5% is acquired with a PCA algorithm, while a diagnostic accuracy of 95.8% is obtained using radial basis function (RBF), PCA-SVM methods. The results prove that a RBF kernel PCA-SVM technique is superior to PCA and conventional SVM (C-SVM) algorithms in classification serum SERS spectra. The study demonstrates that serum SERS, in combination with SVM techniques, has great potential for screening cancerous patients with any solid malignant tumour through a peripheral blood sample.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

NASA Astrophysics Data System (ADS)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

Welding quality evaluation of resistance spot welding using the time-varying inductive reactance signal

NASA Astrophysics Data System (ADS)

Zhang, Hongjie; Hou, Yanyan; Yang, Tao; Zhang, Qian; Zhao, Jian

2018-05-01

In the spot welding process, a high alternating current is applied, resulting in a time-varying electromagnetic field surrounding the welder. When measuring the welding voltage signal, the impedance of the measuring circuit consists of two parts: dynamic resistance relating to weld nugget nucleation event and inductive reactance caused by mutual inductance. The aim of this study is to develop a method to acquire the dynamic reactance signal and to discuss the possibility of using this signal to evaluate the weld quality. For this purpose, a series of experiments were carried out. The reactance signals under different welding conditions were compared and the results showed that the morphological feature of the reactance signal was closely related to the welding current and it was also significantly influenced by some abnormal welding conditions. Some features were extracted from the reactance signal and combined to construct weld nugget strength and diameter prediction models based on the radial basis function (RBF) neural network. In addition, several features were also used to monitor the expulsion in the welding process by using Fisher linear discriminant analysis. The results indicated that using the dynamic reactance signal to evaluate weld quality is possible and feasible.

Estimation of electrical conductivity distribution within the human head from magnetic flux density measurement.

PubMed

Gao, Nuo; Zhu, S A; He, Bin

2005-06-07

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.

A disassembly-free method for evaluation of spiral bevel gear assembly

NASA Astrophysics Data System (ADS)

Jedliński, Łukasz; Jonak, Józef

2017-05-01

The paper presents a novel method for evaluation of assembly of spiral bevel gears. The examination of the approaches to the problem of gear control diagnostics without disassembly has revealed that residual processes in the form of vibrations (or noise) are currently the most suitable to this end. According to the literature, contact pattern is a complex parameter for describing gear position. Therefore, the task is to determine the correlation between contact pattern and gear vibrations. Although the vibration signal contains a great deal of information, it also has a complex spectral structure and contains interferences. For this reason, the proposed method has three variants which determine the effect of preliminary processing of the signal on the results. In Variant 2, stage 1, the vibration signal is subjected to multichannel denoising using a wavelet transform (WT), and in Variant 3 - to a combination of WT and principal component analysis (PCA). This denoising procedure does not occur in Variant 1. Next, we determine the features of the vibration signal in order to focus on information which is crucial regarding the objective of the study. Given the lack of unequivocal premises enabling selection of optimum features, we calculate twenty features, rank them and finally select the appropriate ones using an algorithm. Diagnostic rules were created using artificial neural networks. We investigated the suitability of three network types: multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM).

Control chart pattern recognition using RBF neural network with new training algorithm and practical features.

PubMed

Addeh, Abdoljalil; Khormali, Aminollah; Golilarz, Noorbakhsh Amiri

2018-05-04

The control chart patterns are the most commonly used statistical process control (SPC) tools to monitor process changes. When a control chart produces an out-of-control signal, this means that the process has been changed. In this study, a new method based on optimized radial basis function neural network (RBFNN) is proposed for control chart patterns (CCPs) recognition. The proposed method consists of four main modules: feature extraction, feature selection, classification and learning algorithm. In the feature extraction module, shape and statistical features are used. Recently, various shape and statistical features have been presented for the CCPs recognition. In the feature selection module, the association rules (AR) method has been employed to select the best set of the shape and statistical features. In the classifier section, RBFNN is used and finally, in RBFNN, learning algorithm has a high impact on the network performance. Therefore, a new learning algorithm based on the bees algorithm has been used in the learning module. Most studies have considered only six patterns: Normal, Cyclic, Increasing Trend, Decreasing Trend, Upward Shift and Downward Shift. Since three patterns namely Normal, Stratification, and Systematic are very similar to each other and distinguishing them is very difficult, in most studies Stratification and Systematic have not been considered. Regarding to the continuous monitoring and control over the production process and the exact type detection of the problem encountered during the production process, eight patterns have been investigated in this study. The proposed method is tested on a dataset containing 1600 samples (200 samples from each pattern) and the results showed that the proposed method has a very good performance. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Prediction of HDR quality by combining perceptually transformed display measurements with machine learning

NASA Astrophysics Data System (ADS)

Choudhury, Anustup; Farrell, Suzanne; Atkins, Robin; Daly, Scott

2017-09-01

We present an approach to predict overall HDR display quality as a function of key HDR display parameters. We first performed subjective experiments on a high quality HDR display that explored five key HDR display parameters: maximum luminance, minimum luminance, color gamut, bit-depth and local contrast. Subjects rated overall quality for different combinations of these display parameters. We explored two models | a physical model solely based on physically measured display characteristics and a perceptual model that transforms physical parameters using human vision system models. For the perceptual model, we use a family of metrics based on a recently published color volume model (ICT-CP), which consists of the PQ luminance non-linearity (ST2084) and LMS-based opponent color, as well as an estimate of the display point spread function. To predict overall visual quality, we apply linear regression and machine learning techniques such as Multilayer Perceptron, RBF and SVM networks. We use RMSE and Pearson/Spearman correlation coefficients to quantify performance. We found that the perceptual model is better at predicting subjective quality than the physical model and that SVM is better at prediction than linear regression. The significance and contribution of each display parameter was investigated. In addition, we found that combined parameters such as contrast do not improve prediction. Traditional perceptual models were also evaluated and we found that models based on the PQ non-linearity performed better.

Investigating the limitations of single breath-hold renal artery blood flow measurements using spiral phase contrast MR with R-R interval averaging.

PubMed

Steeden, Jennifer A; Muthurangu, Vivek

2015-04-01

1) To validate an R-R interval averaged golden angle spiral phase contrast magnetic resonance (RAGS PCMR) sequence against conventional cine PCMR for assessment of renal blood flow (RBF) in normal volunteers; and 2) To investigate the effects of motion and heart rate on the accuracy of flow measurements using an in silico simulation. In 20 healthy volunteers RAGS (∼6 sec breath-hold) and respiratory-navigated cine (∼5 min) PCMR were performed in both renal arteries to assess RBF. A simulation of RAGS PCMR was used to assess the effect of heart rate (30-105 bpm), vessel expandability (0-150%) and translational motion (x1.0-4.0) on the accuracy of RBF measurements. There was good agreement between RAGS and cine PCMR in the volunteer study (bias: 0.01 L/min, limits of agreement: -0.04 to +0.06 L/min, P = 0.0001). The simulation demonstrated a positive linear relationship between heart rate and error (r = 0.9894, P < 0.0001), a negative linear relationship between vessel expansion and error (r = -0.9484, P < 0.0001), and a nonlinear, heart rate-dependent relationship between vessel translation and error. We have demonstrated that RAGS PCMR accurately measures RBF in vivo. However, the simulation reveals limitations in this technique at extreme heart rates (<40 bpm, >100 bpm), or when there is significant motion (vessel expandability: >80%, vessel translation: >x2.2). © 2014 Wiley Periodicals, Inc.

Fluorescent microspheres as surrogates in evaluating the efficacy of riverbank filtration for removing Cryptosporidium parvum oocysts and other pathogens

USGS Publications Warehouse

Harvey, Ronald W.; Metge, David W.; Sheets, Rodney A.; Jasperse, Jay

2011-01-01

A major benefit of riverbank filtration (RBF) is that it provides a relatively effective means for pathogen removal. There is a need to conduct more injection-and-recovery transport studies at operating RBF sites in order to properly assess the combined effects of the site heterogeneities and ambient physicochemical conditions, which are difficult to replicate in the lab. For field transport studies involving pathogens, there is considerable interest in using fluorescent carboxylated microspheres (FCM) as surrogates, because they are chemically inert, negatively charged, easy to detect, available in a wide variety of sizes, and have been found to be nonhazardous in tracer applications. Although there have been a number of in-situ studies comparing the subsurface transport behaviors of FCM to those of bacteria and viruses, much less is known about their suitability for investigations of protozoa. Oocysts of the intestinal protozoan pathogen Cryptosporidium spp are of particular concern for many RBF operations because of their ubiquity and persistence in rivers and high resistance to chlorine disinfection. Although microspheres often have proven to be less-than-ideal analogs for capturing the abiotic transport behavior of viruses and bacteria, there is encouraging recent evidence regarding use of FCM as surrogates for C. parvum oocysts. This chapter discusses the potential of fluorescent microspheres as safe and easy-to-detect surrogates for evaluating the efficacy of RBF operations for removing pathogens, particularly Cryptosporidium, from source waters at different points along the flow path.

Role of Mas receptor antagonist (A779) in renal hemodynamics in condition of blocked angiotensin II receptors in rats.

PubMed

Mansoori, A; Oryan, S; Nematbakhsh, M

2016-03-01

The vasodilatory effect of angiotensin 1-7 (Ang 1-7) is exerted in the vascular bed via Mas receptor (MasR) gender dependently. However, the crosstalk between MasR and angiotensin II (Ang II) types 1 and 2 receptors (AT1R and AT2R) may change some actions of Ang 1-7 in renal circulation. In this study by blocking AT1R and AT2R, the role of MasR in kidney hemodynamics was described. In anaesthetized male and female Wistar rats, the effects of saline as vehicle and MasR blockade (A779) were tested on mean arterial pressure (MAP), renal perfusion pressure (RPP), renal blood flow (RBF), and renal vascular resistance (RVR) when both AT1R and AT2R were blocked by losartan and PD123319, respectively. In male rats, when AT1R and AT2R were blocked, there was a tendency for the increase in RBF/wet kidney tissue weight (RBF/KW) to be elevated by A779 as compared with the vehicle (P=0.08), and this was not the case in female rats. The impact of MasR on renal hemodynamics appears not to be sexual dimorphism either when Ang II receptors were blocked. It seems that co-blockade of all AT1R, AT2R, and MasR may alter RBF/ KW in male more than in female rats. These findings support a crosstalk between MasR and Ang II receptors in renal circulation.

Removal of indigenous coliphages and enteric viruses during riverbank filtration from highly polluted river water in Delhi (India).

PubMed

Sprenger, C; Lorenzen, G; Grunert, A; Ronghang, M; Dizer, H; Selinka, H-C; Girones, R; Lopez-Pila, J M; Mittal, A K; Szewzyk, R

2014-06-01

Emerging countries frequently afflicted by waterborne diseases require safe and cost-efficient production of drinking water, a task that is becoming more challenging as many rivers carry a high degree of pollution. A study was conducted on the banks of the Yamuna River, Delhi, India, to ascertain if riverbank filtration (RBF) can significantly improve the quality of the highly polluted surface water in terms of virus removal (coliphages, enteric viruses). Human adenoviruses and noroviruses, both present in the Yamuna River in the range of 10(5) genomes/100 mL, were undetectable after 50 m infiltration and approximately 119 days of underground passage. Indigenous somatic coliphages, used as surrogates of human pathogenic viruses, underwent approximately 5 log10 removal after only 3.8 m of RBF. The initial removal after 1 m was 3.3 log10, and the removal between 1 and 2.4 m and between 2.4 and 3.8 m was 0.7 log10 each. RBF is therefore an excellent candidate to improve the water situation in emerging countries with respect to virus removal.

The identification of high potential archers based on relative psychological coping skills variables: A Support Vector Machine approach

NASA Astrophysics Data System (ADS)

Taha, Zahari; Muazu Musa, Rabiu; Majeed, A. P. P. Abdul; Razali Abdullah, Mohamad; Aizzat Zakaria, Muhammad; Muaz Alim, Muhammad; Arif Mat Jizat, Jessnor; Fauzi Ibrahim, Mohamad

2018-03-01

Support Vector Machine (SVM) has been revealed to be a powerful learning algorithm for classification and prediction. However, the use of SVM for prediction and classification in sport is at its inception. The present study classified and predicted high and low potential archers from a collection of psychological coping skills variables trained on different SVMs. 50 youth archers with the average age and standard deviation of (17.0 ±.056) gathered from various archery programmes completed a one end shooting score test. Psychological coping skills inventory which evaluates the archers level of related coping skills were filled out by the archers prior to their shooting tests. k-means cluster analysis was applied to cluster the archers based on their scores on variables assessed. SVM models, i.e. linear and fine radial basis function (RBF) kernel functions, were trained on the psychological variables. The k-means clustered the archers into high psychologically prepared archers (HPPA) and low psychologically prepared archers (LPPA), respectively. It was demonstrated that the linear SVM exhibited good accuracy and precision throughout the exercise with an accuracy of 92% and considerably fewer error rate for the prediction of the HPPA and the LPPA as compared to the fine RBF SVM. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from the selected psychological coping skills variables examined which would consequently save time and energy during talent identification and development programme.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

NASA Astrophysics Data System (ADS)

Gorji, Taha; Sertel, Elif; Tanik, Aysegul

2017-12-01

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease.

PubMed

Mirinejad, Hossein; Gaweda, Adam E; Brier, Michael E; Zurada, Jacek M; Inanc, Tamer

2017-09-01

Anemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. A Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. Performance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. The proposed method is highly efficient for the control of hemoglobin level, yet provides accurate dosage adjustments in the treatment of CKD anemia. Copyright © 2017 Elsevier B.V. All rights reserved.

Autoregulation of renal blood flow in the conscious dog and the contribution of the tubuloglomerular feedback

PubMed Central

Just, Armin; Wittmann, Uwe; Ehmke, Heimo; Kirchheim, Hartmut R

1998-01-01

The aim of this study was to investigate the autoregulation of renal blood flow under physiological conditions, when challenged by the normal pressure fluctuations, and the contribution of the tubuloglomerular feedback (TGF). The transfer function between 0.0018 and 0.5 Hz was calculated from the spontaneous fluctuations in renal arterial blood pressure (RABP) and renal blood flow (RBF) in conscious resting dogs. The response of RBF to stepwise artificially induced reductions in RABP was also studied (stepwise autoregulation). Under control conditions (n = 12 dogs), the gain of the transfer function started to decrease, indicating improving autoregulation, below 0.06-0.15 Hz (t = 7-17 s). At 0.027 Hz a prominent peak of high gain was found. Below 0.01 Hz (t > 100 s), the gain reached a minimum (maximal autoregulation) of -6.3 ± 0.6 dB. The stepwise autoregulation (n = 4) was much stronger (-19.5 dB). The time delay of the transfer function was remarkably constant from 0.03 to 0.08 Hz (high frequency (HF) range) at 1.7 s and from 0.0034 to 0.01 Hz (low frequency (LF) range) at 14.3 s, respectively. Nifedipine, infused into the renal artery, abolished the stepwise autoregulation (-2.0 ± 1.1 dB, n = 3). The gain of the transfer function (n = 4) remained high down to 0.0034 Hz; in the LF range it was higher than in the control (0.3 ± 1.0 dB, P < 0.05). The time delay in the HF range was reduced to 0.5 s (P < 0.05). After ganglionic blockade (n = 7) no major changes in the transfer function were observed. Under furosemide (frusemide) (40 mg + 10 mg h−1 or 300 mg + 300 mg h−1 i.v.) the stepwise autoregulation was impaired to -7.8 ± 0.3 or -6.7 ± 1.9 dB, respectively (n = 4). In the transfer function (n = 7 or n = 4) the peak at 0.027 Hz was abolished. The delay in the LF range was reduced to -1.1 or -1.6 s, respectively. The transfer gain in the LF range (-5.5 ± 1.2 or -3.8 ± 0.8 dB, respectively) did not differ from the control but was smaller than that under nifedipine (P < 0.05). It is concluded that the ample capacity for regulation of RBF is only partially employed under physiological conditions. The abolition by nifedipine and the negligible effect of ganglionic blockade show that above 0.0034 Hz it is almost exclusively due to autoregulation by the kidney itself. TGF contributes to the maximum autoregulatory capacity, but it is not required for the level of autoregulation expended under physiological conditions. Around 0.027 Hz, TGF even reduces the degree of autoregulation. PMID:9481688

GRACE L1b inversion through a self-consistent modified radial basis function approach

NASA Astrophysics Data System (ADS)

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 separated from ocean dynamic effects, and our hypothesis is that in this way we improve the partitioning of the GRACE signals into land and ocean contributions along the coastline. In particular, we inspect the ice-melting over Greenland from real GRACE data, and we evaluate the ability of the MRBF approach to recover true mass variations along the coastline. Finally, using independent measurements from multiple techniques including GPS vertical motion and altimetry, a validation will be presented to quantify to what extent it is possible to reduce the leakage through the MRBF approach.

Design of the monitoring system at the Sant'Alessio induced riverbank filtration plant (Lucca, Italy)

NASA Astrophysics Data System (ADS)

Rossetto, Rudy; Barbagli, Alessio; Borsi, Iacopo; Mazzanti, Giorgio; Picciaia, Daniele; Vienken, Thomas; Bonari, Enrico

2015-04-01

In Managed Aquifer Recharge (MAR) schemes the monitoring system, for both water quality and quantity issues, plays a key role in assuring that a groundwater recharge plant is really managed. Considering induced Riverbank Filtration (RBF) schemes, while the effect of the augmented filtration consists in an improvement of the quality and quantity of the water infiltrating the aquifer, there is in turn the risk for groundwater contamination, as surface water bodies are highly susceptible to contamination. Within the framework of the MARSOL (2014) EU FPVII-ENV-2013 project, an experimental monitoring system has been designed and will be set in place at the Sant'Alessio RBF well field (Lucca, Italy) to demonstrate the sustainability and the benefits of managing induced RBF versus the unmanaged option. The RBF scheme in Sant'Alessio (Borsi et al. 2014) allows abstraction of an overall amount of about 0,5 m3/s groundwater providing drinking water for about 300000 people of the coastal Tuscany. Water is derived by ten vertical wells set along the Serchio River embankments inducing river water filtration into a high yield (10-2m2/s transmissivity) sand and gravel aquifer. Prior to the monitoring system design, a detailed site characterization has been completed taking advantage of previous and new investigations, the latter performed by means of MOSAIC on-site investigation platform (UFZ). A monitoring network has been set in place in the well field area using existing wells. There groundwater head and the main physico-chemical parameters (temperature, pH, dissolved oxygen, electrical conductivity and redox potential) are routinely monitored. Major geochemical compounds along with a large set of emerging pollutants are analysed (in cooperation with IWW Zentrum Wasser, Germany) both in surface-water and ground-water. The experimental monitoring system (including sensors in surface- and ground-water) has been designed focusing on managing abstraction efficiency and safety at one of the ten productive wells. The groundwater monitoring system consists of a set of six piezometer clusters drilled around a reference well along the main groundwater flowpaths. At each cluster, three piezometers (screened in the penultimate meter) are set at different depths to allow multilevel monitoring and sampling. At six selected piezometers, depending on ongoing hydrogeochemical investigations, six sensors for continuous monitoring of groundwater head, temperature and electrical conductivity will be set in operation. Within the Serchio River, two monitoring stations will be set in operation in order to monitor river head, water temperature and electrical conductivity upstream and downstream the experimental plot. A multi/parameter probe for the detection of selected analytes such nitrates, and selected organics to be defined will also be set in the Serchio River water. Each sensor will constitute a node of a Wireless Sensor Network (WSN). The WSN is based on several data loggers «client» connected via radio to one server point (Gateway), transmitting to a server via GSM-GPRS. This set up, while maintaining the high quality of data transmission, will allow to reduce installation and operational costs. The main characteristic of the conceived monitoring system is that sensors have been selected so to transmit data in an open format. The sensor network prototype will allow to get a substantial sensor cost reduction compared to available commercial solutions. The ultimate goal of this complex monitoring setting will be that of defining the minimum monitoring set up to guarantee efficiency and safety of groundwater withdrawals. Acknowledgements The authors wish to acknowledge GEAL spa for technical support and granting access to the well field. The activities described in this paper are co-financed within the framework of the EU FP7-ENV-2013-WATER-INNO-DEMO MARSOL (Grant Agreement n. 619120). References Borsi, I., Mazzanti, G., Barbagli, A., Rossetto, R., 2014. The riverbank filtration plant in S. Alessio (Lucca): monitoring and modeling activity within EU the FP7 MARSOL project. Acque Sotterranee - Italian Journal of Groundwater, Vol. 3, n. 3/137 MARSOL (2014). Demonstrating Managed Aquifer Recharge as a Solution to Water Scarcity and Drought www.marsol.eu [accessed 4 January 2015

The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Behrang, M.A.; Assareh, E.; Ghanbarzadeh, A.

2010-08-15

The main objective of present study is to predict daily global solar radiation (GSR) on a horizontal surface, based on meteorological variables, using different artificial neural network (ANN) techniques. Daily mean air temperature, relative humidity, sunshine hours, evaporation, and wind speed values between 2002 and 2006 for Dezful city in Iran (32 16'N, 48 25'E), are used in this study. In order to consider the effect of each meteorological variable on daily GSR prediction, six following combinations of input variables are considered: (I)Day of the year, daily mean air temperature and relative humidity as inputs and daily GSR as output.more » (II)Day of the year, daily mean air temperature and sunshine hours as inputs and daily GSR as output. (III)Day of the year, daily mean air temperature, relative humidity and sunshine hours as inputs and daily GSR as output. (IV)Day of the year, daily mean air temperature, relative humidity, sunshine hours and evaporation as inputs and daily GSR as output. (V)Day of the year, daily mean air temperature, relative humidity, sunshine hours and wind speed as inputs and daily GSR as output. (VI)Day of the year, daily mean air temperature, relative humidity, sunshine hours, evaporation and wind speed as inputs and daily GSR as output. Multi-layer perceptron (MLP) and radial basis function (RBF) neural networks are applied for daily GSR modeling based on six proposed combinations. The measured data between 2002 and 2005 are used to train the neural networks while the data for 214 days from 2006 are used as testing data. The comparison of obtained results from ANNs and different conventional GSR prediction (CGSRP) models shows very good improvements (i.e. the predicted values of best ANN model (MLP-V) has a mean absolute percentage error (MAPE) about 5.21% versus 10.02% for best CGSRP model (CGSRP 5)). (author)« less

The effect of feed water dissolved organic carbon concentration and composition on organic micropollutant removal and microbial diversity in soil columns simulating river bank filtration.

PubMed

Bertelkamp, C; van der Hoek, J P; Schoutteten, K; Hulpiau, L; Vanhaecke, L; Vanden Bussche, J; Cabo, A J; Callewaert, C; Boon, N; Löwenberg, J; Singhal, N; Verliefde, A R D

2016-02-01

This study investigated organic micropollutant (OMP) biodegradation rates in laboratory-scale soil columns simulating river bank filtration (RBF) processes. The dosed OMP mixture consisted of 11 pharmaceuticals, 6 herbicides, 2 insecticides and 1 solvent. Columns were filled with soil from a RBF site and were fed with four different organic carbon fractions (hydrophilic, hydrophobic, transphilic and river water organic matter (RWOM)). Additionally, the effect of a short-term OMP/dissolved organic carbon (DOC) shock-load (e.g. quadrupling the OMP concentrations and doubling the DOC concentration) on OMP biodegradation rates was investigated to assess the resilience of RBF systems. The results obtained in this study imply that - in contrast to what is observed for managed aquifer recharge systems operating on wastewater effluent - OMP biodegradation rates are not affected by the type of organic carbon fraction fed to the soil column, in case of stable operation. No effect of a short-term DOC shock-load on OMP biodegradation rates between the different organic carbon fractions was observed. This means that the RBF site simulated in this study is resilient towards transient higher DOC concentrations in the river water. However, a temporary OMP shock-load affected OMP biodegradation rates observed for the columns fed with the river water organic matter (RWOM) and the hydrophilic fraction of the river water organic matter. These different biodegradation rates did not correlate with any of the parameters investigated in this study (cellular adenosine triphosphate (cATP), DOC removal, specific ultraviolet absorbance (SUVA), richness/evenness of the soil microbial population or OMP category (hydrophobicity/charge). Copyright © 2015 Elsevier Ltd. All rights reserved.

Regulation of Synaptic Transmission by RAB-3 and RAB-27 in Caenorhabditis elegans

PubMed Central

Mahoney, Timothy R.; Liu, Qiang; Itoh, Takashi; Luo, Shuo; Hadwiger, Gayla; Vincent, Rose; Wang, Zhao-Wen; Fukuda, Mitsunori

2006-01-01

Rab small GTPases are involved in the transport of vesicles between different membranous organelles. RAB-3 is an exocytic Rab that plays a modulatory role in synaptic transmission. Unexpectedly, mutations in the Caenorhabditis elegans RAB-3 exchange factor homologue, aex-3, cause a more severe synaptic transmission defect as well as a defecation defect not seen in rab-3 mutants. We hypothesized that AEX-3 may regulate a second Rab that regulates these processes with RAB-3. We found that AEX-3 regulates another exocytic Rab, RAB-27. Here, we show that C. elegans RAB-27 is localized to synapse-rich regions pan-neuronally and is also expressed in intestinal cells. We identify aex-6 alleles as containing mutations in rab-27. Interestingly, aex-6 mutants exhibit the same defecation defect as aex-3 mutants. aex-6; rab-3 double mutants have behavioral and pharmacological defects similar to aex-3 mutants. In addition, we demonstrate that RBF-1 (rabphilin) is an effector of RAB-27. Therefore, our work demonstrates that AEX-3 regulates both RAB-3 and RAB-27, that both RAB-3 and RAB-27 regulate synaptic transmission, and that RAB-27 potentially acts through its effector RBF-1 to promote soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) function. PMID:16571673

Novel approach for dam break flow modeling using computational intelligence

NASA Astrophysics Data System (ADS)

Seyedashraf, Omid; Mehrabi, Mohammad; Akhtari, Ali Akbar

2018-04-01

A new methodology based on the computational intelligence (CI) system is proposed and tested for modeling the classic 1D dam-break flow problem. The reason to seek for a new solution lies in the shortcomings of the existing analytical and numerical models. This includes the difficulty of using the exact solutions and the unwanted fluctuations, which arise in the numerical results. In this research, the application of the radial-basis-function (RBF) and multi-layer-perceptron (MLP) systems is detailed for the solution of twenty-nine dam-break scenarios. The models are developed using seven variables, i.e. the length of the channel, the depths of the up-and downstream sections, time, and distance as the inputs. Moreover, the depths and velocities of each computational node in the flow domain are considered as the model outputs. The models are validated against the analytical, and Lax-Wendroff and MacCormack FDM schemes. The findings indicate that the employed CI models are able to replicate the overall shape of the shock- and rarefaction-waves. Furthermore, the MLP system outperforms RBF and the tested numerical schemes. A new monolithic equation is proposed based on the best fitting model, which can be used as an efficient alternative to the existing piecewise analytic equations.

Sustained resveratrol infusion increases natriuresis independent of renal vasodilation.

PubMed

Gordish, Kevin L; Beierwaltes, William H

2014-09-01

Resveratrol is reported to exert cardio-renal protective effects in animal models of pathology, yet the mechanisms underlying these effects are poorly understood. Previously, we reported an i.v. bolus of resveratrol induces renal vasodilation by increasing nitric oxide bioavailability and inhibiting reactive oxygen species. Thus, we hypothesized a sustained infusion of resveratrol would also increase renal blood flow (RBF), and additionally glomerular filtration rate (GFR). We infused vehicle for 30 min followed by 30 min resveratrol at either: 0, 0.5, 1.0, 1.5 mg/min, and measured RBF, renal vascular resistance (RVR), GFR, and urinary sodium excretion. At all three doses, blood pressure and GFR remained unchanged. Control RBF was 7.69 ± 0.84 mL/min/gkw and remained unchanged by 0.5 mg/min resveratrol (7.88 ± 0.94 mL/min/gkw, n = 9), but urinary sodium excretion increased from 2.19 ± 1.1 to 5.07 ± 0.92 μmol/min/gkw (n = 7, P < 0.01). In separate experiments, 1.0 mg/min resveratrol increased RBF by 17%, from 7.16 ± 0.29 to 8.35 ± 0.42 mL/min/gkw (P < 0.01, n = 10), decreased RVR 16% from 13.63 ± 0.65 to 11.36 ± 0.75 ARU (P < 0.003) and increased sodium excretion from 1.57 ± 0.46 to 3.10 ± 0.80 μmol/min/gkw (n = 7, P < 0.04). At the 1.5 mg/min dose, resveratrol increased RBF 12% from 6.76 ± 0.57 to 7.58 ± 0.60 mL/min/gkw (n = 8, P < 0.003), decreased RVR 15% (15.58 ± 1.35 to 13.27 ± 1.14 ARU, P < 0.003) and increased sodium excretion (3.99 ± 1.71 to 7.80 ± 1.51 μmol/min/gkw, n = 8, P < 0.04). We conclude that a constant infusion of resveratrol can induce significant renal vasodilation while not altering GFR or blood pressure. Also, resveratrol infusion produced significant natriuresis at all doses, suggesting it may have a direct effect on renal tubular sodium handling independent of renal perfusion pressure or flow. © 2014 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society.

Resveratrol induces acute endothelium-dependent renal vasodilation mediated through nitric oxide and reactive oxygen species scavenging

PubMed Central

Gordish, Kevin L.

2014-01-01

Resveratrol is suggested to have beneficial cardiovascular and renoprotective effects. Resveratrol increases endothelial nitric oxide synthase (eNOS) expression and nitric oxide (NO) synthesis. We hypothesized resveratrol acts as an acute renal vasodilator, mediated through increased NO production and scavenging of reactive oxygen species (ROS). In anesthetized rats, we found 5.0 mg/kg body weight (bw) of resveratrol increased renal blood flow (RBF) by 8% [from 6.98 ± 0.42 to 7.54 ± 0.17 ml·min−1·gram of kidney weight−1 (gkw); n = 8; P < 0.002] and decreased renal vascular resistance (RVR) by 18% from 15.00 ± 1.65 to 12.32 ± 1.20 arbitrary resistance units (ARU; P < 0.002). To test the participation of NO, we administered 5.0 mg/kg bw resveratrol before and after 10 mg/kg bw of the NOS inhibitor N-nitro-l-arginine methyl ester (l-NAME). l-NAME reduced the increase in RBF to resveratrol by 54% (from 0.59 ± 0.05 to 0.27 ± 0.06 ml·min−1·gkw−1; n = 10; P < 0.001). To test the participation of ROS, we gave 5.0 mg/kg bw resveratrol before and after 1 mg/kg bw tempol, a superoxide dismutase mimetic. Resveratrol increased RBF 7.6% (from 5.91 ± 0.32 to 6.36 ± 0.12 ml·min−1·gkw−1; n = 7; P < 0.001) and decreased RVR 19% (from 18.83 ± 1.37 to 15.27 ± 1.37 ARU). Tempol blocked resveratrol-induced increase in RBF (from 0.45 ± 0.12 to 0.10 ± 0.05 ml·min−1·gkw−1; n = 7; P < 0.03) and the decrease in RVR posttempol was 44% of the control response (3.56 ± 0.34 vs. 1.57 ± 0.21 ARU; n = 7; P < 0.006). We also tested the role of endothelium-derived prostanoids. Two days of 10 mg/kg bw indomethacin pretreatment did not alter basal blood pressure or RBF. Resveratrol-induced vasodilation remained unaffected. We conclude intravenous resveratrol acts as an acute renal vasodilator, partially mediated by increased NO production/NO bioavailability and superoxide scavenging but not by inducing vasodilatory cyclooxygenase products. PMID:24431202

Renoprotective effects of hepatocyte growth factor in the stenotic kidney

PubMed Central

Stewart, Nicholas

2013-01-01

Renal microvascular (MV) damage and loss contribute to the progression of renal injury in renal artery stenosis (RAS). Hepatocyte growth factor (HGF) is a powerful angiogenic and antifibrotic cytokine that we showed to be decreased in the stenotic kidney. We hypothesized that renal HGF therapy will improve renal function mainly by protecting the renal microcirculation. Unilateral RAS was induced in 15 pigs. Six weeks later, single-kidney RBF and GFR were quantified in vivo using multidetector computed tomography (CT). Then, intrarenal rh-HGF or vehicle was randomly administered into the stenotic kidney (RAS, n = 8; RAS+HGF, n = 7). Pigs were observed for 4 additional weeks before CT studies were repeated. Renal MV density was quantified by 3D micro-CT ex vivo and histology, and expression of angiogenic and inflammatory factors, apoptosis, and fibrosis was determined. HGF therapy improved RBF and GFR compared with vehicle-treated pigs. This was accompanied by improved renal expression of angiogenic cytokines (VEGF, p-Akt) and tissue-healing promoters (SDF-1, CXCR4, MMP-9), reduced MV remodeling, apoptosis, and fibrosis, and attenuated renal inflammation. However, HGF therapy did not improve renal MV density, which was similarly reduced in RAS and RAS+HGF compared with controls. Using a clinically relevant animal model of RAS, we showed novel therapeutic effects of a targeted renal intervention. Our results show distinct actions on the existing renal microcirculation and promising renoprotective effects of HGF therapy in RAS. Furthermore, these effects imply plasticity of the stenotic kidney to recuperate its function and underscore the importance of MV integrity in the progression of renal injury in RAS. PMID:23269649

Study on for soluble solids contents measurement of grape juice beverage based on Vis/NIRS and chemomtrics

NASA Astrophysics Data System (ADS)

Wu, Di; He, Yong

2007-11-01

The aim of this study is to investigate the potential of the visible and near infrared spectroscopy (Vis/NIRS) technique for non-destructive measurement of soluble solids contents (SSC) in grape juice beverage. 380 samples were studied in this paper. Smoothing way of Savitzky-Golay and standard normal variate were applied for the pre-processing of spectral data. Least-squares support vector machines (LS-SVM) with RBF kernel function was applied to developing the SSC prediction model based on the Vis/NIRS absorbance data. The determination coefficient for prediction (Rp2) of the results predicted by LS-SVM model was 0. 962 and root mean square error (RMSEP) was 0. 434137. It is concluded that Vis/NIRS technique can quantify the SSC of grape juice beverage fast and non-destructively.. At the same time, LS-SVM model was compared with PLS and back propagation neural network (BP-NN) methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SSC of grape juice beverage. In this study, the generation ability of LS-SVM, PLS and BP-NN models were also investigated. It is concluded that LS-SVM regression method is a promising technique for chemometrics in quantitative prediction.

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

NASA Astrophysics Data System (ADS)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Classification of right-hand grasp movement based on EMOTIV Epoc+

NASA Astrophysics Data System (ADS)

Tobing, T. A. M. L.; Prawito, Wijaya, S. K.

2017-07-01

Combinations of BCT elements for right-hand grasp movement have been obtained, providing the average value of their classification accuracy. The aim of this study is to find a suitable combination for best classification accuracy of right-hand grasp movement based on EEG headset, EMOTIV Epoc+. There are three movement classifications: grasping hand, relax, and opening hand. These classifications take advantage of Event-Related Desynchronization (ERD) phenomenon that makes it possible to differ relaxation, imagery, and movement state from each other. The combinations of elements are the usage of Independent Component Analysis (ICA), spectrum analysis by Fast Fourier Transform (FFT), maximum mu and beta power with their frequency as features, and also classifier Probabilistic Neural Network (PNN) and Radial Basis Function (RBF). The average values of classification accuracy are ± 83% for training and ± 57% for testing. To have a better understanding of the signal quality recorded by EMOTIV Epoc+, the result of classification accuracy of left or right-hand grasping movement EEG signal (provided by Physionet) also be given, i.e.± 85% for training and ± 70% for testing. The comparison of accuracy value from each combination, experiment condition, and external EEG data are provided for the purpose of value analysis of classification accuracy.

Renal Heme Oxygenase-1 Induction with Hemin Augments Renal Hemodynamics, Renal Autoregulation, and Excretory Function

PubMed Central

Botros, Fady T.; Dobrowolski, Leszek; Navar, L. Gabriel

2012-01-01

Heme oxygenases (HO-1; HO-2) catalyze conversion of heme to free iron, carbon monoxide, and biliverdin/bilirubin. To determine the effects of renal HO-1 induction on blood pressure and renal function, normal control rats (n = 7) and hemin-treated rats (n = 6) were studied. Renal clearance studies were performed on anesthetized rats to assess renal function; renal blood flow (RBF) was measured using a transonic flow probe placed around the left renal artery. Hemin treatment significantly induced renal HO-1. Mean arterial pressure and heart rate were not different (115 ± 5 mmHg versus 112 ± 4 mmHg and 331 ± 16 versus 346 ± 10 bpm). However, RBF was significantly higher (9.1 ± 0.8 versus 7.0 ± 0.5 mL/min/g, P < 0.05), and renal vascular resistance was significantly lower (13.0 ± 0.9 versus 16.6 ± 1.4 [mmHg/(mL/min/g)], P < 0.05). Likewise, glomerular filtration rate was significantly elevated (1.4 ± 0.2 versus 1.0 ± 0.1 mL/min/g, P < 0.05), and urine flow and sodium excretion were also higher (18.9 ± 3.9 versus 8.2 ± 1.0 μL/min/g, P < 0.05 and 1.9 ± 0.6 versus 0.2 ± 0.1 μmol/min/g, P < 0.05, resp.). The plateau of the autoregulation relationship was elevated, and renal vascular responses to acute angiotensin II infusion were attenuated in hemin-treated rats reflecting the vasodilatory effect of HO-1 induction. We conclude that renal HO-1 induction augments renal function which may contribute to the antihypertensive effects of HO-1 induction observed in hypertension models. PMID:22518281

Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques.

PubMed

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

2018-05-22

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

New data clustering for RBF classifier of agriculture products from x-ray images

NASA Astrophysics Data System (ADS)

Casasent, David P.; Chen, Xuewen

1999-08-01

Classification of real-time x-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a subsystem for automated non-invasive detection of defective product items on a conveyor belt. We discuss the use of clustering and how it is vital to achieve useful classification. New clustering methods using class identify and new cluster classes are advanced and shown to be of use for this application. Radial basis function neural net classifiers are emphasized. We expect our results to be of use for other classifiers and applications.

Support vector machine multiuser receiver for DS-CDMA signals in multipath channels.

PubMed

Chen, S; Samingan, A K; Hanzo, L

2001-01-01

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

Noncontact diffuse optical assessment of blood flow changes in head and neck free tissue transfer flaps

NASA Astrophysics Data System (ADS)

Huang, Chong; Radabaugh, Jeffrey P.; Aouad, Rony K.; Lin, Yu; Gal, Thomas J.; Patel, Amit B.; Valentino, Joseph; Shang, Yu; Yu, Guoqiang

2015-07-01

Knowledge of tissue blood flow (BF) changes after free tissue transfer may enable surgeons to predict the failure of flap thrombosis at an early stage. This study used our recently developed noncontact diffuse correlation spectroscopy to monitor dynamic BF changes in free flaps without getting in contact with the targeted tissue. Eight free flaps were elevated in patients with head and neck cancer; one of the flaps failed. Multiple BF measurements probing the transferred tissue were performed during and post the surgical operation. Postoperative BF values were normalized to the intraoperative baselines (assigning "1") for the calculation of relative BF change (rBF). The rBF changes over the seven successful flaps were 1.89±0.15, 2.26±0.13, and 2.43±0.13 (mean±standard error), respectively, on postoperative days 2, 4, and 7. These postoperative values were significantly higher than the intraoperative baseline values (p<0.001), indicating a gradual recovery of flap vascularity after the tissue transfer. By contrast, rBF changes observed from the unsuccessful flaps were 1.14 and 1.34, respectively, on postoperative days 2 and 4, indicating less flow recovery. Measurement of BF recovery after flap anastomosis holds the potential to act early to salvage ischemic flaps.

Laser Range and Bearing Finder for Autonomous Missions

NASA Technical Reports Server (NTRS)

Granade, Stephen R.

2004-01-01

NASA has recently re-confirmed their interest in autonomous systems as an enabling technology for future missions. In order for autonomous missions to be possible, highly-capable relative sensor systems are needed to determine an object's distance, direction, and orientation. This is true whether the mission is autonomous in-space assembly, rendezvous and docking, or rover surface navigation. Advanced Optical Systems, Inc. has developed a wide-angle laser range and bearing finder (RBF) for autonomous space missions. The laser RBF has a number of features that make it well-suited for autonomous missions. It has an operating range of 10 m to 5 km, with a 5 deg field of view. Its wide field of view removes the need for scanning systems such as gimbals, eliminating moving parts and making the sensor simpler and space qualification easier. Its range accuracy is 1% or better. It is designed to operate either as a stand-alone sensor or in tandem with a sensor that returns range, bearing, and orientation at close ranges, such as NASA's Advanced Video Guidance Sensor. We have assembled the initial prototype and are currently testing it. We will discuss the laser RBF's design and specifications. Keywords: laser range and bearing finder, autonomous rendezvous and docking, space sensors, on-orbit sensors, advanced video guidance sensor

Noncontact diffuse optical assessment of blood flow changes in head and neck free tissue transfer flaps

PubMed Central

Huang, Chong; Radabaugh, Jeffrey P.; Aouad, Rony K.; Lin, Yu; Gal, Thomas J.; Patel, Amit B.; Valentino, Joseph; Shang, Yu; Yu, Guoqiang

2015-01-01

Abstract. Knowledge of tissue blood flow (BF) changes after free tissue transfer may enable surgeons to predict the failure of flap thrombosis at an early stage. This study used our recently developed noncontact diffuse correlation spectroscopy to monitor dynamic BF changes in free flaps without getting in contact with the targeted tissue. Eight free flaps were elevated in patients with head and neck cancer; one of the flaps failed. Multiple BF measurements probing the transferred tissue were performed during and post the surgical operation. Postoperative BF values were normalized to the intraoperative baselines (assigning “1”) for the calculation of relative BF change (rBF). The rBF changes over the seven successful flaps were 1.89±0.15, 2.26±0.13, and 2.43±0.13 (mean±standard error), respectively, on postoperative days 2, 4, and 7. These postoperative values were significantly higher than the intraoperative baseline values (p<0.001), indicating a gradual recovery of flap vascularity after the tissue transfer. By contrast, rBF changes observed from the unsuccessful flaps were 1.14 and 1.34, respectively, on postoperative days 2 and 4, indicating less flow recovery. Measurement of BF recovery after flap anastomosis holds the potential to act early to salvage ischemic flaps. PMID:26187444

New models to predict depth of infiltration in endometrial carcinoma based on transvaginal sonography.

PubMed

De Smet, F; De Brabanter, J; Van den Bosch, T; Pochet, N; Amant, F; Van Holsbeke, C; Moerman, P; De Moor, B; Vergote, I; Timmerman, D

2006-06-01

Preoperative knowledge of the depth of myometrial infiltration is important in patients with endometrial carcinoma. This study aimed at assessing the value of histopathological parameters obtained from an endometrial biopsy (Pipelle de Cornier; results available preoperatively) and ultrasound measurements obtained after transvaginal sonography with color Doppler imaging in the preoperative prediction of the depth of myometrial invasion, as determined by the final histopathological examination of the hysterectomy specimen (the gold standard). We first collected ultrasound and histopathological data from 97 consecutive women with endometrial carcinoma and divided them into two groups according to surgical stage (Stages Ia and Ib vs. Stages Ic and higher). The areas (AUC) under the receiver-operating characteristics curves of the subjective assessment of depth of invasion by an experienced gynecologist and of the individual ultrasound parameters were calculated. Subsequently, we used these variables to train a logistic regression model and least squares support vector machines (LS-SVM) with linear and RBF (radial basis function) kernels. Finally, these models were validated prospectively on data from 76 new patients in order to make a preoperative prediction of the depth of invasion. Of all ultrasound parameters, the ratio of the endometrial and uterine volumes had the largest AUC (78%), while that of the subjective assessment was 79%. The AUCs of the blood flow indices were low (range, 51-64%). Stepwise logistic regression selected the degree of differentiation, the number of fibroids, the endometrial thickness and the volume of the tumor. Compared with the AUC of the subjective assessment (72%), prospective evaluation of the mathematical models resulted in a higher AUC for the LS-SVM model with an RBF kernel (77%), but this difference was not significant. Single morphological parameters do not improve the predictive power when compared with the subjective assessment of depth of myometrial invasion of endometrial cancer, and blood flow indices do not contribute to the prediction of stage. In this study an LS-SVM model with an RBF kernel gave the best prediction; while this might be more reliable than subjective assessment, confirmation by larger prospective studies is required. Copyright 2006 ISUOG. Published by John Wiley & Sons, Ltd.

Reversal of renal dysfunction by targeted administration of VEGF into the stenotic kidney: a novel potential therapeutic approach.

PubMed

Chade, Alejandro R; Kelsen, Silvia

2012-05-15

Renal microvascular (MV) damage and loss contribute to the progression of renal injury in renovascular disease (RVD). Whether a targeted intervention in renal microcirculation could reverse renal damage is unknown. We hypothesized that intrarenal vascular endothelial growth factor (VEGF) therapy will reverse renal dysfunction and decrease renal injury in experimental RVD. Unilateral renal artery stenosis (RAS) was induced in 14 pigs, as a surrogate of chronic RVD. Six weeks later, renal blood flow (RBF) and glomerular filtration rate (GFR) were quantified in vivo in the stenotic kidney using multidetector computed tomography (CT). Then, intrarenal rhVEGF-165 or vehicle was randomly administered into the stenotic kidneys (n = 7/group), they were observed for 4 additional wk, in vivo studies were repeated, and then renal MV density was quantified by 3D micro-CT, and expression of angiogenic factors and fibrosis was determined. RBF and GFR, MV density, and renal expression of VEGF and downstream mediators such as p-ERK 1/2, Akt, and eNOS were significantly reduced after 6 and at 10 wk of untreated RAS compared with normal controls. Remarkably, administration of VEGF at 6 wk normalized RBF (from 393.6 ± 50.3 to 607.0 ± 45.33 ml/min, P < 0.05 vs. RAS) and GFR (from 43.4 ± 3.4 to 66.6 ± 10.3 ml/min, P < 0.05 vs. RAS) at 10 wk, accompanied by increased angiogenic signaling, augmented renal MV density, and attenuated renal scarring. This study shows promising therapeutic effects of a targeted renal intervention, using an established clinically relevant large-animal model of chronic RAS. It also implies that disruption of renal MV integrity and function plays a pivotal role in the progression of renal injury in the stenotic kidney. Furthermore, it shows a high level of plasticity of renal microvessels to a single-dose VEGF-targeted intervention after established renal injury, supporting promising renoprotective effects of a novel potential therapeutic intervention to treat chronic RVD.

Reversal of renal dysfunction by targeted administration of VEGF into the stenotic kidney: a novel potential therapeutic approach

PubMed Central

Kelsen, Silvia

2012-01-01

Renal microvascular (MV) damage and loss contribute to the progression of renal injury in renovascular disease (RVD). Whether a targeted intervention in renal microcirculation could reverse renal damage is unknown. We hypothesized that intrarenal vascular endothelial growth factor (VEGF) therapy will reverse renal dysfunction and decrease renal injury in experimental RVD. Unilateral renal artery stenosis (RAS) was induced in 14 pigs, as a surrogate of chronic RVD. Six weeks later, renal blood flow (RBF) and glomerular filtration rate (GFR) were quantified in vivo in the stenotic kidney using multidetector computed tomography (CT). Then, intrarenal rhVEGF-165 or vehicle was randomly administered into the stenotic kidneys (n = 7/group), they were observed for 4 additional wk, in vivo studies were repeated, and then renal MV density was quantified by 3D micro-CT, and expression of angiogenic factors and fibrosis was determined. RBF and GFR, MV density, and renal expression of VEGF and downstream mediators such as p-ERK 1/2, Akt, and eNOS were significantly reduced after 6 and at 10 wk of untreated RAS compared with normal controls. Remarkably, administration of VEGF at 6 wk normalized RBF (from 393.6 ± 50.3 to 607.0 ± 45.33 ml/min, P < 0.05 vs. RAS) and GFR (from 43.4 ± 3.4 to 66.6 ± 10.3 ml/min, P < 0.05 vs. RAS) at 10 wk, accompanied by increased angiogenic signaling, augmented renal MV density, and attenuated renal scarring. This study shows promising therapeutic effects of a targeted renal intervention, using an established clinically relevant large-animal model of chronic RAS. It also implies that disruption of renal MV integrity and function plays a pivotal role in the progression of renal injury in the stenotic kidney. Furthermore, it shows a high level of plasticity of renal microvessels to a single-dose VEGF-targeted intervention after established renal injury, supporting promising renoprotective effects of a novel potential therapeutic intervention to treat chronic RVD. PMID:22357917

Noninvasive Imaging of Retinal Morphology and Microvasculature in Obese Mice Using Optical Coherence Tomography and Optical Microangiography

PubMed Central

Zhi, Zhongwei; Chao, Jennifer R.; Wietecha, Tomasz; Hudkins, Kelly L.; Alpers, Charles E.; Wang, Ruikang K.

2014-01-01

Purpose. To evaluate early diabetes-induced changes in retinal thickness and microvasculature in a type 2 diabetic mouse model by using optical coherence tomography (OCT)/optical microangiography (OMAG). Methods. Twenty-two-week-old obese (OB) BTBR mice (n = 10) and wild-type (WT) control mice (n = 10) were imaged. Three-dimensional (3D) data volumes were captured with spectral domain OCT using an ultrahigh-sensitive OMAG scanning protocol for 3D volumetric angiography of the retina and dense A-scan protocol for measurement of the total retinal blood flow (RBF) rate. The thicknesses of the nerve fiber layer (NFL) and that of the NFL to the inner plexiform layer (IPL) were measured and compared between OB and WT mice. The linear capillary densities within intermediate and deep capillary layers were determined by the number of capillaries crossing a 500-μm line. The RBF rate was evaluated using an en face Doppler approach. These quantitative measurements were compared between OB and WT mice. Results. The retinal thickness of the NFL to IPL was significantly reduced in OB mice (P < 0.01) compared to that in WT mice, whereas the NFL thickness between the two was unchanged. 3D depth-resolved OMAG angiography revealed the first in vivo 3D model of mouse retinal microcirculation. Although no obvious differences in capillary vessel densities of the intermediate and deep capillary layers were detected between normal and OB mice, the total RBF rate was significantly lower (P < 0.05) in OB mice than in WT mice. Conclusions. We conclude that OB BTBR mice have significantly reduced NFL–IPL thicknesses and total RBF rates compared with those of WT mice, as imaged by OCT/OMAG. OMAG provides an unprecedented capability for high-resolution depth-resolved imaging of mouse retinal vessels and blood flow that may play a pivotal role in providing a noninvasive method for detecting early microvascular changes in patients with diabetic retinopathy. PMID:24458155

Activation of GLP-1 receptors on vascular smooth muscle cells reduces the autoregulatory response in afferent arterioles and increases renal blood flow.

PubMed

Jensen, Elisa P; Poulsen, Steen S; Kissow, Hannelouise; Holstein-Rathlou, Niels-Henrik; Deacon, Carolyn F; Jensen, Boye L; Holst, Jens J; Sorensen, Charlotte M

2015-04-15

Glucagon-like peptide (GLP)-1 has a range of extrapancreatic effects, including renal effects. The mechanisms are poorly understood, but GLP-1 receptors have been identified in the kidney. However, the exact cellular localization of the renal receptors is poorly described. The aim of the present study was to localize renal GLP-1 receptors and describe GLP-1-mediated effects on the renal vasculature. We hypothesized that renal GLP-1 receptors are located in the renal microcirculation and that activation of these affects renal autoregulation and increases renal blood flow. In vivo autoradiography using (125)I-labeled GLP-1, (125)I-labeled exendin-4 (GLP-1 analog), and (125)I-labeled exendin 9-39 (GLP-1 receptor antagonist) was performed in rodents to localize specific GLP-1 receptor binding. GLP-1-mediated effects on blood pressure, renal blood flow (RBF), heart rate, renin secretion, urinary flow rate, and Na(+) and K(+) excretion were investigated in anesthetized rats. Effects of GLP-1 on afferent arterioles were investigated in isolated mouse kidneys. Specific binding of (125)I-labeled GLP-1, (125)I-labeled exendin-4, and (125)I-labeled exendin 9-39 was observed in the renal vasculature, including afferent arterioles. Infusion of GLP-1 increased blood pressure, RBF, and urinary flow rate significantly in rats. Heart rate and plasma renin concentrations were unchanged. Exendin 9-39 inhibited the increase in RBF. In isolated murine kidneys, GLP-1 and exendin-4 significantly reduced the autoregulatory response of afferent arterioles in response to stepwise increases in pressure. We conclude that GLP-1 receptors are located in the renal vasculature, including afferent arterioles. Activation of these receptors reduces the autoregulatory response of afferent arterioles to acute pressure increases and increases RBF in normotensive rats. Copyright © 2015 the American Physiological Society.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

NASA Astrophysics Data System (ADS)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Meshless Method with Operator Splitting Technique for Transient Nonlinear Bioheat Transfer in Two-Dimensional Skin Tissues

PubMed Central

Zhang, Ze-Wei; Wang, Hui; Qin, Qing-Hua

2015-01-01

A meshless numerical scheme combining the operator splitting method (OSM), the radial basis function (RBF) interpolation, and the method of fundamental solutions (MFS) is developed for solving transient nonlinear bioheat problems in two-dimensional (2D) skin tissues. In the numerical scheme, the nonlinearity caused by linear and exponential relationships of temperature-dependent blood perfusion rate (TDBPR) is taken into consideration. In the analysis, the OSM is used first to separate the Laplacian operator and the nonlinear source term, and then the second-order time-stepping schemes are employed for approximating two splitting operators to convert the original governing equation into a linear nonhomogeneous Helmholtz-type governing equation (NHGE) at each time step. Subsequently, the RBF interpolation and the MFS involving the fundamental solution of the Laplace equation are respectively employed to obtain approximated particular and homogeneous solutions of the nonhomogeneous Helmholtz-type governing equation. Finally, the full fields consisting of the particular and homogeneous solutions are enforced to fit the NHGE at interpolation points and the boundary conditions at boundary collocations for determining unknowns at each time step. The proposed method is verified by comparison of other methods. Furthermore, the sensitivity of the coefficients in the cases of a linear and an exponential relationship of TDBPR is investigated to reveal their bioheat effect on the skin tissue. PMID:25603180

Meshless method with operator splitting technique for transient nonlinear bioheat transfer in two-dimensional skin tissues.

PubMed

Zhang, Ze-Wei; Wang, Hui; Qin, Qing-Hua

2015-01-16

A meshless numerical scheme combining the operator splitting method (OSM), the radial basis function (RBF) interpolation, and the method of fundamental solutions (MFS) is developed for solving transient nonlinear bioheat problems in two-dimensional (2D) skin tissues. In the numerical scheme, the nonlinearity caused by linear and exponential relationships of temperature-dependent blood perfusion rate (TDBPR) is taken into consideration. In the analysis, the OSM is used first to separate the Laplacian operator and the nonlinear source term, and then the second-order time-stepping schemes are employed for approximating two splitting operators to convert the original governing equation into a linear nonhomogeneous Helmholtz-type governing equation (NHGE) at each time step. Subsequently, the RBF interpolation and the MFS involving the fundamental solution of the Laplace equation are respectively employed to obtain approximated particular and homogeneous solutions of the nonhomogeneous Helmholtz-type governing equation. Finally, the full fields consisting of the particular and homogeneous solutions are enforced to fit the NHGE at interpolation points and the boundary conditions at boundary collocations for determining unknowns at each time step. The proposed method is verified by comparison of other methods. Furthermore, the sensitivity of the coefficients in the cases of a linear and an exponential relationship of TDBPR is investigated to reveal their bioheat effect on the skin tissue.

Influence of the rate of infusion on cyclosporine nephrotoxicity in the rat.

PubMed

Finn, W F; McCormack, A J; Sullivan, B A; Hak, L J; Clark, R L

1989-01-01

The effect of the rate of infusion of single and multiple doses of cyclosporine (CsA) on renal function was evaluated in Sprague-Dawley rats. CsA was dissolved in cremophore (Crem) or Tween 80 (Tween) and infused over consecutive 10-min periods at doses of 10, 20, 30 and 40 mg/kg. CsA-Crem and CsA-Tween produced similar and progressive changes in MAP, RBF, and RVR. By the end of the infusion, the mean values (% of control) of MAP (122 +/- 16% and 131 +/- 22%), RBF (56 +/- 11% and 66 +/- 20%), and RVR (222 +/- 38% and 232 +/- 134%) were significantly different from their respective preinfusion values. Infusion of Crem alone resulted in renal vasodilation at low doses and renal vasoconstriction at high doses. Vasoconstriction was not produced by infusion of Tween alone. In addition, animals were treated with vehicle alone (Gp 1), CsA 10 mg/kg/day by injection (Gp 2), or CsA 20 mg/kg/day by i.v. infusion over 4 hr (Gp 3), and were studied at 1 week. Systemic toxicity was greater with the 4-hr infusion as judged by an increase in MAP. The mean values of MAP were 107 +/- 8 (Gp 1), 101 +/- 13 (Gp 2), and 135 +/- 5 mm Hg (Gp 3; p less than 0.05). However, renal function was less severely affected with the 4-hr infusion. The mean values of CIn were 434 +/- 99 (Gp 1), 298 +/- 101 (Gp 2; p less than 0.05), and 425 +/- 114 microL/min/100 g BW (Gp 3); and the mean values for RBF were 2.72 +/- 0.74 (Gp 1), 2.08 +/- 0.17 (Gp 2; p less than 0.05), and 3.35 +/- 0.61 mL/min/100 g BW (Gp 3), respectively. Microangiograms showed marked abnormalities in the intrarenal perfusion pattern in the rats injected with CsA, 10 mg/kg BW. In rats infused over 4 hr with CsA, 20 mg/kg BW, the microangiographic pattern was normal. These studies demonstrate that the acute hemodynamic effects of CsA are directly related to the rate of infusion. Furthermore, the renal toxicity which follows repetitive injection of CsA can be minimized or avoided by administering CsA as a slow infusion. In addition to the total dose administered, the rate of infusion is an important determinant of nephrotoxicity.

Data Assimilation on a Quantum Annealing Computer: Feasibility and Scalability

NASA Astrophysics Data System (ADS)

Nearing, G. S.; Halem, M.; Chapman, D. R.; Pelissier, C. S.

2014-12-01

Data assimilation is one of the ubiquitous and computationally hard problems in the Earth Sciences. In particular, ensemble-based methods require a large number of model evaluations to estimate the prior probability density over system states, and variational methods require adjoint calculations and iteration to locate the maximum a posteriori solution in the presence of nonlinear models and observation operators. Quantum annealing computers (QAC) like the new D-Wave housed at the NASA Ames Research Center can be used for optimization and sampling, and therefore offers a new possibility for efficiently solving hard data assimilation problems. Coding on the QAC is not straightforward: a problem must be posed as a Quadratic Unconstrained Binary Optimization (QUBO) and mapped to a spherical Chimera graph. We have developed a method for compiling nonlinear 4D-Var problems on the D-Wave that consists of five steps: Emulating the nonlinear model and/or observation function using radial basis functions (RBF) or Chebyshev polynomials. Truncating a Taylor series around each RBF kernel. Reducing the Taylor polynomial to a quadratic using ancilla gadgets. Mapping the real-valued quadratic to a fixed-precision binary quadratic. Mapping the fully coupled binary quadratic to a partially coupled spherical Chimera graph using ancilla gadgets. At present the D-Wave contains 512 qbits (with 1024 and 2048 qbit machines due in the next two years); this machine size allows us to estimate only 3 state variables at each satellite overpass. However, QAC's solve optimization problems using a physical (quantum) system, and therefore do not require iterations or calculation of model adjoints. This has the potential to revolutionize our ability to efficiently perform variational data assimilation, as the size of these computers grows in the coming years.

Reconstructing the magnetosphere from data using radial basis functions

NASA Astrophysics Data System (ADS)

Andreeva, Varvara A.; Tsyganenko, Nikolai A.

2016-03-01

A new method is proposed to derive from data magnetospheric magnetic field configurations without any a priori assumptions on the geometry of electric currents. The approach utilizes large sets of archived satellite data and uses an advanced technique to represent the field as a sum of toroidal and poloidal parts, whose generating potentials Ψ1 and Ψ2 are expanded into series of radial basis functions (RBFs) with their nodes regularly distributed over the 3-D modeling domain. The method was tested by reconstructing the inner and high-latitude field within geocentric distances up to 12RE on the basis of magnetometer data of Geotail, Polar, Cluster, Time History of Events and Macroscale Interactions during Substorms, and Van Allen space probes, taken during 1995-2015. Four characteristic states of the magnetosphere before and during a disturbance have been modeled: a quiet prestorm period, storm deepening phase with progressively decreasing SYM-H index, the storm maximum around the negative peak of SYM-H, and the recovery phase. Fitting the RBF model to data faithfully resolved contributions to the total magnetic field from all principal sources, including the westward and eastward ring current, the tail current, diamagnetic currents associated with the polar cusps, and the large-scale effect of the field-aligned currents. For two main phase conditions, the model field exhibited a strong dawn-dusk asymmetry of the low-latitude magnetic depression, extending to low altitudes and partly spreading sunward from the terminator plane in the dusk sector. The RBF model was found to resolve even finer details, such as the bifurcation of the innermost tail current. The method can be further developed into a powerful tool for data-based studies of the magnetospheric currents.

High-NaCl diet impairs dynamic renal blood flow autoregulation in rats with adenine-induced chronic renal failure.

PubMed

Saeed, Aso; DiBona, Gerald F; Grimberg, Elisabeth; Nguy, Lisa; Mikkelsen, Minne Line Nedergaard; Marcussen, Niels; Guron, Gregor

2014-03-15

This study examined the effects of 2 wk of high-NaCl diet on kidney function and dynamic renal blood flow autoregulation (RBFA) in rats with adenine-induced chronic renal failure (ACRF). Male Sprague-Dawley rats received either chow containing adenine or were pair-fed an identical diet without adenine (controls). After 10 wk, rats were randomized to either remain on the same diet (0.6% NaCl) or to be switched to high 4% NaCl chow. Two weeks after randomization, renal clearance experiments were performed under isoflurane anesthesia and dynamic RBFA, baroreflex sensitivity (BRS), systolic arterial pressure variability (SAPV), and heart rate variability were assessed by spectral analytical techniques. Rats with ACRF showed marked reductions in glomerular filtration rate and renal blood flow (RBF), whereas mean arterial pressure and SAPV were significantly elevated. In addition, spontaneous BRS was reduced by ∼50% in ACRF animals. High-NaCl diet significantly increased transfer function fractional gain values between arterial pressure and RBF in the frequency range of the myogenic response (0.06-0.09 Hz) only in ACRF animals (0.3 ± 4.0 vs. -4.4 ± 3.8 dB; P < 0.05). Similarly, a high-NaCl diet significantly increased SAPV in the low-frequency range only in ACRF animals. To conclude, a 2-wk period of a high-NaCl diet in ACRF rats significantly impaired dynamic RBFA in the frequency range of the myogenic response and increased SAPV in the low-frequency range. These abnormalities may increase the susceptibility to hypertensive end-organ injury and progressive renal failure by facilitating pressure transmission to the microvasculature.

Towards automatic musical instrument timbre recognition

NASA Astrophysics Data System (ADS)

Park, Tae Hong

This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer

PubMed Central

Gabere, Musa Nur; Hussein, Mohamed Aly; Aziz, Mohammad Azhar

2016-01-01

Purpose There has been considerable interest in using whole-genome expression profiles for the classification of colorectal cancer (CRC). The selection of important features is a crucial step before training a classifier. Methods In this study, we built a model that uses support vector machine (SVM) to classify cancer and normal samples using Affymetrix exon microarray data obtained from 90 samples of 48 patients diagnosed with CRC. From the 22,011 genes, we selected the 20, 30, 50, 100, 200, 300, and 500 genes most relevant to CRC using the minimum-redundancy–maximum-relevance (mRMR) technique. With these gene sets, an SVM model was designed using four different kernel types (linear, polynomial, radial basis function [RBF], and sigmoid). Results The best model, which used 30 genes and RBF kernel, outperformed other combinations; it had an accuracy of 84% for both ten fold and leave-one-out cross validations in discriminating the cancer samples from the normal samples. With this 30 genes set from mRMR, six classifiers were trained using random forest (RF), Bayes net (BN), multilayer perceptron (MLP), naïve Bayes (NB), reduced error pruning tree (REPT), and SVM. Two hybrids, mRMR + SVM and mRMR + BN, were the best models when tested on other datasets, and they achieved a prediction accuracy of 95.27% and 91.99%, respectively, compared to other mRMR hybrid models (mRMR + RF, mRMR + NB, mRMR + REPT, and mRMR + MLP). Ingenuity pathway analysis was used to analyze the functions of the 30 genes selected for this model and their potential association with CRC: CDH3, CEACAM7, CLDN1, IL8, IL6R, MMP1, MMP7, and TGFB1 were predicted to be CRC biomarkers. Conclusion This model could be used to further develop a diagnostic tool for predicting CRC based on gene expression data from patient samples. PMID:27330311

Renal Autoregulation: New Perspectives Regarding the Protective and Regulatory Roles of the Underlying Mechanisms

PubMed Central

Loutzenhiser, Rodger; Griffin, Karen; Williamson, Geoffrey; Bidani, Anil

2006-01-01

When the kidney is subjected to acute increases in blood pressure (BP), renal blood flow (RBF) and glomerular filtration rate (GFR) are observed to remain relatively constant. Two mechanisms, tubuloglomerular feedback (TGF) and the myogenic response, are thought to act in concert to achieve a precise moment-by-moment regulation of GFR and distal salt delivery. The current view is that this mechanism insulates renal excretory function from fluctuations in BP. Indeed, the concept that renal autoregulation is necessary for normal renal function and volume homeostasis has long been a cornerstone of renal physiology. This article presents a very different view, at least in regard to the myogenic component of this response. We suggest that its primary purpose is to protect the kidney against the damaging effects of hypertension. The arguments advanced take into consideration the unique properties of the afferent arteriolar myogenic response that allow it to protect against the oscillating systolic pressure, and the accruing evidence that when this response is impaired the primary consequence is not a disturbed volume homeostasis, but rather an increased susceptibility to hypertensive injury. It is suggested that redundant and compensatory mechanisms are capable of achieving volume regulation despite considerable fluctuations in distal delivery and the assumed moment-by-moment regulation of renal hemodynamics is questioned. Evidence is presented suggesting that additional mechanisms may exist to maintain ambient levels of RBF and GFR within normal range despite chronic alterations in BP and severely impaired acute responses to pressure. Finally the implications of this new perspective on the divergent roles of the renal myogenic response to pressure versus the TGF response to changes in distal delivery are considered and it is proposed that, in addition to TGF-induced vasoconstrictor responses, vasodepressor responses to reduced distal delivery may play a more critical role in modulating afferent arteriolar reactivity, in order to integrate the regulatory and protective functions of the renal microvasculature. PMID:16603656

[Terahertz Spectroscopic Identification with Deep Belief Network].

PubMed

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

2015-12-01

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

Removal of iron and manganese using granular activated carbon and zeolite in artificial barrier of riverbank filtration

NASA Astrophysics Data System (ADS)

Ismail, Abustan; Harmuni, Halim; Mohd, Remy Rozainy M. A. Z.

2017-04-01

Iron and Manganese was examined from riverbank filtration (RBF) and river water in Sungai Kerian, Lubok Buntar, Serdang Kedah. Water from the RBF was influenced by geochemical and hydro chemical processes in the aquifer that made concentrations of iron (Fe), and manganese (Mn) high, and exceeded the standard values set by the Malaysia Ministry of Health. Therefore, in order to overcome the problem, the artificial barrier was proposed to improve the performance of the RBF. In this study, the capability and performance of granular activated carbon, zeolite and sand were investigated in this research. The effects of dosage, shaking speed, pH and contact time on removal of iron and manganese were studied to determine the best performance. For the removal of iron using granular activated carbon (GAC) and zeolite, the optimum contact time was at 2 hours with 200rpm shaking speed with 5g and 10g at pH 5 with percentage removal of iron was 87.81% and 83.20% respectively. The removal of manganese and zeolite arose sharply in 75 minutes with 90.21% removal, with 100rpm shaking speed. The GAC gave the best performance with 99.39% removal of manganese. The highest removal of manganese was achieved when the adsorbent dosage increased to 10g with shaking speed of 200rpm.

Magnetic resonance elastography can monitor changes in medullary stiffness in response to treatment in the swine ischemic kidney

PubMed Central

Zhang, Xin; Zhu, Xiangyang; Ferguson, Christopher M.; Jiang, Kai; Burningham, Tyson; Lerman, Amir; Lerman, Lilach O.

2018-01-01

Object Low-energy shockwave (SW) therapy attenuates damage in the stenotic kidney (STK) caused by atherosclerotic renal artery stenosis (ARAS). We hypothesized that magnetic resonance elastography (MRE) would detect attenuation of fibrosis following SW in unilateral ARAS kidneys. Materials and Methods Domestic pigs were randomized to control, unilateral ARAS, and ARAS treated with 6 sessions of SW over 3 consecutive weeks (n=7 each) starting after 3 weeks of ARAS or sham. Four weeks after SW treatment, renal fibrosis was evaluated with MRE in-vivo or trichrome staining ex-vivo. Blood pressure, single-kidney renal-blood-flow (RBF) and glomerular-filtration-rate (GFR) were assessed. Results MRE detected increased stiffness in the STK medulla (15.3±2.1 vs. 10.1±0.8 kPa, p<0.05) that moderately correlated with severity of fibrosis (R2=0.501, p<0.01), but did not identify mild STK cortical or contralateral kidney fibrosis. Trichrome staining showed that medullary fibrosis was increased in ARAS and alleviated by SW (10.4±1.8% vs. 2.9±0.2%, p<0.01). SW slightly decreased blood pressure and normalized STK RBF and GFR in ARAS. In the contralateral kidney, SW reversed the increase in RBF and GFR. Conclusion MRE might be a tool for noninvasive monitoring of medullary fibrosis in response to treatment in kidney disease. PMID:29289980

[Rapid identification of hogwash oil by using synchronous fluorescence spectroscopy].

PubMed

Sun, Yan-Hui; An, Hai-Yang; Jia, Xiao-Li; Wang, Juan

2012-10-01

To identify hogwash oil quickly, the characteristic delta lambda of hogwash oil was analyzed by three dimensional fluorescence spectroscopy with parallel factor analysis, and the model was built up by using synchronous fluorescence spectroscopy with support vector machines (SVM). The results showed that the characteristic delta lambda of hogwash oil was 60 nm. Collecting original spectrum of different samples under the condition of characteristic delta lambda 60 nm, the best model was established while 5 principal components were selected from original spectrum and the radial basis function (RBF) was used as the kernel function, and the optimal penalty factor C and kernel function g were 512 and 0.5 respectively obtained by the grid searching and 6-fold cross validation. The discrimination rate of the model was 100% for both training sets and prediction sets. Thus, it is quick and accurate to apply synchronous fluorescence spectroscopy to identification of hogwash oil.

Research on the Direct Carbon Emission Forecast of CHINA'S Provincial Residents Based on Neural Network

NASA Astrophysics Data System (ADS)

Zhang, T.; Zhou, B.; Zhou, S.; Yan, W.

2018-04-01

Global climate change, which mainly effected by human carbon emissions, would affect the regional economic, natural ecological environment, social development and food security in the near future. It's particularly important to make accurate predictions of carbon emissions based on current carbon emissions. This paper accounted out the direct consumption of carbon emissions data from 1995 to 2014 about 30 provinces (the data of Tibet, Hong Kong, Macao and Taiwan is missing) and the whole of China. And it selected the optimal models from BP, RBF and Elman neural network for direct carbon emission prediction, what aim was to select the optimal prediction method and explore the possibility of reaching the peak of residents direct carbon emissions of China in 2030. Research shows that: 1) Residents' direct carbon emissions per capita of all provinces showed an upward trend in 20 years. 2) The accuracy of the prediction results by Elman neural network model is higher than others and more suitable for carbon emission data projections. 3) With the situation of residents' direct carbon emissions free development, the direct carbon emissions will show a fast to slow upward trend in the next few years and began to flatten after 2020, and the direct carbon emissions of per capita will reach the peak in 2032. This is also confirmed that China is expected to reach its peak in carbon emissions by 2030 in theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ura, N.; Carretero, O.A.; Erdoes, E.G.

Studies were done in 2 phases in rats. (1) Bradykinin was added catheter-collected urine, and its hydrolysis was determined by RIA. Three different kiniases were found by application of specific inhibitors. Kininase I-type carboxypeptidase was inhibited by 2-mercaptomethyl-3-guanidinoethyl-thiopropanoic acid, kiniase II by captopril and NEP by phosphoramidon (PA). Surprisingly, NEP was responsible for 68% of total kininase, while kininase I and II contributed only 9 and 23%. (2) To study the effects of inhibition of NEP on renal function, rats were infused with PA (330 ..mu..g/hr/kg, n=6). Urinary kinin level, kininase, GFR, RBF, U/sub Na/V, U/sub K/V and UV weremore » measured. PA decreased total urinary kininase activity from 284 to 58 ng/min/kg (77%, p < 0.01) and increased urinary kinin excretion from 74 to 128 pg/min/kg (73%, p < 0.02), UV from 72 to 82 ..mu..l/min/kg (15%, p < 0.01) and U/sub Na/V from 12 to 17 ..mu.. Eq/min/kg (37%, p < 0.02). PA did not change BP, RBF, GFR or U/sub K/V. /sup 125/I-Tyr-bradykinin infused into the aorta did not appear in urine intact during PA administration. In conclusion, this is the first demonstration of NEP catabolizing kinins in vivo; its inhibition increased the excretion of intrarenally generated kinins. Changes in water and electrolyte excretion may be caused by kinins generated in the distal nephron.« less

On multilevel RBF collocation to solve nonlinear PDEs arising from endogenous stochastic volatility models

NASA Astrophysics Data System (ADS)

Bastani, Ali Foroush; Dastgerdi, Maryam Vahid; Mighani, Abolfazl

2018-06-01

The main aim of this paper is the analytical and numerical study of a time-dependent second-order nonlinear partial differential equation (PDE) arising from the endogenous stochastic volatility model, introduced in [Bensoussan, A., Crouhy, M. and Galai, D., Stochastic equity volatility related to the leverage effect (I): equity volatility behavior. Applied Mathematical Finance, 1, 63-85, 1994]. As the first step, we derive a consistent set of initial and boundary conditions to complement the PDE, when the firm is financed by equity and debt. In the sequel, we propose a Newton-based iteration scheme for nonlinear parabolic PDEs which is an extension of a method for solving elliptic partial differential equations introduced in [Fasshauer, G. E., Newton iteration with multiquadrics for the solution of nonlinear PDEs. Computers and Mathematics with Applications, 43, 423-438, 2002]. The scheme is based on multilevel collocation using radial basis functions (RBFs) to solve the resulting locally linearized elliptic PDEs obtained at each level of the Newton iteration. We show the effectiveness of the resulting framework by solving a prototypical example from the field and compare the results with those obtained from three different techniques: (1) a finite difference discretization; (2) a naive RBF collocation and (3) a benchmark approximation, introduced for the first time in this paper. The numerical results confirm the robustness, higher convergence rate and good stability properties of the proposed scheme compared to other alternatives. We also comment on some possible research directions in this field.

Longitudinal Effects of Teriparatide or Zoledronic Acid on Bone Modeling- and Remodeling-Based Formation in the SHOTZ Study.

PubMed

Dempster, David W; Zhou, Hua; Ruff, Valerie A; Melby, Thomas E; Alam, Jahangir; Taylor, Kathleen A

2018-04-01

Previously, we reported on bone histomorphometry, biochemical markers, and bone mineral density distribution after 6 and 24 months of treatment with teriparatide (TPTD) or zoledronic acid (ZOL) in the SHOTZ study. The study included a 12-month primary study period, with treatment (TPTD 20 μg/d by subcutaneous injection or ZOL 5 mg/yr by intravenous infusion) randomized and double-blind until the month 6 biopsy (TPTD, n = 28; ZOL, n = 30 evaluable), then open-label, with an optional 12-month extension receiving the original treatment. A second biopsy (TPTD, n = 10; ZOL, n = 9) was collected from the contralateral side at month 24. Here we present data on remodeling-based bone formation (RBF), modeling-based bone formation (MBF), and overflow modeling-based bone formation (oMBF, modeling overflow adjacent to RBF sites) in the cancellous, endocortical, and periosteal envelopes. RBF was significantly greater after TPTD versus ZOL in all envelopes at 6 and 24 months, except the periosteal envelope at 24 months. MBF was significantly greater with TPTD in all envelopes at 6 months but not at 24 months. oMBF was significantly greater at 6 months in the cancellous and endocortical envelopes with TPTD, with no significant differences at 24 months. At 6 months, total bone formation surface was also significantly greater in each envelope with TPTD treatment (all p < 0.001). For within-group comparisons from 6 to 24 months, no statistically significant changes were observed in RBF, MBF, or oMBF in any envelope for either the TPTD or ZOL treatment groups. Overall, TPTD treatment was associated with greater bone formation than ZOL. Taken together the data support the view that ZOL is a traditional antiremodeling agent, wheareas TPTD is a proremodeling anabolic agent that increases bone formation, especially that associated with bone remodeling, including related overflow modeling, with substantial modeling-based bone formation early in the course of treatment. © 2017 American Society for Bone and Mineral Research. © 2017 American Society for Bone and Mineral Research.

Donor-funded project's sustainability assessment: a qualitative case study of a results-based financing pilot in Koulikoro region, Mali.

PubMed

Seppey, Mathieu; Ridde, Valéry; Touré, Laurence; Coulibaly, Abdourahmane

2017-12-08

Results-based financing (RBF) is emerging as a new alternative to finance health systems in many African countries. In Mali, a pilot project was conducted to improve demand and supply of health services through financing performance in targeted services. No study has explored the sustainability process of such a project in Africa. This study's objectives were to understand the project's sustainability process and to assess its level of sustainability. Sustainability was examined through its different determinants, phases, levels and contexts. These were explored using qualitative interviews to discern, via critical events, stakeholders' ideas regarding the project's sustainability. Data collection sites were chosen with the participation of different stakeholders, based on a variety of criteria (rural/urban settings, level of participation, RBF participants still present, etc.). Forty-nine stakeholders were then interviewed in six community health centres and two referral health centres (from 11/12/15 to 08/03/16), including health practitioners, administrators, and those involved in implementing and conceptualizing the program (government and NGOs). A theme analysis was done with the software © QDA Miner according to the study's conceptual framework. The results of this project show a weak level of sustainability due to many factors. While some gains could be sustained (ex.: investments in long-term resources, high compatibility of values and codes, adapted design to the implementations contexts, etc.) other intended benefits could not (ex.: end of investments, lack of shared cultural artefacts around RBF, loss of different tasks and procedures, need of more ownership of the project by the local stakeholders). A lack of sustainability planning was observed, and few critical events were associated to phases of sustainability. While this RBF project aimed at increasing health agents' motivation through different mechanisms (supervision, investments, incentives, etc.), these results raise questions on what types of motivation could be more stable and what could be the place of local stakeholders in the project; all this with the aim of more sustained and efficient results.

Acute kidney damage induced by low- and iso-osmolar contrast media in rats: Comparison study with physiologic MRI and histologic-gene examination.

PubMed

Wu, Chen-Jiang; Bao, Mei-Ling; Wang, Qing; Wang, Xiao-Ning; Liu, Xi-Sheng; Shi, Hai-Bin; Zhang, Yu-Dong

2017-01-01

To investigate the physiopathological effects of low- and iso-osmolar contrast media (CM) on renal function with physiologic MRI and histologic-gene examination. Forty-eight rats underwent time-course DWI and DCE-MRI at 3.0 Tesla (T) before and 5-15 min after exposure of CM or saline (Iop.370: 370 mgI/mL iopromide; Iod.320: 320 mgI/mL iodixanol; Iod.270: 270 mgI/mL iodixanol; 4 gI/kg body weight). Intrarenal viscosity was reflected by apparent diffusion coefficient (ADC). Renal physiologies were evaluated by DCE-derived glomerular filtration rate (GFR), renal blood flow (RBF), and renal blood volume (RBV). Potential acute kidney injury (AKI) was determined by histology and the expression of kidney injury molecule 1 (Kim-1). Iop.370 mainly increased ADC in inner-medulla (△ADC IM : 12.3 ± 11.1%; P < 0.001). Iod.320 and Iod.270 mainly decreased ADC in outer-medulla (△ADC IM ; Iod.320: 16.8 ± 7.5%; Iod.270: 18.1 ± 9.5%; P < 0.001) and inner-medulla (△ADC IM ; Iod.320: 28.4 ± 9.3%; Iod.270: 30.3 ± 6.3%; P < 0.001). GFR, RBF and RBV were significantly decreased by Iod.320 (△GFR: 45.5 ± 24.1%; △RBF: 44.6 ± 19.0%; △RBV: 35.2 ± 10.1%; P < 0.001) and Iod.270 (33.2 ± 19.0%; 38.1 ± 15.6%; 30.1 ± 10.1%; P < 0.001), while rarely changed by Iop.370 and saline. Formation of vacuoles and increase in Kim-1 expression was prominently detected in group of Iod.320, while rarely in Iod.270 and Iop.370. Iso-osmolar iodixanol, given at high-dose, produced prominent AKI in nonhydrated rats. This renal dysfunction could be assessed noninvasively by physiologic MRI. 1 J. Magn. Reson. Imaging 2017;45:291-302. © 2016 International Society for Magnetic Resonance in Medicine.

Magnetic resonance elastography can monitor changes in medullary stiffness in response to treatment in the swine ischemic kidney.

PubMed

Zhang, Xin; Zhu, Xiangyang; Ferguson, Christopher Martyn; Jiang, Kai; Burningham, Tyson; Lerman, Amir; Lerman, Lilach Orly

2018-06-01

Low-energy shockwave (SW) therapy attenuates damage in the stenotic kidney (STK) caused by atherosclerotic renal artery stenosis (ARAS). We hypothesized that magnetic resonance elastography (MRE) would detect attenuation of fibrosis following SW in unilateral ARAS kidneys. Domestic pigs were randomized to control, unilateral ARAS, and ARAS treated with 6 sessions of SW over 3 consecutive weeks (n = 7 each) starting after 3 weeks of ARAS or sham. Four weeks after SW treatment, renal fibrosis was evaluated with MRE in vivo or trichrome staining ex vivo. Blood pressure, single-kidney renal-blood-flow (RBF) and glomerular-filtration-rate (GFR) were assessed. MRE detected increased stiffness in the STK medulla (15.3 ± 2.1 vs. 10.1 ± 0.8 kPa, p < 0.05) that moderately correlated with severity of fibrosis (R 2  = 0.501, p < 0.01), but did not identify mild STK cortical or contralateral kidney fibrosis. Trichrome staining showed that medullary fibrosis was increased in ARAS and alleviated by SW (10.4 ± 1.8% vs. 2.9 ± 0.2%, p < 0.01). SW slightly decreased blood pressure and normalized STK RBF and GFR in ARAS. In the contralateral kidney, SW reversed the increase in RBF and GFR. MRE might be a tool for noninvasive monitoring of medullary fibrosis in response to treatment in kidney disease.

The Role of Vasodilator Receptors of Renin-angiotensin System on Nitric Oxide Formation and Kidney Circulation after Angiotensin II Infusion in Renal Ischemia/Reperfusion Rats.

PubMed

Maleki, Maryam; Hasanshahi, Jalal; Moslemi, Fatemeh

2018-01-01

Nitric oxide (NO) as a vasodilator factor has renoprotective effect against renal ischemia. The balance between angiotensin II (Ang II) and NO can affect kidney homeostasis. The aim of this study was to determine NO alteration in response to renin-Ang system vasodilator receptors antagonists (PD123319; Ang II type 2 receptor antagonist and A779; Mas receptor antagonist) in renal ischemia/reperfusion injury (IRI) in rats. Sixty-three Wistar male and female rats were used. Animals from each gender were divided into four groups received saline, Ang II, PD123319 + Ang II, and A779 + Ang II after renal IRI. Renal IRI induced with an adjustable hook. Blood pressure and renal blood flow (RBF) measured continuously. The nitrite levels were measured in serum, kidney, and urine samples. In female rats, the serum and kidney nitrite levels increased significantly by Ang II ( P < 0.05) and decreased significantly ( P < 0.05) when PD123319 was accompanied with Ang II. Such observation was not seen in male. Ang II decreased RBF significantly in all groups ( P < 0.05), while PD + Ang II group showed significant decrease in RBF in comparison with the other groups in female rats ( P < 0.05). Males show more sensibility to Ang II infusion; in fact, it is suggested that there is gender dimorphism in the Ang II and NO production associated with vasodilator receptors.

Privacy preserving RBF kernel support vector machine.

PubMed

Li, Haoran; Xiong, Li; Ohno-Machado, Lucila; Jiang, Xiaoqian

2014-01-01

Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data.

Hydrogeological investigations of river bed clogging at a river bank filtration site along the River Warta, Poland

NASA Astrophysics Data System (ADS)

Przybyłek, Jan; Dragon, Krzysztof; Kaczmarek, Piotr Michał Jan

2017-12-01

River bank filtration (RBF) is a system that enriches groundwater resources by induced infiltration of river water to an aquifer. Problematic during operation of RBF systems is the deterioration of infiltration effectiveness caused by river bed clogging. This situation was observed in the Krajkowo well field which supplies fresh water to the city of Poznań (Poland) during and after the long hydrological drought between the years 1989 and 1992. The present note discusses results of specific hydrogeological research which included drilling of a net of boreholes to a depth of 10 m below river bottom (for sediment sampling as well as for hydrogeological measurements), analyses of grain size distribution and relative density studies. The results obtained have allowed the recognition of the origin of the clogging processes, as well as the documentation of the clogged parts of the river bottom designated for unclogging activities.

Privacy Preserving RBF Kernel Support Vector Machine

PubMed Central

Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Data sharing is challenging but important for healthcare research. Methods for privacy-preserving data dissemination based on the rigorous differential privacy standard have been developed but they did not consider the characteristics of biomedical data and make full use of the available information. This often results in too much noise in the final outputs. We hypothesized that this situation can be alleviated by leveraging a small portion of open-consented data to improve utility without sacrificing privacy. We developed a hybrid privacy-preserving differentially private support vector machine (SVM) model that uses public data and private data together. Our model leverages the RBF kernel and can handle nonlinearly separable cases. Experiments showed that this approach outperforms two baselines: (1) SVMs that only use public data, and (2) differentially private SVMs that are built from private data. Our method demonstrated very close performance metrics compared to nonprivate SVMs trained on the private data. PMID:25013805

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

Production and actions of the anandamide metabolite prostamide E2 in the renal medulla.

PubMed

Ritter, Joseph K; Li, Cao; Xia, Min; Poklis, Justin L; Lichtman, Aron H; Abdullah, Rehab A; Dewey, William L; Li, Pin-Lan

2012-09-01

Medullipin has been proposed to be an antihypertensive lipid hormone released from the renal medulla in response to increased arterial pressure and renal medullary blood flow. Because anandamide (AEA) possesses characteristics of this purported hormone, the present study tested the hypothesis that AEA or one of its metabolites represents medullipin. AEA was demonstrated to be enriched in the kidney medulla compared with cortex. Western blotting and enzymatic analyses of renal cortical and medullary microsomes revealed opposite patterns of enrichment of two AEA-metabolizing enzymes, with fatty acid amide hydrolase higher in the renal cortex and cyclooxygenase-2 (COX-2) higher in the renal medulla. In COX-2 reactions with renal medullary microsomes, prostamide E2, the ethanolamide of prostaglandin E₂, was the major product detected. Intramedullarily infused AEA dose-dependently increased urine volume and sodium and potassium excretion (15-60 nmol/kg/min) but had little effect on mean arterial pressure (MAP). The renal excretory effects of AEA were blocked by intravenous infusion of celecoxib (0.1 μg/kg/min), a selective COX-2 inhibitor, suggesting the involvement of a prostamide intermediate. Plasma kinetic analysis revealed longer elimination half-lives for AEA and prostamide E2 compared with prostaglandin E₂. Intravenous prostamide E2 reduced MAP and increased renal blood flow (RBF), actions opposite to those of angiotensin II. Coinfusion of prostamide E2 inhibited angiotensin II effects on MAP and RBF. These results suggest that AEA and/or its prostamide metabolites in the renal medulla may represent medullipin and function as a regulator of body fluid and MAP.

Disruption of the hormonal network and the enantioselectivity of bifenthrin in trophoblast: maternal-fetal health risk of chiral pesticides.

PubMed

Zhao, Meirong; Zhang, Ying; Zhuang, Shulin; Zhang, Quan; Lu, Chengsheng; Liu, Weiping

2014-07-15

Endocrine-disrupting chemicals (EDCs) can interfere with normal hormone signaling to increase health risks to the maternal-fetal system, yet few studies have been conducted on the currently used chiral EDCs. This work tested the hypothesis that pyrethroids could enantioselectively interfere with trophoblast cells. Cell viability, hormone secretion, and steroidogenesis gene expression of a widely used pyrethroid, bifenthrin (BF), were evaluated in vitro, and the interactions of BF enantiomers with estrogen receptor (ER) were predicted. At low or noncytotoxic concentrations, both progesterone and human chorionic gonadotropin secretion were induced. The expression levels of progesterone receptor and human leukocyte antigen G genes were significantly stimulated. The key regulators of the hormonal cascade, GnRH type-I and its receptor, were both upregulated. The expression levels of selected steroidogenic genes were also significantly altered. Moreover, a consistent enantioselective interference of hormone signaling was observed, and S-BF had greater effects than R-BF. Using molecular docking, the enantioselective endocrine disruption of BF was predicted to be partially due to enantiospecific ER binding affinity. Thus, BF could act through ER to enantioselectively disturb the hormonal network in trophoblast cells. These converging results suggest that the currently used chiral pesticides are of significant concern with respect to maternal-fetal health.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Noncontact diffuse optical assessment of blood flow changes in head and neck free tissue transfer flaps (Conference Presentation)

NASA Astrophysics Data System (ADS)

Huang, Chong; Radabaugh, Jeffrey P.; Aouad, Rony K.; Lin, Yu; Gal, Thomas J.; Patel, Amit B.; Valentino, Joseph; Shang, Yu; Yu, Guoqiang

2016-02-01

Head and neck cancer accounts for 3 to 5% of all cancers in the United States. Primary or salvage surgeries are extensive and often lead to major head and neck defects that require complex reconstructions with local, regional, or free tissue transfer flaps. Knowledge of tissue blood flow (BF) changes after free tissue transfer may enable surgeons to predict the failure of flap thrombosis at an early stage. This study used our recently developed noncontact diffuse correlation spectroscopy to monitor dynamic BF changes in free flaps without getting in contact with the targeted tissue. Eight free flaps were elevated in patients with head and neck cancer; one of the flaps failed. Multiple BF measurements probing the transferred tissue were performed during and post the surgical operation. Postoperative BF values were normalized to the intraoperative baselines (assigning '1') for the calculation of relative BF change (rBF). The rBF changes over the seven successful flaps were 1.89 +/- 0.15, 2.26 +/- 0.13, and 2.43 +/- 0.13 (mean +/- standard error) respectively on postoperative days 2, 4, and 7. These postoperative values were significantly higher than the intraoperative baseline values (p < 0.001), indicating a gradual recovery of flap vascularity after the tissue transfer. By contrast, rBF changes observed from the unsuccessful flap were 1.14 and 1.34 respectively on postoperative days 2 and 4, indicating a less flow recovery. Measurement of BF recovery after flap anastomosis holds the potential to act early to salvage ischemic flaps.

Comparison of ASL and DCE MRI for the non-invasive measurement of renal blood flow: quantification and reproducibility.

PubMed

Cutajar, Marica; Thomas, David L; Hales, Patrick W; Banks, T; Clark, Christopher A; Gordon, Isky

2014-06-01

To investigate the reproducibility of arterial spin labelling (ASL) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and quantitatively compare these techniques for the measurement of renal blood flow (RBF). Sixteen healthy volunteers were examined on two different occasions. ASL was performed using a multi-TI FAIR labelling scheme with a segmented 3D-GRASE imaging module. DCE MRI was performed using a 3D-FLASH pulse sequence. A Bland-Altman analysis was used to assess repeatability of each technique, and determine the degree of correspondence between the two methods. The overall mean cortical renal blood flow (RBF) of the ASL group was 263 ± 41 ml min(-1) [100 ml tissue](-1), and using DCE MRI was 287 ± 70 ml min(-1) [100 ml tissue](-1). The group coefficient of variation (CVg) was 18 % for ASL and 28 % for DCE-MRI. Repeatability studies showed that ASL was more reproducible than DCE with CVgs of 16 % and 25 % for ASL and DCE respectively. Bland-Altman analysis comparing the two techniques showed a good agreement. The repeated measures analysis shows that the ASL technique has better reproducibility than DCE-MRI. Difference analysis shows no significant difference between the RBF values of the two techniques. Reliable non-invasive monitoring of renal blood flow is currently clinically unavailable. Renal arterial spin labelling MRI is robust and repeatable. Renal dynamic contrast-enhanced MRI is robust and repeatable. ASL blood flow values are similar to those obtained using DCE-MRI.

Use of Vis/NIRS for the determination of sugar content of cola soft drinks based on chemometric methods

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-03-01

Three different chemometric methods were performed for the determination of sugar content of cola soft drinks using visible and near infrared spectroscopy (Vis/NIRS). Four varieties of colas were prepared and 180 samples (45 samples for each variety) were selected for the calibration set, while 60 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay, standard normal variate (SNV) and Savitzky-Golay first derivative transformation were applied for the pre-processing of spectral data. The first eleven principal components (PCs) extracted by partial least squares (PLS) analysis were employed as the inputs of BP neural network (BPNN) and least squares-support vector machine (LS-SVM) model. Then the BPNN model with the optimal structural parameters and LS-SVM model with radial basis function (RBF) kernel were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for prediction were 0.971, 1.259 and -0.335 for PLS, 0.986, 0.763, and -0.042 for BPNN, while 0.978, 0.995 and -0.227 for LS-SVM, respectively. All the three methods supplied a high and satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be utilized as a high precision way for the determination of sugar content of cola soft drinks.

A new open-loop fiber optic gyro error compensation method based on angular velocity error modeling.

PubMed

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-02-27

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.42%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.

A New Open-Loop Fiber Optic Gyro Error Compensation Method Based on Angular Velocity Error Modeling

PubMed Central

Zhang, Yanshun; Guo, Yajing; Li, Chunyu; Wang, Yixin; Wang, Zhanqing

2015-01-01

With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. Firstly, the angular velocity error Δω is extracted from OFOG output signals, and then the output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δω is established and thus Δω can be calculated automatically to compensate OFOG errors according to T and u. The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0%, 97.1% and 96.5% relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6%, 1.4% and 1.2%, respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity. PMID:25734642

'The money can be a motivator, to me a little, but mostly PBF just helps me to do better in my job.' An exploration of the motivational mechanisms of performance-based financing for health workers in Malawi.

PubMed

Lohmann, Julia; Wilhelm, Danielle; Kambala, Christabel; Brenner, Stephan; Muula, Adamson S; De Allegri, Manuela

2018-03-01

Performance-based financing (PBF) is assumed to improve health care delivery by motivating health workers to enhance their work performance. However, the exact motivational mechanisms through which PBF is assumed to produce such changes are poorly understood to date. Although PBF is increasingly recognized as a complex health systems intervention, its motivational effect for individual health workers is still often reduced to financial 'carrots and sticks' in the literature and discourse. Aiming to contribute to the development of a more comprehensive understanding of the motivational mechanisms, we explored how PBF impacted health worker motivation in the context of the Malawian Results-based Financing for Maternal and Newborn Health (RBF4MNH) Initiative. We conducted in-depth interviews with 41 nurses, medical assistants and clinical officers from primary- and secondary-level health facilities 1 and 2 years after the introduction of RBF4MNH in 2013. Six categories of motivational mechanisms emerged: RBF4MNH motivated health workers to improve their performance (1) by acting as a periodic wake-up call to deficiencies in their day-to-day practice; (2) by providing direction and goals to work towards; (3) by strengthening perceived ability to perform successfully at work and triggering a sense of accomplishment; (4) by instilling feelings of recognition; (5) by altering social dynamics, improving team work towards a common goal, but also introducing social pressure; and (6) by offering a 'nice to have' opportunity to earn extra income. However, respondents also perceived weaknesses of the intervention design, implementation-related challenges and contextual constraints that kept RBF4MNH from developing its full motivating potential. Our results underline PBF's potential to affect health workers' motivation in ways which go far beyond the direct effects of financial rewards to individuals. We strongly recommend considering all motivational mechanisms more explicitly in future PBF design to fully exploit the approach's capacity for enhancing health worker performance. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

RIVERBANK FILTRATION: EFFECT OF GROUND PASSAGE ON NOM CHARACTER

EPA Science Inventory

Research was conducted to explore the effect of underground travel on the character of the natural organic matter (NOM) originating from the river water source during riverbank filtration (RBF) at three Midwestern US drinking water utilities. Measurements of biodegradable dissolv...

Structured Multi-level Data Fusion and Modelling of Heterogeneous Environmental Data for Future Internet Applications

NASA Astrophysics Data System (ADS)

Sabeur, Zoheir; Chakravarthy, Ajay; Bashevoy, Maxim; Modafferi, Stefano

2013-04-01

The rapid increase in environmental observations which are conducted by Small to Medium Enterprise communities and volunteers using affordable in situ sensors at various scales, in addition to the more established observatories set up by environmental and space agencies using airborne and space-borne sensing technologies is generating serious amounts of BIG data at ever increasing speeds. Furthermore, the emergence of Future Internet technologies and the urgent requirements for the deployment of specific enablers for the delivery of processed environmental knowledge in real-time with advanced situation awareness to citizens has reached paramount importance. Specifically, it has become highly critical now to build and provide services which automate the aggregation of data from various sources, while surmounting the semantic gaps, conflicts and heterogeneity in data sources. The early stage aggregation of data will enable the pre-processing of data from multiple sources while reconciling the temporal gaps in measurement time series, and aligning their respective a-synchronicities. This low level type of data fusion process needs to be automated and chained to more advanced level of data fusion services specialising in observation forecasts at spaces where sensing is not deployed; or at time slices where sensing has not taken place yet. As a result, multi-level fusion services are required among the families of specific enablers for monitoring environments and spaces in the Future Internet. These have been intially deployed and piloted in the ongoing ENVIROFI project of the FI-PPP programme [1]. Automated fusion and modelling of in situ and remote sensing data has been set up and the experimentation successfully conducted using RBF networks for the spatial fusion of water quality parameters measurements from satellite and stationary buoys in the Irish Sea. The RBF networks method scales for the spatial data fusion of multiple types of observation sources. This important approach provides a strong basis for the delivery of environmental observations at desired spatial and temporal scales to multiple users with various needs of spatial and temporal resolutions. It has also led to building robust future internet specific enablers on data fusion, which can indeed be used for multiple usage areas above and beyond the environmental domains of the Future Internet. In this paper, data and processing workflow scenarios shall be described. The fucntionalities of the multi-level fusion services shall be demonstrated and made accessible to the wider communities of the Fututre Internet. [1] The Environmental Observation Web and its Service Applications within the Future Internet. ENVIROFI IP. FP7-2011-ICT-IF Pr.No: 284898 http://www.envirofi.eu/

Overlap among Environmental Databases.

ERIC Educational Resources Information Center

Miller, Betty

1981-01-01

Describes the methodology and results of a study comparing the overlap of Enviroline, Pollution, and the Environmental Periodicals Bibliography files through searches on acid rain, asbestos and water, diesel, glass recycling, Lake Erie, Concorde, reverse osmosis wastewater treatment cost, and Calspan. Nine tables are provided. (RBF)

HIV-1 group P infection: towards a dead-end infection?

PubMed

Alessandri-Gradt, Elodie; De Oliveira, Fabienne; Leoz, Marie; Lemee, Véronique; Robertson, David L; Feyertag, Felix; Ngoupo, Paul-Alain; Mauclere, Philippe; Simon, François; Plantier, Jean-Christophe

2018-06-19

HIV/1 group P (HIV-1/P) is the last HIV/1 group discovered and, to date, constitutes only two strains. To obtain new insight into this divergent group, we screened for new infections by developing specific tools, and analysed phenotypic and genotypic properties of the prototypic strain RBF168. In addition, the follow-up of the unique infected patient monitored so far has raised the knowledge of the natural history of this infection and its therapeutic management. We developed an HIV-1/P specific seromolecular strategy and screened over 29 498 specimen samples. Infectivity and evolution of the gag-30 position, considered as marker of adaptation to human, were explored by successive passages of RBF168 strain onto human peripheral blood mononuclear cells. Natural history and immunovirological responses to combined antiretroviral therapy (cART) were analysed based on CD4 cells and plasmatic viral load evolution. No new infection was detected. Infectivity of RBF168 was found lower, relative to other main HIV groups and the conservative methionine found in the gag-30 position revealed a lack of adaptation to human. The follow-up of the patient during the 5-year ART-free period, showed a relative stability of CD4 cell count with a mean of 326 cells/μl. Initiation of cART led to rapid RNA undetectability with a significant increase of CD4 cells, reaching 687 cells/μl after 8 years. Our results showed that HIV-1/P strains remain extremely rare and could be less adapted and pathogenic than other HIV strains. These data lead to the hypothesis that HIV-1/P infection could evolve towards, or even already corresponds to, a dead-end infection.

Short-term effect of beta-adrenoreceptor blocking agents on ocular blood flow.

PubMed

Sato, T; Muto, T; Ishibashi, Y; Roy, S

2001-10-01

In this study the acute effect of the topically-delivered non-selective beta-blockers timolol and carteolol, and the selective beta-blocker betaxolol, were evaluated with respect to ocular blood flow, intraocular pressure (IOP) and vessel resistance in rabbits' eyes. In a double masked randomized design, one eye of each subject (n = 9) received two drops of 0.5 % timolol or 2 % cartelol or 0.5 % betaxolol ophthalmic solution and a separate group of nine rabbits received two drops of placebo consisting of physiological saline in both eyes to serve as control. Using hydrogen clearance method, ciliary body blood flow (CiBF), choroidal blood flow (CBF), and retinal blood flow (RBF) were measured. IOP and systemic mean arterial pressure (MAP) of each subject were measured under same condition before and after the administration of respective drugs to calculate the ocular perfusion pressure (OPP) and vessel resistance. In timolol- and carteolol-treated eyes significant reduction was observed in IOP (p < 0.01), CiBF (p < 0.01), CBF (p < 0.01) and RBF (p < 0.01) compared to control eyes. However, in betaxolol-treated eyes a marginal reduction in IOP was observed accompanied by significant increase in CiBF (p < 0.01) and RBF (p < 0.05). The non-selective beta-blocker-treated eyes tended to have increased vessel resistance, whereas, selective beta-blocker-treated eyes tended to have decreased vessel resistance. Our current results comparing non-selective and selective beta-blockers suggest that the selective beta-blocker betaxolol may be more appropriate for maintenance of retinal blood flow in situations with low perfusion. Currently the mechanism for regulation of IOP is unclear; however, the findings from this study indicate that decreased CiBF may contribute to reduction in IOP.

A novel mechanism of renal blood flow autoregulation and the autoregulatory role of A1 adenosine receptors in mice.

PubMed

Just, Armin; Arendshorst, William J

2007-11-01

Autoregulation of renal blood flow (RBF) is mediated by a fast myogenic response (MR; approximately 5 s), a slower tubuloglomerular feedback (TGF; approximately 25 s), and potentially additional mechanisms. A1 adenosine receptors (A1AR) mediate TGF in superficial nephrons and contribute to overall autoregulation, but the impact on the other autoregulatory mechanisms is unknown. We studied dynamic autoregulatory responses of RBF to rapid step increases of renal artery pressure in mice. MR was estimated from autoregulation within the first 5 s, TGF from that at 5-25 s, and a third mechanism from 25-100 s. Genetic deficiency of A1AR (A1AR-/-) reduced autoregulation at 5-25 s by 50%, indicating a residual fourth mechanism resembling TGF kinetics but independent of A1AR. MR and third mechanism were unaltered in A1AR-/-. Autoregulation in A1AR-/- was faster at 5-25 than at 25-100 s suggesting two separate mechanisms. Furosemide in wild-type mice (WT) eliminated the third mechanism and enhanced MR, indicating TGF-MR interaction. In A1AR-/-, furosemide did not further impair autoregulation at 5-25 s, but eliminated the third mechanism and enhanced MR. The resulting time course was the same as during furosemide in WT, indicating that A1AR do not affect autoregulation during furosemide inhibition of TGF. We conclude that at least one novel mechanism complements MR and TGF in RBF autoregulation, that is slower than MR and TGF and sensitive to furosemide, but not mediated by A1AR. A fourth mechanism with kinetics similar to TGF but independent of A1AR and furosemide might also contribute. A1AR mediate classical TGF but not TGF-MR interaction.

Spatial variability of groundwater quality of Sabour block, Bhagalpur district (Bihar, India)

NASA Astrophysics Data System (ADS)

Verma, D. K.; Bhunia, Gouri Sankar; Shit, Pravat Kumar; Kumar, S.; Mandal, Jajati; Padbhushan, Rajeev

2017-07-01

This paper examines the quality of groundwater of Sabour block, Bhagalpur district of Bihar state, which lies on the southern region of Indo-Gangetic plains in India. Fifty-nine samples from different sources of water in the block have been collected to determine its suitability for drinking and irrigational purposes. From the samples electrical conductivity (EC), pH and concentrations of Calcium (Ca2+), Magnesium (Mg2+), Sodium (Na+), Potassium (K+), carbonate ion (CO 3 2- ), Bicarbonate ion (HCO 3 - ), Chloride ion (Cl-), and Fluoride (F-) were determined. Surface maps of all the groundwater quality parameters have been prepared using radial basis function (RBF) method. RBF model was used to interpolate data points in a group of multi-dimensional space. Root Mean Square Error (RMSE) is employed to scrutinize the best fit of the model to compare the obtained value. The mean value of pH, EC, Ca2+, Mg2+, Na+, K+, HCO3 -, Cl-, and F- are found to be 7.26, 0.69, 38.98, 34.20, 16.92, 1.19, 0.02, and 0.28, respectively. Distribution of calcium concentration is increasing to the eastern part and K+ concentrations raise to the downstream area in the southwestern part. Low pH concentrations (less than 6.71) occur in eastern part of the block. Spatial variations of hardness in Sabour block portraying maximum concentration in the western part and maximum SAR (more than 4.23) were recorded in the southern part. These results are not exceeding for drinking and irrigation uses recommended by World Health Organization. Therefore, the majority of groundwater samples are found to be safe for drinking and irrigation management practices.

Merchandising Techniques and Libraries.

ERIC Educational Resources Information Center

Green, Sylvie A.

1981-01-01

Proposes that libraries employ modern booksellers' merchandising techniques to improve circulation of library materials. Using displays in various ways, the methods and reasons for weeding out books, replacing worn book jackets, and selecting new books are discussed. Suggestions for learning how to market and 11 references are provided. (RBF)

Optimization of Online Searching by Pre-Recording the Search Statements: A Technique for the HP-2645A Terminal.

ERIC Educational Resources Information Center

Oberhauser, O. C.; Stebegg, K.

1982-01-01

Describes the terminal's capabilities, ways to store and call up lines of statements, cassette tapes needed during searches, and master tape's use for login storage. Advantages of the technique and two sources are listed. (RBF)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dahabieh, Matthew S., E-mail: dahabieh@interchange.ubc.ca; Ooms, Marcel, E-mail: marcel.ooms@mssm.edu; Malcolm, Tom, E-mail: tmalc1@yahoo.com

Transcription from the HIV-1 long terminal repeat (LTR) is mediated by numerous host transcription factors. In this study we characterized an E-box motif (RBE1) within the core promoter that was previously implicated in both transcriptional activation and repression. We show that RBE1 is a binding site for the RBF-2 transcription factor complex (USF1, USF2, and TFII-I), previously shown to bind an upstream viral element, RBE3. The RBE1 and RBE3 elements formed complexes of identical mobility and protein constituents in gel shift assays, both with Jurkat T-cell nuclear extracts and recombinant USF/TFII-I. Furthermore, both elements are regulators of HIV-1 expression; mutationsmore » in LTR-luciferase reporters and in HIV-1 molecular clones resulted in decreased transcription, virion production, and proviral expression in infected cells. Collectively, our data indicate that RBE1 is a bona fide RBF-2 binding site and that the RBE1 and RBE3 elements are necessary for mediating proper transcription from the HIV-1 LTR.« less

Analysis of a utility-interactive wind-photovoltaic hybrid system with battery storage using neural network

NASA Astrophysics Data System (ADS)

Giraud, Francois

1999-10-01

This dissertation investigates the application of neural network theory to the analysis of a 4-kW Utility-interactive Wind-Photovoltaic System (WPS) with battery storage. The hybrid system comprises a 2.5-kW photovoltaic generator and a 1.5-kW wind turbine. The wind power generator produces power at variable speed and variable frequency (VSVF). The wind energy is converted into dc power by a controlled, tree-phase, full-wave, bridge rectifier. The PV power is maximized by a Maximum Power Point Tracker (MPPT), a dc-to-dc chopper, switching at a frequency of 45 kHz. The whole dc power of both subsystems is stored in the battery bank or conditioned by a single-phase self-commutated inverter to be sold to the utility at a predetermined amount. First, the PV is modeled using Artificial Neural Network (ANN). To reduce model uncertainty, the open-circuit voltage VOC and the short-circuit current ISC of the PV are chosen as model input variables of the ANN. These input variables have the advantage of incorporating the effects of the quantifiable and non-quantifiable environmental variants affecting the PV power. Then, a simplified way to predict accurately the dynamic responses of the grid-linked WPS to gusty winds using a Recurrent Neural Network (RNN) is investigated. The RNN is a single-output feedforward backpropagation network with external feedback, which allows past responses to be fed back to the network input. In the third step, a Radial Basis Functions (RBF) Network is used to analyze the effects of clouds on the Utility-Interactive WPS. Using the irradiance as input signal, the network models the effects of random cloud movement on the output current, the output voltage, the output power of the PV system, as well as the electrical output variables of the grid-linked inverter. Fourthly, using RNN, the combined effects of a random cloud and a wind gusts on the system are analyzed. For short period intervals, the wind speed and the solar radiation are considered as the sole sources of power, whose variations influence the system variables. Since both subsystems have different dynamics, their respective responses are expected to impact differently the whole system behavior. The dispatchability of the battery-supported system as well as its stability and reliability during gusts and/or cloud passage is also discussed. In the fifth step, the goal is to determine to what extent the overall power quality of the grid would be affected by a proliferation of Utility-interactive hybrid system and whether recourse to bulky or individual filtering and voltage controller is necessary. The final stage of the research includes a steady-state analysis of two-year operation (May 96--Apr 98) of the system, with a discussion on system reliability, on any loss of supply probability, and on the effects of the randomness in the wind and solar radiation upon the system design optimization.

Changes in the Landscape: New Alignments and Aims in the World of Videotex and Information.

ERIC Educational Resources Information Center

Borrell, Jerry

1982-01-01

Discusses changes in politics, industry, consumer buying, and commerce resulting from the technological implementation of information handling and describes the problems and successes of such videotex systems as the British Broadcasting Corporation's CEEFAX and the British Post Office's Prestel. (RBF)

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

PubMed Central

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

2017-01-01

As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems. PMID:28422080

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

PubMed

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

2017-04-19

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

A hybrid wavelet de-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series.

PubMed

Wang, Dong; Borthwick, Alistair G; He, Handan; Wang, Yuankun; Zhu, Jieyu; Lu, Yuan; Xu, Pengcheng; Zeng, Xiankui; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Liu, Jiufu; Zou, Ying; He, Ruimin

2018-01-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, wavelet de-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. Compared to three other generic methods, the results generated by WD-REPA model presented invariably smaller error measures which means the forecasting capability of the WD-REPA model is better than other models. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series. Copyright © 2017 Elsevier Inc. All rights reserved.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

PubMed

Kavitha, Muthu Subash; Asano, Akira; Taguchi, Akira; Heo, Min-Suk

2013-09-01

To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

STUDY OF WATER QUALITY IMPROVEMENTS DURING RIVERBANK FILTRATION AT THREE MIDWESTERN UNITED STATES DRINKING WATER UTILITIES

EPA Science Inventory

Riverbank filtration (RBF) is a process during which surface water is subjected to subsurface flow prior to extraction from wells. During infiltration and soil passage, surface water is subjected to a combination of physical, chemical, and biological processes such as filtration...

Books at Auction: The Art of Deaccessioning.

ERIC Educational Resources Information Center

Model, Peter

1981-01-01

Lack of space and budget cuts force many libraries to weed out books and to be selective in accepting collection bequests. The approach for deciding which books to weed out and whether to dispose of these books through dealers or auctions are decisions private clubs and many libraries must make. (RBF)

Application of support vector machine for the separation of mineralised zones in the Takht-e-Gonbad porphyry deposit, SE Iran

NASA Astrophysics Data System (ADS)

Mahvash Mohammadi, Neda; Hezarkhani, Ardeshir

2018-07-01

Classification of mineralised zones is an important factor for the analysis of economic deposits. In this paper, the support vector machine (SVM), a supervised learning algorithm, based on subsurface data is proposed for classification of mineralised zones in the Takht-e-Gonbad porphyry Cu-deposit (SE Iran). The effects of the input features are evaluated via calculating the accuracy rates on the SVM performance. Ultimately, the SVM model, is developed based on input features namely lithology, alteration, mineralisation, the level and, radial basis function (RBF) as a kernel function. Moreover, the optimal amount of parameters λ and C, using n-fold cross-validation method, are calculated at level 0.001 and 0.01 respectively. The accuracy of this model is 0.931 for classification of mineralised zones in the Takht-e-Gonbad porphyry deposit. The results of the study confirm the efficiency of SVM method for classification the mineralised zones.

An algorithm of improving speech emotional perception for hearing aid

NASA Astrophysics Data System (ADS)

Xi, Ji; Liang, Ruiyu; Fei, Xianju

2017-07-01

In this paper, a speech emotion recognition (SER) algorithm was proposed to improve the emotional perception of hearing-impaired people. The algorithm utilizes multiple kernel technology to overcome the drawback of SVM: slow training speed. Firstly, in order to improve the adaptive performance of Gaussian Radial Basis Function (RBF), the parameter determining the nonlinear mapping was optimized on the basis of Kernel target alignment. Then, the obtained Kernel Function was used as the basis kernel of Multiple Kernel Learning (MKL) with slack variable that could solve the over-fitting problem. However, the slack variable also brings the error into the result. Therefore, a soft-margin MKL was proposed to balance the margin against the error. Moreover, the relatively iterative algorithm was used to solve the combination coefficients and hyper-plane equations. Experimental results show that the proposed algorithm can acquire an accuracy of 90% for five kinds of emotions including happiness, sadness, anger, fear and neutral. Compared with KPCA+CCA and PIM-FSVM, the proposed algorithm has the highest accuracy.

Automatic classification of sleep stages based on the time-frequency image of EEG signals.

PubMed

Bajaj, Varun; Pachori, Ram Bilas

2013-12-01

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obtain the time-frequency image (TFI). The segmentation of TFI has been performed based on the frequency-bands of the rhythms of EEG signals. The features derived from the histogram of segmented TFI have been used as an input feature set to multiclass least squares support vector machines (MC-LS-SVM) together with the radial basis function (RBF), Mexican hat wavelet, and Morlet wavelet kernel functions for automatic classification of sleep stages from EEG signals. The experimental results are presented to show the effectiveness of the proposed method for classification of sleep stages from EEG signals. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Novel agents that downregulate EGFR, HER2, and HER3 in parallel

PubMed Central

Ferreira, Renan Barroso; Law, Mary Elizabeth; Jahn, Stephan Christopher; Davis, Bradley John; Heldermon, Coy Don; Reinhard, Mary; Castellano, Ronald Keith; Law, Brian Keith

2015-01-01

EGFR, HER2, and HER3 contribute to the initiation and progression of human cancers, and are therapeutic targets for monoclonal antibodies and tyrosine kinase inhibitors. An important source of resistance to these agents arises from functional redundancy among EGFR, HER2, and HER3. EGFR family members contain conserved extracellular structures that are stabilized by disulfide bonds. Compounds that disrupt extracellular disulfide bonds could inactivate EGFR, HER2, and HER3 in unison. Here we describe the identification of compounds that kill breast cancer cells that overexpress EGFR or HER2. Cell death parallels downregulation of EGFR, HER2, and HER3. These compounds disrupt disulfide bonds and are termed Disulfide Bond Disrupting Agents (DDAs). DDA RBF3 exhibits anticancer efficacy in vivo at 40 mg/kg without evidence of toxicity. DDAs may complement existing EGFR-, HER2-, and HER3-targeted agents that function through alternate mechanisms of action, and combination regimens with these existing drugs may overcome therapeutic resistance. PMID:25865227

SVM Classifier - a comprehensive java interface for support vector machine classification of microarray data.

PubMed

Pirooznia, Mehdi; Deng, Youping

2006-12-12

Graphical user interface (GUI) software promotes novelty by allowing users to extend the functionality. SVM Classifier is a cross-platform graphical application that handles very large datasets well. The purpose of this study is to create a GUI application that allows SVM users to perform SVM training, classification and prediction. The GUI provides user-friendly access to state-of-the-art SVM methods embodied in the LIBSVM implementation of Support Vector Machine. We implemented the java interface using standard swing libraries. We used a sample data from a breast cancer study for testing classification accuracy. We achieved 100% accuracy in classification among the BRCA1-BRCA2 samples with RBF kernel of SVM. We have developed a java GUI application that allows SVM users to perform SVM training, classification and prediction. We have demonstrated that support vector machines can accurately classify genes into functional categories based upon expression data from DNA microarray hybridization experiments. Among the different kernel functions that we examined, the SVM that uses a radial basis kernel function provides the best performance. The SVM Classifier is available at http://mfgn.usm.edu/ebl/svm/.

Renal effects of felodipine: a review of experimental evidence and clinical data.

PubMed

DiBona, G F

1990-01-01

The dihydropyridine calcium channel antagonist felodipine has a wide spectrum of effects on the kidney. From a variety of studies in normotensive and hypertensive animals and human subjects, felodipine produces a decrease in renal vascular resistance that, although predominantly dependent on the decrease in mean arterial pressure (MAP), may be associated with an increase in renal blood flow (RBF). The glomerular filtration rate (GFR) is unchanged. In response to acute felodipine administration, the significant diuresis and natriuresis observed is caused by a direct inhibitory effect on net renal tubular sodium and water reabsorption. While the acute natriuretic response to felodipine administration is modulated by compensatory adaptations over the remainder of the 24-h period and during chronic treatment, the negative sodium balance established is sustained over the duration of the treatment. Renal sodium and water retention are not observed and there is little effect on renal potassium handling. As a vasodilator antihypertensive agent, felodipine produces renal vasodilatation (normal or increased but not decreased RBF) without adverse effects on the GFR or renal sodium and water retention.

Iterative refinement of implicit boundary models for improved geological feature reproduction

NASA Astrophysics Data System (ADS)

Martin, Ryan; Boisvert, Jeff B.

2017-12-01

Geological domains contain non-stationary features that cannot be described by a single direction of continuity. Non-stationary estimation frameworks generate more realistic curvilinear interpretations of subsurface geometries. A radial basis function (RBF) based implicit modeling framework using domain decomposition is developed that permits introduction of locally varying orientations and magnitudes of anisotropy for boundary models to better account for the local variability of complex geological deposits. The interpolation framework is paired with a method to automatically infer the locally predominant orientations, which results in a rapid and robust iterative non-stationary boundary modeling technique that can refine locally anisotropic geological shapes automatically from the sample data. The method also permits quantification of the volumetric uncertainty associated with the boundary modeling. The methodology is demonstrated on a porphyry dataset and shows improved local geological features.

Understanding the undelaying mechanism of HA-subtyping in the level of physic-chemical characteristics of protein.

PubMed

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L; Hemmatzadeh, Farhid; Ebrahimie, Esmaeil

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

PubMed Central

Ebrahimi, Mansour; Aghagolzadeh, Parisa; Shamabadi, Narges; Tahmasebi, Ahmad; Alsharifi, Mohammed; Adelson, David L.

2014-01-01

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics. PMID:24809455

Effects of sediment-associated extractable metals, degree of sediment grain sorting, and dissolved organic carbon upon Cryptosporidium parvum removal and transport within riverbank filtration sediments, Sonoma County, California.

PubMed

Metge, David W; Harvey, Ronald W; Aiken, George R; Anders, Robert; Lincoln, George; Jasperse, Jay; Hill, Mary C

2011-07-01

Oocysts of the protozoan pathogen Cryptosporidium parvum are of particular concern for riverbank filtration (RBF) operations because of their persistence, ubiquity, and resistance to chlorine disinfection. At the Russian River RBF site (Sonoma County, CA), transport of C. parvum oocysts and oocyst-sized (3 μm) carboxylate-modified microspheres through poorly sorted (sorting indices, σ(1), up to 3.0) and geochemically heterogeneous sediments collected between 2 and 25 m below land surface (bls) were assessed. Removal was highly sensitive to variations in both the quantity of extractable metals (mainly Fe and Al) and degree of grain sorting. In flow-through columns, there was a log-linear relationship (r(2) = 0.82 at p < 0.002) between collision efficiency (α, the probability that colloidal collisions with grain surfaces would result in attachment) and extractable metals, and a linear relationship (r(2) = 0.99 at p < 0.002) between α and σ(1). Collectively, variability in extractable metals and grain sorting accounted for ∼83% of the variability in α (at p < 0.0002) along the depth profiles. Amendments of 2.2 mg L(-1) of Russian River dissolved organic carbon (DOC) reduced α for oocysts by 4-5 fold. The highly reactive hydrophobic organic acid (HPOA) fraction was particularly effective in re-entraining sediment-attached microspheres. However, the transport-enhancing effects of the riverine DOC did not appear to penetrate very deeply into the underlying sediments, judging from high α values (∼1.0) observed for oocysts being advected through unamended sediments collected at ∼2 m bls. This study suggests that in evaluating the efficacy of RBF operations to remove oocysts, it may be necessary to consider not only the geochemical nature and size distribution of the sediment grains, but also the degrees of sediment sorting and the concentration, reactivity, and penetration of the source water DOC.

Explaining Support Vector Machines: A Color Based Nomogram

PubMed Central

Van Belle, Vanya; Van Calster, Ben; Van Huffel, Sabine; Suykens, Johan A. K.; Lisboa, Paulo

2016-01-01

Problem setting Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models. Objective In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto) not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables. Results Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant). When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable. Conclusions This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method. PMID:27723811

[Influence of non-sodium restricted diet with diuretics on plasma rennin, renal blood flow and in patients with cirrhotic ascites].

PubMed

Zhu, Yin-fang; Gu, Xi-bing; Zhu, Hong-ying; Yang, Xiao-juan; Wang, Dong; Yu, Ping

2013-02-01

To explore influence of sodium restricted diet and non-sodium restricted diet on plasma rennin (PRA), angiotensin II (All), ALD, renal blood flow (RBF) and subside of ascites in patients with cirrhotic ascites. Eighty cases of hepatitis B with cirrhotic ascites were randomly divided into sodium restricted diet group and non-sodium restricted diet group. 39 cases were in non-sodium restricted diet group, taking sodium chloride 6500-8000 mg daily; 41 cases were in sodium restricted diet group, taking sodium chloride 5000 mg daily. Both groups received diuretics furosemide and spironolactone. Blood sodium, urine sodium, PRA, AII, ALD, RBF ascites subsiding were compared after treatment. In non-sodium restricted diet group, blood sodium and urine sodium increased 10 days after treatment compared with those before treatment, and compared with those of sodium restricted diet group 10 days after treatment, P <0. 01. RBF increased compared with that before treatment, and compared with that of sodium restricted diet group 10 days after treatment, P < 0. 01. Renal damage induced by low blood sodium after treatment was less in non-sodium restricted diet group than that in sodium restricted diet group, P <0. 05. Ascites disappearance upon discharge was more in sodium restricted diet group than that in non-sodium restricted diet group, P <0. 01. Time of ascites disappearance was shorter in non-sodium restricted diet group than that in sodium restricted diet group, P < 0. 01. Compared with sodium restricted diet, while using diuretics of both groups, non-sodium restricted diet can increase level of blood sodium, thus increasing excretion of urine sodium and diuretic effect. It can also decrease levels of PRA, AII and ALD, increase renal blood flow and prevent renal damage induced by low blood sodium and facilitate subsiding of ascites.

Glucagon-like peptide-1 acutely affects renal blood flow and urinary flow rate in spontaneously hypertensive rats despite significantly reduced renal expression of GLP-1 receptors.

PubMed

Ronn, Jonas; Jensen, Elisa P; Wewer Albrechtsen, Nicolai J; Holst, Jens Juul; Sorensen, Charlotte M

2017-12-01

Glucagon-like peptide-1 (GLP-1) is an incretin hormone increasing postprandial insulin release. GLP-1 also induces diuresis and natriuresis in humans and rodents. The GLP-1 receptor is extensively expressed in the renal vascular tree in normotensive rats where acute GLP-1 treatment leads to increased mean arterial pressure (MAP) and increased renal blood flow (RBF). In hypertensive animal models, GLP-1 has been reported both to increase and decrease MAP. The aim of this study was to examine expression of renal GLP-1 receptors in spontaneously hypertensive rats (SHR) and to assess the effect of acute intrarenal infusion of GLP-1. We hypothesized that GLP-1 would increase diuresis and natriuresis and reduce MAP in SHR. Immunohistochemical staining and in situ hybridization for the GLP-1 receptor were used to localize GLP-1 receptors in the kidney. Sevoflurane-anesthetized normotensive Sprague-Dawley rats and SHR received a 20 min intrarenal infusion of GLP-1 and changes in MAP, RBF, heart rate, dieresis, and natriuresis were measured. The vasodilatory effect of GLP-1 was assessed in isolated interlobar arteries from normo- and hypertensive rats. We found no expression of GLP-1 receptors in the kidney from SHR. However, acute intrarenal infusion of GLP-1 increased MAP, RBF, dieresis, and natriuresis without affecting heart rate in both rat strains. These results suggest that the acute renal effects of GLP-1 in SHR are caused either by extrarenal GLP-1 receptors activating other mechanisms (e.g., insulin) to induce the renal changes observed or possibly by an alternative renal GLP-1 receptor. © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

Nucleolar Proteome Analysis and Proteasomal Activity Assays Reveal a Link between Nucleolus and 26S Proteasome in A. thaliana

PubMed Central

Montacié, Charlotte; Durut, Nathalie; Opsomer, Alison; Palm, Denise; Comella, Pascale; Picart, Claire; Carpentier, Marie-Christine; Pontvianne, Frederic; Carapito, Christine; Schleiff, Enrico; Sáez-Vásquez, Julio

2017-01-01

In all eukaryotic cells, the nucleolus is functionally and structurally linked to rRNA synthesis and ribosome biogenesis. This compartment contains as well factors involved in other cellular activities, but the functional interconnection between non-ribosomal activities and the nucleolus (structure and function) still remains an open question. Here, we report a novel mass spectrometry analysis of isolated nucleoli from Arabidopsis thaliana plants using the FANoS (Fluorescence Assisted Nucleolus Sorting) strategy. We identified many ribosome biogenesis factors (RBF) and proteins non-related with ribosome biogenesis, in agreement with the recognized multi-functionality of the nucleolus. Interestingly, we found that 26S proteasome subunits localize in the nucleolus and demonstrated that proteasome activity and nucleolus organization are intimately linked to each other. Proteasome subunits form discrete foci in the disorganized nucleolus of nuc1.2 plants. Nuc1.2 protein extracts display reduced proteasome activity in vitro compared to WT protein extracts. Remarkably, proteasome activity in nuc1.2 is similar to proteasome activity in WT plants treated with proteasome inhibitors (MG132 or ALLN). Finally, we show that MG132 treatment induces disruption of nucleolar structures in WT but not in nuc1.2 plants. Altogether, our data suggest a functional interconnection between nucleolus structure and proteasome activity. PMID:29104584

Comparison of SVM RBF-NN and DT for crop and weed identification based on spectral measurement over corn fields

USDA-ARS?s Scientific Manuscript database

It is important to find an appropriate pattern-recognition method for in-field plant identification based on spectral measurement in order to classify the crop and weeds accurately. In this study, the method of Support Vector Machine (SVM) was evaluated and compared with two other methods, Decision ...

Model, analysis, and evaluation of the effects of analog VLSI arithmetic on linear subspace-based image recognition.

PubMed

Carvajal, Gonzalo; Figueroa, Miguel

2014-07-01

Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Gaussian process regression for tool wear prediction

NASA Astrophysics Data System (ADS)

Kong, Dongdong; Chen, Yongjie; Li, Ning

2018-05-01

To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.

Intraocular Pressure, Blood Pressure, and Retinal Blood Flow Autoregulation: A Mathematical Model to Clarify Their Relationship and Clinical Relevance

PubMed Central

Guidoboni, Giovanna; Harris, Alon; Cassani, Simone; Arciero, Julia; Siesky, Brent; Amireskandari, Annahita; Tobe, Leslie; Egan, Patrick; Januleviciene, Ingrida; Park, Joshua

2014-01-01

Purpose. This study investigates the relationship between intraocular pressure (IOP) and retinal hemodynamics and predicts how arterial blood pressure (BP) and blood flow autoregulation (AR) influence this relationship. Methods. A mathematical model is developed to simulate blood flow in the central retinal vessels and retinal microvasculature as current flowing through a network of resistances and capacitances. Variable resistances describe active and passive diameter changes due to AR and IOP. The model is validated by using clinically measured values of retinal blood flow and velocity. The model simulations for six theoretical patients with high, normal, and low BP (HBP-, NBP-, LBP-) and functional or absent AR (-wAR, -woAR) are compared with clinical data. Results. The model predicts that NBPwAR and HBPwAR patients can regulate retinal blood flow (RBF) as IOP varies between 15 and 23 mm Hg and between 23 and 29 mm Hg, respectively, whereas LBPwAR patients do not adequately regulate blood flow if IOP is 15 mm Hg or higher. Hemodynamic alterations would be noticeable only if IOP changes occur outside of the regulating range, which, most importantly, depend on BP. The model predictions are consistent with clinical data for IOP reduction via surgery and medications and for cases of induced IOP elevation. Conclusions. The theoretical model results suggest that the ability of IOP to induce noticeable changes in retinal hemodynamics depends on the levels of BP and AR of the individual. These predictions might help to explain the inconsistencies found in the clinical literature concerning the relationship between IOP and retinal hemodynamics. PMID:24876284

Intraocular pressure, blood pressure, and retinal blood flow autoregulation: a mathematical model to clarify their relationship and clinical relevance.

PubMed

Guidoboni, Giovanna; Harris, Alon; Cassani, Simone; Arciero, Julia; Siesky, Brent; Amireskandari, Annahita; Tobe, Leslie; Egan, Patrick; Januleviciene, Ingrida; Park, Joshua

2014-05-29

This study investigates the relationship between intraocular pressure (IOP) and retinal hemodynamics and predicts how arterial blood pressure (BP) and blood flow autoregulation (AR) influence this relationship. A mathematical model is developed to simulate blood flow in the central retinal vessels and retinal microvasculature as current flowing through a network of resistances and capacitances. Variable resistances describe active and passive diameter changes due to AR and IOP. The model is validated by using clinically measured values of retinal blood flow and velocity. The model simulations for six theoretical patients with high, normal, and low BP (HBP-, NBP-, LBP-) and functional or absent AR (-wAR, -woAR) are compared with clinical data. The model predicts that NBPwAR and HBPwAR patients can regulate retinal blood flow (RBF) as IOP varies between 15 and 23 mm Hg and between 23 and 29 mm Hg, respectively, whereas LBPwAR patients do not adequately regulate blood flow if IOP is 15 mm Hg or higher. Hemodynamic alterations would be noticeable only if IOP changes occur outside of the regulating range, which, most importantly, depend on BP. The model predictions are consistent with clinical data for IOP reduction via surgery and medications and for cases of induced IOP elevation. The theoretical model results suggest that the ability of IOP to induce noticeable changes in retinal hemodynamics depends on the levels of BP and AR of the individual. These predictions might help to explain the inconsistencies found in the clinical literature concerning the relationship between IOP and retinal hemodynamics. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

PubMed

Siddiqui, Muhammad Faisal; Reza, Ahmed Wasif; Kanesan, Jeevan

2015-01-01

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data

NASA Astrophysics Data System (ADS)

Ashrafi, Mohammad; Chua, Lloyd Hock Chye; Quek, Chai; Qin, Xiaosheng

2017-02-01

Current state-of-the-art online neuro fuzzy models (NFMs) such as DENFIS (Dynamic Evolving Neural-Fuzzy Inference System) have been used for runoff forecasting. Online NFMs adopt a local learning approach and are able to adapt to changes continuously. The DENFIS model however requires upper/lower bound for normalization and also the number of rules increases monotonically. This requirement makes the model unsuitable for use in basins with limited data, since a priori data is required. In order to address this and other drawbacks of current online models, the Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) is adopted in this study for forecast applications in basins with limited data. GSETSK is a fully-online NFM which updates its structure and parameters based on the most recent data. The model does not require the need for historical data and adopts clustering and rule pruning techniques to generate a compact and up-to-date rule-base. GSETSK was used in two forecast applications, rainfall-runoff (a catchment in Sweden) and river routing (Lower Mekong River) forecasts. Each of these two applications was studied under two scenarios: (i) there is no prior data, and (ii) only limited data is available (1 year for the Swedish catchment and 1 season for the Mekong River). For the Swedish Basin, GSETSK model results were compared to available results from a calibrated HBV (Hydrologiska Byråns Vattenbalansavdelning) model. For the Mekong River, GSETSK results were compared against the URBS (Unified River Basin Simulator) model. Both comparisons showed that results from GSETSK are comparable with the physically based models, which were calibrated with historical data. Thus, even though GSETSK was trained with a very limited dataset in comparison with HBV or URBS, similar results were achieved. Similarly, further comparisons between GSETSK with DENFIS and the RBF (Radial Basis Function) models highlighted further advantages of GSETSK as having a rule-base (compared to opaque RBF) which is more compact, up-to-date and more easily interpretable.

Interdependency of Cystathione γ-Lyase and Cystathione β-Synthase in Hydrogen Sulfide–Induced Blood Pressure Regulation in Rats

PubMed Central

Roy, Alexander; Khan, Abdul H.; Islam, Mohammed T.; Prieto, Minolfa C.; Majid, Dewan S.A.

2012-01-01

Background Hydrogen sulfide (H2S), an endogenous vasoactive agent, is produced by cystathionine γ-lyase (CGL) and cystathionine β-synthase (CBS) enzymes. This study was conducted to evaluate the relative contribution of these enzymes in regulating systemic arterial pressure. Methods Sprague–Dawley rats were chronically treated with CGL inhibitor, -propargylglycine (PAG, 37.5 mg/kg/day; intraperitoneally, i.p.) or CBS inhibitor, aminooxyacetic acid (AOA, 8.75 mg/kg/day; i.p.) or in combination for 4 weeks and the effects on arterial pressure (tail-cuff plethysmography) and renal excretory function (24 h urine collections using metabolic cages) were assessed once in a week. Changes in renal blood flow (RBF; Ultrasonic flowmetry) and glomerular filtration rate (GFR; Inulin clearance) were assessed in acute experiments in anesthetized rats at the end of treatment period. Results Compared to vehicle treated control group, only the rats with combination therapy showed a decrease in urinary sulfate excretion rate (248 ± 47 vs. 591 ± 70 nmol/24 h; marker for endogenous H2S level) which was associated with an increase in mean arterial pressure (MAP; 130 ± 2 vs. 99 ± 2 mm Hg). Urine flow and sodium excretion were also increased in combination group as consequent to the increase in MAP. GFR did not alter due to these treatments but RBF was lowered (4 ± 0.3 vs. 7 ± 0.4 ml/min/g) only in the combination group compared to the control group. Conclusion These findings indicate that a deficiency in one enzyme's activity could be compensated by the activity of the other to maintain the endogenous H2S level, the deficiency of which modulates systemic and renal vascular resistances leading to the development of hypertension. PMID:21866187

Recent advances in numerical PDEs

NASA Astrophysics Data System (ADS)

Zuev, Julia Michelle

In this thesis, we investigate four neighboring topics, all in the general area of numerical methods for solving Partial Differential Equations (PDEs). Topic 1. Radial Basis Functions (RBF) are widely used for multi-dimensional interpolation of scattered data. This methodology offers smooth and accurate interpolants, which can be further refined, if necessary, by clustering nodes in select areas. We show, however, that local refinements with RBF (in a constant shape parameter [varepsilon] regime) may lead to the oscillatory errors associated with the Runge phenomenon (RP). RP is best known in the case of high-order polynomial interpolation, where its effects can be accurately predicted via Lebesgue constant L (which is based solely on the node distribution). We study the RP and the applicability of Lebesgue constant (as well as other error measures) in RBF interpolation. Mainly, we allow for a spatially variable shape parameter, and demonstrate how it can be used to suppress RP-like edge effects and to improve the overall stability and accuracy. Topic 2. Although not as versatile as RBFs, cubic splines are useful for interpolating grid-based data. In 2-D, we consider a patch representation via Hermite basis functions s i,j ( u, v ) = [Special characters omitted.] h mn H m ( u ) H n ( v ), as opposed to the standard bicubic representation. Stitching requirements for the rectangular non-equispaced grid yield a 2-D tridiagonal linear system AX = B, where X represents the unknown first derivatives. We discover that the standard methods for solving this NxM system do not take advantage of the spline-specific format of the matrix B. We develop an alternative approach using this specialization of the RHS, which allows us to pre-compute coefficients only once, instead of N times. MATLAB implementation of our fast 2-D cubic spline algorithm is provided. We confirm analytically and numerically that for large N ( N > 200), our method is at least 3 times faster than the standard algorithm and is just as accurate. Topic 3. The well-known ADI-FDTD method for solving Maxwell's curl equations is second-order accurate in space/time, unconditionally stable, and computationally efficient. We research Richardson extrapolation -based techniques to improve time discretization accuracy for spatially oversampled ADI-FDTD. A careful analysis of temporal accuracy, computational efficiency, and the algorithm's overall stability is presented. Given the context of wave- type PDEs, we find that only a limited number of extrapolations to the ADI-FDTD method are beneficial, if its unconditional stability is to be preserved. We propose a practical approach for choosing the size of a time step that can be used to improve the efficiency of the ADI-FDTD algorithm, while maintaining its accuracy and stability. Topic 4. Shock waves and their energy dissipation properties are critical to understanding the dynamics controlling the MHD turbulence. Numerical advection algorithms used in MHD solvers (e.g. the ZEUS package) introduce undesirable numerical viscosity. To counteract its effects and to resolve shocks numerically, Richtmyer and von Neumann's artificial viscosity is commonly added to the model. We study shock power by analyzing the influence of both artificial and numerical viscosity on energy decay rates. Also, we analytically characterize the numerical diffusivity of various advection algorithms by quantifying their diffusion coefficients e.

Equivalent magnetic vector potential model for low-frequency magnetic exposure assessment

NASA Astrophysics Data System (ADS)

Diao, Y. L.; Sun, W. N.; He, Y. Q.; Leung, S. W.; Siu, Y. M.

2017-10-01

In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort—the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.

The effect of traditional Persian music on the cardiac functioning of young Iranian women.

PubMed

Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh; Khosroshai, Hamid Tayebi; Javanshir, Elnaz

In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are not similar. Therefore, in the present study, we have sought to examine the effects of traditional Persian music on the cardiac function in young women. Twenty-two healthy females participated in this study. ECG signals were recorded in two conditions: rest and music. For each of the 21 ECG signals (15 morphological and six wavelet based feature) features were extracted. SVM classifier was used for the classification of ECG signals during and before the music. The results showed that the mean of heart rate, the mean amplitude of R-wave, T-wave, and P-wave decreased in response to music. Time-frequency analysis revealed that the mean of the absolute values of the detail coefficients at higher scales increased during rest. The overall accuracy of 91.6% was achieved using polynomial kernel and RBF kernel. Using linear kernel, the best result (with the accuracy rate of 100%) was attained. Copyright © 2016. Published by Elsevier B.V.

Equivalent magnetic vector potential model for low-frequency magnetic exposure assessment.

PubMed

Diao, Y L; Sun, W N; He, Y Q; Leung, S W; Siu, Y M

2017-09-21

In this paper, a novel source model based on a magnetic vector potential for the assessment of induced electric field strength in a human body exposed to the low-frequency (LF) magnetic field of an electrical appliance is presented. The construction of the vector potential model requires only a single-component magnetic field to be measured close to the appliance under test, hence relieving considerable practical measurement effort-the radial basis functions (RBFs) are adopted for the interpolation of discrete measurements; the magnetic vector potential model can then be directly constructed by summing a set of simple algebraic functions of RBF parameters. The vector potentials are then incorporated into numerical calculations as the equivalent source for evaluations of the induced electric field in the human body model. The accuracy and effectiveness of the proposed model are demonstrated by comparing the induced electric field in a human model to that of the full-wave simulation. This study presents a simple and effective approach for modelling the LF magnetic source. The result of this study could simplify the compliance test procedure for assessing an electrical appliance regarding LF magnetic exposure.

Distribution of intrarenal blood flow consequent to left atrial balloon inflation.

PubMed

Passmore, J C; Stremel, R W; Hock, C E; Allen, R L; Bradford, W B

1985-01-01

The effects of inflation of a balloon within, and consequent distension of, the left atrium (LABI, left atrial balloon inflation) on total renal blood flow (RBF) and intrarenal blood flow distribution were measured and compared to values obtained from another group of dogs that were hemorrhaged (HEM) to the same level of hypotension as that produced by LABI, a mean systemic arterial pressure of 88 mm Hg. Kidney wt/kg, RBF/kg body wt, and urine flow were markedly reduced during the hemorrhage period in the HEM group when compared to values obtained during the experimental period for the LABI group. Data from the freeze-dissection (133Xe) analysis revealed that the percentage distribution of blood flow as renal outer cortical (OC) blood flow was less (26%) in the HEM group than in the LABI group (50%), this latter value being very similar to that of control dogs that experienced no hypotension (49%). LABI better maintains OC blood flow and urine flow when compared to HEM at the same systemic blood pressure, suggesting a role for cardiopulmonary receptors in reflex sympathetic control of renal blood flow distribution during hypotension.

Differential influence of urbanisation on Coccidian infection in two passerine birds.

PubMed

Delgado-V, Carlos A; French, Kris

2015-06-01

Urbanisation has the potential to increase the risk of parasitism on wildlife. Although some ectoparasite groups appear unaffected, different responses are hypothesised for parasites with simpler life histories such as gastrointestinal parasites. Red-browed finches (RBF) and the superb fairywrens (SFW), two native passerine birds affected by urbanisation, were examined for Coccidian parasites along an urbanisation gradient in New South Wales, Australia, in order to detect if prevalence might be directly related to the degree of urbanisation. Influence of urbanisation on Coccidian infection was differential. In RBF, the prevalence of Isospora increased significantly in more urbanised areas but prevalence did not change between breeding and non-breeding seasons. In contrast, in SFW, the degree of urbanisation did not significantly change with the degree of urbanisation, and season exhibited no significant effects on the prevalence of coccidians. Diet, behaviour and habits are suspected to be the most influential factors on the variation seen between both species where granivorous and gregarious species are significantly infected. Since the dynamics of urban wildlife-pathogen interactions is largely unexplored, more studies are needed to corroborate if this pattern of Isospora infections can be extended to other passerine birds in cities from Australia and overseas.

High quality optical microangiography of ocular microcirculation and measurement of total retinal blood flow in mouse eye

NASA Astrophysics Data System (ADS)

Zhi, Zhongwei; Yin, Xin; Dziennis, Suzan; Alpers, Charles E.; Wang, Ruikang K.

2013-03-01

Visualization and measurement of retinal blood flow (RBF) is important to the diagnosis and management of different eye diseases, including diabetic retinopathy. Optical microangiography (OMAG) is developed for generating 3D dynamic microcirculation image and later refined into ultra-high sensitive OMAG (UHS-OMAG) for true capillary vessels imaging. Here, we present the application of OMAG imaging technique for visualization of depth-resolved vascular network within retina and choroid as well as measurement of total retinal blood flow in mice. A fast speed spectral domain OCT imaging system at 820nm with a line scan rate of 140 kHz was developed to image mouse posterior eye. By applying UHS-OMAG scanning protocol and processing algorithm, we achieved true capillary level imaging of retina and choroid vasculature in mouse eye. The vascular pattern within different retinal layers and choroid was presented. An en face Doppler OCT approach [1] without knowing Doppler angle was adopted for the measurement of total retinal blood flow. The axial blood flow velocity is measured in an en face plane by raster scanning and the flow is calculated by integrating over the vessel area of the central retinal artery.

Research on the feature extraction and pattern recognition of the distributed optical fiber sensing signal

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Sun, Qi; Pi, Shaohua; Wu, Hongyan

2014-09-01

In this paper, feature extraction and pattern recognition of the distributed optical fiber sensing signal have been studied. We adopt Mel-Frequency Cepstral Coefficient (MFCC) feature extraction, wavelet packet energy feature extraction and wavelet packet Shannon entropy feature extraction methods to obtain sensing signals (such as speak, wind, thunder and rain signals, etc.) characteristic vectors respectively, and then perform pattern recognition via RBF neural network. Performances of these three feature extraction methods are compared according to the results. We choose MFCC characteristic vector to be 12-dimensional. For wavelet packet feature extraction, signals are decomposed into six layers by Daubechies wavelet packet transform, in which 64 frequency constituents as characteristic vector are respectively extracted. In the process of pattern recognition, the value of diffusion coefficient is introduced to increase the recognition accuracy, while keeping the samples for testing algorithm the same. Recognition results show that wavelet packet Shannon entropy feature extraction method yields the best recognition accuracy which is up to 97%; the performance of 12-dimensional MFCC feature extraction method is less satisfactory; the performance of wavelet packet energy feature extraction method is the worst.

Detailing the relation between renal T2* and renal tissue pO2 using an integrated approach of parametric magnetic resonance imaging and invasive physiological measurements.

PubMed

Pohlmann, Andreas; Arakelyan, Karen; Hentschel, Jan; Cantow, Kathleen; Flemming, Bert; Ladwig, Mechthild; Waiczies, Sonia; Seeliger, Erdmann; Niendorf, Thoralf

2014-08-01

This study was designed to detail the relation between renal T2* and renal tissue pO2 using an integrated approach that combines parametric magnetic resonance imaging (MRI) and quantitative physiological measurements (MR-PHYSIOL). Experiments were performed in 21 male Wistar rats. In vivo modulation of renal hemodynamics and oxygenation was achieved by brief periods of aortic occlusion, hypoxia, and hyperoxia. Renal perfusion pressure (RPP), renal blood flow (RBF), local cortical and medullary tissue pO2, and blood flux were simultaneously recorded together with T2*, T2 mapping, and magnetic resonance-based kidney size measurements (MR-PHYSIOL). Magnetic resonance imaging was carried out on a 9.4-T small-animal magnetic resonance system. Relative changes in the invasive quantitative parameters were correlated with relative changes in the parameters derived from MRI using Spearman analysis and Pearson analysis. Changes in T2* qualitatively reflected tissue pO2 changes induced by the interventions. T2* versus pO2 Spearman rank correlations were significant for all interventions, yet quantitative translation of T2*/pO2 correlations obtained for one intervention to another intervention proved not appropriate. The closest T2*/pO2 correlation was found for hypoxia and recovery. The interlayer comparison revealed closest T2*/pO2 correlations for the outer medulla and showed that extrapolation of results obtained for one renal layer to other renal layers must be made with due caution. For T2* to RBF relation, significant Spearman correlations were deduced for all renal layers and for all interventions. T2*/RBF correlations for the cortex and outer medulla were even superior to those between T2* and tissue pO2. The closest T2*/RBF correlation occurred during hypoxia and recovery. Close correlations were observed between T2* and kidney size during hypoxia and recovery and for occlusion and recovery. In both cases, kidney size correlated well with renal vascular conductance, as did renal vascular conductance with T2*. Our findings indicate that changes in T2* qualitatively mirror changes in renal tissue pO2 but are also associated with confounding factors including vascular volume fraction and tubular volume fraction. Our results demonstrate that MR-PHYSIOL is instrumental to detail the link between renal tissue pO2 and T2* in vivo. Unravelling the link between regional renal T2* and tissue pO2, including the role of the T2* confounding parameters vascular and tubular volume fraction and oxy-hemoglobin dissociation curve, requires further research. These explorations are essential before the quantitative capabilities of parametric MRI can be translated from experimental research to improved clinical understanding of hemodynamics/oxygenation in kidney disorders.

Effects of sediment-associated extractable metals, degree of sediment grain sorting, and dissolved organic carbon upon Cryptosporidium parvum removal and transport within riverbank filtration sediments, Sonoma County, California

USGS Publications Warehouse

Metge, D.W.; Harvey, R.W.; Aiken, G.R.; Anders, R.; Lincoln, G.; Jasperse, James; Hill, M.C.

2011-01-01

Oocysts of the protozoan pathogen Cryptosporidium parvum are of particular concern for riverbank filtration (RBF) operations because of their persistence, ubiquity, and resistance to chlorine disinfection. At the Russian River RBF site (Sonoma County, CA), transport of C. parvumoocysts and oocyst-sized (3 μm) carboxylate-modified microspheres through poorly sorted (sorting indices, σ1, up to 3.0) and geochemically heterogeneous sediments collected between 2 and 25 m below land surface (bls) were assessed. Removal was highly sensitive to variations in both the quantity of extractable metals (mainly Fe and Al) and degree of grain sorting. In flow-through columns, there was a log–linear relationship (r2 = 0.82 at p < 0.002) between collision efficiency (α, the probability that colloidal collisions with grain surfaces would result in attachment) and extractable metals, and a linear relationship (r2 = 0.99 at p < 0.002) between α and σ1. Collectively, variability in extractable metals and grain sorting accounted for ∼83% of the variability in α (at p < 0.0002) along the depth profiles. Amendments of 2.2 mg L–1 of Russian River dissolved organic carbon (DOC) reduced α for oocysts by 4–5 fold. The highly reactive hydrophobic organic acid (HPOA) fraction was particularly effective in re-entraining sediment-attached microspheres. However, the transport-enhancing effects of the riverine DOC did not appear to penetrate very deeply into the underlying sediments, judging from high α values (∼1.0) observed for oocysts being advected through unamended sediments collected at ∼2 m bls. This study suggests that in evaluating the efficacy of RBF operations to remove oocysts, it may be necessary to consider not only the geochemical nature and size distribution of the sediment grains, but also the degrees of sediment sorting and the concentration, reactivity, and penetration of the source water DOC.

Assessment of the microbial removal capabilities of riverbank filtration

NASA Astrophysics Data System (ADS)

Partinoudi, V.; Collins, M.; Margolin, A.; Brannaka, L.

2003-04-01

Riverbank filtrate includes both groundwater and river water that has percolated through the banks or bed of a river to an extraction well. One of the primary objectives of this study was to assess the microbial removal capabilities of riverbank filtration (RBF) independent of any groundwater dilution, i.e. a worse case scenario. A total of five sites were chosen: the Pembroke Waterworks (NH), the Milford State Fish Hatchery (NH), Jackson (NH) (where an infiltration gallery exists), Louisville Water Company (KY), and Cedar Rapids (IA). This study has been monitoring total coliforms, E.coli and aerobic spore forming bacteria amongst other water quality parameters over the past twelve months. Male specific (MS2) and somatic coliphage viruses were also monitored intensively for two weeks, using a single agar overlay and a two-step enrichment method, in December 2002 in Louisville, KY and in Cedar Rapids, IA. This intensive coliphage monitoring was followed by the collection of samples for special analysis of enteric viruses (Adenovirus type 40 and 41, Astrovirus, Poliovirus, Coxsackie virus, Rotavirus and Echovirus). The virus samples were analyzed using the ICC-nPCR method, due to its high specificity and sensitivity. Typical river water total coliforms, E.coli and aerobic spore forming bacteria concentrations ranged between 43-145000 CFU/100mL, 0-24192 CFU/100mL and 83-1997 CFU/100mL, respectively. All three of these microbial concentrations were below detection limits (<1CFU/100mL) in the riverbank filtration extraction well water, even after eliminating the “dilution” effects with groundwater. The male specific and the somatic coliphages ranged between 328-491 PFU/25mL and 3-21 PFU/25mL, respectively, in the river water. The concentration of the male specific coliphages was reduced by as much as 77% by the riverbank passage whereas the concentrations of the somatic coliphages were reduced by 100%. In summary the sites evaluated in this study indicated the conservative effectiveness of RBF in removing bacteria and virus indicators. Any groundwater dilution with the RBF extract should contribute to even lower microbial concentrations.

Quantification of single-kidney glomerular filtration rate with electron-beam computed tomography

NASA Astrophysics Data System (ADS)

Lerman, Lilach O.; Ritman, Erik L.; Pelaez, Laura I.; Sheedy, Patrick F., II; Krier, James D.

2000-04-01

The ability to accurately and noninvasively quantify single- kidney GFR could be invaluable for assessment of renal function. We developed a model that enables this measurement with EBCT. To examine the reliability of this method, EBCT renal flow and volume studies after contrast media administration were performed in pigs with unilateral renal artery stenosis (Group 1), controls (Group 2), and simultaneously with inulin clearance (Group 3). Renal flow curves, obtained from the bilateral renal cortex and medulla, depicted transit of the contrast through the vascular and tubular compartments, and were fitted using extended gamma- variate functions. Renal blood flow was calculated as the sum of products of cortical and medullary perfusions and volumes. Normalized GFR (mL/min/cc) was calculated using the rate (maximal slope) of proximal tubular contrast accumulation, and EBCT-GFR as normalized GFR* cortical volume. In Group 1, the decreased GFR of the stenotic kidney correlated well with its decreased volume and RBF, and with the degree of stenosis (r equals -0.99). In Group 3, EBCT-GFR correlated well with inulin clearance (slope 1.1, r equals 0.81). This novel approach can be very useful for quantification of concurrent regional hemodynamics and function in the intact kidneys, in a manner potentially applicable to humans.

Developing a Drosophila Model of Schwannomatosis

DTIC Science & Technology

2013-02-01

Drosophila melanogaster has become an important model system for cancer studies. Reduced redundancy in the Drosophila genome compared with that of...of high-resolution deletion coverage of the Drosophila melanogaster genome . Nat. Genet. 36, 288-292. Pastor-Pareja, J. C., Wu, M. and Xu. T. (2008...microarray analysis of the entire Drosophila melanogaster genome and compared gene expression profiles of wild type, dCap-D3 and rbf1 mutant

Relativistic Corrections to the Properties of the Alkali Fluorides

NASA Technical Reports Server (NTRS)

Dyall, Kenneth G.; Partridge, Harry

1993-01-01

Relativistic corrections to the bond lengths, dissociation energies and harmonic frequencies of KF, RbF and CsF have been obtained at the self-consistent field level by dissociating to ions. The relativistic corrections to the bond lengths, harmonic frequencies and dissociation energies to the ions are very small, due to the ionic nature of these molecules and the similarity of the relativistic and nonrelativistic ionic radii.

Hybrid diffuse optical techniques for continuous hemodynamic measurement in gastrocnemius during plantar flexion exercise

NASA Astrophysics Data System (ADS)

Henry, Brad; Zhao, Mingjun; Shang, Yu; Uhl, Timothy; Thomas, D. Travis; Xenos, Eleftherios S.; Saha, Sibu P.; Yu, Guoqiang

2015-12-01

Occlusion calibrations and gating techniques have been recently applied by our laboratory for continuous and absolute diffuse optical measurements of forearm muscle hemodynamics during handgrip exercises. The translation of these techniques from the forearm to the lower limb is the goal of this study as various diseases preferentially affect muscles in the lower extremity. This study adapted a hybrid near-infrared spectroscopy and diffuse correlation spectroscopy system with a gating algorithm to continuously quantify hemodynamic responses of medial gastrocnemius during plantar flexion exercises in 10 healthy subjects. The outcomes from optical measurement include oxy-, deoxy-, and total hemoglobin concentrations, blood oxygen saturation, and relative changes in blood flow (rBF) and oxygen consumption rate (rV˙O2). We calibrated rBF and rV˙O2 profiles with absolute baseline values of BF and V˙O2 obtained by venous and arterial occlusions, respectively. Results from this investigation were comparable to values from similar studies. Additionally, significant correlation was observed between resting local muscle BF measured by the optical technique and whole limb BF measured concurrently by a strain gauge venous plethysmography. The extensive hemodynamic and metabolic profiles during exercise will allow for future comparison studies to investigate the diagnostic value of hybrid technologies in muscles affected by disease.

Evidence for a Humoral Mechanism in Volume Expansion Natriuresis

PubMed Central

Kaloyanides, George J.; Azer, Maher

1971-01-01

The role of a humoral mechanism in the natriuresis induced by volume expansion was evaluated using an isolated dog kidney perfused by a second dog which had been pretreated with desoxycorticosterone acetate (DOCA). Expansion of the perfusion dog with an equilibrated volume of blood from a reservoir, resulted in an increase in UnaV (sodium excretion) from 153.6±27.9 (sem) to 345.5±57.8 μEq/min, P<0.001. FEna (fractional sodium excretion) increased from 3.4±0.6 to 8.1±1.2%, P<0.01. The natriuresis occurred in the face of a significant decrease in Cin, RBF, and renal arterial pressure, and in the absence of any change in plasma protein concentration or packed cell volume. In a control group of experiments, sodium excretion did not change when the perfusion dog was not volume expanded, although Cin (inulin clearance) and RBF (renal blood flow) decreased to the same degree as in the expanded group. These data support the conclusion that volume expansion of the perfusion dog either stimulated the release of a natriuretic factor or suppressed the release of an antinatriuretic factor which was manifested by an increase in sodium excretion in the isolated kidney. PMID:5097568

A Temperature Compensation Method for Piezo-Resistive Pressure Sensor Utilizing Chaotic Ions Motion Algorithm Optimized Hybrid Kernel LSSVM.

PubMed

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

2016-10-14

A piezo-resistive pressure sensor is made of silicon, the nature of which is considerably influenced by ambient temperature. The effect of temperature should be eliminated during the working period in expectation of linear output. To deal with this issue, an approach consists of a hybrid kernel Least Squares Support Vector Machine (LSSVM) optimized by a chaotic ions motion algorithm presented. To achieve the learning and generalization for excellent performance, a hybrid kernel function, constructed by a local kernel as Radial Basis Function (RBF) kernel, and a global kernel as polynomial kernel is incorporated into the Least Squares Support Vector Machine. The chaotic ions motion algorithm is introduced to find the best hyper-parameters of the Least Squares Support Vector Machine. The temperature data from a calibration experiment is conducted to validate the proposed method. With attention on algorithm robustness and engineering applications, the compensation result shows the proposed scheme outperforms other compared methods on several performance measures as maximum absolute relative error, minimum absolute relative error mean and variance of the averaged value on fifty runs. Furthermore, the proposed temperature compensation approach lays a foundation for more extensive research.

Design and landing dynamic analysis of reusable landing leg for a near-space manned capsule

NASA Astrophysics Data System (ADS)

Yue, Shuai; Nie, Hong; Zhang, Ming; Wei, Xiaohui; Gan, Shengyong

2018-06-01

To improve the landing performance of a near-space manned capsule under various landing conditions, a novel landing system is designed that employs double chamber and single chamber dampers in the primary and auxiliary struts, respectively. A dynamic model of the landing system is established, and the damper parameters are determined by employing the design method. A single-leg drop test with different initial pitch angles is then conducted to compare and validate the simulation model. Based on the validated simulation model, seven critical landing conditions regarding nine crucial landing responses are found by combining the radial basis function (RBF) surrogate model and adaptive simulated annealing (ASA) optimization method. Subsequently, the adaptability of the landing system under critical landing conditions is analyzed. The results show that the simulation effectively results match the test results, which validates the accuracy of the dynamic model. In addition, all of the crucial responses under their corresponding critical landing conditions satisfy the design specifications, demonstrating the feasibility of the landing system.

Triazine-Carbon Nanotubes: New Platforms for the Design of Flavin Receptors.

PubMed

Lucío, María Isabel; Pichler, Federica; Ramírez, José Ramón; de la Hoz, Antonio; Sánchez-Migallón, Ana; Hadad, Caroline; Quintana, Mildred; Giulani, Angela; Bracamonte, Maria Victoria; Fierro, Jose L G; Tavagnacco, Claudio; Herrero, María Antonia; Prato, Maurizio; Vázquez, Ester

2016-06-20

The synthesis of functionalised carbon nanotubes as receptors for riboflavin (RBF) is reported. Carbon nanotubes, both single-walled and multi-walled, have been functionalised with 1,3,5-triazines and p-tolyl chains by aryl radical addition under microwave irradiation and the derivatives have been fully characterised by using a range of techniques. The interactions between riboflavin and the hybrids were analysed by using fluorescence and UV/Vis spectroscopic techniques. The results show that the attached functional groups minimise the π-π stacking interactions between riboflavin and the nanotube walls. Comparison of p-tolyl groups with the triazine groups shows that the latter have stronger interactions with riboflavin because of the presence of hydrogen bonds. Moreover, the triazine derivatives follow the Stern-Volmer relationship and show a high association constant with riboflavin. In this way, artificial receptors in catalytic processes could be designed through specific control of the interaction between functionalised carbon nanotubes and riboflavin. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform.

PubMed

Tripathy, Rajesh K; Zamora-Mendez, Alejandro; de la O Serna, José A; Paternina, Mario R Arrieta; Arrieta, Juan G; Naik, Ganesh R

2018-01-01

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

PubMed Central

Tripathy, Rajesh K.; Zamora-Mendez, Alejandro; de la O Serna, José A.; Paternina, Mario R. Arrieta; Arrieta, Juan G.; Naik, Ganesh R.

2018-01-01

Accurate detection and classification of life-threatening ventricular arrhythmia episodes such as ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) from electrocardiogram (ECG) is a challenging problem for patient monitoring and defibrillation therapy. This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes. The ECG signal is decomposed into various oscillatory modes using digital Taylor-Fourier transform (DTFT). The magnitude feature and a novel phase feature namely the phase difference (PD) are evaluated from the mode Taylor-Fourier coefficients of ECG signal. The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs. VF, non-shock vs. shock and VF vs. non-VF arrhythmia episodes. The accuracy, sensitivity, and specificity values obtained using the proposed method are 89.81, 86.38, and 93.97%, respectively for the classification of Non-VF and VF episodes. Comparison with the performance of the state-of-the-art features demonstrate the advantages of the proposition.

SVM-Based Synthetic Fingerprint Discrimination Algorithm and Quantitative Optimization Strategy

PubMed Central

Chen, Suhang; Chang, Sheng; Huang, Qijun; He, Jin; Wang, Hao; Huang, Qiangui

2014-01-01

Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors—the ridge distance features, global gray features, frequency feature and Harris Corner feature—are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%. PMID:25347063

Absolute quantification of regional renal blood flow in swine by dynamic contrast-enhanced magnetic resonance imaging using a blood pool contrast agent.

PubMed

Lüdemann, Lutz; Nafz, Benno; Elsner, Franz; Grosse-Siestrup, Christian; Meissler, Michael; Kaufels, Nicola; Rehbein, Hagen; Persson, Pontus B; Michaely, Henrik J; Lengsfeld, Philipp; Voth, Matthias; Gutberlet, Matthias

2009-03-01

To evaluate for the first time in an animal model the possibility of absolute regional quantification of renal medullary and cortical perfusion by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a blood pool contrast agent. A total of 18 adult female pigs (age, 16-22 weeks; body weight, 45-65 kg; no dietary restrictions) were investigated by DCE-MRI. Absolute renal blood flow (RBF) measured by an ultrasound transit time flow probe around the renal vein was used as the standard of reference. An inflatable stainless cuff placed around the renal artery near its origin from the abdominal aorta was used to reduce RBF to 60%, 40%, and 20% of the baseline flow. The last measurement was performed with the cuff fully reopened. Absolute RBF values during these 4 perfusion states were compared with the results of DCE-MRI performed on a 1.5-T scanner with an 8-channel phased-array surface coil. All scans were acquired in breath-hold technique in the coronal plane using a field of view of 460 mm.Each dynamic scan commenced with a set of five 3D T1-weighted gradient echo sequences with different flip angles (alpha = 2 degrees, 5 degrees, 10 degrees, 20 degrees, 30 degrees): TE, 0.88 milliseconds; TR, 2.65 milliseconds; slice thickness, 8.8 mm for 4 slices; acquisition matrix, 128 x 128; and acquisitions, 4. These data served to calculate 3D intrinsic longitudinal relaxation rate maps (R10) and magnetization (M0). Immediately after these images, the dynamic 3D T1-weighted gradient echo images were acquired with the same parameters and a constant alpha = 30 degrees, half Fourier, 1 acquisition, 64 frames, a time interval of 1.65 seconds between each frame, and a total duration of 105.6. Three milliliters of an albumin-binding blood pool contrast agent (0.25 mmol/mL gadofosveset trisodium, Vasovist, Bayer Schering Pharma AG, Berlin, Germany) was injected at a rate of 3 mL/s. Perfusion was calculated using the arterial input function from the aorta, which was extracted from the dynamic relaxation rate change maps and perfusion images were calculated on a voxel-by-voxel basis using a singular value decomposition. In 11 pigs, 4 different perfusion states were investigated sequentially. The reduced kidney perfusion measured by ultrasound highly correlated with total renal blood flow determined by DCE-MRI, P < 0.001. The correlation coefficient between both measurements was 0.843. Regional cortical and medullary renal flow was also highly correlated (r = 0.77/0.78, P < 0.001) with the degree of flow reduction. Perfusion values smaller than 50 mL/min/100 cm were overestimated by MRI, high perfusion values slightly underestimated. DCE-MRI using a blood pool contrast agent allows absolute quantification of total kidney perfusion as well as separate determination of cortical and medullary flow. The results show that our technique has sufficient accuracy and reproducibility to be transferred to the clinical setting.

Double Ramp Loss Based Reject Option Classifier

DTIC Science & Technology

2015-05-22

choose 10% of these points uniformly at random and flip their labels. 2. Ionosphere Dataset [2] : This dataset describes the problem of discrimi- nating...good versus bad radars based on whether they send some useful infor- mation about the Ionosphere . There are 34 variables and 351 observations. 3... Ionosphere dataset (nonlinear classifiers using RBF kernel for both the approaches) d LDR (C = 2, γ = 0.125) LDH (C = 16, γ = 0.125) Risk RR Acc(unrej

Process for the production of .sup.18 F-2-deoxy-2-fluoro-D-glucose

DOEpatents

Elmaleh, David R.; Levy, Shlomo; Shiue, Chyng-Yann; Wolf, Alfred P.

1986-01-01

Process for the production of 2-deoxy-2-fluoro-D-glucose and the corresponding .sup.18 F-compound in which methyl 4,6-O-benzylidine-3-O-methyl-2-O-trifluoromethanesulfonyl-.beta.-D-mannopy ranoside is reacted with a triflating reagent, the resulting compound reacted with CsHF.sub.2, RbF or the corresponding .sup.18 F-compounds, and thereafter the alkyl groups removed by hydrolysis.

Automatic extraction of numeric strings in unconstrained handwritten document images

NASA Astrophysics Data System (ADS)

Haji, M. Mehdi; Bui, Tien D.; Suen, Ching Y.

2011-01-01

Numeric strings such as identification numbers carry vital pieces of information in documents. In this paper, we present a novel algorithm for automatic extraction of numeric strings in unconstrained handwritten document images. The algorithm has two main phases: pruning and verification. In the pruning phase, the algorithm first performs a new segment-merge procedure on each text line, and then using a new regularity measure, it prunes all sequences of characters that are unlikely to be numeric strings. The segment-merge procedure is composed of two modules: a new explicit character segmentation algorithm which is based on analysis of skeletal graphs and a merging algorithm which is based on graph partitioning. All the candidate sequences that pass the pruning phase are sent to a recognition-based verification phase for the final decision. The recognition is based on a coarse-to-fine approach using probabilistic RBF networks. We developed our algorithm for the processing of real-world documents where letters and digits may be connected or broken in a document. The effectiveness of the proposed approach is shown by extensive experiments done on a real-world database of 607 documents which contains handwritten, machine-printed and mixed documents with different types of layouts and levels of noise.

Compensator-based 6-DOF control for probe asteroid-orbital-frame hovering with actuator limitations

NASA Astrophysics Data System (ADS)

Liu, Xiaosong; Zhang, Peng; Liu, Keping; Li, Yuanchun

2016-05-01

This paper is concerned with 6-DOF control of a probe hovering in the orbital frame of an asteroid. Considering the requirements of the scientific instruments pointing direction and orbital position in practical missions, the coordinate control of relative attitude and orbit between the probe and target asteroid is imperative. A 6-DOF dynamic equation describing the relative translational and rotational motion of a probe in the asteroid's orbital frame is derived, taking the irregular gravitation, model and parameter uncertainties and external disturbances into account. An adaptive sliding mode controller is employed to guarantee the convergence of the state error, where the adaptation law is used to estimate the unknown upper bound of system uncertainty. Then the controller is improved to deal with the practical problem of actuator limitations by introducing a RBF neural network compensator, which is used to approximate the difference between the actual control with magnitude constraint and the designed nominal control law. The closed-loop system is proved to be asymptotically stable through the Lyapunov stability analysis. Numerical simulations are performed to compare the performances of the preceding designed control laws. Simulation results demonstrate the validity of the control scheme using the compensator-based adaptive sliding mode control law in the presence of actuator limitations, system uncertainty and external disturbance.

Early superoxide scavenging accelerates renal microvascular rarefaction and damage in the stenotic kidney.

PubMed

Kelsen, Silvia; He, Xiaochen; Chade, Alejandro R

2012-08-15

Renal artery stenosis (RAS), the main cause of chronic renovascular disease (RVD), is associated with significant oxidative stress. Chronic RVD induces renal injury partly by promoting renal microvascular (MV) damage and blunting MV repair in the stenotic kidney. We tested the hypothesis that superoxide anion plays a pivotal role in MV dysfunction, reduction of MV density, and progression of renal injury in the stenotic kidney. RAS was induced in 14 domestic pigs and observed for 6 wk. Seven RAS pigs were chronically treated with the superoxide dismutase mimetic tempol (RAS+T) to reduce oxidative stress. Single-kidney hemodynamics and function were quantified in vivo using multidetector computer tomography (CT) and renal MV density was quantified ex vivo using micro-CT. Expression of angiogenic, inflammatory, and apoptotic factors was measured in renal tissue, and renal apoptosis and fibrosis were quantified in tissue sections. The degree of RAS and blood pressure were similarly increased in RAS and RAS+T. Renal blood flow (RBF) and glomerular filtration rate (GFR) were reduced in the stenotic kidney (280.1 ± 36.8 and 34.2 ± 3.1 ml/min, P < 0.05 vs. control). RAS+T kidneys showed preserved GFR (58.5 ± 6.3 ml/min, P = not significant vs. control) but a similar decreases in RBF (293.6 ± 85.2 ml/min) and further decreases in MV density compared with RAS. These changes were accompanied by blunted angiogenic signaling and increased apoptosis and fibrosis in the stenotic kidney of RAS+T compared with RAS. The current study shows that tempol administration provided limited protection to the stenotic kidney. Despite preserved GFR, renal perfusion was not improved by tempol, and MV density was further reduced compared with untreated RAS, associated with increased renal apoptosis and fibrosis. These results suggest that a tight balance of the renal redox status is necessary for a normal MV repair response to injury, at least at the early stage of RVD, and raise caution regarding antioxidant strategies in RAS.