Data classification using metaheuristic Cuckoo Search technique for Levenberg Marquardt back propagation (CSLM) algorithm

NASA Astrophysics Data System (ADS)

Nawi, Nazri Mohd.; Khan, Abdullah; Rehman, M. Z.

2015-05-01

A nature inspired behavior metaheuristic techniques which provide derivative-free solutions to solve complex problems. One of the latest additions to the group of nature inspired optimization procedure is Cuckoo Search (CS) algorithm. Artificial Neural Network (ANN) training is an optimization task since it is desired to find optimal weight set of a neural network in training process. Traditional training algorithms have some limitation such as getting trapped in local minima and slow convergence rate. This study proposed a new technique CSLM by combining the best features of two known algorithms back-propagation (BP) and Levenberg Marquardt algorithm (LM) for improving the convergence speed of ANN training and avoiding local minima problem by training this network. Some selected benchmark classification datasets are used for simulation. The experiment result show that the proposed cuckoo search with Levenberg Marquardt algorithm has better performance than other algorithm used in this study.

Application of principal component regression and artificial neural network in FT-NIR soluble solids content determination of intact pear fruit

NASA Astrophysics Data System (ADS)

Ying, Yibin; Liu, Yande; Fu, Xiaping; Lu, Huishan

2005-11-01

The artificial neural networks (ANNs) have been used successfully in applications such as pattern recognition, image processing, automation and control. However, majority of today's applications of ANNs is back-propagate feed-forward ANN (BP-ANN). In this paper, back-propagation artificial neural networks (BP-ANN) were applied for modeling soluble solid content (SSC) of intact pear from their Fourier transform near infrared (FT-NIR) spectra. One hundred and sixty-four pear samples were used to build the calibration models and evaluate the models predictive ability. The results are compared to the classical calibration approaches, i.e. principal component regression (PCR), partial least squares (PLS) and non-linear PLS (NPLS). The effects of the optimal methods of training parameters on the prediction model were also investigated. BP-ANN combine with principle component regression (PCR) resulted always better than the classical PCR, PLS and Weight-PLS methods, from the point of view of the predictive ability. Based on the results, it can be concluded that FT-NIR spectroscopy and BP-ANN models can be properly employed for rapid and nondestructive determination of fruit internal quality.

Displacement back analysis for a high slope of the Dagangshan Hydroelectric Power Station based on BP neural network and particle swarm optimization.

PubMed

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes.

Prediction of air pollutant concentration based on sparse response back-propagation training feedforward neural networks.

PubMed

Ding, Weifu; Zhang, Jiangshe; Leung, Yee

2016-10-01

In this paper, we predict air pollutant concentration using a feedforward artificial neural network inspired by the mechanism of the human brain as a useful alternative to traditional statistical modeling techniques. The neural network is trained based on sparse response back-propagation in which only a small number of neurons respond to the specified stimulus simultaneously and provide a high convergence rate for the trained network, in addition to low energy consumption and greater generalization. Our method is evaluated on Hong Kong air monitoring station data and corresponding meteorological variables for which five air quality parameters were gathered at four monitoring stations in Hong Kong over 4 years (2012-2015). Our results show that our training method has more advantages in terms of the precision of the prediction, effectiveness, and generalization of traditional linear regression algorithms when compared with a feedforward artificial neural network trained using traditional back-propagation.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network.

PubMed

Song, Xianzhi; Peng, Chi; Li, Gensheng; He, Zhenguo; Wang, Haizhu

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells.

Optimization of Operation Parameters for Helical Flow Cleanout with Supercritical CO2 in Horizontal Wells Using Back-Propagation Artificial Neural Network

PubMed Central

Song, Xianzhi; Peng, Chi; Li, Gensheng

2016-01-01

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. PMID

Application of Gaussian beam ray-equivalent model and back-propagation artificial neural network in laser diode fast axis collimator assembly.

PubMed

Yu, Hao; Rossi, Giammarco; Braglia, Andrea; Perrone, Guido

2016-08-10

The paper presents the development of a tool based on a back-propagation artificial neural network to assist in the accurate positioning of the lenses used to collimate the beam from semiconductor laser diodes along the so-called fast axis. After training using a Gaussian beam ray-equivalent model, the network is capable of indicating the tilt, decenter, and defocus of such lenses from the measured field distribution, so the operator can determine the errors with respect to the actual lens position and optimize the diode assembly procedure. An experimental validation using a typical configuration exploited in multi-emitter diode module assembly and fast axis collimating lenses with different focal lengths and numerical apertures is reported.

Displacement Back Analysis for a High Slope of the Dagangshan Hydroelectric Power Station Based on BP Neural Network and Particle Swarm Optimization

PubMed Central

Liang, Zhengzhao; Gong, Bin; Tang, Chunan; Zhang, Yongbin; Ma, Tianhui

2014-01-01

The right bank high slope of the Dagangshan Hydroelectric Power Station is located in complicated geological conditions with deep fractures and unloading cracks. How to obtain the mechanical parameters and then evaluate the safety of the slope are the key problems. This paper presented a displacement back analysis for the slope using an artificial neural network model (ANN) and particle swarm optimization model (PSO). A numerical model was established to simulate the displacement increment results, acquiring training data for the artificial neural network model. The backpropagation ANN model was used to establish a mapping function between the mechanical parameters and the monitoring displacements. The PSO model was applied to initialize the weights and thresholds of the backpropagation (BP) network model and determine suitable values of the mechanical parameters. Then the elastic moduli of the rock masses were obtained according to the monitoring displacement data at different excavation stages, and the BP neural network model was proved to be valid by comparing the measured displacements, the displacements predicted by the BP neural network model, and the numerical simulation using the back-analyzed parameters. The proposed model is useful for rock mechanical parameters determination and instability investigation of rock slopes. PMID:25140345

Non-invasive prediction of bloodstain age using the principal component and a back propagation artificial neural network

NASA Astrophysics Data System (ADS)

Sun, Huimin; Meng, Yaoyong; Zhang, Pingli; Li, Yajing; Li, Nan; Li, Caiyun; Guo, Zhiyou

2017-09-01

The age determination of bloodstains is an important and immediate challenge for forensic science. No reliable methods are currently available for estimating the age of bloodstains. Here we report a method for determining the age of bloodstains at different storage temperatures. Bloodstains were stored at 37 °C, 25 °C, 4 °C, and  -20 °C for 80 d. Bloodstains were measured using Raman spectroscopy at various time points. The principal component and a back propagation artificial neural network model were then established for estimating the age of the bloodstains. The results were ideal; the square of correlation coefficient was up to 0.99 (R 2  >  0.99) and the root mean square error of the prediction at lowest reached 55.9829 h. This method is real-time, non-invasive, non-destructive and highly efficiency. It may well prove that Raman spectroscopy is a promising tool for the estimation of the age of bloodstains.

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Data fusion in data scarce areas using a back-propagation artificial neural network model: a case study of the South China Sea

NASA Astrophysics Data System (ADS)

Wang, Zheng; Mao, Zhihua; Xia, Junshi; Du, Peijun; Shi, Liangliang; Huang, Haiqing; Wang, Tianyu; Gong, Fang; Zhu, Qiankun

2018-06-01

The cloud cover for the South China Sea and its coastal area is relatively large throughout the year, which limits the potential application of optical remote sensing. A HJ-charge-coupled device (HJ-CCD) has the advantages of wide field, high temporal resolution, and short repeat cycle. However, this instrument suffers from its use of only four relatively low-quality bands which can't adequately resolve the features of long wavelengths. The Landsat Enhanced Thematic Mapper-plus (ETM+) provides high-quality data, however, the Scan Line Corrector (SLC) stopped working and caused striping of remote sensed images, which dramatically reduced the coverage of the ETM+ data. In order to combine the advantages of the HJ-CCD and Landsat ETM+ data, we adopted a back-propagation artificial neural network (BP-ANN) to fuse these two data types for this study. The results showed that the fused output data not only have the advantage of data intactness for the HJ-CCD, but also have the advantages of the multi-spectral and high radiometric resolution of the ETM+ data. Moreover, the fused data were analyzed qualitatively, quantitatively and from a practical application point of view. Experimental studies indicated that the fused data have a full spatial distribution, multi-spectral bands, high radiometric resolution, a small difference between the observed and fused output data, and a high correlation between the observed and fused data. The excellent performance in its practical application is a further demonstration that the fused data are of high quality.

Computation of Ground-State Properties in Molecular Systems: Back-Propagation with Auxiliary-Field Quantum Monte Carlo.

PubMed

Motta, Mario; Zhang, Shiwei

2017-11-14

We address the computation of ground-state properties of chemical systems and realistic materials within the auxiliary-field quantum Monte Carlo method. The phase constraint to control the Fermion phase problem requires the random walks in Slater determinant space to be open-ended with branching. This in turn makes it necessary to use back-propagation (BP) to compute averages and correlation functions of operators that do not commute with the Hamiltonian. Several BP schemes are investigated, and their optimization with respect to the phaseless constraint is considered. We propose a modified BP method for the computation of observables in electronic systems, discuss its numerical stability and computational complexity, and assess its performance by computing ground-state properties in several molecular systems, including small organic molecules.

Optimizing hidden layer node number of BP network to estimate fetal weight

NASA Astrophysics Data System (ADS)

Su, Juan; Zou, Yuanwen; Lin, Jiangli; Wang, Tianfu; Li, Deyu; Xie, Tao

2007-12-01

The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.

Analog Delta-Back-Propagation Neural-Network Circuitry

NASA Technical Reports Server (NTRS)

Eberhart, Silvio

1990-01-01

Changes in synapse weights due to circuit drifts suppressed. Proposed fully parallel analog version of electronic neural-network processor based on delta-back-propagation algorithm. Processor able to "learn" when provided with suitable combinations of inputs and enforced outputs. Includes programmable resistive memory elements (corresponding to synapses), conductances (synapse weights) adjusted during learning. Buffer amplifiers, summing circuits, and sample-and-hold circuits arranged in layers of electronic neurons in accordance with delta-back-propagation algorithm.

On-line dynamic monitoring automotive exhausts: using BP-ANN for distinguishing multi-components

NASA Astrophysics Data System (ADS)

Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

2017-10-01

Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3 5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2 and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Cascade Back-Propagation Learning in Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2003-01-01

The cascade back-propagation (CBP) algorithm is the basis of a conceptual design for accelerating learning in artificial neural networks. The neural networks would be implemented as analog very-large-scale integrated (VLSI) circuits, and circuits to implement the CBP algorithm would be fabricated on the same VLSI circuit chips with the neural networks. Heretofore, artificial neural networks have learned slowly because it has been necessary to train them via software, for lack of a good on-chip learning technique. The CBP algorithm is an on-chip technique that provides for continuous learning in real time. Artificial neural networks are trained by example: A network is presented with training inputs for which the correct outputs are known, and the algorithm strives to adjust the weights of synaptic connections in the network to make the actual outputs approach the correct outputs. The input data are generally divided into three parts. Two of the parts, called the "training" and "cross-validation" sets, respectively, must be such that the corresponding input/output pairs are known. During training, the cross-validation set enables verification of the status of the input-to-output transformation learned by the network to avoid over-learning. The third part of the data, termed the "test" set, consists of the inputs that are required to be transformed into outputs; this set may or may not include the training set and/or the cross-validation set. Proposed neural-network circuitry for on-chip learning would be divided into two distinct networks; one for training and one for validation. Both networks would share the same synaptic weights.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

Lifetime prediction for organic coating under alternating hydrostatic pressure by artificial neural network

PubMed Central

Tian, Wenliang; Meng, Fandi; Liu, Li; Li, Ying; Wang, Fuhui

2017-01-01

A concept for prediction of organic coatings, based on the alternating hydrostatic pressure (AHP) accelerated tests, has been presented. An AHP accelerated test with different pressure values has been employed to evaluate coating degradation. And a back-propagation artificial neural network (BP-ANN) has been established to predict the service property and the service lifetime of coatings. The pressure value (P), immersion time (t) and service property (impedance modulus |Z|) are utilized as the parameters of the network. The average accuracies of the predicted service property and immersion time by the established network are 98.6% and 84.8%, respectively. The combination of accelerated test and prediction method by BP-ANN is promising to evaluate and predict coating property used in deep sea. PMID:28094340

21 CFR 872.3910 - Backing and facing for an artificial tooth.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Backing and facing for an artificial tooth. 872.3910 Section 872.3910 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... artificial tooth. (a) Identification. A backing and facing for an artificial tooth is a device intended for...

Low back pain: conservative treatment with artificial shock absorbers.

PubMed

Wosk, J; Voloshin, A S

1985-03-01

A new method of conservative treatment for low back pain (LBP) was studied by follow-up investigation of 382 patients during the last five years. The attempt to reduce repetitive impulsive intervertebral impact in the troublesome S1-L5-4 area by significant improvement of the foot's attenuational capacity through artificial viscoelastic shock absorbing was prompted by the authors' work on decreased capability of LBP spines to attenuate axially propagated walking stresses. Viscoelastic shoe inserts were used in addition to light flexible shoes as artificial shock absorbing devices. Maximal amplitudes of bone oscillation during walking were reduced by about 40% by the viscoelastic inserts. Rapid and surprisingly significant improvement of pain syndrome and patient mobility occurred in about 80% of the patients. The accelerographic patterns recorded on a sacrum of patient with LBP were unusual for a healthy subject; they usually disappeared after treatment in LBP cases. Results suggested that poor walking impact attenuation was a true cause for prolonging intervertebral structures overstrain and consequent degeneration. It seemed logical that as spine damage could be explained primarily by prolonged impulsive overstrain, treatment must include viscoelastic inserts which increase foot shock absorbing capacity and help cushion the spine.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

PubMed

Niu, Xiao-ying; Shao, Li-min; Zhao, Zhi-lei; Zhang, Xiao-yu

2012-08-01

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.

One-dimensional inversion of geo-electrical resistivity sounding data using artificial neural networks—a case study

NASA Astrophysics Data System (ADS)

Singh, U. K.; Tiwari, R. K.; Singh, S. B.

2005-02-01

This paper deals with the application of artificial neural networks (ANN) technique for the study of a case history using 1-D inversion of vertical electrical resistivity sounding (VES) data from the Puga valley, Kashmir, India. The study area is important for its rich geothermal resources as well as from the tectonic point of view as it is located near the collision boundary of the Indo-Asian crustal plates. In order to understand the resistivity structure and layer thicknesses, we used here three-layer feedforward neural networks to model and predict measured VES data. Three algorithms, e.g. back-propagation (BP), adaptive back-propagation (ABP) and Levenberg-Marquardt algorithm (LMA) were applied to the synthetic as well as real VES field data and efficiency of supervised training network are compared. Analyses suggest that LMA is computationally faster and give results, which are comparatively more accurate and consistent than BP and ABP. The results obtained using the ANN inversions are remarkably correlated with the available borehole litho-logs. The feasibility study suggests that ANN methods offer an excellent complementary tool for the direct detection of layered resistivity structure.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN.

PubMed

Sen, Alper; Gümüsay, M Umit; Kavas, Aktül; Bulucu, Umut

2008-09-25

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

An intelligent switch with back-propagation neural network based hybrid power system

NASA Astrophysics Data System (ADS)

Perdana, R. H. Y.; Fibriana, F.

2018-03-01

The consumption of conventional energy such as fossil fuels plays the critical role in the global warming issues. The carbon dioxide, methane, nitrous oxide, etc. could lead the greenhouse effects and change the climate pattern. In fact, 77% of the electrical energy is generated from fossil fuels combustion. Therefore, it is necessary to use the renewable energy sources for reducing the conventional energy consumption regarding electricity generation. This paper presents an intelligent switch to combine both energy resources, i.e., the solar panels as the renewable energy with the conventional energy from the State Electricity Enterprise (PLN). The artificial intelligence technology with the back-propagation neural network was designed to control the flow of energy that is distributed dynamically based on renewable energy generation. By the continuous monitoring on each load and source, the dynamic pattern of the intelligent switch was better than the conventional switching method. The first experimental results for 60 W solar panels showed the standard deviation of the trial at 0.7 and standard deviation of the experiment at 0.28. The second operation for a 900 W of solar panel obtained the standard deviation of the trial at 0.05 and 0.18 for the standard deviation of the experiment. Moreover, the accuracy reached 83% using this method. By the combination of the back-propagation neural network with the observation of energy usage of the load using wireless sensor network, each load can be evenly distributed and will impact on the reduction of conventional energy usage.

Back-propagation learning of infinite-dimensional dynamical systems.

PubMed

Tokuda, Isao; Tokunaga, Ryuji; Aihara, Kazuyuki

2003-10-01

This paper presents numerical studies of applying back-propagation learning to a delayed recurrent neural network (DRNN). The DRNN is a continuous-time recurrent neural network having time delayed feedbacks and the back-propagation learning is to teach spatio-temporal dynamics to the DRNN. Since the time-delays make the dynamics of the DRNN infinite-dimensional, the learning algorithm and the learning capability of the DRNN are different from those of the ordinary recurrent neural network (ORNN) having no time-delays. First, two types of learning algorithms are developed for a class of DRNNs. Then, using chaotic signals generated from the Mackey-Glass equation and the Rössler equations, learning capability of the DRNN is examined. Comparing the learning algorithms, learning capability, and robustness against noise of the DRNN with those of the ORNN and time delay neural network, advantages as well as disadvantages of the DRNN are investigated.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

PubMed Central

Şen, Alper; Gümüşay, M. Ümit; Kavas, Aktül; Bulucu, Umut

2008-01-01

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN. PMID:27873854

Artificial intelligence in the diagnosis of low back pain.

PubMed

Mann, N H; Brown, M D

1991-04-01

Computerized methods are used to recognize the characteristics of patient pain drawings. Artificial neural network (ANN) models are compared with expert predictions and traditional statistical classification methods when placing the pain drawings of low back pain patients into one of five clinically significant categories. A discussion is undertaken outlining the differences in these classifiers and the potential benefits of the ANN model as an artificial intelligence technique.

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

PubMed

Zhao, Xin; Han, Meng; Ding, Lili; Calin, Adrian Cantemir

2018-01-01

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society.

Typing SNP based on the near-infrared spectroscopy and artificial neural network

NASA Astrophysics Data System (ADS)

Ren, Li; Wang, Wei-Peng; Gao, Yu-Zhen; Yu, Xiao-Wei; Xie, Hong-Ping

2009-07-01

Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G > A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.

HF Propagation Effects Caused by an Artificial Plasma Cloud in the Ionosphere

NASA Astrophysics Data System (ADS)

Joshi, D. R.; Groves, K. M.; McNeil, W. J.; Caton, R. G.; Parris, R. T.; Pedersen, T. R.; Cannon, P. S.; Angling, M. J.; Jackson-Booth, N. K.

2014-12-01

In a campaign carried out by the NASA sounding rocket team, the Air Force Research Laboratory (AFRL) launched two sounding rockets in the Kwajalein Atoll, Marshall Islands, in May 2013 known as the Metal Oxide Space Cloud (MOSC) experiment to study the interactions of artificial ionization and the background plasma and measure the effects on high frequency (HF) radio wave propagation. The rockets released samarium metal vapor in the lower F-region of the ionosphere that ionized forming a plasma cloud that persisted for tens of minutes to hours in the post-sunset period. Data from the experiments has been analyzed to understand the impacts of the artificial ionization on HF radio wave propagation. Swept frequency HF links transiting the artificial ionization region were employed to produce oblique ionograms that clearly showed the effects of the samarium cloud. Ray tracing has been used to successfully model the effects of the ionized cloud. Comparisons between observations and modeled results will be presented, including model output using the International Reference Ionosphere (IRI), the Parameterized Ionospheric Model (PIM) and PIM constrained by electron density profiles measured with the ALTAIR radar at Kwajalein. Observations and modeling confirm that the cloud acted as a divergent lens refracting energy away from direct propagation paths and scattering energy at large angles relative to the initial propagation direction. The results confirm that even small amounts of ionized material injected in the upper atmosphere can result in significant changes to the natural propagation environment.

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS

PubMed Central

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-01-01

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas. PMID:26426030

Temporal and Spatial Simulation of Atmospheric Pollutant PM2.5 Changes and Risk Assessment of Population Exposure to Pollution Using Optimization Algorithms of the Back Propagation-Artificial Neural Network Model and GIS.

PubMed

Zhang, Ping; Hong, Bo; He, Liang; Cheng, Fei; Zhao, Peng; Wei, Cailiang; Liu, Yunhui

2015-09-29

PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi'an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO₂, and NO₂, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors' variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.

21 CFR 872.3910 - Backing and facing for an artificial tooth.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Backing and facing for an artificial tooth. 872.3910 Section 872.3910 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... use in fabrication of a fixed or removable dental appliance, such as a crown or bridge. The backing...

21 CFR 872.3910 - Backing and facing for an artificial tooth.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Backing and facing for an artificial tooth. 872.3910 Section 872.3910 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... use in fabrication of a fixed or removable dental appliance, such as a crown or bridge. The backing...

21 CFR 872.3910 - Backing and facing for an artificial tooth.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Backing and facing for an artificial tooth. 872.3910 Section 872.3910 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN... use in fabrication of a fixed or removable dental appliance, such as a crown or bridge. The backing...

Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

NASA Astrophysics Data System (ADS)

Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping

2017-09-01

To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

Optical back propagation for fiber optic networks with hybrid EDFA Raman amplification.

PubMed

Liang, Xiaojun; Kumar, Shiva

2017-03-06

We have investigated an optical back propagation (OBP) method to compensate for propagation impairments in fiber optic networks with lumped Erbium doped fiber amplifier (EDFA) and/or distributed Raman amplification. An OBP module consists of an optical phase conjugator (OPC), optical amplifiers and dispersion varying fibers (DVFs). We derived a semi-analytical expression that calculates the dispersion profile of DVF. The OBP module acts as a nonlinear filter that fully compensates for the nonlinear distortions due to signal propagation in a transmission fiber, and is applicable for fiber optic networks with reconfigurable optical add-drop multiplexers (ROADMs). We studied a wavelength division multiplexing (WDM) network with 3000 km transmission distance and 64-quadrature amplitude modulation (QAM) modulation. OBP brings 5.8 dB, 5.9 dB and 6.1 dB Q-factor gains over linear compensation for systems with full EDFA amplification, hybrid EDFA/Raman amplification, and full Raman amplification, respectively. In contrast, digital back propagation (DBP) or OPC-only systems provide only 0.8 ~ 1.5 dB Q-factor gains.

Appraisal of artificial neural network for forecasting of economic parameters

NASA Astrophysics Data System (ADS)

Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

2017-01-01

The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

A stable second order method for training back propagation networks

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.

1993-01-01

A simple method for improving the learning rate of the back-propagation algorithm is described. The basis of the method is that approximate second order corrections can be incorporated in the output units. The extended method leads to significant improvements in the convergence rate.

Orbit-product representation and correction of Gaussian belief propagation

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

Johnson, Jason K; Chertkov, Michael; Chernyak, Vladimir

We present a new interpretation of Gaussian belief propagation (GaBP) based on the 'zeta function' representation of the determinant as a product over orbits of a graph. We show that GaBP captures back-tracking orbits of the graph and consider how to correct this estimate by accounting for non-backtracking orbits. We show that the product over non-backtracking orbits may be interpreted as the determinant of the non-backtracking adjacency matrix of the graph with edge weights based on the solution of GaBP. An efficient method is proposed to compute a truncated correction factor including all non-backtracking orbits up to a specified length.

Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks.

PubMed

Abdollahi, Yadollah; Sairi, Nor Asrina; Said, Suhana Binti Mohd; Abouzari-lotf, Ebrahim; Zakaria, Azmi; Sabri, Mohd Faizul Bin Mohd; Islam, Aminul; Alias, Yatimah

2015-11-05

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up. Copyright © 2015 Elsevier B.V. All rights reserved.

An intercomparison of artificial intelligence approaches for polar scene identification

NASA Technical Reports Server (NTRS)

Tovinkere, V. R.; Penaloza, M.; Logar, A.; Lee, J.; Weger, R. C.; Berendes, T. A.; Welch, R. M.

1993-01-01

The following six different artificial-intelligence (AI) approaches to polar scene identification are examined: (1) a feed forward back propagation neural network, (2) a probabilistic neural network, (3) a hybrid neural network, (4) a 'don't care' feed forward perception model, (5) a 'don't care' feed forward back propagation neural network, and (6) a fuzzy logic based expert system. The ten classes into which six AVHRR local-coverage arctic scenes were classified were: water, solid sea ice, broken sea ice, snow-covered mountains, land, stratus over ice, stratus over water, cirrus over water, cumulus over water, and multilayer cloudiness. It was found that 'don't care' back propagation neural network produced the highest accuracies. This approach has also low CPU requirement.

Authorship attribution of source code by using back propagation neural network based on particle swarm optimization

PubMed Central

Xu, Guoai; Li, Qi; Guo, Yanhui; Zhang, Miao

2017-01-01

Authorship attribution is to identify the most likely author of a given sample among a set of candidate known authors. It can be not only applied to discover the original author of plain text, such as novels, blogs, emails, posts etc., but also used to identify source code programmers. Authorship attribution of source code is required in diverse applications, ranging from malicious code tracking to solving authorship dispute or software plagiarism detection. This paper aims to propose a new method to identify the programmer of Java source code samples with a higher accuracy. To this end, it first introduces back propagation (BP) neural network based on particle swarm optimization (PSO) into authorship attribution of source code. It begins by computing a set of defined feature metrics, including lexical and layout metrics, structure and syntax metrics, totally 19 dimensions. Then these metrics are input to neural network for supervised learning, the weights of which are output by PSO and BP hybrid algorithm. The effectiveness of the proposed method is evaluated on a collected dataset with 3,022 Java files belong to 40 authors. Experiment results show that the proposed method achieves 91.060% accuracy. And a comparison with previous work on authorship attribution of source code for Java language illustrates that this proposed method outperforms others overall, also with an acceptable overhead. PMID:29095934

Back-Propagation Operation for Analog Neural Network Hardware with Synapse Components Having Hysteresis Characteristics

PubMed Central

Ueda, Michihito; Nishitani, Yu; Kaneko, Yukihiro; Omote, Atsushi

2014-01-01

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware. PMID:25393715

PSF estimation for defocus blurred image based on quantum back-propagation neural network

NASA Astrophysics Data System (ADS)

Gao, Kun; Zhang, Yan; Shao, Xiao-guang; Liu, Ying-hui; Ni, Guoqiang

2010-11-01

Images obtained by an aberration-free system are defocused blur due to motion in depth and/or zooming. The precondition of restoring the degraded image is to estimate point spread function (PSF) of the imaging system as precisely as possible. But it is difficult to identify the analytic model of PSF precisely due to the complexity of the degradation process. Inspired by the similarity between the quantum process and imaging process in the probability and statistics fields, one reformed multilayer quantum neural network (QNN) is proposed to estimate PSF of the defocus blurred image. Different from the conventional artificial neural network (ANN), an improved quantum neuron model is used in the hidden layer instead, which introduces a 2-bit controlled NOT quantum gate to control output and adopts 2 texture and edge features as the input vectors. The supervised back-propagation learning rule is adopted to train network based on training sets from the historical images. Test results show that this method owns excellent features of high precision and strong generalization ability.

Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

ERIC Educational Resources Information Center

Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

2013-01-01

As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

[Study on the growth, development and artificial propagation of Hypericum ascyron].

PubMed

Chen, Yu-mei; Zhang, Ke-qin; Song, Bai-jun; Zhao, Gui-ying; Wang, Zhen-hui; Wang, Li-mei; Chang, Wei-yi; Cong, Li-xin

2011-06-01

To explore the morphological changes, growth conditions and artificial propagation of Hypericum ascyron. The morphological changes were observed and recorded in the scene, the height and diameter of the plants were measured; the growth Verhaulst model was set up with the SPSS 17.0 software; the sexual reproduction and asexual reproduction were carried out in artificial cultivation. Hypericum ascyron started germinating in late April each year, branching in late May, flowering in late June, the period of full bearing was in early August, seeds were mature in early October. The Verhaulst models of the increase in the height (H), the quantity of leaf pairs (L) and the branching (B) were, H = 127.109/(1 + 23.744 x e(-0.062t)), L = 23.343/(1 + 11.303 x e(-0.062t)), B = 22.037/(1 + 73.068 x e(-0.068t)). The survival rate of whole graft and segmentation plant were 100% and 67.2% respectively on asexual reproduction; on the sexual reproduction, the seed germination rate was 15.2%, the survival rate of transplant seedlings was 36%. The period of growth and development of Hypericum ascyron is from April to October and it can be carried out artificial propagation.

Neural network for processing both spatial and temporal data with time based back-propagation

NASA Technical Reports Server (NTRS)

Villarreal, James A. (Inventor); Shelton, Robert O. (Inventor)

1993-01-01

Neural networks are computing systems modeled after the paradigm of the biological brain. For years, researchers using various forms of neural networks have attempted to model the brain's information processing and decision-making capabilities. Neural network algorithms have impressively demonstrated the capability of modeling spatial information. On the other hand, the application of parallel distributed models to the processing of temporal data has been severely restricted. The invention introduces a novel technique which adds the dimension of time to the well known back-propagation neural network algorithm. In the space-time neural network disclosed herein, the synaptic weights between two artificial neurons (processing elements) are replaced with an adaptable-adjustable filter. Instead of a single synaptic weight, the invention provides a plurality of weights representing not only association, but also temporal dependencies. In this case, the synaptic weights are the coefficients to the adaptable digital filters. Novelty is believed to lie in the disclosure of a processing element and a network of the processing elements which are capable of processing temporal as well as spacial data.

[Fishery resource protection by artificial propagation in hydroelectric development: Lixianjiang River drainage in Yunnan as an example].

PubMed

Yang, Yong-Hong; Yang, Jun-Xing; Pan, Xiao-Fu; Zhou, Wei; Yang, Mei-Lin

2011-04-01

Hydroelectric developments can result in a number of negative environmental consequences. Conservation aquaculture is a branch of science derived from conservation and population recovery studies on endangered fishes. Here we discuss the impacts on fishes caused by hydropower projects in Lixianjiang, and evaluate effects and problems on the propagation of Parazacco spilurus, Hemibagrus pluriradiatus, Neolissochilus benasi and Semilabeo obscurus. A successful propagation project includes foraging ecology in fields, pond cultivation, juvenile fish raising, prevention and curing on fish disease, genetic management, artificial releasing and population monitoring. Artificial propagation is the practicable act on genetic intercommunication, preventing population deterioration for fishes in upper and lower reaches of the dam. For long-term planning, fish stocks are not suitable for many kind of fishes, but can prevent fishes from going extinct in the wild. Basic data collection on fish ecology, parent fish hunting, prevention on fish disease are the most important factors on artificial propagation. Strengthening the genetic management of stock population for keeping a higher genetic diversity can increase the success of stock enhancement. The works on Lixianjiang provide a new model for river fish protection. To make sure the complicated project works well, project plans, commission contracts, base line monitoring and techniques on artificial reproduction must be considered early. Last, fishery conservation should be considered alongside location development.

Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

PubMed

Olawoyin, Richard

2016-10-01

The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary effects of alternative artificial propagation programs: implications for viability of endangered anadromous salmonids

PubMed Central

McClure, Michelle M; Utter, Fred M; Baldwin, Casey; Carmichael, Richard W; Hassemer, Peter F; Howell, Philip J; Spruell, Paul; Cooney, Thomas D; Schaller, Howard A; Petrosky, Charles E

2008-01-01

Most hatchery programs for anadromous salmonids have been initiated to increase the numbers of fish for harvest, to mitigate for habitat losses, or to increase abundance in populations at low abundance. However, the manner in which these programs are implemented can have significant impacts on the evolutionary trajectory and long-term viability of populations. In this paper, we review the potential benefits and risks of hatchery programs relative to the conservation of species listed under the US Endangered Species Act. To illustrate, we present the range of potential effects within a population as well as among populations of Chinook salmon (Oncorhynchus tshawytscha) where changes to major hatchery programs are being considered. We apply evolutionary considerations emerging from these examples to suggest broader principles for hatchery uses that are consistent with conservation goals. We conclude that because of the evolutionary risks posed by artificial propagation programs, they should not be viewed as a substitute for addressing other limiting factors that prevent achieving viability. At the population level, artificial propagation programs that are implemented as a short-term approach to avoid imminent extinction are more likely to achieve long-term population viability than approaches that rely on long-term supplementation. In addition, artificial propagation programs can have out-of-population impacts that should be considered in conservation planning. PMID:25567637

Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

NASA Astrophysics Data System (ADS)

Jin, Huijin; Wu, Sujun; Peng, Yuncheng

2013-12-01

In this study, an artificial neural network (ANN) was employed to predict the contact fatigue life of alloy cast steel rolls (ACSRs) as a function of alloy composition, heat treatment parameters, and contact stress by utilizing the back-propagation algorithm. The ANN was trained and tested using experimental data and a very good performance of the neural network was achieved. The well-trained neural network was then adopted to predict the contact fatigue life of chromium alloyed cast steel rolls with different alloy compositions and heat treatment processes. The prediction results showed that the maximum value of contact fatigue life was obtained with quenching at 960 °C, tempering at 520 °C, and under the contact stress of 2355 MPa. The optimal alloy composition was C-0.54, Si-0.66, Mn-0.67, Cr-4.74, Mo-0.46, V-0.13, Ni-0.34, and Fe-balance (wt.%). Some explanations of the predicted results from the metallurgical viewpoints are given. A convenient and powerful method of optimizing alloy composition and heat treatment parameters of ACSRs has been developed.

Localized water reverberation phases and its impact on back-projection images

NASA Astrophysics Data System (ADS)

Yue, H.; Castillo, J.; Yu, C.; Meng, L.; Zhan, Z.

2017-12-01

Coherent radiators imaged by back-projections (BP) are commonly interpreted as part of the rupture process. Nevertheless, artifacts introduced by structure related phases are rarely discriminated from the rupture process. In this study, we adopt the logic of empirical Greens' function analysis (EGF) to discriminate between rupture and structure effect. We re-examine the waveforms and BP images of the 2012 Mw 7.2 Indian Ocean earthquake and an EGF event (Mw 6.2). The P wave codas of both events present similar shape with characteristic period of approximately 10 s, which are back-projected as coherent radiators near the trench. S wave BP doesn't image energy radiation near the trench. We interpret those coda waves as localized water reverberation phases excited near the trench. We perform a 2D waveform modeling using realistic bathymetry model, and find that the sharp near-trench bathymetry traps the acoustic water waves forming localized reverberation phases. These waves can be imaged as coherent near-trench radiators with similar features as that in the observations. We present a set of methodology to discriminate between the rupture and propagation effects in BP images, which can serve as a criterion of subevent identification.

An accelerated training method for back propagation networks

NASA Technical Reports Server (NTRS)

Shelton, Robert O. (Inventor)

1993-01-01

The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

Back analysis of geomechanical parameters in underground engineering using artificial bee colony.

PubMed

Zhu, Changxing; Zhao, Hongbo; Zhao, Ming

2014-01-01

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

NASA Astrophysics Data System (ADS)

Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

2018-06-01

A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

Effective Multifocus Image Fusion Based on HVS and BP Neural Network

PubMed Central

Yang, Yong

2014-01-01

The aim of multifocus image fusion is to fuse the images taken from the same scene with different focuses to obtain a resultant image with all objects in focus. In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. The clearer pixels are then used to construct the initial fused image. Thirdly, the focused regions are detected by measuring the similarity between the source images and the initial fused image followed by morphological opening and closing operations. Finally, the final fused image is obtained by a fusion rule for those focused regions. Experimental results show that the proposed method can provide better performance and outperform several existing popular fusion methods in terms of both objective and subjective evaluations. PMID:24683327

BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass

PubMed Central

Pi, Erxu; Mantri, Nitin; Ngai, Sai Ming; Lu, Hongfei; Du, Liqun

2013-01-01

Temperature is one of the most significant environmental factors that affects germination of grass seeds. Reliable prediction of the optimal temperature for seed germination is crucial for determining the suitable regions and favorable sowing timing for turf grass cultivation. In this study, a back-propagation-artificial-neural-network-aided dual quintic equation (BP-ANN-QE) model was developed to improve the prediction of the optimal temperature for seed germination. This BP-ANN-QE model was used to determine optimal sowing times and suitable regions for three Cynodon dactylon cultivars (C. dactylon, ‘Savannah’ and ‘Princess VII’). Prediction of the optimal temperature for these seeds was based on comprehensive germination tests using 36 day/night (high/low) temperature regimes (both ranging from 5/5 to 40/40°C with 5°C increments). Seed germination data from these temperature regimes were used to construct temperature-germination correlation models for estimating germination percentage with confidence intervals. Our tests revealed that the optimal high/low temperature regimes required for all the three bermudagrass cultivars are 30/5, 30/10, 35/5, 35/10, 35/15, 35/20, 40/15 and 40/20°C; constant temperatures ranging from 5 to 40°C inhibited the germination of all three cultivars. While comparing different simulating methods, including DQEM, Bisquare ANN-QE, and BP-ANN-QE in establishing temperature based germination percentage rules, we found that the R2 values of germination prediction function could be significantly improved from about 0.6940–0.8177 (DQEM approach) to 0.9439–0.9813 (BP-ANN-QE). These results indicated that our BP-ANN-QE model has better performance than the rests of the compared models. Furthermore, data of the national temperature grids generated from monthly-average temperature for 25 years were fit into these functions and we were able to map the germination percentage of these C. dactylon cultivars in the national scale of

Day-Ahead PM2.5 Concentration Forecasting Using WT-VMD Based Decomposition Method and Back Propagation Neural Network Improved by Differential Evolution

PubMed Central

Wang, Deyun; Liu, Yanling; Luo, Hongyuan; Yue, Chenqiang; Cheng, Sheng

2017-01-01

Accurate PM2.5 concentration forecasting is crucial for protecting public health and atmospheric environment. However, the intermittent and unstable nature of PM2.5 concentration series makes its forecasting become a very difficult task. In order to improve the forecast accuracy of PM2.5 concentration, this paper proposes a hybrid model based on wavelet transform (WT), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by differential evolution (DE) algorithm. Firstly, WT is employed to disassemble the PM2.5 concentration series into a number of subsets with different frequencies. Secondly, VMD is applied to decompose each subset into a set of variational modes (VMs). Thirdly, DE-BP model is utilized to forecast all the VMs. Fourthly, the forecast value of each subset is obtained through aggregating the forecast results of all the VMs obtained from VMD decomposition of this subset. Finally, the final forecast series of PM2.5 concentration is obtained by adding up the forecast values of all subsets. Two PM2.5 concentration series collected from Wuhan and Tianjin, respectively, located in China are used to test the effectiveness of the proposed model. The results demonstrate that the proposed model outperforms all the other considered models in this paper. PMID:28704955

Multi-Array Back-Projections of The 2015 Gorkha Earthquake With Physics-Based Aftershock Calibrations

NASA Astrophysics Data System (ADS)

Meng, L.; Zhang, A.; Yagi, Y.

2015-12-01

The 2015 Mw 7.8 Nepal-Gorkha earthquake with casualties of over 9,000 people is the most devastating disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Its rupture process is well imaged by the teleseismic MUSIC back-projections (BP). Here, we perform independent back-projections of high-frequency recordings (0.5-2 Hz) from the Australian seismic network (AU), the North America network (NA) and the European seismic network (EU), located in complementary orientations. Our results of all three arrays show unilateral linear rupture path to the east of the hypocenter. But the propagating directions and the inferred rupture speeds differ significantly among different arrays. To understand the spatial uncertainties of the BP analysis, we image four moderate-size (M5~6) aftershocks based on the timing correction derived from the alignment of the initial P-wave of the mainshock. We find that the apparent source locations inferred from BP are systematically biased along the source-array orientation, which can be explained by the uncertainty of the 3D velocity structure deviated from the 1D reference model (e.g. IASP91). We introduced a slowness error term in travel time as a first-order calibration that successfully mitigates the source location discrepancies of different arrays. The calibrated BP results of three arrays are mutually consistent and reveal a unilateral rupture propagating eastward at a speed of 2.7 km/s along the down-dip edge of the locked Himalaya thrust zone over ~ 150 km, in agreement with a narrow slip distribution inferred from finite source inversions.

Application of artificial neural networks to chemostratigraphy

NASA Astrophysics Data System (ADS)

Malmgren, BjöRn A.; Nordlund, Ulf

1996-08-01

Artificial neural networks, a branch of artificial intelligence, are computer systems formed by a number of simple, highly interconnected processing units that have the ability to learn a set of target vectors from a set of associated input signals. Neural networks learn by self-adjusting a set of parameters, using some pertinent algorithm to minimize the error between the desired output and network output. We explore the potential of this approach in solving a problem involving classification of geochemical data. The data, taken from the literature, are derived from four late Quaternary zones of volcanic ash of basaltic and rhyolithic origin from the Norwegian Sea. These ash layers span the oxygen isotope zones 1, 5, 7, and 11, respectively (last 420,000 years). The data consist of nine geochemical variables (oxides) determined in each of 183 samples. We employed a three-layer back propagation neural network to assess its efficiency to optimally differentiate samples from the four ash zones on the basis of their geochemical composition. For comparison, three statistical pattern recognition techniques, linear discriminant analysis, the k-nearest neighbor (k-NN) technique, and SIMCA (soft independent modeling of class analogy), were applied to the same data. All of these showed considerably higher error rates than the artificial neural network, indicating that the back propagation network was indeed more powerful in correctly classifying the ash particles to the appropriate zone on the basis of their geochemical composition.

A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

PubMed Central

Wang, Zhongbin; Xu, Xihua; Si, Lei; Ji, Rui; Liu, Xinhua; Tan, Chao

2016-01-01

In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others. PMID:27123002

Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses.

PubMed

Leng, Xiang'zi; Wang, Jinhua; Ji, Haibo; Wang, Qin'geng; Li, Huiming; Qian, Xin; Li, Fengying; Yang, Meng

2017-08-01

Size-fractionated heavy metal concentrations were observed in airborne particulate matter (PM) samples collected from 2014 to 2015 (spanning all four seasons) from suburban (Xianlin) and industrial (Pukou) areas in Nanjing, a megacity of southeast China. Rapid prediction models of size-fractionated metals were established based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters. About 38% and 77% of PM 2.5 concentrations in Xianlin and Pukou, respectively, were beyond the Chinese National Ambient Air Quality Standard limit of 75 μg/m 3 . Nearly all elements had higher concentrations in industrial areas, and in winter among the four seasons. Anthropogenic elements such as Pb, Zn, Cd and Cu showed larger percentages in the fine fraction (ø≤2.5 μm), whereas the crustal elements including Al, Ba, Fe, Ni, Sr and Ti showed larger percentages in the coarse fraction (ø > 2.5 μm). SVM showed a higher training correlation coefficient (R), and lower mean absolute error (MAE) as well as lower root mean square error (RMSE), than MLR and BP-ANN for most metals. All the three methods showed better prediction results for Ni, Al, V, Cd and As, whereas relatively poor for Cr and Fe. The daily airborne metal concentrations in 2015 were then predicted by the fully trained SVM models and the results showed the heaviest pollution of airborne heavy metals occurred in December and January, whereas the lightest pollution occurred in June and July. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive laser link reconfiguration using constraint propagation

NASA Technical Reports Server (NTRS)

Crone, M. S.; Julich, P. M.; Cook, L. M.

1993-01-01

This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network.

PubMed

Zhao, Guo; Wang, Hui; Liu, Gang

2017-07-03

Abstract : In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd 2+ in the presence of Cu 2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu 2+ concentration on the stripping response to Cd 2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd 2+ in the presence of Cu 2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd 2+ and the stripping peak currents of Cu 2+ and Cd 2+ . The factors affecting the SWASV detection of Cd 2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd 2+ in soil samples with satisfactory results.

Flank wears Simulation by using back propagation neural network when cutting hardened H-13 steel in CNC End Milling

NASA Astrophysics Data System (ADS)

Hazza, Muataz Hazza F. Al; Adesta, Erry Y. T.; Riza, Muhammad

2013-12-01

High speed milling has many advantages such as higher removal rate and high productivity. However, higher cutting speed increase the flank wear rate and thus reducing the cutting tool life. Therefore estimating and predicting the flank wear length in early stages reduces the risk of unaccepted tooling cost. This research presents a neural network model for predicting and simulating the flank wear in the CNC end milling process. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the flank wear length. Then the measured data have been used to train the developed neural network model. Artificial neural network (ANN) was applied to predict the flank wear length. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed flank wear which indicates the validity of the models.

Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

1995-01-01

This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

Reliability analysis of C-130 turboprop engine components using artificial neural network

NASA Astrophysics Data System (ADS)

Qattan, Nizar A.

In this study, we predict the failure rate of Lockheed C-130 Engine Turbine. More than thirty years of local operational field data were used for failure rate prediction and validation. The Weibull regression model and the Artificial Neural Network model including (feed-forward back-propagation, radial basis neural network, and multilayer perceptron neural network model); will be utilized to perform this study. For this purpose, the thesis will be divided into five major parts. First part deals with Weibull regression model to predict the turbine general failure rate, and the rate of failures that require overhaul maintenance. The second part will cover the Artificial Neural Network (ANN) model utilizing the feed-forward back-propagation algorithm as a learning rule. The MATLAB package will be used in order to build and design a code to simulate the given data, the inputs to the neural network are the independent variables, the output is the general failure rate of the turbine, and the failures which required overhaul maintenance. In the third part we predict the general failure rate of the turbine and the failures which require overhaul maintenance, using radial basis neural network model on MATLAB tool box. In the fourth part we compare the predictions of the feed-forward back-propagation model, with that of Weibull regression model, and radial basis neural network model. The results show that the failure rate predicted by the feed-forward back-propagation artificial neural network model is closer in agreement with radial basis neural network model compared with the actual field-data, than the failure rate predicted by the Weibull model. By the end of the study, we forecast the general failure rate of the Lockheed C-130 Engine Turbine, the failures which required overhaul maintenance and six categorical failures using multilayer perceptron neural network (MLP) model on DTREG commercial software. The results also give an insight into the reliability of the engine

Artificial Neural Network-Based Three-dimensional Continuous Response Relationship Construction of 3Cr20Ni10W2 Heat-Resisting Alloy and Its Application in Finite Element Simulation

NASA Astrophysics Data System (ADS)

Li, Le; Wang, Li-yong

2018-04-01

The application of accurate constitutive relationship in finite element simulation would significantly contribute to accurate simulation results, which plays a critical role in process design and optimization. In this investigation, the true stress-strain data of 3Cr20Ni10W2 heat-resisting alloy were obtained from a series of isothermal compression tests conducted in a wide temperature range of 1203-1403 K and strain rate range of 0.01-10 s-1 on a Gleeble 1500 testing machine. Then the constitutive relationship was modeled by an optimally constructed and well-trained back-propagation artificial neural network (BP-ANN). The evaluation of the BP-ANN model revealed that it has admirable performance in characterizing and predicting the flow behaviors of 3Cr20Ni10W2 heat-resisting alloy. Meanwhile, a comparison between improved Arrhenius-type constitutive equation and BP-ANN model shows that the latter has higher accuracy. Consequently, the developed BP-ANN model was used to predict abundant stress-strain data beyond the limited experimental conditions and construct the three-dimensional continuous response relationship for temperature, strain rate, strain, and stress. Finally, the three-dimensional continuous response relationship was applied to the numerical simulation of isothermal compression tests. The results show that such constitutive relationship can significantly promote the accuracy improvement of numerical simulation for hot forming processes.

[Reproductive biology and artificial propagation of Acipenser sinensis below Gezhouba Dam].

PubMed

Liu, Jian-yi; Wei, Qi-wei; Chen, Xi-hua; Yang, De-guo; Du, Hao; Zhu, Yong-jiu

2007-06-01

A total of 36 females and 21 males of Chinese sturgeon Acipenser sinensis were caught in 1998-2004 excluding 2002 to study the characteristics of their reproductive biology and the effect of their artificial propagation. The results showed that the body length (BL), body mass (BM) and age of the females were 240-320 cm, 140-432 kg, and 15-30 years, and those of the males were 153-284 cm, 70-244 kg and 12-26 years, respectively. The inducing rate was 93.1% for females and 100% for males, and the ova had 7 different colors. The absolute fecundity was 200,000-590,000 eggs, with an average of 358,000 eggs, and the relative fecundity to BM was 820-3,020 eggs per kg, with an average of 1,590 eggs per kg. The sperm had 4 different colors. The absolute sperm quantity obtained from one male was 1,000-5,952 ml, with an average of 2,597.8 ml, and the relative sperm quantity to BM was 1.25-31.24 ml . kg(-1), with an average of 13.3 ml . kg(-1). During the study period, the average fertilization rate in artificial propagation was 63.7%, and the hatching rate was 48.1%, with 4,762,000 fry obtained. Compared with the data in 1976, the natural reproductive capacity of the Chinese sturgeon broodstocks declined greatly.

Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.

PubMed

Duan, Xiaoran; Yang, Yongli; Tan, Shanjuan; Wang, Sihua; Feng, Xiaolei; Cui, Liuxin; Feng, Feifei; Yu, Songcheng; Wang, Wei; Wu, Yongjun

2017-08-01

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.

Forecasting Zakat collection using artificial neural network

NASA Astrophysics Data System (ADS)

Sy Ahmad Ubaidillah, Sh. Hafizah; Sallehuddin, Roselina

2013-04-01

'Zakat', "that which purifies" or "alms", is the giving of a fixed portion of one's wealth to charity, generally to the poor and needy. It is one of the five pillars of Islam, and must be paid by all practicing Muslims who have the financial means (nisab). 'Nisab' is the minimum level to determine whether there is a 'zakat' to be paid on the assets. Today, in most Muslim countries, 'zakat' is collected through a decentralized and voluntary system. Under this voluntary system, 'zakat' committees are established, which are tasked with the collection and distribution of 'zakat' funds. 'Zakat' promotes a more equitable redistribution of wealth, and fosters a sense of solidarity amongst members of the 'Ummah'. The Malaysian government has established a 'zakat' center at every state to facilitate the management of 'zakat'. The center has to have a good 'zakat' management system to effectively execute its functions especially in the collection and distribution of 'zakat'. Therefore, a good forecasting model is needed. The purpose of this study is to develop a forecasting model for Pusat Zakat Pahang (PZP) to predict the total amount of collection from 'zakat' of assets more precisely. In this study, two different Artificial Neural Network (ANN) models using two different learning algorithms are developed; Back Propagation (BP) and Levenberg-Marquardt (LM). Both models are developed and compared in terms of their accuracy performance. The best model is determined based on the lowest mean square error and the highest correlations values. Based on the results obtained from the study, BP neural network is recommended as the forecasting model to forecast the collection from 'zakat' of assets for PZP.

Applying artificial intelligence technology to support decision-making in nursing: A case study in Taiwan.

PubMed

Liao, Pei-Hung; Hsu, Pei-Ti; Chu, William; Chu, Woei-Chyn

2015-06-01

This study applied artificial intelligence to help nurses address problems and receive instructions through information technology. Nurses make diagnoses according to professional knowledge, clinical experience, and even instinct. Without comprehensive knowledge and thinking, diagnostic accuracy can be compromised and decisions may be delayed. We used a back-propagation neural network and other tools for data mining and statistical analysis. We further compared the prediction accuracy of the previous methods with an adaptive-network-based fuzzy inference system and the back-propagation neural network, identifying differences in the questions and in nurse satisfaction levels before and after using the nursing information system. This study investigated the use of artificial intelligence to generate nursing diagnoses. The percentage of agreement between diagnoses suggested by the information system and those made by nurses was as much as 87 percent. When patients are hospitalized, we can calculate the probability of various nursing diagnoses based on certain characteristics. © The Author(s) 2013.

Novel two-way artificial boundary condition for 2D vertical water wave propagation modelled with Radial-Basis-Function Collocation Method

NASA Astrophysics Data System (ADS)

Mueller, A.

2018-04-01

A new transparent artificial boundary condition for the two-dimensional (vertical) (2DV) free surface water wave propagation modelled using the meshless Radial-Basis-Function Collocation Method (RBFCM) as boundary-only solution is derived. The two-way artificial boundary condition (2wABC) works as pure incidence, pure radiation and as combined incidence/radiation BC. In this work the 2wABC is applied to harmonic linear water waves; its performance is tested against the analytical solution for wave propagation over horizontal sea bottom, standing and partially standing wave as well as wave interference of waves with different periods.

Self-learning fuzzy controllers based on temporal back propagation

NASA Technical Reports Server (NTRS)

Jang, Jyh-Shing R.

1992-01-01

This paper presents a generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner. This methodology, termed temporal back propagation, is model-insensitive in the sense that it can deal with plants that can be represented in a piecewise-differentiable format, such as difference equations, neural networks, GMDH structures, and fuzzy models. Regardless of the numbers of inputs and outputs of the plants under consideration, the proposed approach can either refine the fuzzy if-then rules if human experts, or automatically derive the fuzzy if-then rules obtained from human experts are not available. The inverted pendulum system is employed as a test-bed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Spread prediction model of continuous steel tube based on BP neural network

NASA Astrophysics Data System (ADS)

Zhai, Jian-wei; Yu, Hui; Zou, Hai-bei; Wang, San-zhong; Liu, Li-gang

2017-07-01

According to the geometric pass of roll and technological parameters of three-roller continuous mandrel rolling mill in a factory, a finite element model is established to simulate the continuous rolling process of seamless steel tube, and the reliability of finite element model is verified by comparing with the simulation results and actual results of rolling force, wall thickness and outer diameter of the tube. The effect of roller reduction, roller rotation speed and blooming temperature on the spread rule is studied. Based on BP(Back Propagation) neural network technology, a spread prediction model of continuous rolling tube is established for training wall thickness coefficient and spread coefficient of the continuous rolling tube, and the rapid and accurate prediction of continuous rolling tube size is realized.

Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.

PubMed

Ma, Jing; Yu, Jiong; Hao, Guangshu; Wang, Dan; Sun, Yanni; Lu, Jianxin; Cao, Hongcui; Lin, Feiyan

2017-02-20

The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC. The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch. In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.

The effect of back and feet support on oscillometric blood pressure measurements.

PubMed

Ringrose, Jennifer S; Wong, Jonathan; Yousefi, Farahnaz; Padwal, Raj

2017-08-01

Recommendations to support the back and feet during blood pressure (BP) measurement are not always followed in clinical practice. Our objective was to determine to what extent back and feet support affects mean oscillometric BP measurements. Eighty-five consecutive, consenting participants 18 years or older with systolic BP readings 80-220 mmHg and diastolic BP readings 50-120 mmHg and arm circumferences of 25-43 cm were recruited. BP was measured using an Omron HEM 907 oscillometric device. Back and feet support were examined independently. First, while the feet were supported, two sets of three BP readings were taken in random order: one with the back supported and one with the back unsupported. Next, with the back supported, two sets of three BP readings were taken in random order: one with the feet dangling and one with feet supported. The mean age of the participants was 52.0±20.7 years and the mean arm circumference was 31.0±3.2 cm; 62% were women and 49% had hypertension. The mean BP levels with the back unsupported were slightly higher than those with the back supported (119.8±15.5/69.9±8.9 vs. 119.2±16.4/68.2±8.8 mmHg; difference of 0.7±4.9/1.8±3.0; P=0.21 for systolic and <0.0001 for diastolic comparisons). The mean BP levels with feet dangling were slightly lower than with feet supported (120.3±16.3/72.6±8.9 vs. 121.2±16.1/72.9±8.6 mmHg; difference of -0.9±4.1/-0.3±2.8; P=0.04 for systolic and <0.36 for diastolic comparisons). Systolic BP differences were greater than or equal to 5 mmHg in 34% (back phase) and 23% (feet phase) of the participants. Provision of back and feet support has a small effect on the mean oscillometric BP. The magnitude of effect is greatest on diastolic BP when the back is unsupported.

Propagation based phase retrieval of simulated intensity measurements using artificial neural networks

NASA Astrophysics Data System (ADS)

Kemp, Z. D. C.

2018-04-01

Determining the phase of a wave from intensity measurements has many applications in fields such as electron microscopy, visible light optics, and medical imaging. Propagation based phase retrieval, where the phase is obtained from defocused images, has shown significant promise. There are, however, limitations in the accuracy of the retrieved phase arising from such methods. Sources of error include shot noise, image misalignment, and diffraction artifacts. We explore the use of artificial neural networks (ANNs) to improve the accuracy of propagation based phase retrieval algorithms applied to simulated intensity measurements. We employ a phase retrieval algorithm based on the transport-of-intensity equation to obtain the phase from simulated micrographs of procedurally generated specimens. We then train an ANN with pairs of retrieved and exact phases, and use the trained ANN to process a test set of retrieved phase maps. The total error in the phase is significantly reduced using this method. We also discuss a variety of potential extensions to this work.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Chromatic characterization of a three-channel colorimeter using back-propagation neural networks

NASA Astrophysics Data System (ADS)

Pardo, P. J.; Pérez, A. L.; Suero, M. I.

2004-09-01

This work describes a method for the chromatic characterization of a three-channel colorimeter of recent design and construction dedicated to color vision research. The colorimeter consists of two fixed monochromators and a third monochromator interchangeable with a cathode ray tube or any other external light source. Back-propagation neural networks were used for the chromatic characterization to establish the relationship between each monochromator's input parameters and the tristimulus values of each chromatic stimulus generated. The results showed the effectiveness of this type of neural-network-based system for the chromatic characterization of the stimuli produced by any monochromator.

AAAIC '88 - Aerospace Applications of Artificial Intelligence; Proceedings of the Fourth Annual Conference, Dayton, OH, Oct. 25-27, 1988. Volumes 1 2

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

Johnson, J.R.; Netrologic, Inc., San Diego, CA)

1988-01-01

Topics presented include integrating neural networks and expert systems, neural networks and signal processing, machine learning, cognition and avionics applications, artificial intelligence and man-machine interface issues, real time expert systems, artificial intelligence, and engineering applications. Also considered are advanced problem solving techniques, combinational optimization for scheduling and resource control, data fusion/sensor fusion, back propagation with momentum, shared weights and recurrency, automatic target recognition, cybernetics, optical neural networks.

Blood pressure (BP) assessment-from BP level to BP variability.

PubMed

Feber, Janusz; Litwin, Mieczyslaw

2016-07-01

The assessment of blood pressure (BP) can be challenging in children, especially in very young individuals, due to their variable body size and lack of cooperation. In the absence of data relating BP with cardiovascular outcomes in children, there is a need to convert absolute BP values (in mmHg) into age-, gender- and height appropriate BP percentiles or Z-scores in order to compare a patient's BP with the BP of healthy children of the same age, but also of children of different ages. Traditionally, the interpretation of BP has been based mainly on the assessment of the BP level obtained by office, home or 24-h BP monitoring. Recent studies suggest that it is not only BP level (i.e. average BP) but also BP variability that is clinically important for the development of target organ damage, including the progression of chronic kidney disease. In this review we describe current methods to evaluate of BP level, outline available methods for BP variability assessment and discuss the clinical consequences of BP variability, including its potential role in the management of hypertension.

Propagating gene expression fronts in a one-dimensional coupled system of artificial cells

NASA Astrophysics Data System (ADS)

Tayar, Alexandra M.; Karzbrun, Eyal; Noireaux, Vincent; Bar-Ziv, Roy H.

2015-12-01

Living systems employ front propagation and spatiotemporal patterns encoded in biochemical reactions for communication, self-organization and computation. Emulating such dynamics in minimal systems is important for understanding physical principles in living cells and in vitro. Here, we report a one-dimensional array of DNA compartments in a silicon chip as a coupled system of artificial cells, offering the means to implement reaction-diffusion dynamics by integrated genetic circuits and chip geometry. Using a bistable circuit we programmed a front of protein synthesis propagating in the array as a cascade of signal amplification and short-range diffusion. The front velocity is maximal at a saddle-node bifurcation from a bistable regime with travelling fronts to a monostable regime that is spatially homogeneous. Near the bifurcation the system exhibits large variability between compartments, providing a possible mechanism for population diversity. This demonstrates that on-chip integrated gene circuits are dynamical systems driving spatiotemporal patterns, cellular variability and symmetry breaking.

Curriculum Assessment Using Artificial Neural Network and Support Vector Machine Modeling Approaches: A Case Study. IR Applications. Volume 29

ERIC Educational Resources Information Center

Chen, Chau-Kuang

2010-01-01

Artificial Neural Network (ANN) and Support Vector Machine (SVM) approaches have been on the cutting edge of science and technology for pattern recognition and data classification. In the ANN model, classification accuracy can be achieved by using the feed-forward of inputs, back-propagation of errors, and the adjustment of connection weights. In…

D Coordinate Transformation Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Konakoglu, B.; Cakır, L.; Gökalp, E.

2016-10-01

Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network.

PubMed

Liu, Ruixin; Zhang, Xiaodong; Zhang, Lu; Gao, Xiaojie; Li, Huiling; Shi, Junhan; Li, Xuelin

2014-06-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue.

Bitterness intensity prediction of berberine hydrochloride using an electronic tongue and a GA-BP neural network

PubMed Central

LIU, RUIXIN; ZHANG, XIAODONG; ZHANG, LU; GAO, XIAOJIE; LI, HUILING; SHI, JUNHAN; LI, XUELIN

2014-01-01

The aim of this study was to predict the bitterness intensity of a drug using an electronic tongue (e-tongue). The model drug of berberine hydrochloride was used to establish a bitterness prediction model (BPM), based on the taste evaluation of bitterness intensity by a taste panel, the data provided by the e-tongue and a genetic algorithm-back-propagation neural network (GA-BP) modeling method. The modeling characteristics of the GA-BP were compared with those of multiple linear regression, partial least square regression and BP methods. The determination coefficient of the BPM was 0.99965±0.00004, the root mean square error of cross-validation was 0.1398±0.0488 and the correlation coefficient of the cross-validation between the true and predicted values was 0.9959±0.0027. The model is superior to the other three models based on these indicators. In conclusion, the model established in this study has a high fitting degree and may be used for the bitterness prediction modeling of berberine hydrochloride of different concentrations. The model also provides a reference for the generation of BPMs of other drugs. Additionally, the algorithm of the study is able to conduct a rapid and accurate quantitative analysis of the data provided by the e-tongue. PMID:24926369

Characteristics of VLF wave propagation in the Earth's magnetosphere in the presence of an artificial density duct

NASA Astrophysics Data System (ADS)

Pasmanik, Dmitry; Demekhov, Andrei

We study the propagation of VLF waves in the Earth's ionosphere and magnetosphere in the presence of large-scale artificial plasma inhomogeneities which can be created by HF heating facilities like HAARP and ``Sura''. A region with enhanced cold plasma density can be formed due to the action of HF heating. This region is extended along geomagnetic field (up to altitudes of several thousand km) and has rather small size across magnetic field (about 1 degree). The geometric-optical approximation is used to study wave propagation. The plasma density and ion composition are calculated with the use of SAMI2 model, which was modified to take the effect of HF heating into account. We calculate ray trajectories of waves with different initial frequency and wave-normal angles and originating at altitudes of about 100 km in the region near the heating area. The source of such waves could be the lightning discharges, modulated HF heating of the ionosphere, or VLF transmitters. Variation of the wave amplitude along the ray trajectories due to refraction is considered and spatial distribution of wave intensity in the magnetosphere is analyzed. We show that the presence of such a density disturbances can lead to significant changes of wave propagation trajectories, in particular, to efficient guiding of VLF waves in this region. This can result in a drastic increase of the VLF-wave intensity in the density duct. The dependence of wave propagation properties on parameters of heating facility operation regime is considered. We study the variation of the spatial distribution of VLF wave intensity related to the slow evolution of the artificial inhomogeneity during the heating.

Discovering weighted patterns in intron sequences using self-adaptive harmony search and back-propagation algorithms.

PubMed

Huang, Yin-Fu; Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete.

50 CFR 23.64 - What factors are considered in making a finding that a plant is artificially propagated?

Code of Federal Regulations, 2010 CFR

2010-10-01

... 50 Wildlife and Fisheries 6 2010-10-01 2010-10-01 false What factors are considered in making a... INTERNATIONAL TRADE IN ENDANGERED SPECIES OF WILD FAUNA AND FLORA (CITES) Factors Considered in Making Certain Findings § 23.64 What factors are considered in making a finding that a plant is artificially propagated...

Nanophotonic particle simulation and inverse design using artificial neural networks.

PubMed

Peurifoy, John; Shen, Yichen; Jing, Li; Yang, Yi; Cano-Renteria, Fidel; DeLacy, Brendan G; Joannopoulos, John D; Tegmark, Max; Soljačić, Marin

2018-06-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

Nanophotonic particle simulation and inverse design using artificial neural networks

PubMed Central

Peurifoy, John; Shen, Yichen; Jing, Li; Cano-Renteria, Fidel; DeLacy, Brendan G.; Joannopoulos, John D.; Tegmark, Max

2018-01-01

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical. PMID:29868640

A CFBPN Artificial Neural Network Model for Educational Qualitative Data Analyses: Example of Students' Attitudes Based on Kellerts' Typologies

ERIC Educational Resources Information Center

Yorek, Nurettin; Ugulu, Ilker

2015-01-01

In this study, artificial neural networks are suggested as a model that can be "trained" to yield qualitative results out of a huge amount of categorical data. It can be said that this is a new approach applied in educational qualitative data analysis. In this direction, a cascade-forward back-propagation neural network (CFBPN) model was…

Discovering Weighted Patterns in Intron Sequences Using Self-Adaptive Harmony Search and Back-Propagation Algorithms

PubMed Central

Wang, Chia-Ming; Liou, Sing-Wu

2013-01-01

A hybrid self-adaptive harmony search and back-propagation mining system was proposed to discover weighted patterns in human intron sequences. By testing the weights under a lazy nearest neighbor classifier, the numerical results revealed the significance of these weighted patterns. Comparing these weighted patterns with the popular intron consensus model, it is clear that the discovered weighted patterns make originally the ambiguous 5SS and 3SS header patterns more specific and concrete. PMID:23737711

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network

PubMed Central

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-01-01

Background: Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. Method: A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Results: Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. Conclusions: The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced. PMID:28077893

The Prediction of the Risk Level of Pulmonary Embolism and Deep Vein Thrombosis through Artificial Neural Network.

PubMed

Agharezaei, Laleh; Agharezaei, Zhila; Nemati, Ali; Bahaadinbeigy, Kambiz; Keynia, Farshid; Baneshi, Mohammad Reza; Iranpour, Abedin; Agharezaei, Moslem

2016-10-01

Venous thromboembolism is a common cause of mortality among hospitalized patients and yet it is preventable through detecting the precipitating factors and a prompt diagnosis by specialists. The present study has been carried out in order to assist specialists in the diagnosis and prediction of the risk level of pulmonary embolism in patients, by means of artificial neural network. A number of 31 risk factors have been used in this study in order to evaluate the conditions of 294 patients hospitalized in 3 educational hospitals affiliated with Kerman University of Medical Sciences. Two types of artificial neural networks, namely Feed-Forward Back Propagation and Elman Back Propagation, were compared in this study. Through an optimized artificial neural network model, an accuracy and risk level index of 93.23 percent was achieved and, subsequently, the results have been compared with those obtained from the perfusion scan of the patients. 86.61 percent of high risk patients diagnosed through perfusion scan diagnostic method were also diagnosed correctly through the method proposed in the present study. The results of this study can be a good resource for physicians, medical assistants, and healthcare staff to diagnose high risk patients more precisely and prevent the mortalities. Additionally, expenses and other unnecessary diagnostic methods such as perfusion scans can be efficiently reduced.

Propagation of back-arc extension into the arc lithosphere in the southern New Hebrides volcanic arc

NASA Astrophysics Data System (ADS)

Patriat, M.; Collot, J.; Danyushevsky, L.; Fabre, M.; Meffre, S.; Falloon, T.; Rouillard, P.; Pelletier, B.; Roach, M.; Fournier, M.

2015-09-01

New geophysical data acquired during three expeditions of the R/V Southern Surveyor in the southern part of the North Fiji Basin allow us to characterize the deformation of the upper plate at the southern termination of the New Hebrides subduction zone, where it bends eastward along the Hunter Ridge. Unlike the northern end of the Tonga subduction zone, on the other side of the North Fiji Basin, the 90° bend does not correspond to the transition from a subduction zone to a transform fault, but it is due to the progressive retreat of the New Hebrides trench. The subduction trench retreat is accommodated in the upper plate by the migration toward the southwest of the New Hebrides arc and toward the south of the Hunter Ridge, so that the direction of convergence remains everywhere orthogonal to the trench. In the back-arc domain, the active deformation is characterized by propagation of the back-arc spreading ridge into the Hunter volcanic arc. The N-S spreading axis propagates southward and penetrates in the arc, where it connects to a sinistral strike-slip zone via an oblique rift. The collision of the Loyalty Ridge with the New Hebrides arc, less than two million years ago, likely initiated this deformation pattern and the fragmentation of the upper plate. In this particular geodynamic setting, with an oceanic lithosphere subducting beneath a highly sheared volcanic arc, a wide range of primitive subduction-related magmas has been produced including adakites, island arc tholeiites, back-arc basin basalts, and medium-K subduction-related lavas.

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network

PubMed Central

Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. PMID:29420584

A novel stock forecasting model based on High-order-fuzzy-fluctuation Trends and Back Propagation Neural Network.

PubMed

Guan, Hongjun; Dai, Zongli; Zhao, Aiwu; He, Jie

2018-01-01

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method.

A novel and generalized approach in the inversion of geoelectrical resistivity data using Artificial Neural Networks (ANN)

NASA Astrophysics Data System (ADS)

Raj, A. Stanley; Srinivas, Y.; Oliver, D. Hudson; Muthuraj, D.

2014-03-01

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model.

Error vector magnitude based parameter estimation for digital filter back-propagation mitigating SOA distortions in 16-QAM.

PubMed

Amiralizadeh, Siamak; Nguyen, An T; Rusch, Leslie A

2013-08-26

We investigate the performance of digital filter back-propagation (DFBP) using coarse parameter estimation for mitigating SOA nonlinearity in coherent communication systems. We introduce a simple, low overhead method for parameter estimation for DFBP based on error vector magnitude (EVM) as a figure of merit. The bit error rate (BER) penalty achieved with this method has negligible penalty as compared to DFBP with fine parameter estimation. We examine different bias currents for two commercial SOAs used as booster amplifiers in our experiments to find optimum operating points and experimentally validate our method. The coarse parameter DFBP efficiently compensates SOA-induced nonlinearity for both SOA types in 80 km propagation of 16-QAM signal at 22 Gbaud.

Oppositely directed pairs of propagating rifts in back-arc basins: Double saloon door seafloor spreading during subduction rollback

NASA Astrophysics Data System (ADS)

Martin, A. K.

2006-06-01

When a continent breaks up into two plates, which then separate from each other about a rotation pole, it can be shown that if initial movement is taken up by lithospheric extension, asthenospheric breakthrough and oceanic accretion propagate toward the pole of rotation. Such a propagating rift model is then applied to an embryonic centrally located rift which evolves into two rifts propagating in opposite directions. The resultant rhombic shape of the modeled basin, initially underlain entirely by thinned continental crust, is very similar to the Oligocene to Burdigalian back-arc evolution of the Valencia Trough and the Liguro-Provencal Basin in the western Mediterranean. Existing well and seismic stratigraphic data confirm that a rift did initiate in the Gulf of Lion and propagated southwest into the Valencia Trough. Similarly, seismic refraction, gravity, and heat flow data demonstrate that maximum extension within the Valencia Trough/Liguro-Provencal Basin occurred in an axial position close to the North Balearic Fracture Zone. The same model of oppositely propagating rifts, when applied to the Burdigalian/Langhian episode of back-arc oceanic accretion within the Liguro-Provencal and Algerian basins, predicts a number of features which are borne out by existing geological and geophysical, particularly magnetic data. These include the orientation of subparallel magnetic anomalies, presumed to be seafloor spreading isochrons, in both basins; concave-to-the-west fracture zones southwest of the North Balearic Fracture Zone, and concave-to-the-east fracture zones to its northeast; a spherical triangular area of NW oriented seafloor spreading isochrons southwest of Sardinia; the greater NW extension of the central (youngest?) magnetic anomaly within this triangular area, in agreement with the model-predicted northwestward propagation of a rift in this zone; successively more central (younger) magnetic anomalies abutting thinned continental crust nearer to the pole of

Recognition of edible oil by using BP neural network and laser induced fluorescence spectrum

NASA Astrophysics Data System (ADS)

Mu, Tao-tao; Chen, Si-ying; Zhang, Yin-chao; Guo, Pan; Chen, He; Zhang, Hong-yan; Liu, Xiao-hua; Wang, Yuan; Bu, Zhi-chao

2013-09-01

In order to accomplish recognition of the different edible oil we set up a laser induced fluorescence spectrum system in the laboratory based on Laser induced fluorescence spectrum technology, and then collect the fluorescence spectrum of different edible oil by using that system. Based on this, we set up a fluorescence spectrum database of different cooking oil. It is clear that there are three main peak position of different edible oil from fluorescence spectrum chart. Although the peak positions of all cooking oil were almost the same, the relative intensity of different edible oils was totally different. So it could easily accomplish that oil recognition could take advantage of the difference of relative intensity. Feature invariants were extracted from the spectrum data, which were chosen from the fluorescence spectrum database randomly, before distinguishing different cooking oil. Then back propagation (BP) neural network was established and trained by the chosen data from the spectrum database. On that basis real experiment data was identified by BP neural network. It was found that the overall recognition rate could reach as high as 83.2%. Experiments showed that the laser induced fluorescence spectrum of different cooking oil was very different from each other, which could be used to accomplish the oil recognition. Laser induced fluorescence spectrum technology, combined BP neural network，was fast, high sensitivity, non-contact, and high recognition rate. It could become a new technique to accomplish the edible oil recognition and quality detection.

Detection of Foreign Matter in Transfusion Solution Based on Gaussian Background Modeling and an Optimized BP Neural Network

PubMed Central

Zhou, Fuqiang; Su, Zhen; Chai, Xinghua; Chen, Lipeng

2014-01-01

This paper proposes a new method to detect and identify foreign matter mixed in a plastic bottle filled with transfusion solution. A spin-stop mechanism and mixed illumination style are applied to obtain high contrast images between moving foreign matter and a static transfusion background. The Gaussian mixture model is used to model the complex background of the transfusion image and to extract moving objects. A set of features of moving objects are extracted and selected by the ReliefF algorithm, and optimal feature vectors are fed into the back propagation (BP) neural network to distinguish between foreign matter and bubbles. The mind evolutionary algorithm (MEA) is applied to optimize the connection weights and thresholds of the BP neural network to obtain a higher classification accuracy and faster convergence rate. Experimental results show that the proposed method can effectively detect visible foreign matter in 250-mL transfusion bottles. The misdetection rate and false alarm rate are low, and the detection accuracy and detection speed are satisfactory. PMID:25347581

[Application of artificial neural networks in forecasting the number of circulatory system diseases death toll].

PubMed

Zhang, Ying; Shao, Yi; Shang, Kezheng; Wang, Shigong; Wang, Jinyan

2014-09-01

Set up the model of forecasting the number of circulatorys death toll based on back-propagation (BP) artificial neural networks discuss the relationship between the circulatory system diseases death toll meteorological factors and ambient air pollution. The data of tem deaths, meteorological factors, and ambient air pollution within the m 2004 to 2009 in Nanjing were collected. On the basis of analyzing the ficient between CSDDT meteorological factors and ambient air pollution, leutral network model of CSDDT was built for 2004 - 2008 based on factors and ambient air pollution within the same time, and the data of 2009 est the predictive power of the model. There was a closely system diseases relationship between meteorological factors, ambient air pollution and the circulatory system diseases death toll. The ANN model structure was 17 -16 -1, 17 input notes, 16 hidden notes and 1 output note. The training precision was 0. 005 and the final error was 0. 004 999 42 after 487 training steps. The results of forecast show that predict accuracy over 78. 62%. This method is easy to be finished with smaller error, and higher ability on circulatory system death toll on independent prediction, which can provide a new method for forecasting medical-meteorological forecast and have the value of further research.

Validation of artificial neural network models for predicting biochemical markers associated with male infertility.

PubMed

Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B

2016-08-01

Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous

[The Identification of the Origin of Chinese Wolfberry Based on Infrared Spectral Technology and the Artificial Neural Network].

PubMed

Li, Zhong; Liu, Ming-de; Ji, Shou-xiang

2016-03-01

The Fourier Transform Infrared Spectroscopy (FTIR) is established to find the geographic origins of Chinese wolfberry quickly. In the paper, the 45 samples of Chinese wolfberry from different places of Qinghai Province are to be surveyed by FTIR. The original data matrix of FTIR is pretreated with common preprocessing and wavelet transform. Compared with common windows shifting smoothing preprocessing, standard normal variation correction and multiplicative scatter correction, wavelet transform is an effective spectrum data preprocessing method. Before establishing model through the artificial neural networks, the spectra variables are compressed by means of the wavelet transformation so as to enhance the training speed of the artificial neural networks, and at the same time the related parameters of the artificial neural networks model are also discussed in detail. The survey shows even if the infrared spectroscopy data is compressed to 1/8 of its original data, the spectral information and analytical accuracy are not deteriorated. The compressed spectra variables are used for modeling parameters of the backpropagation artificial neural network (BP-ANN) model and the geographic origins of Chinese wolfberry are used for parameters of export. Three layers of neural network model are built to predict the 10 unknown samples by using the MATLAB neural network toolbox design error back propagation network. The number of hidden layer neurons is 5, and the number of output layer neuron is 1. The transfer function of hidden layer is tansig, while the transfer function of output layer is purelin. Network training function is trainl and the learning function of weights and thresholds is learngdm. net. trainParam. epochs=1 000, while net. trainParam. goal = 0.001. The recognition rate of 100% is to be achieved. It can be concluded that the method is quite suitable for the quick discrimination of producing areas of Chinese wolfberry. The infrared spectral analysis technology

High-order Two-way Artificial Boundary Conditions for Nonlinear Wave Propagation with Backscattering

NASA Technical Reports Server (NTRS)

Fibich, Gadi; Tsynkov, Semyon

2000-01-01

When solving linear scattering problems, one typically first solves for the impinging wave in the absence of obstacles. Then, by linear superposition, the original problem is reduced to one that involves only the scattered waves driven by the values of the impinging field at the surface of the obstacles. In addition, when the original domain is unbounded, special artificial boundary conditions (ABCs) that would guarantee the reflectionless propagation of waves have to be set at the outer boundary of the finite computational domain. The situation becomes conceptually different when the propagation equation is nonlinear. In this case the impinging and scattered waves can no longer be separated, and the problem has to be solved in its entirety. In particular, the boundary on which the incoming field values are prescribed, should transmit the given incoming waves in one direction and simultaneously be transparent to all the outgoing waves that travel in the opposite direction. We call this type of boundary conditions two-way ABCs. In the paper, we construct the two-way ABCs for the nonlinear Helmholtz equation that models the laser beam propagation in a medium with nonlinear index of refraction. In this case, the forward propagation is accompanied by backscattering, i.e., generation of waves in the direction opposite to that of the incoming signal. Our two-way ABCs generate no reflection of the backscattered waves and at the same time impose the correct values of the incoming wave. The ABCs are obtained for a fourth-order accurate discretization to the Helmholtz operator; the fourth-order grid convergence is corroborated experimentally by solving linear model problems. We also present solutions in the nonlinear case using the two-way ABC which, unlike the traditional Dirichlet boundary condition, allows for direct calculation of the magnitude of backscattering.

Fast discrimination of traditional Chinese medicine according to geographical origins with FTIR spectroscopy and advanced pattern recognition techniques

NASA Astrophysics Data System (ADS)

Li, Ning; Wang, Yan; Xu, Kexin

2006-08-01

Combined with Fourier transform infrared (FTIR) spectroscopy and three kinds of pattern recognition techniques, 53 traditional Chinese medicine danshen samples were rapidly discriminated according to geographical origins. The results showed that it was feasible to discriminate using FTIR spectroscopy ascertained by principal component analysis (PCA). An effective model was built by employing the Soft Independent Modeling of Class Analogy (SIMCA) and PCA, and 82% of the samples were discriminated correctly. Through use of the artificial neural network (ANN)-based back propagation (BP) network, the origins of danshen were completely classified.

Modeling the Malaysian motor insurance claim using artificial neural network and adaptive NeuroFuzzy inference system

NASA Astrophysics Data System (ADS)

Mohd Yunos, Zuriahati; Shamsuddin, Siti Mariyam; Ismail, Noriszura; Sallehuddin, Roselina

2013-04-01

Artificial neural network (ANN) with back propagation algorithm (BP) and ANFIS was chosen as an alternative technique in modeling motor insurance claims. In particular, an ANN and ANFIS technique is applied to model and forecast the Malaysian motor insurance data which is categorized into four claim types; third party property damage (TPPD), third party bodily injury (TPBI), own damage (OD) and theft. This study is to determine whether an ANN and ANFIS model is capable of accurately predicting motor insurance claim. There were changes made to the network structure as the number of input nodes, number of hidden nodes and pre-processing techniques are also examined and a cross-validation technique is used to improve the generalization ability of ANN and ANFIS models. Based on the empirical studies, the prediction performance of the ANN and ANFIS model is improved by using different number of input nodes and hidden nodes; and also various sizes of data. The experimental results reveal that the ANFIS model has outperformed the ANN model. Both models are capable of producing a reliable prediction for the Malaysian motor insurance claims and hence, the proposed method can be applied as an alternative to predict claim frequency and claim severity.

[Artificial propagation and embryonic development of Neolissochilus benasi].

PubMed

Pan, Xiao-Fu; Liu, Qian; Wang, Xiao-Ai; Yang, Jun-Xing; Chen, Xiao-Yong; Li, Zai-Yun; Li, Lie

2013-12-01

From 2009 to 2011, luteinizing hormone releasing hormone analogue (LHRH-A2) mixed with domperidon (DOM) was successfully applied during the artificial propagation of Neolissochilus benasi. Totally, 60 females and 100 males were injected with the hormone mixture, resulting in 47 (78.3%) females and 92 (92.0%) males being successfully spawned. A total of 1,986-5 854 eggs were spawned per female with an egg diameter varying between 2.2-2.8 mm, and an average nucleus deviation rate of 73.2%. Sperm density, vitality and life span were 16.32±2.89×10(9)/mL, 60.6±3.2% and 70.2±5.3 s, respectively. On the whole, the embryonic development of N. benasi was similar to that of zebra fish-albeit relatively slower-lasting approximately 120 hours. The development itself can be divided into six discrete stages: zygote, cleavage, blastula, gastrula, segmentation and hatching. Results showed that the average hatching rate was 32.4%, with 86.5% of larvae surviving 45 days after hatching. During embryonic development, deformities commonly occurred on the mouth, chest, ocular region, especially in the spinal column. To try to attempt improving future breeding efforts, we provided a survey of the embryonic developmental difficulties of N. benasi using LHRH-A2 followed by several potential solutions, including providing suitable breeding conditions and minimizing capture stresses.

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network.

PubMed

Huang, Li; Huang, Jian; Wang, Wei

2018-01-18

Resettlement affects not only the resettlers' production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers' sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries.

The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network

PubMed Central

Huang, Li; Huang, Jian

2018-01-01

Resettlement affects not only the resettlers’ production activities and life but also, directly or indirectly, the normal operation of power stations, the sustainable development of the resettlers, and regional social stability. Therefore, a scientific evaluation index system for the sustainable development of reservoir resettlement must be established that fits Chinese national conditions and not only promotes reservoir resettlement research but also improves resettlement practice. This essay builds an evaluation index system for resettlers’ sustainable development based on a back-propagation (BP) neural network, which can be adopted in China, taking the resettlement necessitated by step hydropower stations along the Wujiang River cascade as an example. The assessment results show that the resettlement caused by step power stations along the Wujiang River is sustainable, and this evaluation supports the conclusion that national policies and regulations, which are undergoing constant improvement, and resettlement has increasingly improved. The results provide a reference for hydropower reservoir resettlement in developing countries. PMID:29346305

Gross domestic product estimation based on electricity utilization by artificial neural network

NASA Astrophysics Data System (ADS)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

78 FR 60270 - BP America Inc., BP Corporation North America Inc., BP America Production Company, and BP Energy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-01

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. IN13-15-000] BP America Inc., BP Corporation North America Inc., BP America Production Company, and BP Energy Company; Notice of Designation of Commission Staff as Non-Decisional With respect to an order issued by the Commission on August...

Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

NASA Astrophysics Data System (ADS)

Yu, Yali; Wang, Mengxia; Lima, Dimas

2018-04-01

In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

Artificial neural networks in Space Station optimal attitude control

NASA Astrophysics Data System (ADS)

Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

1992-08-01

Innovative techniques of using 'Artificial Neural Networks' (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using Control Moment Gyros (CMGs) are investigated. The first technique uses a feedforward ANN with multilayer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second techique uses a single layer feedforward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

Risk factors for Apgar score using artificial neural networks.

PubMed

Ibrahim, Doaa; Frize, Monique; Walker, Robin C

2006-01-01

Artificial Neural Networks (ANNs) have been used in identifying the risk factors for many medical outcomes. In this paper, the risk factors for low Apgar score are introduced. This is the first time, to our knowledge, that the ANNs are used for Apgar score prediction. The medical domain of interest used is the perinatal database provided by the Perinatal Partnership Program of Eastern and Southeastern Ontario (PPPESO). The ability of the feed forward back propagation ANNs to generate strong predictive model with the most influential variables is tested. Finally, minimal sets of variables (risk factors) that are important in predicting Apgar score outcome without degrading the ANN performance are identified.

Applying Gradient Descent in Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

Rainwater propagation through snow during artificial rain-on-snow events

NASA Astrophysics Data System (ADS)

Juras, Roman; Würzer, Sebastian; Pavlasek, Jiri; Jonas, Tobias

2016-04-01

The mechanism of rainwater propagation and runoff generation during rain-on-snow (ROS) is still insufficiently known. Understanding rainwater behaviour within the natural snowpack is crucial especially for forecasting of natural hazards like floods and wet snow avalanches. In this study, rainwater percolation through snow was investigated by sprinkling the naturally stable isotope deuterium on snow and conduct hydrograph separation on samples collected from the snowpack runoff. This allowed quantifying the contribution of rainwater and snowmelt in the water released from the snowpack. Four field experiments were carried out during the winter 2015 in the vicinity of Davos, Switzerland. A 1 by 1 m block of natural snow cover was isolated from the surrounding snowpack to enable a closed water balance. This experimental snow sample was exposed to artificial rainfall with 41 mm of deuterium enriched water. The sprinkling was run in four 30 minutes intervals separated by three 30 minutes non-sprinkling intervals. The runoff from the snow cube was monitored quantitatively by a snow lysimeter and output water was continuously sampled for the deuterium concentration. Further, snowpack properties were analysed before and after the sprinkling, including vertical profiles of snow density, liquid water content (LWC) and deuterium concentration. One experiment conducted on cold snowpack showed that rainwater propagated much faster as compared to three experiments conducted on ripe isothermal snowpack. Our data revealed that sprinkled rainwater initially pushed out pre-event LWC or mixed with meltwater created within the snowpack. Hydrographs from every single experiment showed four pronounced peaks, with the first peak always consisted of less rainwater than the following ones. The partial contribution of rainwater to the total runoff consistently increased over the course of the experiment, but never exceeded 63 %. Moreover, the development of preferential paths after the first

Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

NASA Astrophysics Data System (ADS)

Podder, M. S.; Majumder, C. B.

2017-11-01

An artificial neural network (ANN) model was developed to predict the phycoremediation efficiency of Chlorella pyrenoidosa for the removal of both As(III) and As(V) from synthetic wastewater based on 49 data-sets obtained from experimental study and increased the data using CSCF technique. The data were divided into training (60%) validation (20%) and testing (20%) sets. The data collected was used for training a three-layer feed-forward back propagation (BP) learning algorithm having 4-5-1 architecture. The model used tangent sigmoid transfer function at input to hidden layer ( tansing) while a linear transfer function ( purelin) was used at output layer. Comparison between experimental results and model results gave a high correlation coefficient (R allANN 2 equal to 0.99987 for both ions and exhibited that the model was able to predict the phycoremediation of As(III) and As(V) from wastewater. Experimental parameters influencing phycoremediation process like pH, inoculum size, contact time and initial arsenic concentration [either As(III) or As(V)] were investigated. A contact time of 168 h was mainly required for achieving equilibrium at pH 9.0 with an inoculum size of 10% (v/v). At optimum conditions, metal ion uptake enhanced with increasing initial metal ion concentration.

Prevalence and tracking of back pain from childhood to adolescence

PubMed Central

2011-01-01

Background It is generally acknowledged that back pain (BP) is a common condition already in childhood. However, the development until early adulthood is not well understood and, in particular, not the individual tracking pattern. The objectives of this paper are to show the prevalence estimates of BP, low back pain (LBP), mid back pain (MBP), neck pain (NP), and care-seeking because of BP at three different ages (9, 13 and15 years) and how the BP reporting tracks over these age groups over three consecutive surveys. Methods A longitudinal cohort study was carried out from the years of 1997 till 2005, collecting interview data from children who were sampled to be representative of Danish schoolchildren. BP was defined overall and specifically in the three spinal regions as having reported pain within the past month. The prevalence estimates and the various patterns of BP reporting over time are presented as percentages. Results Of the 771 children sampled, 62%, 57%, and 58% participated in the three back surveys and 34% participated in all three. The prevalence estimates for children at the ages of 9, 13, and 15, respectively, were for BP 33%, 28%, and 48%; for LBP 4%, 22%, and 36%; for MBP 20%, 13%, and 35%; and for NP 10%, 7%, and 15%. Seeking care for BP increased from 6% and 8% at the two youngest ages to 34% at the oldest. Only 7% of the children who participated in all three surveys reported BP each time and 30% of these always reported no pain. The patterns of development differed for the three spinal regions and between genders. Status at the previous survey predicted status at the next survey, so that those who had pain before were more likely to report pain again and vice versa. This was most pronounced for care-seeking. Conclusion It was confirmed that BP starts early in life, but the patterns of onset and development over time vary for different parts of the spine and between genders. Because of these differences, it is recommended to report on BP in

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management

Prediction of BP reactivity to talking using hybrid soft computing approaches.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2014-01-01

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R (2)), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

Artificial neural networks in Space Station optimal attitude control

NASA Astrophysics Data System (ADS)

Kumar, Renjith R.; Seywald, Hans; Deshpande, Samir M.; Rahman, Zia

1995-01-01

Innovative techniques of using "artificial neural networks" (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second technique uses a single layer feed-forward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.

Determination of zinc oxide content of mineral medicine calamine using near-infrared spectroscopy based on MIV and BP-ANN algorithm

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Chen, Long; Sun, Yangbo; Bai, Yu; Huang, Bisheng; Chen, Keli

2018-03-01

Near-infrared (NIR) spectroscopy has been widely used in the analysis fields of traditional Chinese medicine. It has the advantages of fast analysis, no damage to samples and no pollution. In this research, a fast quantitative model for zinc oxide (ZnO) content in mineral medicine calamine was explored based on NIR spectroscopy. NIR spectra of 57 batches of calamine samples were collected and the first derivative (FD) method was adopted for conducting spectral pretreatment. The content of ZnO in calamine sample was determined using ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy. 57 batches of calamine samples were categorized into calibration and prediction set using the Kennard-Stone (K-S) algorithm. Firstly, in the calibration set, to calculate the correlation coefficient (r) between the absorbance value and the ZnO content of corresponding samples at each wave number. Next, according to the square correlation coefficient (r2) value to obtain the top 50 wave numbers to compose the characteristic spectral bands (4081.8-4096.3, 4188.9-4274.7, 4335.4, 4763.6,4794.4-4802.1, 4809.9, 4817.6-4875.4 cm- 1), which were used to establish the quantitative model of ZnO content using back propagation artificial neural network (BP-ANN) algorithm. Then, the 50 wave numbers were operated by the mean impact value (MIV) algorithm to choose wave numbers whose absolute value of MIV greater than or equal to 25, to obtain the optimal characteristic spectral bands (4875.4-4836.9, 4223.6-4080.9 cm- 1). And then, both internal cross and external validation were used to screen the number of hidden layer nodes of BP-ANN. Finally, the number 4 of hidden layer nodes was chosen as the best. At last, the BP-ANN model was found to enjoy a high accuracy and strong forecasting capacity for analyzing ZnO content in calamine samples ranging within 42.05-69.98%, with relative mean square error of cross validation (RMSECV) of 1.66% and coefficient of

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools

NASA Astrophysics Data System (ADS)

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-01

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp = 0.9180 and RMSEP = 2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine.

Back Propagation Artificial Neural Network and Its Application in Fault Detection of Condenser Failure in Thermo Plant

NASA Astrophysics Data System (ADS)

Ismail, Firas B.; Thiruchelvam, Vinesh

2013-06-01

Steam condenser is one of the most important equipment in steam power plants. If the steam condenser trips it may lead to whole unit shutdown, which is economically burdensome. Early condenser trips monitoring is crucial to maintain normal and safe operational conditions. In the present work, artificial intelligent monitoring systems specialized in condenser outages has been proposed and coded within the MATLAB environment. The training and validation of the system has been performed using real operational measurements captured from the control system of selected steam power plant. An integrated plant data preparation scheme for condenser outages with related operational variables has been proposed. Condenser outages under consideration have been detected by developed system before the plant control system"

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

NASA Astrophysics Data System (ADS)

Komeilian, H.; Mohyeddin Bateni, S.; Xu, T.; Nielson, J.

2015-05-01

Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009-2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson's correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

NASA Astrophysics Data System (ADS)

Swastika, Windra

2017-03-01

A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

A lithology identification method for continental shale oil reservoir based on BP neural network

NASA Astrophysics Data System (ADS)

Han, Luo; Fuqiang, Lai; Zheng, Dong; Weixu, Xia

2018-06-01

The Dongying Depression and Jiyang Depression of the Bohai Bay Basin consist of continental sedimentary facies with a variable sedimentary environment and the shale layer system has a variety of lithologies and strong heterogeneity. It is difficult to accurately identify the lithologies with traditional lithology identification methods. The back propagation (BP) neural network was used to predict the lithology of continental shale oil reservoirs. Based on the rock slice identification, x-ray diffraction bulk rock mineral analysis, scanning electron microscope analysis, and the data of well logging and logging, the lithology was divided with carbonate, clay and felsic as end-member minerals. According to the core-electrical relationship, the frequency histogram was then used to calculate the logging response range of each lithology. The lithology-sensitive curves selected from 23 logging curves (GR, AC, CNL, DEN, etc) were chosen as the input variables. Finally, the BP neural network training model was established to predict the lithology. The lithology in the study area can be divided into four types: mudstone, lime mudstone, lime oil-mudstone, and lime argillaceous oil-shale. The logging responses of lithology were complicated and characterized by the low values of four indicators and medium values of two indicators. By comparing the number of hidden nodes and the number of training times, we found that the number of 15 hidden nodes and 1000 times of training yielded the best training results. The optimal neural network training model was established based on the above results. The lithology prediction results of BP neural network of well XX-1 showed that the accuracy rate was over 80%, indicating that the method was suitable for lithology identification of continental shale stratigraphy. The study provided the basis for the reservoir quality and oily evaluation of continental shale reservoirs and was of great significance to shale oil and gas exploration.

Incidence of back pain in adolescent athletes: a prospective study.

PubMed

Mueller, Steffen; Mueller, Juliane; Stoll, Josefine; Prieske, Olaf; Cassel, Michael; Mayer, Frank

2016-01-01

Recently, the incidence rate of back pain (BP) in adolescents has been reported at 21%. However, the development of BP in adolescent athletes is unclear. Hence, the purpose of this study was to examine the incidence of BP in young elite athletes in relation to gender and type of sport practiced. Subjective BP was assessed in 321 elite adolescent athletes (m/f 57%/43%; 13.2 ± 1.4 years; 163.4 ± 11.4 cm; 52.6 ± 12.6 kg; 5.0 ± 2.6 training yrs; 7.6 ± 5.3 training h/week). Initially, all athletes were free of pain. The main outcome criterion was the incidence of back pain [%] analyzed in terms of pain development from the first measurement day (M1) to the second measurement day (M2) after 2.0 ± 1.0 year. Participants were classified into athletes who developed back pain (BPD) and athletes who did not develop back pain (nBPD). BP (acute or within the last 7 days) was assessed with a 5-step face scale (face 1-2 = no pain; face 3-5 = pain). BPD included all athletes who reported faces 1 and 2 at M1 and faces 3 to 5 at M2. nBPD were all athletes who reported face 1 or 2 at both M1 and M2. Data was analyzed descriptively. Additionally, a Chi 2 test was used to analyze gender- and sport-specific differences ( p  = 0.05). Thirty-two athletes were categorized as BPD (10%). The gender difference was 5% (m/f: 12%/7%) but did not show statistical significance ( p  = 0.15). The incidence of BP ranged between 6 and 15% for the different sport categories. Game sports (15%) showed the highest, and explosive strength sports (6%) the lowest incidence. Anthropometrics or training characteristics did not significantly influence BPD ( p  = 0.14 gender to p  = 0.90 sports; r 2  = 0.0825). BP incidence was lower in adolescent athletes compared to young non-athletes and even to the general adult population. Consequently, it can be concluded that high-performance sports do not lead to an additional increase in back pain incidence

Predicting high-risk preterm birth using artificial neural networks.

PubMed

Catley, Christina; Frize, Monique; Walker, C Robin; Petriu, Dorina C

2006-07-01

A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patient's obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the network's sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model.

Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

NASA Astrophysics Data System (ADS)

Maleki, E.

2015-12-01

Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters.

Artificial neural network modelling of a large-scale wastewater treatment plant operation.

PubMed

Güçlü, Dünyamin; Dursun, Sükrü

2010-11-01

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.

Effect of back massage intervention on anxiety, comfort, and physiologic responses in patients with congestive heart failure.

PubMed

Chen, Wei-Ling; Liu, Gin-Jen; Yeh, Shu-Hui; Chiang, Ming-Chu; Fu, Mao-Young; Hsieh, Yuan-Kai

2013-05-01

Patients suffering from congestive heart failure (CHF) frequently feel physical suffering and anxiety. The researchers investigated whether back massage could reduce anxiety, discomfort, and physical suffering in patients with CHF. The effects of gender and severity-dependent response of back massage on anxiety and discomfort in patients were also analyzed. The study used a quasi-experimental design with one group pretest and posttest. Sixty-four participants were recruited in southern Taiwan. The modified State Anxiety Inventory, the discomfort Visual Analogue Scale, electronic blood pressure (BP) gauges, stethoscopes and the pulse oximetry were used in this study. The participants' systolic BP (F (3, 189)=18.91, p<0.01), diastolic BP (F (3, 189)=13.40, p<0.01), heart rate (F (3, 189)=26.28, p<0.01), and respiratory rates (F (3, 189)=5.77, p<0.01) were significantly decreased after back massage. Oxygen saturation levels showed significant increases (F (3, 189)=42.82, p<0.01). Male participants revealed a more significant reduction in anxiety than the female participants (F (1, 50)=7.27, p=0.01). Those with more severe heart failure and greater levels of anxiety (F (2, 61)=4.31, p=0.02) and systolic BP (F (2, 61)=3.86, p=0.03) demonstrated significantly greater responses to back massage. Back massage significantly reduced anxiety in the study population. Systolic BP decreased to a greater degree in the male participants, particularly in those with severe heart failure and greater levels of anxiety and higher systolic BP. This study was conducted without a control group. Randomized clinical trials are needed to validate the effectiveness of back massage on patients with CHF.

Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network

NASA Astrophysics Data System (ADS)

Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T.

2014-05-01

We have reconstructed third-dimension specimen information from convergent-beam electron diffraction (CBED) patterns simulated using the stacked-Bloch-wave method. By reformulating the stacked-Bloch-wave formalism as an artificial neural network and optimizing with resilient back propagation, we demonstrate specimen orientation reconstructions with depth resolutions down to 5 nm. To show our algorithm's ability to analyze realistic data, we also discuss and demonstrate our algorithm reconstructing from noisy data and using a limited number of CBED disks. Applicability of this reconstruction algorithm to other specimen parameters is discussed.

Improving back projection imaging with a novel physics-based aftershock calibration approach: A case study of the 2015 Gorkha earthquake

NASA Astrophysics Data System (ADS)

Meng, Lingsen; Zhang, Ailin; Yagi, Yuji

2016-01-01

The 2015 Mw 7.8 Nepal-Gorkha earthquake with casualties of over 9000 people was the most devastating disaster to strike Nepal since the 1934 Nepal-Bihar earthquake. Its rupture process was imaged by teleseismic back projections (BP) of seismograms recorded by three, large regional networks in Australia, North America, and Europe. The source images of all three arrays reveal a unilateral eastward rupture; however, the propagation directions and speeds differ significantly between the arrays. To understand the spatial uncertainties of the BP analyses, we analyze four moderate size aftershocks recorded by all three arrays exactly as had been conducted for the main shock. The apparent source locations inferred from BPs are systematically biased from the catalog locations, as a result of a slowness error caused by three-dimensional Earth structures. We introduce a physics-based slowness correction that successfully mitigates the source location discrepancies among the arrays. Our calibrated BPs are found to be mutually consistent and reveal a unilateral rupture propagating eastward at a speed of 2.7 km/s, localized in a relatively narrow and deep swath along the downdip edge of the locked Himalayan thrust zone. We find that the 2015 Gorkha earthquake was a localized rupture that failed to break the entire Himalayan décollement to the surface, which can be regarded as an intermediate event during the interseismic period of larger Himalayan ruptures that break the whole seismogenic zone width. Thus, our physics-based slowness correction is an important technical improvement of BP, mitigating spatial uncertainties and improving the robustness of single and multiarray studies.

Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

NASA Astrophysics Data System (ADS)

Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.

2016-09-01

Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.

New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

NASA Astrophysics Data System (ADS)

Mofavvaz, Shirin; Sohrabi, Mahmoud Reza; Nezamzadeh-Ejhieh, Alireza

2017-07-01

In the present study, artificial neural networks (ANNs) and least squares support vector machines (LS-SVM) as intelligent methods based on absorption spectra in the range of 230-300 nm have been used for determination of antihistamine decongestant contents. In the first step, one type of network (feed-forward back-propagation) from the artificial neural network with two different training algorithms, Levenberg-Marquardt (LM) and gradient descent with momentum and adaptive learning rate back-propagation (GDX) algorithm, were employed and their performance was evaluated. The performance of the LM algorithm was better than the GDX algorithm. In the second one, the radial basis network was utilized and results compared with the previous network. In the last one, the other intelligent method named least squares support vector machine was proposed to construct the antihistamine decongestant prediction model and the results were compared with two of the aforementioned networks. The values of the statistical parameters mean square error (MSE), Regression coefficient (R2), correlation coefficient (r) and also mean recovery (%), relative standard deviation (RSD) used for selecting the best model between these methods. Moreover, the proposed methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them.

Determining quality of caviar from Caspian Sea based on Raman spectroscopy and using artificial neural networks.

PubMed

Mohamadi Monavar, H; Afseth, N K; Lozano, J; Alimardani, R; Omid, M; Wold, J P

2013-07-15

The purpose of this study was to evaluate the feasibility of Raman spectroscopy for predicting purity of caviars. The 93 wild caviar samples of three different types, namely; Beluga, Asetra and Sevruga were analysed by Raman spectroscopy in the range 1995 cm(-1) to 545 cm(-1). Also, 60 samples from combinations of every two types were examined. The chemical origin of the samples was identified by reference measurements on pure samples. Linear chemometric methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used for data visualisation and classification which permitted clear distinction between different caviars. Non-linear methods like Artificial Neural Networks (ANN) were used to classify caviar samples. Two different networks were tested in the classification: Probabilistic Neural Network with Radial-Basis Function (PNN) and Multilayer Feed Forward Networks with Back Propagation (BP-NN). In both cases, scores of principal components (PCs) were chosen as input nodes for the input layer in PC-ANN models in order to reduce the redundancy of data and time of training. Leave One Out (LOO) cross validation was applied in order to check the performance of the networks. Results of PCA indicated that, features like type and purity can be used to discriminate different caviar samples. These findings were also supported by LDA with efficiency between 83.77% and 100%. These results were confirmed with the results obtained by developed PC-ANN models, able to classify pure caviar samples with 93.55% and 71.00% accuracy in BP network and PNN, respectively. In comparison, LDA, PNN and BP-NN models for predicting caviar types have 90.3%, 73.1% and 91.4% accuracy. Partial least squares regression (PLSR) models were built under cross validation and tested with different independent data sets, yielding determination coefficients (R(2)) of 0.86, 0.83, 0.92 and 0.91 with root mean square error (RMSE) of validation of 0.32, 0.11, 0.03 and 0.09 for

Learning topic models by belief propagation.

PubMed

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Intelligent sensing sensory quality of Chinese rice wine using near infrared spectroscopy and nonlinear tools.

PubMed

Ouyang, Qin; Chen, Quansheng; Zhao, Jiewen

2016-02-05

The approach presented herein reports the application of near infrared (NIR) spectroscopy, in contrast with human sensory panel, as a tool for estimating Chinese rice wine quality; concretely, to achieve the prediction of the overall sensory scores assigned by the trained sensory panel. Back propagation artificial neural network (BPANN) combined with adaptive boosting (AdaBoost) algorithm, namely BP-AdaBoost, as a novel nonlinear algorithm, was proposed in modeling. First, the optimal spectra intervals were selected by synergy interval partial least square (Si-PLS). Then, BP-AdaBoost model based on the optimal spectra intervals was established, called Si-BP-AdaBoost model. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient (Rp) and root mean square error of prediction (RMSEP) in prediction set. Si-BP-AdaBoost showed excellent performance in comparison with other models. The best Si-BP-AdaBoost model was achieved with Rp=0.9180 and RMSEP=2.23 in the prediction set. It was concluded that NIR spectroscopy combined with Si-BP-AdaBoost was an appropriate method for the prediction of the sensory quality in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Effect of Back Massage Intervention on Anxiety, Comfort, and Physiologic Responses in Patients with Congestive Heart Failure

PubMed Central

Chen, Wei-Ling; Liu, Gin-Jen; Chiang, Ming-Chu; Fu, Mao-Young; Hsieh, Yuan-Kai

2013-01-01

Abstract Background Patients suffering from congestive heart failure (CHF) frequently feel physical suffering and anxiety. Objectives The researchers investigated whether back massage could reduce anxiety, discomfort, and physical suffering in patients with CHF. The effects of gender and severity-dependent response of back massage on anxiety and discomfort in patients were also analyzed. Design The study used a quasi-experimental design with one group pretest and posttest. Participants Sixty-four participants were recruited in southern Taiwan. Outcome measures The modified State Anxiety Inventory, the discomfort Visual Analogue Scale, electronic blood pressure (BP) gauges, stethoscopes and the pulse oximetry were used in this study. Results The participants' systolic BP (F (3, 189)=18.91, p<0.01), diastolic BP (F (3, 189)=13.40, p<0.01), heart rate (F (3, 189)=26.28, p<0.01), and respiratory rates (F (3, 189)=5.77, p<0.01) were significantly decreased after back massage. Oxygen saturation levels showed significant increases (F (3, 189)=42.82, p<0.01). Male participants revealed a more significant reduction in anxiety than the female participants (F (1, 50)=7.27, p=0.01). Those with more severe heart failure and greater levels of anxiety (F (2, 61)=4.31, p=0.02) and systolic BP (F (2, 61)=3.86, p=0.03) demonstrated significantly greater responses to back massage. Conclusions Back massage significantly reduced anxiety in the study population. Systolic BP decreased to a greater degree in the male participants, particularly in those with severe heart failure and greater levels of anxiety and higher systolic BP. This study was conducted without a control group. Randomized clinical trials are needed to validate the effectiveness of back massage on patients with CHF. PMID:23186129

Application of back propagation artificial neural network on genetic variants in adiponectin ADIPOQ, peroxisome proliferator-activated receptor-γ, and retinoid X receptor-α genes and type 2 diabetes risk in a Chinese Han population.

PubMed

Shi, Hui; Lu, Ying; Du, Juan; Du, Wencong; Ye, Xinhua; Yu, Xiaofang; Ma, Jianhua; Cheng, Jinluo; Gao, Yanqin; Cao, Yuanyuan; Zhou, Ling; Li, Qian

2012-03-01

Our study was designed to explore the applied characteristics of the back propagation artificial neural network (BPANN) on studying the genetic variants in adipnectin ADIPOQ, peroxisome proliferator-activated receptor (PPAR)-γ, and retinoid X receptor-α (RXR-α) genes and type 2 diabetes mellitus (T2DM) risks in a Chinese Han population. We used BPANN as the fitting model based on data gathered from T2DM patients (n=913) and normal controls (n=1,001). The mean impact value (MIV) for each input variables were calculated, and the sequence of the factors according to their absolute MIVs was sorted. The results from BPANN were compared with multiple logistic regression analysis, and the generalized multifactor dimensionality reduction (GMDR) method was used to calculate the joint effects of ADIPOQ, PPAR-γ, and RXR-α genes. By BPANN analysis, the sequence according to the importance of the T2DM risk factors was in the order of serum adiponectin level, rs3856806, rs7649121, hypertension, rs3821799, rs17827276, rs12495941, rs4240711, age, rs16861194, waist circumference, rs2241767, rs2920502, rs1063539, alcohol drinking, smoking, hyperlipoproteinemia, gender, rs3132291, T2DM family history, rs4842194, rs822394, rs1801282, rs1045570, rs16861205, rs6537944, body mass index, rs266729, and rs1801282. However, compared with multiple logistic regression analysis, only 11 factors were statistically significant. After overweight and obesity were taken as environment adjustment factors into the analysis, model A2 B4 C5 C6 C8 (rs3856806, rs4240711, rs7649121, rs3821799, rs12495941) was the best model (coefficient of variation consistency=10/10, P=0.0107) in the GMDR method. These results suggested the interactions of ADIPOQ, PPAR-γ, and RXR-α genes might play a role in susceptibility to T2DM. BPANN could be used to analyze the risk factors of diseases and provide more complicated relationships between inputs and outputs.

Efficient, balanced, transmission line RF circuits by back propagation of common impedance nodes.

PubMed

Markhasin, Evgeny; Hu, Jianping; Su, Yongchao; Herzfeld, Judith; Griffin, Robert G

2013-06-01

We present a new, efficient strategy for designing fully balanced transmission line RF circuits for solid state NMR probes based on back propagation of common impedance nodes (BPCIN). In this approach, the impedance node phenomenon is the sole means of achieving mutual RF isolation and balance in all RF channels. BPCIN is illustrated using a custom double resonance 3.2 mm MAS probe operating at 500 MHz ((1)H) and 125 MHz ((13)C). When fully optimized, the probe is capable of producing high homogeneity (810°/90° ratios of 86% and 89% for (1)H and (13)C, respectively) and high efficiency (γB1=100 kHz for (1)H and (13)C at 70 W and 180 W of RF input, respectively; up to 360 kHz for (1)H). The probe's performance is illustrated by 2D MAS correlation spectra of microcrystals of the tripeptide N-f-MLF-OH and hydrated amyloid fibrils of the protein PI3-SH3. Copyright © 2013 Elsevier Inc. All rights reserved.

Early driver fatigue detection from electroencephalography signals using artificial neural networks.

PubMed

King, L M; Nguyen, H T; Lal, S K L

2006-01-01

This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training optimization technique called the magnified gradient function (MGF). This technique reduces the time required for training by modifying the standard back propagation (SBP) algorithm. The MGF is shown to classify professional driver fatigue with 81.49% accuracy (80.53% sensitivity, 82.44% specificity) and non-professional driver fatigue with 83.06% accuracy (84.04% sensitivity and 82.08% specificity).

Trunk Muscle Activity during Drop Jump Performance in Adolescent Athletes with Back Pain.

PubMed

Mueller, Steffen; Stoll, Josefine; Mueller, Juliane; Cassel, Michael; Mayer, Frank

2017-01-01

In the context of back pain, great emphasis has been placed on the importance of trunk stability, especially in situations requiring compensation of repetitive, intense loading induced during high-performance activities, e.g., jumping or landing. This study aims to evaluate trunk muscle activity during drop jump in adolescent athletes with back pain (BP) compared to athletes without back pain (NBP). Eleven adolescent athletes suffering back pain (BP: m/f: n = 4/7; 15.9 ± 1.3 y; 176 ± 11 cm; 68 ± 11 kg; 12.4 ± 10.5 h/we training) and 11 matched athletes without back pain (NBP: m/f: n = 4/7; 15.5 ± 1.3 y; 174 ± 7 cm; 67 ± 8 kg; 14.9 ± 9.5 h/we training) were evaluated. Subjects conducted 3 drop jumps onto a force plate (ground reaction force). Bilateral 12-lead SEMG (surface Electromyography) was applied to assess trunk muscle activity. Ground contact time [ms], maximum vertical jump force [N], jump time [ms] and the jump performance index [m/s] were calculated for drop jumps. SEMG amplitudes (RMS: root mean square [%]) for all 12 single muscles were normalized to MIVC (maximum isometric voluntary contraction) and analyzed in 4 time windows (100 ms pre- and 200 ms post-initial ground contact, 100 ms pre- and 200 ms post-landing) as outcome variables. In addition, muscles were grouped and analyzed in ventral and dorsal muscles, as well as straight and transverse trunk muscles. Drop jump ground reaction force variables did not differ between NBP and BP ( p > 0.05). Mm obliquus externus and internus abdominis presented higher SEMG amplitudes (1.3-1.9-fold) for BP ( p < 0.05). Mm rectus abdominis, erector spinae thoracic/lumbar and latissimus dorsi did not differ ( p > 0.05). The muscle group analysis over the whole jumping cycle showed statistically significantly higher SEMG amplitudes for BP in the ventral ( p = 0.031) and transverse muscles ( p = 0.020) compared to NBP. Higher activity of transverse, but not straight, trunk muscles might indicate a specific

An HMGA2-IGF2BP2 Axis Regulates Myoblast Proliferation and Myogenesis

PubMed Central

Li, Zhizhong; Gilbert, Jason A.; Zhang, Yunyu; Zhang, Minsi; Qiu, Qiong; Ramanujan, Krishnan; Shavlakadze, Tea; Eash, John K.; Scaramozza, Annarita; Goddeeris, Matthew M.; Kirsch, David G.; Campbell, Kevin P.; Brack, Andrew S.; Glass, David J.

2013-01-01

Summary A group of genes that are highly and specifically expressed in proliferating skeletal myoblasts during myogenesis was identified. Expression of one of these genes, Hmga2, increases coincident with satellite cell activation, and later its expression significantly declines correlating with fusion of myoblasts into myotubes. Hmga2 knockout mice exhibit impaired muscle development and reduced myoblast proliferation, while overexpression of HMGA2 promotes myoblast growth. This perturbation in proliferation can be explained by the finding that HMGA2 directly regulates the RNA-binding protein IGF2BP2. Add-back of IGF2BP2 rescues the phenotype. IGF2BP2 in turn binds to and controls the translation of a set of mRNAs, including c-myc, Sp1, and Igf1r. These data demonstrate that the HMGA2-IGF2BP2 axis functions as a key regulator of satellite cell activation and therefore skeletal muscle development. PMID:23177649

An HMGA2-IGF2BP2 axis regulates myoblast proliferation and myogenesis.

PubMed

Li, Zhizhong; Gilbert, Jason A; Zhang, Yunyu; Zhang, Minsi; Qiu, Qiong; Ramanujan, Krishnan; Shavlakadze, Tea; Eash, John K; Scaramozza, Annarita; Goddeeris, Matthew M; Kirsch, David G; Campbell, Kevin P; Brack, Andrew S; Glass, David J

2012-12-11

A group of genes that are highly and specifically expressed in proliferating skeletal myoblasts during myogenesis was identified. Expression of one of these genes, Hmga2, increases coincident with satellite cell activation, and later its expression significantly declines correlating with fusion of myoblasts into myotubes. Hmga2 knockout mice exhibit impaired muscle development and reduced myoblast proliferation, while overexpression of HMGA2 promotes myoblast growth. This perturbation in proliferation can be explained by the finding that HMGA2 directly regulates the RNA-binding protein IGF2BP2. Add-back of IGF2BP2 rescues the phenotype. IGF2BP2 in turn binds to and controls the translation of a set of mRNAs, including c-myc, Sp1, and Igf1r. These data demonstrate that the HMGA2-IGF2BP2 axis functions as a key regulator of satellite cell activation and therefore skeletal muscle development. Copyright © 2012 Elsevier Inc. All rights reserved.

Fixing convergence of Gaussian belief propagation

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

Johnson, Jason K; Bickson, Danny; Dolev, Danny

Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper we develop a double-loop algorithm for forcing convergence of GaBP. Our method computes the correct MAP estimate even in cases where standard GaBP would not have converged. We further extend this construction to compute least-squares solutions of over-constrained linear systems. We believe that our construction has numerous applications, since the GaBP algorithm ismore » linked to solution of linear systems of equations, which is a fundamental problem in computer science and engineering. As a case study, we discuss the linear detection problem. We show that using our new construction, we are able to force convergence of Montanari's linear detection algorithm, in cases where it would originally fail. As a consequence, we are able to increase significantly the number of users that can transmit concurrently.« less

Determining the Viscosity Coefficient for Viscoelastic Wave Propagation in Rock Bars

NASA Astrophysics Data System (ADS)

Niu, Leilei; Zhu, Wancheng; Li, Shaohua; Guan, Kai

2018-05-01

Rocks with microdefects exhibit viscoelastic behavior during stress wave propagation. The viscosity coefficient of the wave can be used to characterize the attenuation as the wave propagates in rock. In this study, a long artificial bar with a readily adjustable viscosity coefficient was fabricated to investigate stress wave attenuation. The viscoelastic behavior of the artificial bar under dynamic loading was investigated, and the initial viscoelastic coefficient was obtained based on the amplitude attenuation of the incident harmonic wave. A one-dimensional wave propagation program was compiled to reproduce the time history of the stress wave measured during the experiments, and the program was well fitted to the Kelvin-Voigt model. The attenuation and dispersion of the stress wave in long artificial viscoelastic bars were quantified to accurately determine the viscoelastic coefficient. Finally, the method used to determine the viscoelastic coefficient of a long artificial bar based on the experiments and numerical simulations was extended to determine the viscoelastic coefficient of a short rock bar. This study provides a new method of determining the viscosity coefficient of rock.

The Propagation of Cosmic Rays from the Galactic Wind Termination Shock: Back to the Galaxy?

NASA Astrophysics Data System (ADS)

Merten, Lukas; Bustard, Chad; Zweibel, Ellen G.; Becker Tjus, Julia

2018-05-01

Although several theories exist for the origin of cosmic rays (CRs) in the region between the spectral “knee” and “ankle,” this problem is still unsolved. A variety of observations suggest that the transition from Galactic to extragalactic sources occurs in this energy range. In this work, we examine whether a Galactic wind that eventually forms a termination shock far outside the Galactic plane can contribute as a possible source to the observed flux in the region of interest. Previous work by Bustard et al. estimated that particles can be accelerated to energies above the “knee” up to R max = 1016 eV for parameters drawn from a model of a Milky Way wind. A remaining question is whether the accelerated CRs can propagate back into the Galaxy. To answer this crucial question, we simulate the propagation of the CRs using the low-energy extension of the CRPropa framework, based on the solution of the transport equation via stochastic differential equations. The setup includes all relevant processes, including three-dimensional anisotropic spatial diffusion, advection, and corresponding adiabatic cooling. We find that, assuming realistic parameters for the shock evolution, a possible Galactic termination shock can contribute significantly to the energy budget in the “knee” region and above. We estimate the resulting produced neutrino fluxes and find them to be below measurements from IceCube and limits by KM3NeT.

Cryopreservation of veliger larvae of trumpet shell, Charonia sauliae: an essential preparation to artificial propagation

NASA Astrophysics Data System (ADS)

Kang, Kyoung Ho; Zhang, Zhifeng; Bao, Zhenmin; Shao, Mingyu

2009-09-01

Trumpet shell, Charonia sauliae, is an endangered and valuable species, but its artificial propagation protocol has not been successfully established. To estimate the possibility of cryopreservation for larvae of C. sauliae, which is a potential preparation for its artificial reproduction and further research, in this study a protocol for the cryopreservation of veliger larvae of trumpet shell was optimized. Through a two-step cryopreservation procedure, four kinds of cryoprotectants (ethylene glycol, 1, 2-propanediol, dimethyl sulfoxide and glycerol) were employed at three concentrations (1.0, 1.5 and 2.0 molL-1) respectively and survival rates of larvae were determined after a storage of 1h. The larvae frozen with these four cryoprotectants after 1 h storage were cultured, and then survival rates were determined at 24, 72 and 120 h after thawing. Dimethyl sulfoxide at a concentration of 1.5 molL-1 showed the best protective effect in all experiments ( p<0.05). And survival rates of larvae frozen with dimethyl sulfoxide were determined after 1, 7 and 15 d of storage. The survival rates of larvae frozen with 1.5 molL-1 dimethyl sulfoxide after 1 h, 1 d, 7 d and 15 d of storage were 80.77% ±7.51%, 80.34% ±11.28%, 83.10% ±9.14% and 77.23% ±6.22% respectively. No significant differences in survival rates of larvae frozen with dimethyl sulfoxide were observed after various storage periods ( p>0.05).

Research on Environmental Adjustment of Cloud Ranch Based on BP Neural Network PID Control

NASA Astrophysics Data System (ADS)

Ren, Jinzhi; Xiang, Wei; Zhao, Lin; Wu, Jianbo; Huang, Lianzhen; Tu, Qinggang; Zhao, Heming

2018-01-01

In order to make the intelligent ranch management mode replace the traditional artificial one gradually, this paper proposes a pasture environment control system based on cloud server, and puts forward the PID control algorithm based on BP neural network to control temperature and humidity better in the pasture environment. First, to model the temperature and humidity (controlled object) of the pasture, we can get the transfer function. Then the traditional PID control algorithm and the PID one based on BP neural network are applied to the transfer function. The obtained step tracking curves can be seen that the PID controller based on BP neural network has obvious superiority in adjusting time and error, etc. This algorithm, calculating reasonable control parameters of the temperature and humidity to control environment, can be better used in the cloud service platform.

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

NASA Technical Reports Server (NTRS)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

An artificial stress asperity for initialization of spontaneous rupture propagation - a parametric study of a dynamic model with linear slip-weakening friction

NASA Astrophysics Data System (ADS)

Galis, M.; Pelties, C.; Kristek, J.; Moczo, P.

2012-04-01

Artificial procedures are used to initiate spontaneous rupture on faults with the linear slip-weakening (LSW) friction law. Probably the most frequent technique is the stress asperity. It is important to minimize effects of the artificial initialization on the phase of the spontaneous rupture propagation. The effects may strongly depend on the geometry and size of the asperity, spatial distribution of the stress in and around the asperity, and a maximum stress-overshoot value. A square initialization zone with the stress discontinuously falling down at the asperity border to the level of the initial stress has been frequently applied (e.g., in the SCEC verification exercise). Galis et al. (2010) and Bizzarri (2010) independently introduced the elliptical asperity with a smooth spatial stress distribution in and around the asperity. In both papers the width of smoothing/tapering zone was only ad-hoc defined. Numerical simulations indicate that the ADER-DG method can account for a discontinuous-stress initialization more accurately than the FE method. Considering the ADER-DG solution a reference we performed numerical simulations in order to define the width of the smoothing/tapering zone to be used in the FE and FD-FE hybrid methods for spontaneous rupture propagation. We considered different sizes of initialization zone, different shapes of the initialization zone (square, circle, ellipse), different spatial distributions of stress (smooth, discontinuous), and different stress-overshoot values to investigate conditions of the spontaneous rupture propagation. We compare our numerical results with the 2D and 3D estimates by Andrews (1976a,b), Day (1982), Campillo & Ionescu (1997), Favreau at al. (1999) and Uenishi & Rice (2003, 2004). Results of our study may help modelers to better setup the initialization zone in order to avoid, e.g., a too large initialization zone and reduce numerical artifacts.

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

Jing, Yaqi; Meng, Qinghao, E-mail: qh-meng@tju.edu.cn; Qi, Peifeng

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classificationmore » rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.« less

Artificial Intelligence Techniques to Optimize the EDC/NHS-Mediated Immobilization of Cellulase on Eudragit L-100

PubMed Central

Zhang, Yu; Xu, Jing-Liang; Yuan, Zhen-Hong; Qi, Wei; Liu, Yun-Yun; He, Min-Chao

2012-01-01

Two artificial intelligence techniques, namely artificial neural network (ANN) and genetic algorithm (GA) were combined to be used as a tool for optimizing the covalent immobilization of cellulase on a smart polymer, Eudragit L-100. 1-Ethyl-3-(3-dimethyllaminopropyl) carbodiimide (EDC) concentration, N-hydroxysuccinimide (NHS) concentration and coupling time were taken as independent variables, and immobilization efficiency was taken as the response. The data of the central composite design were used to train ANN by back-propagation algorithm, and the result showed that the trained ANN fitted the data accurately (correlation coefficient R2 = 0.99). Then a maximum immobilization efficiency of 88.76% was searched by genetic algorithm at a EDC concentration of 0.44%, NHS concentration of 0.37% and a coupling time of 2.22 h, where the experimental value was 87.97 ± 6.45%. The application of ANN based optimization by GA is quite successful. PMID:22942683

Artificial neural network in breast lesions from fine-needle aspiration cytology smear.

PubMed

Subbaiah, R M; Dey, Pranab; Nijhawan, Raje

2014-03-01

Artificial neural networks (ANNs) are applied in engineering and certain medical fields. ANN has immense potential and is rarely been used in breast lesions. In this present study, we attempted to build up a complete robust back propagation ANN model based on cytomorphological data, morphometric data, nuclear densitometric data, and gray level co-occurrence matrix (GLCM) of ductal carcinoma and fibroadenomas of breast cases diagnosed on fine-needle aspiration cytology (FNAC). We selected 52 cases of fibroadenomas and 60 cases of infiltrating ductal carcinoma of breast diagnosed on FNAC by two cytologists. Essential cytological data was quantitated by two independent cytologists (SRM, PD). With the help of Image J software, nuclear morphomeric, densitometric, and GLCM features were measured in all the cases on hematoxylin and eosin-stained smears. With the available data, an ANN model was built up with the help of Neurointelligence software. The network was designed as 41-20-1 (41 input nodes, 20 hidden nodes, 1 output node). The network was trained by the online back propagation algorithm and 500 iterations were done. Learning was adjusted after every iteration. ANN model correctly identified all cases of fibroadenomas and infiltrating carcinomas in the test set. This is one of the first successful composite ANN models of breast carcinomas. This basic model can be used to diagnose the gray zone area of the breast lesions on FNAC. We assume that this model may have far-reaching implications in future. Copyright © 2013 Wiley Periodicals, Inc.

Effect of filtration of signals of brain activity on quality of recognition of brain activity patterns using artificial intelligence methods

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Musatov, Vyachaslav Yu.

2018-02-01

In present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.

Glucose Synthesis in a Protein-Based Artificial Photosynthesis System.

PubMed

Lu, Hao; Yuan, Wenqiao; Zhou, Jack; Chong, Parkson Lee-Gau

2015-09-01

The objective of this study was to understand glucose synthesis of a protein-based artificial photosynthesis system affected by operating conditions, including the concentrations of reactants, reaction temperature, and illumination. Results from non-vesicle-based glyceraldehyde-3-phosphate (GAP) and glucose synthesis showed that the initial concentrations of ribulose-1,5-bisphosphate (RuBP) and adenosine triphosphate (ATP), lighting source, and temperature significantly affected glucose synthesis. Higher initial concentrations of RuBP and ATP significantly enhanced GAP synthesis, which was linearly correlated to glucose synthesis, confirming the proper functions of all catalyzing enzymes in the system. White fluorescent light inhibited artificial photosynthesis and reduced glucose synthesis by 79.2 % compared to in the dark. The reaction temperature of 40 °C was optimum, whereas lower or higher temperature reduced glucose synthesis. Glucose synthesis in the vesicle-based artificial photosynthesis system reconstituted with bacteriorhodopsin, F 0 F 1 ATP synthase, and polydimethylsiloxane-methyloxazoline-polydimethylsiloxane triblock copolymer was successfully demonstrated. This system efficiently utilized light-induced ATP to drive glucose synthesis, and 5.2 μg ml(-1) glucose was synthesized in 0.78-ml reaction buffer in 7 h. Light-dependent reactions were found to be the bottleneck of the studied artificial photosynthesis system.

Sythesis of MCMC and Belief Propagation

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

Ahn, Sungsoo; Chertkov, Michael; Shin, Jinwoo

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach whichmore » allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.« less

Correcting wave predictions with artificial neural networks

NASA Astrophysics Data System (ADS)

Makarynskyy, O.; Makarynska, D.

2003-04-01

The predictions of wind waves with different lead times are necessary in a large scope of coastal and open ocean activities. Numerical wave models, which usually provide this information, are based on deterministic equations that do not entirely account for the complexity and uncertainty of the wave generation and dissipation processes. An attempt to improve wave parameters short-term forecasts based on artificial neural networks is reported. In recent years, artificial neural networks have been used in a number of coastal engineering applications due to their ability to approximate the nonlinear mathematical behavior without a priori knowledge of interrelations among the elements within a system. The common multilayer feed-forward networks, with a nonlinear transfer functions in the hidden layers, were developed and employed to forecast the wave characteristics over one hour intervals starting from one up to 24 hours, and to correct these predictions. Three non-overlapping data sets of wave characteristics, both from a buoy, moored roughly 60 miles west of the Aran Islands, west coast of Ireland, were used to train and validate the neural nets involved. The networks were trained with error back propagation algorithm. Time series plots and scatterplots of the wave characteristics as well as tables with statistics show an improvement of the results achieved due to the correction procedure employed.

Data fusion with artificial neural networks (ANN) for classification of earth surface from microwave satellite measurements

NASA Technical Reports Server (NTRS)

Lure, Y. M. Fleming; Grody, Norman C.; Chiou, Y. S. Peter; Yeh, H. Y. Michael

1993-01-01

A data fusion system with artificial neural networks (ANN) is used for fast and accurate classification of five earth surface conditions and surface changes, based on seven SSMI multichannel microwave satellite measurements. The measurements include brightness temperatures at 19, 22, 37, and 85 GHz at both H and V polarizations (only V at 22 GHz). The seven channel measurements are processed through a convolution computation such that all measurements are located at same grid. Five surface classes including non-scattering surface, precipitation over land, over ocean, snow, and desert are identified from ground-truth observations. The system processes sensory data in three consecutive phases: (1) pre-processing to extract feature vectors and enhance separability among detected classes; (2) preliminary classification of Earth surface patterns using two separate and parallely acting classifiers: back-propagation neural network and binary decision tree classifiers; and (3) data fusion of results from preliminary classifiers to obtain the optimal performance in overall classification. Both the binary decision tree classifier and the fusion processing centers are implemented by neural network architectures. The fusion system configuration is a hierarchical neural network architecture, in which each functional neural net will handle different processing phases in a pipelined fashion. There is a total of around 13,500 samples for this analysis, of which 4 percent are used as the training set and 96 percent as the testing set. After training, this classification system is able to bring up the detection accuracy to 94 percent compared with 88 percent for back-propagation artificial neural networks and 80 percent for binary decision tree classifiers. The neural network data fusion classification is currently under progress to be integrated in an image processing system at NOAA and to be implemented in a prototype of a massively parallel and dynamically reconfigurable Modular

Scene segmentation of natural images using texture measures and back-propagation

NASA Technical Reports Server (NTRS)

Sridhar, Banavar; Phatak, Anil; Chatterji, Gano

1993-01-01

Knowledge of the three-dimensional world is essential for many guidance and navigation applications. A sequence of images from an electro-optical sensor can be processed using optical flow algorithms to provide a sparse set of ranges as a function of azimuth and elevation. A natural way to enhance the range map is by interpolation. However, this should be undertaken with care since interpolation assumes continuity of range. The range is continuous in certain parts of the image and can jump at object boundaries. In such situations, the ability to detect homogeneous object regions by scene segmentation can be used to determine regions in the range map that can be enhanced by interpolation. The use of scalar features derived from the spatial gray-level dependence matrix for texture segmentation is explored. Thresholding of histograms of scalar texture features is done for several images to select scalar features which result in a meaningful segmentation of the images. Next, the selected scalar features are used with a neural net to automate the segmentation procedure. Back-propagation is used to train the feed forward neural network. The generalization of the network approach to subsequent images in the sequence is examined. It is shown that the use of multiple scalar features as input to the neural network result in a superior segmentation when compared with a single scalar feature. It is also shown that the scalar features, which are not useful individually, result in a good segmentation when used together. The methodology is applied to both indoor and outdoor images.

Identification and discrimination of oral asaccharolytic Eubacterium spp. by pyrolysis mass spectrometry and artificial neural networks.

PubMed

Goodacre, R; Hiom, S J; Cheeseman, S L; Murdoch, D; Weightman, A J; Wade, W G

1996-02-01

Curie-point pyrolysis mass spectra were obtained from 29 oral asaccharolytic Eubacterium strains and 6 abscess isolates previously identified as Peptostreptococcus heliotrinreducens. Pyrolysis mass spectrometry (PyMS) with cluster analysis was able to clarify the taxonomic position of this group of organisms. Artificial neural networks (ANNS) were then trained by supervised learning (with the back-propagation algorithm) to recognize the strains from their pyrolysis mass spectra; all Eubacterium strains were correctly identified, and the abscess isolates were identified as un-named Eubacterium taxon C2 and were distinct from the type strain of P. heliotrinreducens. These results demonstrate that the combination of PyMS and ANNs provides a rapid and accurate identification technique.

Spectrally Shaped DP-16QAM Super-Channel Transmission with Multi-Channel Digital Back-Propagation

PubMed Central

Maher, Robert; Xu, Tianhua; Galdino, Lidia; Sato, Masaki; Alvarado, Alex; Shi, Kai; Savory, Seb J.; Thomsen, Benn C.; Killey, Robert I.; Bayvel, Polina

2015-01-01

The achievable transmission capacity of conventional optical fibre communication systems is limited by nonlinear distortions due to the Kerr effect and the difficulty in modulating the optical field to effectively use the available fibre bandwidth. In order to achieve a high information spectral density (ISD), while simultaneously maintaining transmission reach, multi-channel fibre nonlinearity compensation and spectrally efficient data encoding must be utilised. In this work, we use a single coherent super-receiver to simultaneously receive a DP-16QAM super-channel, consisting of seven spectrally shaped 10GBd sub-carriers spaced at the Nyquist frequency. Effective nonlinearity mitigation is achieved using multi-channel digital back-propagation (MC-DBP) and this technique is combined with an optimised forward error correction implementation to demonstrate a record gain in transmission reach of 85%; increasing the maximum transmission distance from 3190 km to 5890 km, with an ISD of 6.60 b/s/Hz. In addition, this report outlines for the first time, the sensitivity of MC-DBP gain to linear transmission line impairments and defines a trade-off between performance and complexity. PMID:25645457

TopBP1 functions with 53BP1 in the G1 DNA damage checkpoint

PubMed Central

Cescutti, Rachele; Negrini, Simona; Kohzaki, Masaoki; Halazonetis, Thanos D

2010-01-01

TopBP1 is a checkpoint protein that colocalizes with ATR at sites of DNA replication stress. In this study, we show that TopBP1 also colocalizes with 53BP1 at sites of DNA double-strand breaks (DSBs), but only in the G1-phase of the cell cycle. Recruitment of TopBP1 to sites of DNA replication stress was dependent on BRCT domains 1–2 and 7–8, whereas recruitment to sites of DNA DSBs was dependent on BRCT domains 1–2 and 4–5. The BRCT domains 4–5 interacted with 53BP1 and recruitment of TopBP1 to sites of DNA DSBs in G1 was dependent on 53BP1. As TopBP1 contains a domain important for ATR activation, we examined whether it contributes to the G1 cell cycle checkpoint. By monitoring the entry of irradiated G1 cells into S-phase, we observed a checkpoint defect after siRNA-mediated depletion of TopBP1, 53BP1 or ATM. Thus, TopBP1 may mediate the checkpoint function of 53BP1 in G1. PMID:20871591

TopBP1 functions with 53BP1 in the G1 DNA damage checkpoint.

PubMed

Cescutti, Rachele; Negrini, Simona; Kohzaki, Masaoki; Halazonetis, Thanos D

2010-11-03

TopBP1 is a checkpoint protein that colocalizes with ATR at sites of DNA replication stress. In this study, we show that TopBP1 also colocalizes with 53BP1 at sites of DNA double-strand breaks (DSBs), but only in the G1-phase of the cell cycle. Recruitment of TopBP1 to sites of DNA replication stress was dependent on BRCT domains 1-2 and 7-8, whereas recruitment to sites of DNA DSBs was dependent on BRCT domains 1-2 and 4-5. The BRCT domains 4-5 interacted with 53BP1 and recruitment of TopBP1 to sites of DNA DSBs in G1 was dependent on 53BP1. As TopBP1 contains a domain important for ATR activation, we examined whether it contributes to the G1 cell cycle checkpoint. By monitoring the entry of irradiated G1 cells into S-phase, we observed a checkpoint defect after siRNA-mediated depletion of TopBP1, 53BP1 or ATM. Thus, TopBP1 may mediate the checkpoint function of 53BP1 in G1.

HF propagation results from the Metal Oxide Space Cloud (MOSC) experiment

NASA Astrophysics Data System (ADS)

Joshi, Dev; Groves, Keith M.; McNeil, William; Carrano, Charles; Caton, Ronald G.; Parris, Richard T.; Pederson, Todd R.; Cannon, Paul S.; Angling, Matthew; Jackson-Booth, Natasha

2017-06-01

With support from the NASA sounding rocket program, the Air Force Research Laboratory launched two sounding rockets in the Kwajalein Atoll, Marshall Islands in May 2013 known as the Metal Oxide Space Cloud experiment. The rockets released samarium metal vapor at preselected altitudes in the lower F region that ionized forming a plasma cloud. Data from Advanced Research Project Agency Long-range Tracking and Identification Radar incoherent scatter radar and high-frequency (HF) radio links have been analyzed to understand the impacts of the artificial ionization on radio wave propagation. The HF radio wave ray-tracing toolbox PHaRLAP along with ionospheric models constrained by electron density profiles measured with the ALTAIR radar have been used to successfully model the effects of the cloud on HF propagation. Up to three new propagation paths were created by the artificial plasma injections. Observations and modeling confirm that the small amounts of ionized material injected in the lower F region resulted in significant changes to the natural HF propagation environment.

Steps toward quantitative infrasound propagation modeling

NASA Astrophysics Data System (ADS)

Waxler, Roger; Assink, Jelle; Lalande, Jean-Marie; Velea, Doru

2016-04-01

Realistic propagation modeling requires propagation models capable of incorporating the relevant physical phenomena as well as sufficiently accurate atmospheric specifications. The wind speed and temperature gradients in the atmosphere provide multiple ducts in which low frequency sound, infrasound, can propagate efficiently. The winds in the atmosphere are quite variable, both temporally and spatially, causing the sound ducts to fluctuate. For ground to ground propagation the ducts can be borderline in that small perturbations can create or destroy a duct. In such cases the signal propagation is very sensitive to fluctuations in the wind, often producing highly dispersed signals. The accuracy of atmospheric specifications is constantly improving as sounding technology develops. There is, however, a disconnect between sound propagation and atmospheric specification in that atmospheric specifications are necessarily statistical in nature while sound propagates through a particular atmospheric state. In addition infrasonic signals can travel to great altitudes, on the order of 120 km, before refracting back to earth. At such altitudes the atmosphere becomes quite rare causing sound propagation to become highly non-linear and attenuating. Approaches to these problems will be presented.

Rapid Simulation of Blast Wave Propagation in Built Environments Using Coarse-Grain Based Intelligent Modeling Methods

DTIC Science & Technology

2011-04-01

experiments was performed using an artificial neural network to try to capture the nonlinearities. The radial Gaussian artificial neural network system...Modeling Blast-Wave Propagation using Artificial Neural Network Methods‖, in International Journal of Advanced Engineering Informatics, Elsevier

Effect of Backing Plate Thermal Property on Friction Stir Welding of 25-mm-Thick AA6061

NASA Astrophysics Data System (ADS)

Upadhyay, Piyush; Reynolds, Anthony

2014-04-01

By using backing plates made out of materials with widely varying thermal diffusivity this work seeks to elucidate the effects of the root side thermal boundary condition on weld process variables and resulting joint properties. Welds were made in 25.4-mm-thick AA6061 using ceramic, titanium, steel, and aluminum as backing plate (BP) material. Welds were also made using a "composite backing plate" consisting of longitudinal narrow strip of low diffusivity material at the center and two side plates of high diffusivity aluminum. Stir zone temperature during the welding was measured using two thermocouples spot welded at the core of the probe: one at the midplane height and another near the tip of the probe corresponding to the root of the weld. Steady state midplane probe temperatures for all the BPs used were found to be very similar. Near root peak temperature, however, varied significantly among weld made with different BPs all other things being equal. Whereas the near root and midplane temperature were the same in the case of ceramic backing plate, the root peak temperature was 318 K (45 °C) less than the midplane temperature in the case of aluminum BP. The trends of nugget hardness and grain size in through thickness direction were in agreement with the measured probe temperatures. Hardness and tensile test results show that the use of composite BP results in stronger joint compared to monolithic steel BP.

Artificial neural network-aided image analysis system for cell counting.

PubMed

Sjöström, P J; Frydel, B R; Wahlberg, L U

1999-05-01

In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, i.e., systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell recognition, an automated cell counter was constructed using a combination of artificial intelligence and standard image analysis methods. Artificial neural network (ANN) methods were applied on digitized microscopy fields without pre-ANN feature extraction. A three-layer feed-forward network with extensive weight sharing in the first hidden layer was employed and trained on 1,830 examples using the error back-propagation algorithm on a Power Macintosh 7300/180 desktop computer. The optimal number of hidden neurons was determined and the trained system was validated by comparison with blinded human counts. System performance at 50x and lO0x magnification was evaluated. The correlation index at 100x magnification neared person-to-person variability, while 50x magnification was not useful. The system was approximately six times faster than an experienced human. ANN-based automated cell counting in noisy histological preparations is feasible. Consistent histology and computer power are crucial for system performance. The system provides several benefits, such as speed of analysis and consistency, and frees up personnel for other tasks.

Classification of cardiac patient states using artificial neural networks

PubMed Central

Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

2003-01-01

Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network

PubMed Central

Ahmed, Afaz Uddin; Tariqul Islam, Mohammad; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. PMID:25133214

A novel user classification method for femtocell network by using affinity propagation algorithm and artificial neural network.

PubMed

Ahmed, Afaz Uddin; Islam, Mohammad Tariqul; Ismail, Mahamod; Kibria, Salehin; Arshad, Haslina

2014-01-01

An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation.

Artificial neural network intelligent method for prediction

NASA Astrophysics Data System (ADS)

Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

2017-09-01

Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Function approximation and documentation of sampling data using artificial neural networks.

PubMed

Zhang, Wenjun; Barrion, Albert

2006-11-01

Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field. Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors. BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.

Discrimination of liver cancer in cellular level based on backscatter micro-spectrum with PCA algorithm and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Jing; Wang, Cheng; Cai, Gan; Dong, Xiaona

2016-10-01

The incidence and mortality rate of the primary liver cancer are very high and its postoperative metastasis and recurrence have become important factors to the prognosis of patients. Circulating tumor cells (CTC), as a new tumor marker, play important roles in the early diagnosis and individualized treatment. This paper presents an effective method to distinguish liver cancer based on the cellular scattering spectrum, which is a non-fluorescence technique based on the fiber confocal microscopic spectrometer. Combining the principal component analysis (PCA) with back propagation (BP) neural network were utilized to establish an automatic recognition model for backscatter spectrum of the liver cancer cells from blood cell. PCA was applied to reduce the dimension of the scattering spectral data which obtained by the fiber confocal microscopic spectrometer. After dimensionality reduction by PCA, a neural network pattern recognition model with 2 input layer nodes, 11 hidden layer nodes, 3 output nodes was established. We trained the network with 66 samples and also tested it. Results showed that the recognition rate of the three types of cells is more than 90%, the relative standard deviation is only 2.36%. The experimental results showed that the fiber confocal microscopic spectrometer combining with the algorithm of PCA and BP neural network can automatically identify the liver cancer cell from the blood cells. This will provide a better tool for investigating the metastasis of liver cancers in vivo, the biology metabolic characteristics of liver cancers and drug transportation. Additionally, it is obviously referential in practical application.

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs.

PubMed

Kaur, Gurmanik; Arora, Ajat Shatru; Jain, Vijender Kumar

2017-01-01

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination ( R 2 ) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R 2  = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R 2  = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

PubMed

Martínez-Blanco, Ma Del Rosario; Ornelas-Vargas, Gerardo; Solís-Sánchez, Luis Octavio; Castañeda-Miranada, Rodrigo; Vega-Carrillo, Héctor René; Celaya-Padilla, José M; Garza-Veloz, Idalia; Martínez-Fierro, Margarita; Ortiz-Rodríguez, José Manuel

2016-11-01

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multilingual vocal emotion recognition and classification using back propagation neural network

NASA Astrophysics Data System (ADS)

Kayal, Apoorva J.; Nirmal, Jagannath

2016-03-01

This work implements classification of different emotions in different languages using Artificial Neural Networks (ANN). Mel Frequency Cepstral Coefficients (MFCC) and Short Term Energy (STE) have been considered for creation of feature set. An emotional speech corpus consisting of 30 acted utterances per emotion has been developed. The emotions portrayed in this work are Anger, Joy and Neutral in each of English, Marathi and Hindi languages. Different configurations of Artificial Neural Networks have been employed for classification purposes. The performance of the classifiers has been evaluated by False Negative Rate (FNR), False Positive Rate (FPR), True Positive Rate (TPR) and True Negative Rate (TNR).

Numerical Simulation and Artificial Neural Network Modeling for Predicting Welding-Induced Distortion in Butt-Welded 304L Stainless Steel Plates

NASA Astrophysics Data System (ADS)

Narayanareddy, V. V.; Chandrasekhar, N.; Vasudevan, M.; Muthukumaran, S.; Vasantharaja, P.

2016-02-01

In the present study, artificial neural network modeling has been employed for predicting welding-induced angular distortions in autogenous butt-welded 304L stainless steel plates. The input data for the neural network have been obtained from a series of three-dimensional finite element simulations of TIG welding for a wide range of plate dimensions. Thermo-elasto-plastic analysis was carried out for 304L stainless steel plates during autogenous TIG welding employing double ellipsoidal heat source. The simulated thermal cycles were validated by measuring thermal cycles using thermocouples at predetermined positions, and the simulated distortion values were validated by measuring distortion using vertical height gauge for three cases. There was a good agreement between the model predictions and the measured values. Then, a multilayer feed-forward back propagation neural network has been developed using the numerically simulated data. Artificial neural network model developed in the present study predicted the angular distortion accurately.

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network

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

Liu, X.; Wilcox, G.L.

1993-12-31

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences.more » Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.« less

Estimating tree bole volume using artificial neural network models for four species in Turkey.

PubMed

Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

2010-01-01

Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.

Monthly monsoon rainfall forecasting using artificial neural networks

NASA Astrophysics Data System (ADS)

Ganti, Ravikumar

2014-10-01

Indian agriculture sector heavily depends on monsoon rainfall for successful harvesting. In the past, prediction of rainfall was mainly performed using regression models, which provide reasonable accuracy in the modelling and forecasting of complex physical systems. Recently, Artificial Neural Networks (ANNs) have been proposed as efficient tools for modelling and forecasting. A feed-forward multi-layer perceptron type of ANN architecture trained using the popular back-propagation algorithm was employed in this study. Other techniques investigated for modeling monthly monsoon rainfall include linear and non-linear regression models for comparison purposes. The data employed in this study include monthly rainfall and monthly average of the daily maximum temperature in the North Central region in India. Specifically, four regression models and two ANN model's were developed. The performance of various models was evaluated using a wide variety of standard statistical parameters and scatter plots. The results obtained in this study for forecasting monsoon rainfalls using ANNs have been encouraging. India's economy and agricultural activities can be effectively managed with the help of the availability of the accurate monsoon rainfall forecasts.

Catalytic Ignition and Upstream Reaction Propagation in Monolith Reactors

NASA Technical Reports Server (NTRS)

Struk, Peter M.; Dietrich, Daniel L.; Miller, Fletcher J.; T'ien, James S.

2007-01-01

Using numerical simulations, this work demonstrates a concept called back-end ignition for lighting-off and pre-heating a catalytic monolith in a power generation system. In this concept, a downstream heat source (e.g. a flame) or resistive heating in the downstream portion of the monolith initiates a localized catalytic reaction which subsequently propagates upstream and heats the entire monolith. The simulations used a transient numerical model of a single catalytic channel which characterizes the behavior of the entire monolith. The model treats both the gas and solid phases and includes detailed homogeneous and heterogeneous reactions. An important parameter in the model for back-end ignition is upstream heat conduction along the solid. The simulations used both dry and wet CO chemistry as a model fuel for the proof-of-concept calculations; the presence of water vapor can trigger homogenous reactions, provided that gas-phase temperatures are adequately high and there is sufficient fuel remaining after surface reactions. With sufficiently high inlet equivalence ratio, back-end ignition occurs using the thermophysical properties of both a ceramic and metal monolith (coated with platinum in both cases), with the heat-up times significantly faster for the metal monolith. For lower equivalence ratios, back-end ignition occurs without upstream propagation. Once light-off and propagation occur, the inlet equivalence ratio could be reduced significantly while still maintaining an ignited monolith as demonstrated by calculations using complete monolith heating.

Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Chattopadhyay, Surajit; Bandyopadhyay, Goutami

2007-01-01

Present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Networks models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with learning rate 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found skillful. But, Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Artificial neural networks applied to flow prediction scenarios in Tomebamba River - Paute watershed, for flood and water quality control and management at City of Cuenca Ecuador

NASA Astrophysics Data System (ADS)

Cisneros, Felipe; Veintimilla, Jaime

2013-04-01

The main aim of this research is to create a model of Artificial Neural Networks (ANN) that allows predicting the flow in Tomebamba River both, at real time and in a certain day of year. As inputs we are using information of rainfall and flow of the stations along of the river. This information is organized in scenarios and each scenario is prepared to a specific area. The information is acquired from the hydrological stations placed in the watershed using an electronic system developed at real time and it supports any kind or brands of this type of sensors. The prediction works very good three days in advance This research includes two ANN models: Back propagation and a hybrid model between back propagation and OWO-HWO. These last two models have been tested in a preliminary research. To validate the results we are using some error indicators such as: MSE, RMSE, EF, CD and BIAS. The results of this research reached high levels of reliability and the level of error are minimal. These predictions are useful for flood and water quality control and management at City of Cuenca Ecuador

An Emotional ANN (EANN) approach to modeling rainfall-runoff process

NASA Astrophysics Data System (ADS)

Nourani, Vahid

2017-01-01

This paper presents the first hydrological implementation of Emotional Artificial Neural Network (EANN), as a new generation of Artificial Intelligence-based models for daily rainfall-runoff (r-r) modeling of the watersheds. Inspired by neurophysiological form of brain, in addition to conventional weights and bias, an EANN includes simulated emotional parameters aimed at improving the network learning process. EANN trained by a modified version of back-propagation (BP) algorithm was applied to single and multi-step-ahead runoff forecasting of two watersheds with two distinct climatic conditions. Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose. The overall comparison of the obtained results of the r-r modeling indicates that the EANN could outperform the conventional feed forward neural network (FFNN) model up to 13% and 34% in terms of training and verification efficiency criteria, respectively. The superiority of EANN over classic ANN is due to its ability to recognize and distinguish dry (rainless days) and wet (rainy days) situations using hormonal parameters of the artificial emotional system.

On-line Tool Wear Detection on DCMT070204 Carbide Tool Tip Based on Noise Cutting Audio Signal using Artificial Neural Network

NASA Astrophysics Data System (ADS)

Prasetyo, T.; Amar, S.; Arendra, A.; Zam Zami, M. K.

2018-01-01

This study develops an on-line detection system to predict the wear of DCMT070204 tool tip during the cutting process of the workpiece. The machine used in this research is CNC ProTurn 9000 to cut ST42 steel cylinder. The audio signal has been captured using the microphone placed in the tool post and recorded in Matlab. The signal is recorded at the sampling rate of 44.1 kHz, and the sampling size of 1024. The recorded signal is 110 data derived from the audio signal while cutting using a normal chisel and a worn chisel. And then perform signal feature extraction in the frequency domain using Fast Fourier Transform. Feature selection is done based on correlation analysis. And tool wear classification was performed using artificial neural networks with 33 input features selected. This artificial neural network is trained with back propagation method. Classification performance testing yields an accuracy of 74%.

Synaptic Contacts Enhance Cell-to-Cell Tau Pathology Propagation.

PubMed

Calafate, Sara; Buist, Arjan; Miskiewicz, Katarzyna; Vijayan, Vinoy; Daneels, Guy; de Strooper, Bart; de Wit, Joris; Verstreken, Patrik; Moechars, Diederik

2015-05-26

Accumulation of insoluble Tau protein aggregates and stereotypical propagation of Tau pathology through the brain are common hallmarks of tauopathies, including Alzheimer's disease (AD). Propagation of Tau pathology appears to occur along connected neurons, but whether synaptic contacts between neurons are facilitating propagation has not been demonstrated. Using quantitative in vitro models, we demonstrate that, in parallel to non-synaptic mechanisms, synapses, but not merely the close distance between the cells, enhance the propagation of Tau pathology between acceptor hippocampal neurons and Tau donor cells. Similarly, in an artificial neuronal network using microfluidic devices, synapses and synaptic activity are promoting neuronal Tau pathology propagation in parallel to the non-synaptic mechanisms. Our work indicates that the physical presence of synaptic contacts between neurons facilitate Tau pathology propagation. These findings can have implications for synaptic repair therapies, which may turn out to have adverse effects by promoting propagation of Tau pathology. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Active action potential propagation but not initiation in thalamic interneuron dendrites

PubMed Central

Casale, Amanda E.; McCormick, David A.

2012-01-01

Inhibitory interneurons of the dorsal lateral geniculate nucleus of the thalamus modulate the activity of thalamocortical cells in response to excitatory input through the release of inhibitory neurotransmitter from both axons and dendrites. The exact mechanisms by which release can occur from dendrites are, however, not well understood. Recent experiments using calcium imaging have suggested that Na/K based action potentials can evoke calcium transients in dendrites via local active conductances, making the back-propagating action potential a candidate for dendritic neurotransmitter release. In this study, we employed high temporal and spatial resolution voltage-sensitive dye imaging to assess the characteristics of dendritic voltage deflections in response to Na/K action potentials in interneurons of the mouse dorsal lateral geniculate nucleus. We found that trains or single action potentials elicited by somatic current injection or local synaptic stimulation led to action potentials that rapidly and actively back-propagated throughout the entire dendritic arbor and into the fine filiform dendritic appendages known to release GABAergic vesicles. Action potentials always appeared first in the soma or proximal dendrite in response to somatic current injection or local synaptic stimulation, and the rapid back-propagation into the dendritic arbor depended upon voltage-gated sodium and TEA-sensitive potassium channels. Our results indicate that thalamic interneuron dendrites integrate synaptic inputs that initiate action potentials, most likely in the axon initial segment, that then back-propagate with high-fidelity into the dendrites, resulting in a nearly synchronous release of GABA from both axonal and dendritic compartments. PMID:22171033

The Elimination of Fire Hazard Due to Back Fires

NASA Technical Reports Server (NTRS)

Theodorsen, Theodore; Freeman, Ira M

1933-01-01

A critical study was made of the operation of a type of back-fire arrester used to reduce the fire hazard of aircraft engines. A flame arrester consisting of a pack or plug of alternate flat and corrugated plates of thin metal was installed in the intake pipe of a gasoline engines; an auxiliary spark plug inserted in the intake manifold permitted the production of artificial back fires at will. It was found possible to design a plug which prevented all back fires from reaching the carburetor.

Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami; Gholami, Raoof; Sadeghzadeh, Farhad

2014-06-01

Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN). Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

NASA Astrophysics Data System (ADS)

Vitela, Javier E.; Martinell, Julio J.

1998-02-01

In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

Application of an artificial neural network to pump card diagnosis

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

Ashenayi, K.; Lea, J.F.; Kemp, F.

1994-12-01

Beam pumping is the most frequently used artificial-lift technique for oil production. Downhole pump cards are used to evaluate performance of the pumping unit. Pump cards can be generated from surface dynamometer cards using a 1D wave equation with viscous damping, as suggested by Gibbs and Neely. Pump cards contain significant information describing the behavior of the pump. However, interpretation of these cards is tedious and time-consuming; hence, an automated system capable of interpreting these cards could speed interpretation and warn of pump failures. This work presents the results of a DOS-based computer program capable of correctly classifying pump cards.more » The program uses a hybrid artificial neural network (ANN) to identify significant features of the pump card. The hybrid ANN uses classical and sinusoidal perceptrons. The network is trained using an error-back-propagation technique. The program correctly identified pump problems for more than 180 different training and test pump cards. The ANN takes a total of 80 data points as input. Sixty data points are collected from the pump card perimeter, and the remaining 20 data points represent the slope at selected points on the pump card perimeter. Pump problem conditions are grouped into 11 distinct classes. The network is capable of identifying one or more of these problem conditions for each pump card. Eight examples are presented and discussed.« less

In vitro propagation of Paphiopedilum orchids.

PubMed

Zeng, Songjun; Huang, Weichang; Wu, Kunlin; Zhang, Jianxia; da Silva, Jaime A Teixeira; Duan, Jun

2016-01-01

Paphiopedilum is one of the most popular and rare orchid genera. Members of the genus are sold and exhibited as pot plants and cut flowers. Wild populations of Paphiopedilum are under the threat of extinction due to over-collection and loss of suitable habitats. A reduction in their commercial value through large-scale propagation in vitro is an option to reduce pressure from illegal collection, to attempt to meet commercial needs and to re-establish threatened species back into the wild. Although they are commercially propagated via asymbiotic seed germination, Paphiopedilum are considered to be difficult to propagate in vitro, especially by plant regeneration from tissue culture. This review aims to cover the most important aspects and to provide an up-to-date research progress on in vitro propagation of Paphiopedilum and to emphasize the importance of further improving tissue culture protocols for ex vitro-derived explants.

Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

NASA Astrophysics Data System (ADS)

Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo

2017-12-01

Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.

Analysis of pulse thermography using similarities between wave and diffusion propagation

NASA Astrophysics Data System (ADS)

Gershenson, M.

2017-05-01

Pulse thermography or thermal wave imaging are commonly used as nondestructive evaluation (NDE) method. While the technical aspect has evolve with time, theoretical interpretation is lagging. Interpretation is still using curved fitting on a log log scale. A new approach based directly on the governing differential equation is introduced. By using relationships between wave propagation and the diffusive propagation of thermal excitation, it is shown that one can transform from solutions in one type of propagation to the other. The method is based on the similarities between the Laplace transforms of the diffusion equation and the wave equation. For diffusive propagation we have the Laplace variable s to the first power, while for the wave propagation similar equations occur with s2. For discrete time the transformation between the domains is performed by multiplying the temperature data vector by a matrix. The transform is local. The performance of the techniques is tested on synthetic data. The application of common back projection techniques used in the processing of wave data is also demonstrated. The combined use of the transform and back projection makes it possible to improve both depth and lateral resolution of transient thermography.

Polymer-Based Black Phosphorus (bP) Hybrid Materials by in Situ Radical Polymerization: An Effective Tool To Exfoliate bP and Stabilize bP Nanoflakes

PubMed Central

2018-01-01

Black phosphorus (bP) has been recently investigated for next generation nanoelectronic multifunctional devices. However, the intrinsic instability of exfoliated bP (the bP nanoflakes) toward both moisture and air has so far overshadowed its practical implementation. In order to contribute to fill this gap, we report here the preparation of new hybrid polymer-based materials where bP nanoflakes (bPn) exhibit a significantly improved stability. The new materials have been prepared by different synthetic paths including: (i) the mixing of conventionally liquid-phase exfoliated bP (in dimethyl sulfoxide, DMSO) with poly(methyl methacrylate) (PMMA) solution; (ii) the direct exfoliation of bP in a polymeric solution; (iii) the in situ radical polymerization after exfoliating bP in the liquid monomer (methyl methacrylate, MMA). This last methodology concerns the preparation of stable suspensions of bPn–MMA by sonication-assisted liquid-phase exfoliation (LPE) of bP in the presence of MMA followed by radical polymerization. The hybrids characteristics have been compared in order to evaluate the bP dispersion and the effectiveness of the bPn interfacial interactions with polymer chains aimed at their long-term environmental stabilization. The passivation of the bPn is particularly effective when the hybrid material is prepared by in situ polymerization. By using this synthetic methodology, the nanoflakes, even if with a gradient of dispersion (size of aggregates), preserve their chemical structure from oxidation (as proved by both Raman and 31P-solid state NMR studies) and are particularly stable to air and UV light exposure. The feasibility of this approach, capable of efficiently exfoliating bP while protecting the bPn, has been then verified by using different vinyl monomers (styrene and N-vinylpyrrolidone), thus obtaining hybrids where the nanoflakes are embedded in polymer matrices with a variety of intriguing thermal, mechanical, and solubility characteristics.

Resource constrained design of artificial neural networks using comparator neural network

NASA Technical Reports Server (NTRS)

Wah, Benjamin W.; Karnik, Tanay S.

1992-01-01

We present a systematic design method executed under resource constraints for automating the design of artificial neural networks using the back error propagation algorithm. Our system aims at finding the best possible configuration for solving the given application with proper tradeoff between the training time and the network complexity. The design of such a system is hampered by three related problems. First, there are infinitely many possible network configurations, each may take an exceedingly long time to train; hence, it is impossible to enumerate and train all of them to completion within fixed time, space, and resource constraints. Second, expert knowledge on predicting good network configurations is heuristic in nature and is application dependent, rendering it difficult to characterize fully in the design process. A learning procedure that refines this knowledge based on examples on training neural networks for various applications is, therefore, essential. Third, the objective of the network to be designed is ill-defined, as it is based on a subjective tradeoff between the training time and the network cost. A design process that proposes alternate configurations under different cost-performance tradeoff is important. We have developed a Design System which schedules the available time, divided into quanta, for testing alternative network configurations. Its goal is to select/generate and test alternative network configurations in each quantum, and find the best network when time is expended. Since time is limited, a dynamic schedule that determines the network configuration to be tested in each quantum is developed. The schedule is based on relative comparison of predicted training times of alternative network configurations using comparator network paradigm. The comparator network has been trained to compare training times for a large variety of traces of TSSE-versus-time collected during back-propagation learning of various applications.

Role of AMPA and NMDA receptors and back-propagating action potentials in spike timing-dependent plasticity.

PubMed

Fuenzalida, Marco; Fernández de Sevilla, David; Couve, Alejandro; Buño, Washington

2010-01-01

The cellular mechanisms that mediate spike timing-dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSP(AMPA) and EPSP(NMDA)) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSP(AMPA) with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSP(AMPA) by combining SC stimulation, an imposed EPSP(AMPA)-like depolarization, and BAP or by coupling the EPSP(NMDA) evoked under sustained depolarization (approximately -40 mV) and BAP. In Mg(2+)-free solution EPSP(NMDA) and BAP also produced LTP. Suppression of EPSP(NMDA) or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.

The modelling of lead removal from water by deep eutectic solvents functionalized CNTs: artificial neural network (ANN) approach.

PubMed

Fiyadh, Seef Saadi; AlSaadi, Mohammed Abdulhakim; AlOmar, Mohamed Khalid; Fayaed, Sabah Saadi; Hama, Ako R; Bee, Sharifah; El-Shafie, Ahmed

2017-11-01

The main challenge in the lead removal simulation is the behaviour of non-linearity relationships between the process parameters. The conventional modelling technique usually deals with this problem by a linear method. The substitute modelling technique is an artificial neural network (ANN) system, and it is selected to reflect the non-linearity in the interaction among the variables in the function. Herein, synthesized deep eutectic solvents were used as a functionalized agent with carbon nanotubes as adsorbents of Pb 2+ . Different parameters were used in the adsorption study including pH (2.7 to 7), adsorbent dosage (5 to 20 mg), contact time (3 to 900 min) and Pb 2+ initial concentration (3 to 60 mg/l). The number of experimental trials to feed and train the system was 158 runs conveyed in laboratory scale. Two ANN types were designed in this work, the feed-forward back-propagation and layer recurrent; both methods are compared based on their predictive proficiency in terms of the mean square error (MSE), root mean square error, relative root mean square error, mean absolute percentage error and determination coefficient (R 2 ) based on the testing dataset. The ANN model of lead removal was subjected to accuracy determination and the results showed R 2 of 0.9956 with MSE of 1.66 × 10 -4 . The maximum relative error is 14.93% for the feed-forward back-propagation neural network model.

An artificial neural network to predict resting energy expenditure in obesity.

PubMed

Disse, Emmanuel; Ledoux, Séverine; Bétry, Cécile; Caussy, Cyrielle; Maitrepierre, Christine; Coupaye, Muriel; Laville, Martine; Simon, Chantal

2017-09-01

The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m -2 ) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m -2 . External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive

BP network for atorvastatin effect evaluation from ultrasound images features classification

NASA Astrophysics Data System (ADS)

Fang, Mengjie; Yang, Xin; Liu, Yang; Xu, Hongwei; Liang, Huageng; Wang, Yujie; Ding, Mingyue

2013-10-01

Atherosclerotic lesions at the carotid artery are a major cause of emboli or atheromatous debris, resulting in approximately 88% of ischemic strokes in the USA in 2006. Stroke is becoming the most common cause of death worldwide, although patient management and prevention strategies have reduced stroke rate considerably over the past decades. Many research studies have been carried out on how to quantitatively evaluate local arterial effects for potential carotid disease treatments. As an inexpensive, convenient and fast means of detection, ultrasonic medical testing has been widespread in the world, so it is very practical to use ultrasound technology in the prevention and treatment of carotid atherosclerosis. This paper is dedicated to this field. Currently, many ultrasound image characteristics on carotid plaque have been proposed. After screening a large number of features (including 26 morphological and 85 texture features), we have got six shape characteristics and six texture characteristics in the combination. In order to test the validity and accuracy of these combined features, we have established a Back-Propagation (BP) neural network to classify atherosclerosis plaques between atorvastatin group and placebo group. The leave-one-case-out protocol was utilized on a database of 768 carotid ultrasound images of 12 patients (5 subjects of placebo group and 7 subjects of atorvastatin group) for the evaluation. The classification results showed that the combined features and classification have good recognition ability, with the overall accuracy 83.93%, sensitivity 82.14%, specificity 85.20%, positive predictive value 79.86%, negative predictive value 86.98%, Matthew's correlation coefficient 67.08%, and Youden's index 67.34%. And the receiver operating characteristic (ROC) curve in our test also performed well.

Activation and propagation of tumor infiltrating lymphocytes on clinical-grade designer artificial antigen presenting cells for adoptive immunotherapy of melanoma

PubMed Central

Forget, Marie-Andrée; Malu, Shruti; Liu, Hui; Toth, Christopher; Maiti, Sourindra; Kale, Charuta; Haymaker, Cara; Bernatchez, Chantale; Huls, Helen; Wang, Ena; Marincola, Francesco M.; Hwu, Patrick; Cooper, Laurence J. N.; Radvanyi, Laszlo G.

2014-01-01

PURPOSE Adoptive cell therapy (ACT) with autologous tumor infiltrating lymphocytes (TIL) is a therapy for metastatic melanoma with response rates up to 50%. However, the generation of the TIL transfer product is challenging, requiring pooled allogeneic normal donor peripheral blood mononuclear cells (PBMC) used in vitro as “feeders” to support a rapid expansion protocol (REP). Here, we optimized a platform to propagate TIL to a clinical scale using K562-cells genetically modified to express costimulatory molecules such as CD86, CD137-ligand and membrane-bound IL-15 to function as artificial antigen-presenting cell (aAPC) as an alternative to using PBMC feeders. EXPERIMENTAL DESIGN We used aAPC or γ-irradiated PBMC feeders to propagate TIL and measured rates of expansion. The activation and differentiation state was evaluated by flow cytometry and differential gene expression analyses. Clonal diversity was assessed based on pattern of T-cell receptor (TCR) usage. T-cell effector function was measured by evaluation of cytotoxic granule content and killing of target cells. RESULTS The aAPC propagated TIL at numbers equivalent to that found with PBMC feeders, while increasing the frequency of CD8+ T-cell expansion with a comparable effector-memory phenotype. mRNA profiling revealed an up-regulation of genes in the Wnt and stem-cell pathways with the aAPC. The aAPC platform did not skew clonal diversity and CD8+ T cells showed comparable anti-tumor function as those expanded with PBMC feeders. CONCLUSIONS TIL can be rapidly expanded with aAPC to clinical scale generating T cells with similar phenotypic and effector profiles as with PBMC feeders. These data support the clinical-application of aAPC to manufacture TIL for the treatment of melanoma. PMID:25304728

Rupture Processes of the Mw8.3 Sea of Okhotsk Earthquake and Aftershock Sequences from 3-D Back Projection Imaging

NASA Astrophysics Data System (ADS)

Jian, P. R.; Hung, S. H.; Meng, L.

2014-12-01

On May 24, 2013, the largest deep earthquake ever recorded in history occurred on the southern tip of the Kamchatka Island, where the Pacific Plate subducts underneath the Okhotsk Plate. Previous 2D beamforming back projection (BP) of P- coda waves suggests the mainshock ruptured bilaterally along a horizontal fault plane determined by the global centroid moment tensor solution. On the other hand, the multiple point source inversion of P and SH waveforms argued that the earthquake comprises a sequence of 6 subevents not located on a single plane but actually distributed in a zone that extends 64 km horizontally and 35 km in depth. We then apply a three-dimensional MUSIC BP approach to resolve the rupture processes of the manishock and two large aftershocks (M6.7) with no a priori setup of preferential orientations of the planar rupture. The maximum pseudo-spectrum of high-frequency P wave in a sequence of time windows recorded by the densely-distributed stations from US and EU Array are used to image 3-D temporal and spatial rupture distribution. The resulting image confirms that the nearly N-S striking but two antiparallel rupture stages. The first subhorizontal rupture initially propagates toward the NNE direction, while at 18 s later it directs reversely to the SSW and concurrently shifts downward to 35 km deeper lasting for about 20 s. The rupture lengths in the first NNE-ward and second SSW-ward stage are about 30 km and 85 km; the estimated rupture velocities are 3 km/s and 4.25 km/s, respectively. Synthetic experiments are undertaken to assess the capability of the 3D MUSIC BP for the recovery of spatio-temporal rupture processes. Besides, high frequency BP images based on the EU-Array data show two M6.7 aftershocks are more likely to rupture on the vertical fault planes.

Accurate orbit propagation in the presence of planetary close encounters

NASA Astrophysics Data System (ADS)

Amato, Davide; Baù, Giulio; Bombardelli, Claudio

2017-09-01

We present an efficient strategy for the numerical propagation of small Solar system objects undergoing close encounters with massive bodies. The trajectory is split into several phases, each of them being the solution of a perturbed two-body problem. Formulations regularized with respect to different primaries are employed in two subsequent phases. In particular, we consider the Kustaanheimo-Stiefel regularization and a novel set of non-singular orbital elements pertaining to the Dromo family. In order to test the proposed strategy, we perform ensemble propagations in the Earth-Sun Circular Restricted 3-Body Problem (CR3BP) using a variable step size and order multistep integrator and an improved version of Everhart's radau solver of 15th order. By combining the trajectory splitting with regularized equations of motion in short-term propagations (1 year), we gain up to six orders of magnitude in accuracy with respect to the classical Cowell's method for the same computational cost. Moreover, in the propagation of asteroid (99942) Apophis through its 2029 Earth encounter, the position error stays within 100 metres after 100 years. In general, as to improve the performance of regularized formulations, the trajectory must be split between 1.2 and 3 Hill radii from the Earth. We also devise a robust iterative algorithm to stop the integration of regularized equations of motion at a prescribed physical time. The results rigorously hold in the CR3BP, and similar considerations may apply when considering more complex models. The methods and algorithms are implemented in the naples fortran 2003 code, which is available online as a GitHub repository.

Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models.

PubMed

Pappu, J Sharon Mano; Gummadi, Sathyanarayana N

2016-11-01

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development of Artificial Neural Network Model for Diesel Fuel Properties Prediction using Vibrational Spectroscopy.

PubMed

Bolanča, Tomislav; Marinović, Slavica; Ukić, Sime; Jukić, Ante; Rukavina, Vinko

2012-06-01

This paper describes development of artificial neural network models which can be used to correlate and predict diesel fuel properties from several FTIR-ATR absorbances and Raman intensities as input variables. Multilayer feed forward and radial basis function neural networks have been used to rapid and simultaneous prediction of cetane number, cetane index, density, viscosity, distillation temperatures at 10% (T10), 50% (T50) and 90% (T90) recovery, contents of total aromatics and polycyclic aromatic hydrocarbons of commercial diesel fuels. In this study two-phase training procedures for multilayer feed forward networks were applied. While first phase training algorithm was constantly the back propagation one, two second phase training algorithms were varied and compared, namely: conjugate gradient and quasi Newton. In case of radial basis function network, radial layer was trained using K-means radial assignment algorithm and three different radial spread algorithms: explicit, isotropic and K-nearest neighbour. The number of hidden layer neurons and experimental data points used for the training set have been optimized for both neural networks in order to insure good predictive ability by reducing unnecessary experimental work. This work shows that developed artificial neural network models can determine main properties of diesel fuels simultaneously based on a single and fast IR or Raman measurement.

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Fault detection and classification in electrical power transmission system using artificial neural network.

PubMed

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

Usefulness of Enzyme-linked Immunosorbent Assay Using Recombinant BP180 and BP230 for Serodiagnosis and Monitoring Disease Activity of Bullous Pemphigoid

PubMed Central

Lee, Eui Hyung; Kim, Yeon Hee; Kim, Sinyoung; Kim, Song-ee

2012-01-01

Background Bullous pemphigoid (BP) is an autoimmune subepidermal bullous disease associated with autoantibodies against BP180 and BP230. Enzyme-linked immunosorbent assay (ELISA) is a sensitive tool for the detection of immunoglobulin G (IgG) anti-BP180 and anti-BP230 autoantibodies. Objective The aim of this study was to evaluate the usefulness of ELISA for diagnosing and monitoring the disease activity of BP. Methods We evaluated serum IgG levels of anti-BP180 and anti-BP230 autoantibodies in 47 BP patients, 16 epidermolysis bullosa aquisita patients, and 15 healthy volunteers using ELISA. Through retrospective review of the medical records, the clinical characteristics of BP including disease activity, duration, pruritus severity and peripheral blood eosinophil counts were assessed. Results The sensitivity of BP180 ELISA was 97.9%, BP230 ELISA 72.3%, and a combination of the two was 100%. The specificity of BP180 ELISA was 90.3%, BP230 ELISA 100%, and a combination of the two was 90.3%. BP180 ELISA scores showed strong associations with disease activity, pruritus severity, peripheral blood eosinophil counts, and disease duration, whereas BP230 ELISA scores did not. Conclusion BP180 and BP230 ELISAs are highly sensitive methods for the diagnosis of BP, and BP180 ELISA, in particular, is a sensitive tool for monitoring the disease activity of BP. PMID:22363155

Optimality in Microwave-Assisted Drying of Aloe Vera ( Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling

NASA Astrophysics Data System (ADS)

Das, Chandan; Das, Arijit; Kumar Golder, Animes

2016-10-01

The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. `Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.

Performance Evaluation of Public Non-Profit Hospitals Using a BP Artificial Neural Network: The Case of Hubei Province in China

PubMed Central

Li, Chunhui; Yu, Chuanhua

2013-01-01

To provide a reference for evaluating public non-profit hospitals in the new environment of medical reform, we established a performance evaluation system for public non-profit hospitals. The new “input-output” performance model for public non-profit hospitals is based on four primary indexes (input, process, output and effect) that include 11 sub-indexes and 41 items. The indicator weights were determined using the analytic hierarchy process (AHP) and entropy weight method. The BP neural network was applied to evaluate the performance of 14 level-3 public non-profit hospitals located in Hubei Province. The most stable BP neural network was produced by comparing different numbers of neurons in the hidden layer and using the “Leave-one-out” Cross Validation method. The performance evaluation system we established for public non-profit hospitals could reflect the basic goal of the new medical health system reform in China. Compared with PLSR, the result indicated that the BP neural network could be used effectively for evaluating the performance public non-profit hospitals. PMID:23955238

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Back-Projection Cortical Potential Imaging: Theory and Results.

PubMed

Haor, Dror; Shavit, Reuven; Shapiro, Moshe; Geva, Amir B

2017-07-01

Electroencephalography (EEG) is the single brain monitoring technique that is non-invasive, portable, passive, exhibits high-temporal resolution, and gives a directmeasurement of the scalp electrical potential. Amajor disadvantage of the EEG is its low-spatial resolution, which is the result of the low-conductive skull that "smears" the currents coming from within the brain. Recording brain activity with both high temporal and spatial resolution is crucial for the localization of confined brain activations and the study of brainmechanismfunctionality, whichis then followed by diagnosis of brain-related diseases. In this paper, a new cortical potential imaging (CPI) method is presented. The new method gives an estimation of the electrical activity on the cortex surface and thus removes the "smearing effect" caused by the skull. The scalp potentials are back-projected CPI (BP-CPI) onto the cortex surface by building a well-posed problem to the Laplace equation that is solved by means of the finite elements method on a realistic head model. A unique solution to the CPI problem is obtained by introducing a cortical normal current estimation technique. The technique is based on the same mechanism used in the well-known surface Laplacian calculation, followed by a scalp-cortex back-projection routine. The BP-CPI passed four stages of validation, including validation on spherical and realistic head models, probabilistic analysis (Monte Carlo simulation), and noise sensitivity tests. In addition, the BP-CPI was compared with the minimum norm estimate CPI approach and found superior for multi-source cortical potential distributions with very good estimation results (CC >0.97) on a realistic head model in the regions of interest, for two representative cases. The BP-CPI can be easily incorporated in different monitoring tools and help researchers by maintaining an accurate estimation for the cortical potential of ongoing or event-related potentials in order to have better

Classification of a Driver's cognitive workload levels using artificial neural network on ECG signals.

PubMed

Tjolleng, Amir; Jung, Kihyo; Hong, Wongi; Lee, Wonsup; Lee, Baekhee; You, Heecheon; Son, Joonwoo; Park, Seikwon

2017-03-01

An artificial neural network (ANN) model was developed in the present study to classify the level of a driver's cognitive workload based on electrocardiography (ECG). ECG signals were measured on 15 male participants while they performed a simulated driving task as a primary task with/without an N-back task as a secondary task. Three time-domain ECG measures (mean inter-beat interval (IBI), standard deviation of IBIs, and root mean squared difference of adjacent IBIs) and three frequencydomain ECG measures (power in low frequency, power in high frequency, and ratio of power in low and high frequencies) were calculated. To compensate for individual differences in heart response during the driving tasks, a three-step data processing procedure was performed to ECG signals of each participant: (1) selection of two most sensitive ECG measures, (2) definition of three (low, medium, and high) cognitive workload levels, and (3) normalization of the selected ECG measures. An ANN model was constructed using a feed-forward network and scaled conjugate gradient as a back-propagation learning rule. The accuracy of the ANN classification model was found satisfactory for learning data (95%) and testing data (82%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Artificial neural network modeling of the water quality index using land use areas as predictors.

PubMed

Gazzaz, Nabeel M; Yusoff, Mohd Kamil; Ramli, Mohammad Firuz; Juahir, Hafizan; Aris, Ahmad Zaharin

2015-02-01

This paper describes the design of an artificial neural network (ANN) model to predict the water quality index (WQI) using land use areas as predictors. Ten-year records of land use statistics and water quality data for Kinta River (Malaysia) were employed in the modeling process. The most accurate WQI predictions were obtained with the network architecture 7-23-1; the back propagation training algorithm; and a learning rate of 0.02. The WQI forecasts of this model had significant (p < 0.01), positive, very high correlation (ρs = 0.882) with the measured WQI values. Sensitivity analysis revealed that the relative importance of the land use classes to WQI predictions followed the order: mining > rubber > forest > logging > urban areas > agriculture > oil palm. These findings show that the ANNs are highly reliable means of relating water quality to land use, thus integrating land use development with river water quality management.

Human recognition based on head-shoulder contour extraction and BP neural network

NASA Astrophysics Data System (ADS)

Kong, Xiao-fang; Wang, Xiu-qin; Gu, Guohua; Chen, Qian; Qian, Wei-xian

2014-11-01

In practical application scenarios like video surveillance and human-computer interaction, human body movements are uncertain because the human body is a non-rigid object. Based on the fact that the head-shoulder part of human body can be less affected by the movement, and will seldom be obscured by other objects, in human detection and recognition, a head-shoulder model with its stable characteristics can be applied as a detection feature to describe the human body. In order to extract the head-shoulder contour accurately, a head-shoulder model establish method with combination of edge detection and the mean-shift algorithm in image clustering has been proposed in this paper. First, an adaptive method of mixture Gaussian background update has been used to extract targets from the video sequence. Second, edge detection has been used to extract the contour of moving objects, and the mean-shift algorithm has been combined to cluster parts of target's contour. Third, the head-shoulder model can be established, according to the width and height ratio of human head-shoulder combined with the projection histogram of the binary image, and the eigenvectors of the head-shoulder contour can be acquired. Finally, the relationship between head-shoulder contour eigenvectors and the moving objects will be formed by the training of back-propagation (BP) neural network classifier, and the human head-shoulder model can be clustered for human detection and recognition. Experiments have shown that the method combined with edge detection and mean-shift algorithm proposed in this paper can extract the complete head-shoulder contour, with low calculating complexity and high efficiency.

[Study on artificial neural network combined with multispectral remote sensing imagery for forest site evaluation].

PubMed

Gong, Yin-Xi; He, Cheng; Yan, Fei; Feng, Zhong-Ke; Cao, Meng-Lei; Gao, Yuan; Miao, Jie; Zhao, Jin-Long

2013-10-01

Multispectral remote sensing data containing rich site information are not fully used by the classic site quality evaluation system, as it merely adopts artificial ground survey data. In order to establish a more effective site quality evaluation system, a neural network model which combined remote sensing spectra factors with site factors and site index relations was established and used to study the sublot site quality evaluation in the Wangyedian Forest Farm in Inner Mongolia Province, Chifeng City. Based on the improved back propagation artificial neural network (BPANN), this model combined multispectral remote sensing data with sublot survey data, and took larch as example, Through training data set sensitivity analysis weak or irrelevant factor was excluded, the size of neural network was simplified, and the efficiency of network training was improved. This optimal site index prediction model had an accuracy up to 95.36%, which was 9.83% higher than that of the neural network model based on classic sublot survey data, and this shows that using multi-spectral remote sensing and small class survey data to determine the status of larch index prediction model has the highest predictive accuracy. The results fully indicate the effectiveness and superiority of this method.

Classification and prediction of rice wines with different marked ages by using a voltammetric electronic tongue.

PubMed

Wei, Zhenbo; Wang, Jun; Ye, Linshuang

2011-08-15

A voltammetric electronic tongue (VE-tongue) was developed to discriminate the difference between Chinese rice wines in this research. Three types of Chinese rice wine with different marked ages (1, 3, and 5 years) were classified by the VE-tongue by principal component analysis (PCA) and cluster analysis (CA). The VE-tongue consisted of six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium) in a standard three-electrode configuration. The multi-frequency large amplitude pulse voltammetry (MLAPV), which consisted of four segments of 1 Hz, 10 Hz, 100 Hz, and 1000 Hz, was applied as the potential waveform. The three types of Chinese rice wine could be classified accurately by PCA and CA, and some interesting regularity is shown in the score plots with the help of PCA. Two regression models, partial least squares (PLS) and back-error propagation-artificial neural network (BP-ANN), were used for wine age prediction. The regression results showed that the marked ages of the three types of Chinese rice wine were successfully predicted using PLS and BP-ANN. Copyright © 2011 Elsevier B.V. All rights reserved.

Designing Artificial Neural Networks Using Particle Swarm Optimization Algorithms

PubMed Central

Vázquez, Roberto A.

2015-01-01

Artificial Neural Network (ANN) design is a complex task because its performance depends on the architecture, the selected transfer function, and the learning algorithm used to train the set of synaptic weights. In this paper we present a methodology that automatically designs an ANN using particle swarm optimization algorithms such as Basic Particle Swarm Optimization (PSO), Second Generation of Particle Swarm Optimization (SGPSO), and a New Model of PSO called NMPSO. The aim of these algorithms is to evolve, at the same time, the three principal components of an ANN: the set of synaptic weights, the connections or architecture, and the transfer functions for each neuron. Eight different fitness functions were proposed to evaluate the fitness of each solution and find the best design. These functions are based on the mean square error (MSE) and the classification error (CER) and implement a strategy to avoid overtraining and to reduce the number of connections in the ANN. In addition, the ANN designed with the proposed methodology is compared with those designed manually using the well-known Back-Propagation and Levenberg-Marquardt Learning Algorithms. Finally, the accuracy of the method is tested with different nonlinear pattern classification problems. PMID:26221132

Estimating EQ-5D values from the Oswestry Disability Index and numeric rating scales for back and leg pain.

PubMed

Carreon, Leah Y; Bratcher, Kelly R; Das, Nandita; Nienhuis, Jacob B; Glassman, Steven D

2014-04-15

Cross-sectional cohort. The purpose of this study is to determine whether the EuroQOL-5D (EQ-5D) can be derived from commonly available low back disease-specific health-related quality of life measures. The Oswestry Disability Index (ODI) and numeric rating scales (0-10) for back pain (BP) and leg pain (LP) are widely used disease-specific measures in patients with lumbar degenerative disorders. Increasingly, the EQ-5D is being used as a measure of utility due to ease of administration and scoring. The EQ-5D, ODI, BP, and LP were prospectively collected in 14,544 patients seen in clinic for lumbar degenerative disorders. Pearson correlation coefficients for paired observations from multiple time points between ODI, BP, LP, and EQ-5D were determined. Regression modeling was done to compute the EQ-5D score from the ODI, BP, and LP. The mean age was 53.3 ± 16.4 years and 41% were male. Correlations between the EQ-5D and the ODI, BP, and LP were statistically significant (P < 0.0001) with correlation coefficients of -0.77, -0.50, and -0.57, respectively. The regression equation: [0.97711 + (-0.00687 × ODI) + (-0.01488 × LP) + (-0.01008 × BP)] to predict EQ-5D, had an R2 of 0.61 and a root mean square error of 0.149. The model using ODI alone had an R2 of 0.57 and a root mean square error of 0.156. The model using the individual ODI items had an R2 of 0.64 and a root mean square error of 0.143. The correlation coefficient between the observed and estimated EQ-5D score was 0.78. There was no statistically significant difference between the actual EQ-5D (0.553 ± 0.238) and the estimated EQ-5D score (0.553 ± 0.186) using the ODI, BP, and LP regression model. However, rounding off the coefficients to less than 5 decimal places produced less accurate results. Unlike previous studies showing a robust relationship between low back-specific measures and the Short Form-6D, a similar relationship was not seen between the ODI, BP, LP, and the EQ-5D. Thus, the EQ-5D cannot be

BP pledges to cut emissions

NASA Astrophysics Data System (ADS)

Showstack, Randy

British Petroleum (BP), one of the world's biggest oil companies that could become even bigger if a merger with Amoco is approved, announced on September 18 that it will cut its emissions of greenhouse gases by 10% from a 1990 baseline of 40 million tons of carbon dioxide between now and the year 2010.The target, which is double the amount of emissions reductions that industrialized nations agreed to under the Kyoto protocol on climate change, will now stand next to BP's financial targets, said John Browne, group chief executive of BP.

Modeling the low-velocity impact characteristics of woven glass epoxy composite laminates using artificial neural networks

NASA Astrophysics Data System (ADS)

Mathivanan, N. Rajesh; Mouli, Chandra

2012-12-01

In this work, a new methodology based on artificial neural networks (ANN) has been developed to study the low-velocity impact characteristics of woven glass epoxy laminates of EP3 grade. To train and test the networks, multiple impact cases have been generated using statistical analysis of variance (ANOVA). Experimental tests were performed using an instrumented falling-weight impact-testing machine. Different impact velocities and impact energies on different thicknesses of laminates were considered as the input parameters of the ANN model. This model is a feed-forward back-propagation neural network. Using the input/output data of the experiments, the model was trained and tested. Further, the effects of the low-velocity impact response of the laminates at different energy levels were investigated by studying the cause-effect relationship among the influential factors using response surface methodology. The most significant parameter is determined from the other input variables through ANOVA.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation

PubMed Central

Scellier, Benjamin; Bengio, Yoshua

2017-01-01

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task. PMID

Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a back-propagation neural network algorithm

NASA Astrophysics Data System (ADS)

Han, Hyung-Suk

2012-12-01

The indoor noise of a ship is usually determined using the A-weighted sound pressure level. However, in order to better understand this phenomenon, evaluation parameters that more accurately reflect the human sense of hearing are required. To find the level of the satisfaction index of the noise inside a naval vessel such as "Loudness" and "Annoyance", psycho-acoustic evaluation of various sound recordings from the naval vessel was performed in a laboratory. The objective of this paper is to develop a single index of "Loudness" and "Annoyance" for noise inside a naval vessel according to a psycho-acoustic evaluation by using psychological responses such as Noise Rating (NR), Noise Criterion (NC), Room Criterion (RC), Preferred Speech Interference Level (PSIL) and loudness level. Additionally, in order to determine a single index of satisfaction for noise such as "Loudness" and "Annoyance", with respect to a human's sense of hearing, a back-propagation neural network is applied.

Equalization enhanced phase noise in Nyquist-spaced superchannel transmission systems using multi-channel digital back-propagation

PubMed Central

Xu, Tianhua; Liga, Gabriele; Lavery, Domaniç; Thomsen, Benn C.; Savory, Seb J.; Killey, Robert I.; Bayvel, Polina

2015-01-01

Superchannel transmission spaced at the symbol rate, known as Nyquist spacing, has been demonstrated for effectively maximizing the optical communication channel capacity and spectral efficiency. However, the achievable capacity and reach of transmission systems using advanced modulation formats are affected by fibre nonlinearities and equalization enhanced phase noise (EEPN). Fibre nonlinearities can be effectively compensated using digital back-propagation (DBP). However EEPN which arises from the interaction between laser phase noise and dispersion cannot be efficiently mitigated, and can significantly degrade the performance of transmission systems. Here we report the first investigation of the origin and the impact of EEPN in Nyquist-spaced superchannel system, employing electronic dispersion compensation (EDC) and multi-channel DBP (MC-DBP). Analysis was carried out in a Nyquist-spaced 9-channel 32-Gbaud DP-64QAM transmission system. Results confirm that EEPN significantly degrades the performance of all sub-channels of the superchannel system and that the distortions are more severe for the outer sub-channels, both using EDC and MC-DBP. It is also found that the origin of EEPN depends on the relative position between the carrier phase recovery module and the EDC (or MC-DBP) module. Considering EEPN, diverse coding techniques and modulation formats have to be applied for optimizing different sub-channels in superchannel systems. PMID:26365422

Comparisons between physics-based, engineering, and statistical learning models for outdoor sound propagation.

PubMed

Hart, Carl R; Reznicek, Nathan J; Wilson, D Keith; Pettit, Chris L; Nykaza, Edward T

2016-05-01

Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.

RanBP2 modulates Cox11 and hexokinase I activities and haploinsufficiency of RanBP2 causes deficits in glucose metabolism.

PubMed

Aslanukov, Azamat; Bhowmick, Reshma; Guruju, Mallikarjuna; Oswald, John; Raz, Dorit; Bush, Ronald A; Sieving, Paul A; Lu, Xinrong; Bock, Cheryl B; Ferreira, Paulo A

2006-10-01

The Ran-binding protein 2 (RanBP2) is a large multimodular and pleiotropic protein. Several molecular partners with distinct functions interacting specifically with selective modules of RanBP2 have been identified. Yet, the significance of these interactions with RanBP2 and the genetic and physiological role(s) of RanBP2 in a whole-animal model remain elusive. Here, we report the identification of two novel partners of RanBP2 and a novel physiological role of RanBP2 in a mouse model. RanBP2 associates in vitro and in vivo and colocalizes with the mitochondrial metallochaperone, Cox11, and the pacemaker of glycolysis, hexokinase type I (HKI) via its leucine-rich domain. The leucine-rich domain of RanBP2 also exhibits strong chaperone activity toward intermediate and mature folding species of Cox11 supporting a chaperone role of RanBP2 in the cytosol during Cox11 biogenesis. Cox11 partially colocalizes with HKI, thus supporting additional and distinct roles in cell function. Cox11 is a strong inhibitor of HKI, and RanBP2 suppresses the inhibitory activity of Cox11 over HKI. To probe the physiological role of RanBP2 and its role in HKI function, a mouse model harboring a genetically disrupted RanBP2 locus was generated. RanBP2(-/-) are embryonically lethal, and haploinsufficiency of RanBP2 in an inbred strain causes a pronounced decrease of HKI and ATP levels selectively in the central nervous system. Inbred RanBP2(+/-) mice also exhibit deficits in growth rates and glucose catabolism without impairment of glucose uptake and gluconeogenesis. These phenotypes are accompanied by a decrease in the electrophysiological responses of photosensory and postreceptoral neurons. Hence, RanBP2 and its partners emerge as critical modulators of neuronal HKI, glucose catabolism, energy homeostasis, and targets for metabolic, aging disorders and allied neuropathies.

Prevalence and management of back pain in adolescent idiopathic scoliosis patients: A retrospective study

PubMed Central

Théroux, Jean; Le May, Sylvie; Fortin, Carole; Labelle, Hubert

2015-01-01

BACKGROUND: Back pain (BP) has often been associated with adolescent idiopathic scoliosis (AIS), which is a three-dimensional deviation of the vertebral column. In adolescents, chronic pain appears to be a predictor of health care utilization and has a negative impact on physical, psychological and family well-being. In this population, BP tends to be persistent and may be a predictor of BP in adulthood. OBJECTIVE: To document the prevalence and management of BP in AIS patients. METHODS: A retrospective chart review of AIS patients who were referred to Sainte-Justine University Teaching Hospital (Montreal, Quebec) from 2006 to 2011 was conducted. RESULTS: A total of 310 randomly selected charts were reviewed. Nearly one-half of the patients (47.3%) mentioned that they experienced BP, most commonly in the lumbar (19.7%) and thoracic regions (7.7%). The type of BP was documented in only 36% (n=112) of the charts. Pain intensity was specified in only 21% (n=65) of the charts. In approximately 80% (n=248) of the charts, no pain management treatment plan was documented. CONCLUSIONS: The prevalence of BP was moderately high among the present sample of adolescents with AIS. An improved system for documenting BP assessment, type, treatment plan and treatment effectiveness would improve pain management for these patients. PMID:25831076

Back-Propagation of Physiological Action Potential Output in Dendrites of Slender-Tufted L5A Pyramidal Neurons

PubMed Central

Grewe, Benjamin F.; Bonnan, Audrey; Frick, Andreas

2009-01-01

Pyramidal neurons of layer 5A are a major neocortical output type and clearly distinguished from layer 5B pyramidal neurons with respect to morphology, in vivo firing patterns, and connectivity; yet knowledge of their dendritic properties is scant. We used a combination of whole-cell recordings and Ca2+ imaging techniques in vitro to explore the specific dendritic signaling role of physiological action potential patterns recorded in vivo in layer 5A pyramidal neurons of the whisker-related ‘barrel cortex’. Our data provide evidence that the temporal structure of physiological action potential patterns is crucial for an effective invasion of the main apical dendrites up to the major branch point. Both the critical frequency enabling action potential trains to invade efficiently and the dendritic calcium profile changed during postnatal development. In contrast to the main apical dendrite, the more passive properties of the short basal and apical tuft dendrites prevented an efficient back-propagation. Various Ca2+ channel types contributed to the enhanced calcium signals during high-frequency firing activity, whereas A-type K+ and BKCa channels strongly suppressed it. Our data support models in which the interaction of synaptic input with action potential output is a function of the timing, rate and pattern of action potentials, and dendritic location. PMID:20508744

Universal Artificial Antigen Presenting Cells to Selectively Propagate T Cells Expressing Chimeric Antigen Receptor Independent of Specificity

PubMed Central

Rushworth, David; Jena, Bipulendu; Olivares, Simon; Maiti, Sourindra; Briggs, Neima; Somanchi, Srinivas; Dai, Jianliang; Lee, Dean; Cooper, Laurence J. N.

2014-01-01

T cells genetically modified to stably express immunoreceptors are being assessed for therapeutic potential in clinical trials. T cells expressing a chimeric antigen receptor (CAR) are endowed with a new specificity to target tumor-associated antigen (TAA) independent of major histocompatibility complex. Our approach to non-viral gene transfer in T cells uses ex vivo numeric expansion of CAR+ T cells on irradiated artificial antigen presenting cells (aAPC) bearing the targeted TAA. The requirement for aAPC to express a desired TAA limits the human application of CARs with multiple specificities when selective expansion through co-culture with feeder cells is sought. As an alternative to expressing individual TAAs on aAPC, we expressed one ligand that could activate CAR+ T cells for sustained proliferation independent of specificity. We expressed a CAR ligand (designated CARL) that binds the conserved IgG4 extracellular domain of CAR and demonstrated CARL+ aAPC propagate CAR+ T cells of multiple specificities. CARL avoids technical issues and costs associated with deploying clinical-grade aAPC for each TAA targeted by a given CAR. Employing CARL enables one aAPC to numerically expand all CAR+ T cells containing the IgG4 domain, and simplifies expansion, testing, and clinical translation of CAR+ T cells of any specificity. PMID:24714354

Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict

NASA Astrophysics Data System (ADS)

Mandal, Sumantra; Sivaprasad, P. V.; Venugopal, S.; Murthy, K. P. N.

2006-09-01

An artificial neural network (ANN) model is developed to predict the constitutive flow behaviour of austenitic stainless steels during hot deformation. The input parameters are alloy composition and process variables whereas flow stress is the output. The model is based on a three-layer feed-forward ANN with a back-propagation learning algorithm. The neural network is trained with an in-house database obtained from hot compression tests on various grades of austenitic stainless steels. The performance of the model is evaluated using a wide variety of statistical indices. Good agreement between experimental and predicted data is obtained. The correlation between individual alloying elements and high temperature flow behaviour is investigated by employing the ANN model. The results are found to be consistent with the physical phenomena. The model can be used as a guideline for new alloy development.

Analyte species and concentration identification using differentially functionalized microcantilever arrays and artificial neural networks

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

Senesac, Larry R; Datskos, Panos G; Sepaniak, Michael J

2006-01-01

In the present work, we have performed analyte species and concentration identification using an array of ten differentially functionalized microcantilevers coupled with a back-propagation artificial neural network pattern recognition algorithm. The array consists of ten nanostructured silicon microcantilevers functionalized by polymeric and gas chromatography phases and macrocyclic receptors as spatially dense, differentially responding sensing layers for identification and quantitation of individual analyte(s) and their binary mixtures. The array response (i.e. cantilever bending) to analyte vapor was measured by an optical readout scheme and the responses were recorded for a selection of individual analytes as well as several binary mixtures. Anmore » artificial neural network (ANN) was designed and trained to recognize not only the individual analytes and binary mixtures, but also to determine the concentration of individual components in a mixture. To the best of our knowledge, ANNs have not been applied to microcantilever array responses previously to determine concentrations of individual analytes. The trained ANN correctly identified the eleven test analyte(s) as individual components, most with probabilities greater than 97%, whereas it did not misidentify an unknown (untrained) analyte. Demonstrated unique aspects of this work include an ability to measure binary mixtures and provide both qualitative (identification) and quantitative (concentration) information with array-ANN-based sensor methodologies.« less

History of Artificial Gravity. Chapter 3

NASA Technical Reports Server (NTRS)

Clement, Gilles; Bukley, Angie; Paloski, William

2006-01-01

This chapter reviews the past and current projects on artificial gravity during space missions. The idea of a rotating wheel-like space station providing artificial gravity goes back in the writings of Tsiolkovsky, Noordung, and Wernher von Braun. Its most famous fictional representation is in the film 2001: A Space Odyssey, which also depicts spin-generated artificial gravity aboard a space station and a spaceship bound for Jupiter. The O Neill-type space colony provides another classic illustration of this technique. A more realistic approach to rotating the space station is to provide astronauts with a smaller centrifuge contained within a spacecraft. The astronauts would go into it for a workout, and get their gravity therapeutic dose for a certain period of time, daily or a few times a week. This simpler concept is current being tested during ground-based studies in several laboratories around the world.

Intermediary LEO propagation including higher order zonal harmonics

NASA Astrophysics Data System (ADS)

Hautesserres, Denis; Lara, Martin

2017-04-01

Two new intermediary orbits of the artificial satellite problem are proposed. The analytical solutions include higher order effects of the geopotential, and are obtained by means of a torsion transformation applied to the quasi-Keplerian system resulting after the elimination of the parallax simplification, for the first intermediary, and after the elimination of the parallax and perigee simplifications, for the second one. The new intermediaries perform notably well for low Earth orbits propagation, are free from special functions, and result advantageous, both in accuracy and efficiency, when compared to the standard Cowell integration of the J_2 problem, thus providing appealing alternatives for onboard, short-term, orbit propagation under limited computational resources.

21 CFR 872.3910 - Backing and facing for an artificial tooth.

Code of Federal Regulations, 2010 CFR

2010-04-01

... use in fabrication of a fixed or removable dental appliance, such as a crown or bridge. The backing... is made of porcelain or plastic. (b) Classification. Class I (general controls). The device is exempt...

Critical Parameters of the Initiation Zone for Spontaneous Dynamic Rupture Propagation

NASA Astrophysics Data System (ADS)

Galis, M.; Pelties, C.; Kristek, J.; Moczo, P.; Ampuero, J. P.; Mai, P. M.

2014-12-01

Numerical simulations of rupture propagation are used to study both earthquake source physics and earthquake ground motion. Under linear slip-weakening friction, artificial procedures are needed to initiate a self-sustained rupture. The concept of an overstressed asperity is often applied, in which the asperity is characterized by its size, shape and overstress. The physical properties of the initiation zone may have significant impact on the resulting dynamic rupture propagation. A trial-and-error approach is often necessary for successful initiation because 2D and 3D theoretical criteria for estimating the critical size of the initiation zone do not provide general rules for designing 3D numerical simulations. Therefore, it is desirable to define guidelines for efficient initiation with minimal artificial effects on rupture propagation. We perform an extensive parameter study using numerical simulations of 3D dynamic rupture propagation assuming a planar fault to examine the critical size of square, circular and elliptical initiation zones as a function of asperity overstress and background stress. For a fixed overstress, we discover that the area of the initiation zone is more important for the nucleation process than its shape. Comparing our numerical results with published theoretical estimates, we find that the estimates by Uenishi & Rice (2004) are applicable to configurations with low background stress and small overstress. None of the published estimates are consistent with numerical results for configurations with high background stress. We therefore derive new equations to estimate the initiation zone size in environments with high background stress. Our results provide guidelines for defining the size of the initiation zone and overstress with minimal effects on the subsequent spontaneous rupture propagation.

A Survey of Symplectic and Collocation Integration Methods for Orbit Propagation

NASA Technical Reports Server (NTRS)

Jones, Brandon A.; Anderson, Rodney L.

2012-01-01

Demands on numerical integration algorithms for astrodynamics applications continue to increase. Common methods, like explicit Runge-Kutta, meet the orbit propagation needs of most scenarios, but more specialized scenarios require new techniques to meet both computational efficiency and accuracy needs. This paper provides an extensive survey on the application of symplectic and collocation methods to astrodynamics. Both of these methods benefit from relatively recent theoretical developments, which improve their applicability to artificial satellite orbit propagation. This paper also details their implementation, with several tests demonstrating their advantages and disadvantages.

Artificial propagation of coregonines in the management of the Laurentian Great Lakes

USGS Publications Warehouse

Todd, Thomas N.

1986-01-01

Numerous stresses caused wide fluctuations in the abundance of Great Lakes coregonine fishes during the last century. State, Provincial, and Federal agencies attempted to bolster these fisheries by stocking more than 32 billion fry of lake whitefish (Coregonus clupeaformis) and 6 billion fry of lake herring (C. artedii) over a period of about 90 years (1870-1960). Propagation efforts were unsuccessful in arresting the decline of these fishes, perhaps because the stocking densities were too low. It appears that stocking densities must exceed 41% of the natural hatch to produce measurable success in a planting program that augments natural reproduction. Stocking of any of the Great Lakes with lake whitefish at these levels would require several billion fry per lake annually. Such a program is too large to be practical and intensified protection of the remaining stocks would be more cost effective. A species such as the shortnose cisco (C. reighardi) which has only a small number of extant individuals, and can therefore be significantly augmented with fewer stocked fish, may be a much better candidate for propagation than is the lake whitefish. Propagation of coregonines in the Great Lakes should be considered only in localities that have little or no natural recruitment and then only for rehabilitation, and only if accompanied by adequate assessment of the performance of the stocked fish.

Evaluation of factors associated with severe and frequent back pain in high school athletes.

PubMed

Noll, Matias; Silveira, Erika Aparecida; Avelar, Ivan Silveira de

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14-20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01-1.33), posture while writing (PR: 1.41, CI: 1.27-1.58), and posture while using a computer (PR: 1.39, CI: 1.26-1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01-1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01-1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes.

Improving BP control through electronic communications: an economic evaluation.

PubMed

Fishman, Paul A; Cook, Andrea J; Anderson, Melissa L; Ralston, James D; Catz, Sheryl L; Carrell, David; Carlson, James; Green, Beverly B

2013-09-01

Web-based collaborative approaches to managing chronic illness show promise for both improving health outcomes and increasing the efficiency of the healthcare system. Analyze the cost-effectiveness of the Electronic Communications and Home Blood Pressure Monitoring to Improve Blood Pressure Control (e-BP) study, a randomized controlled trial that used a patient-shared electronic medical record, home blood pressure (BP) monitoring, and web-based pharmacist care to improve BP control (<140/90 mm Hg). Incremental cost-effectiveness analysis conducted from a health plan perspective. Cost-effectiveness of home BP monitoring and web-based pharmacist care estimated for percent change in patients with controlled BP and cost per mm Hg in diastolic and systolic BP relative to usual care and home BP monitoring alone. A 1% improvement in number of patients with controlled BP using home BP monitoring and web-based pharmacist care-the e-BP program-costs $16.65 (95% confidence interval: 15.37- 17.94) relative to home BP monitoring and web training alone. Each mm HG reduction in systolic and diastolic BP achieved through the e-BP program costs $65.29 (59.91-70.67) relativeto home BP monitoring and web tools only. Life expectancy was increased at an incremental cost of $1850 (1635-2064) and $2220 (1745-2694) per year of life saved for men and women, respectively. Web-based collaborative care can be used to achieve BP control at a relatively low cost. Future research should examine the cost impact of potential long-term clinical improvements.

The epidemiology of back pain and its relationship with depression, psychosis, anxiety, sleep disturbances, and stress sensitivity: Data from 43 low- and middle-income countries.

PubMed

Stubbs, Brendon; Koyanagi, Ai; Thompson, Trevor; Veronese, Nicola; Carvalho, Andre F; Solomi, Marco; Mugisha, James; Schofield, Patricia; Cosco, Theodore; Wilson, Nicky; Vancampfort, Davy

Back pain (BP) is a leading cause of global disability. However, population-based studies investigating its impact on mental health outcomes are lacking, particularly among low- and middle-income countries (LMICs). Thus, the primary aims of this study were to: (1) determine the epidemiology of BP in 43 LMICs; (2) explore the relationship between BP and mental health (depression spectrum, psychosis spectrum, anxiety, sleep disturbances and stress). Data on 190,593 community-dwelling adults aged ≥18 years from the World Health Survey (WHS) 2002-2004 were analyzed. The presence of past-12 month psychotic symptoms and depression was established using questions from the Composite International Diagnostic Interview. Anxiety, sleep problems, stress sensitivity, and any BP or chronic BP (CBP) during the previous 30 days were also self-reported. Multivariable logistic regression analyses were undertaken. The overall prevalence of any BP and CBP were 35.1% and 6.9% respectively. Significant associations with any BP were observed for subsyndromal depression [OR (odds ratio)=2.21], brief depressive episode (OR=2.64), depressive episode (OR=2.88), psychosis diagnosis with symptoms (OR=2.05), anxiety (OR=2.12), sleep disturbance (OR=2.37) and the continuous variable of stress sensitivity. Associations were generally more pronounced for chronic BP. Our data establish that BP is associated with elevated mental health comorbidity in LMICs. Integrated interventions that address back pain and metal health comorbidities might be an important next step to tackle this considerable burden. Copyright © 2016 Elsevier Inc. All rights reserved.

Synaptic depolarization is more effective than back-propagating action potentials during induction of associative long-term potentiation in hippocampal pyramidal neurons.

PubMed

Hardie, Jason; Spruston, Nelson

2009-03-11

Long-term potentiation (LTP) requires postsynaptic depolarization that can result from EPSPs paired with action potentials or larger EPSPs that trigger dendritic spikes. We explored the relative contribution of these sources of depolarization to LTP induction during synaptically driven action potential firing in hippocampal CA1 pyramidal neurons. Pairing of a weak test input with a strong input resulted in large LTP (approximately 75% increase) when the weak and strong inputs were both located in the apical dendrites. This form of LTP did not require somatic action potentials. When the strong input was located in the basal dendrites, the resulting LTP was smaller (< or =25% increase). Pairing the test input with somatically evoked action potentials mimicked this form of LTP. Thus, back-propagating action potentials may contribute to modest LTP, but local synaptic depolarization and/or dendritic spikes mediate a stronger form of LTP that requires spatial proximity of the associated synaptic inputs.

Ran-binding protein 5 (RanBP5) is related to the nuclear transport factor importin-beta but interacts differently with RanBP1.

PubMed Central

Deane, R; Schäfer, W; Zimmermann, H P; Mueller, L; Görlich, D; Prehn, S; Ponstingl, H; Bischoff, F R

1997-01-01

We report the identification and characterization of a novel 124-kDa Ran binding protein, RanBP5. This protein is related to importin-beta, the key mediator of nuclear localization signal (NLS)-dependent nuclear transport. RanBP5 was identified by two independent methods: it was isolated from HeLa cells by using its interaction with RanGTP in an overlay assay to monitor enrichment, and it was also found by the yeast two-hybrid selection method with RanBP1 as bait. RanBP5 binds to RanBP1 as part of a trimeric RanBP1-Ran-RanBP5 complex. Like importin-beta, RanBP5 strongly binds the GTP-bound form of Ran, stabilizing it against both intrinsic and RanGAP1-induced GTP hydrolysis and also against nucleotide exchange. The GAP resistance of the RanBP5-RanGTP complex can be relieved by RanBP1, which might reflect an in vivo role for RanBP1. RanBP5 is a predominantly cytoplasmic protein that can bind to nuclear pore complexes. We propose that RanBP5 is a mediator of a nucleocytoplasmic transport pathway that is distinct from the importin-alpha-dependent import of proteins with a classical NLS. PMID:9271386

Artificial neural network modeling and optimization of ultrahigh pressure extraction of green tea polyphenols.

PubMed

Xi, Jun; Xue, Yujing; Xu, Yinxiang; Shen, Yuhong

2013-11-01

In this study, the ultrahigh pressure extraction of green tea polyphenols was modeled and optimized by a three-layer artificial neural network. A feed-forward neural network trained with an error back-propagation algorithm was used to evaluate the effects of pressure, liquid/solid ratio and ethanol concentration on the total phenolic content of green tea extracts. The neural network coupled with genetic algorithms was also used to optimize the conditions needed to obtain the highest yield of tea polyphenols. The obtained optimal architecture of artificial neural network model involved a feed-forward neural network with three input neurons, one hidden layer with eight neurons and one output layer including single neuron. The trained network gave the minimum value in the MSE of 0.03 and the maximum value in the R(2) of 0.9571, which implied a good agreement between the predicted value and the actual value, and confirmed a good generalization of the network. Based on the combination of neural network and genetic algorithms, the optimum extraction conditions for the highest yield of green tea polyphenols were determined as follows: 498.8 MPa for pressure, 20.8 mL/g for liquid/solid ratio and 53.6% for ethanol concentration. The total phenolic content of the actual measurement under the optimum predicated extraction conditions was 582.4 ± 0.63 mg/g DW, which was well matched with the predicted value (597.2mg/g DW). This suggests that the artificial neural network model described in this work is an efficient quantitative tool to predict the extraction efficiency of green tea polyphenols. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Tunneling in BP-MoS2 heterostructure

NASA Astrophysics Data System (ADS)

Liu, Xiaochi; Qu, Deshun; Kim, Changsik; Ahmed, Faisal; Yoo, Won Jong

Tunnel field effect transistor (TFET) is considered to be a leading option for achieving SS <60 mV/dec. In this work, black phosphorus (BP) and molybdenum disulfide (MoS2) heterojunction devices are fabricated. We find that thin BP flake and MoS2 form normal p-n junctions, tunneling phenomena can be observed when BP thickness increases to certain level. PEO:CsClO4 is applied on the surface of the device together with a side gate electrode patterned together with source and drain electrodes. The Fermi level of MoS2 on top of BP layer can be modulated by the side gating, and this enables to vary the MoS2-BP tunnel diode property from off-state to on-state. Since tunneling is the working mechanism of MoS2-BP junction, and PEO:CsClO4\\ possesses ultra high dielectric constant and small equivalent oxide thickness (EOT), a low SS of 55 mV/dec is obtained from MoS2-BP TFET. This work was supported by the Global Research Laboratory and Global Frontier R&D Programs at the Center for Hybrid Interface Materials, both funded by the Ministry of Science, ICT & Future Planning via the National Research Foundation of Korea (NRF).

AI in CALL--Artificially Inflated or Almost Imminent?

ERIC Educational Resources Information Center

Schulze, Mathias

2008-01-01

The application of techniques from artificial intelligence (AI) to CALL has commonly been referred to as intelligent CALL (ICALL). ICALL is only slightly older than the "CALICO Journal", and this paper looks back at a quarter century of published research mainly in North America and by North American scholars. This "inventory…

Propagation failures, breathing pulses, and backfiring in an excitable reaction-diffusion system.

PubMed

Manz, Niklas; Steinbock, Oliver

2006-09-01

We report results from experiments with a pseudo-one-dimensional Belousov-Zhabotinsky reaction that employs 1,4-cyclohexanedione as its organic substrate. This excitable system shows traveling oxidation pulses and pulse trains that can undergo complex sequences of propagation failures. Moreover, we present examples for (i) breathing pulses that undergo periodic changes in speed and size and (ii) backfiring pulses that near their back repeatedly generate new pulses propagating in opposite direction.

Trailer siting issues: BP Texas City.

PubMed

Kaszniak, Mark; Holmstrom, Donald

2008-11-15

On 23 March, 2005, a series of explosions and fires occurred at the BP Texas City refinery during the startup of an isomerization (ISOM) process unit. Fifteen workers were killed and about 180 others were injured. All of the fatalities were contract workers; the deaths and most of the serious injuries occurred in and around temporary office trailers that had been sited near a blowdown drum and stack open to the atmosphere as part of ongoing turnaround activities in an adjacent unit. Due to problems that developed during the ISOM startup, flammable hydrocarbon liquid overfilled the blowdown drum and stack which resulted in a geyser-like release out the top into the atmosphere. The flammable hydrocarbons fell to the ground releasing vapors that were likely ignited from a nearby idling diesel pickup truck. A total of 44 trailers were damaged by the blast pressure wave that propagated through the refinery when the vapor cloud exploded. Thirteen trailers were totally destroyed and workers were injured in trailers as far as 479ft away from the release. The focus of this paper will be on trailer siting issues, including: need for work/office trailers within process units, adequacy of risk analysis methods in API RP 752, and minimum safe distance requirements

BP180 Is Critical in the Autoimmunity of Bullous Pemphigoid

PubMed Central

Liu, Yale; Li, Liang; Xia, Yumin

2017-01-01

Bullous pemphigoid (BP) is by far the most common autoimmune blistering dermatosis that mainly occurs in the elderly. The BP180 is a transmembrane glycoprotein, which is highly immunodominant in BP. The structure and location of BP180 indicate that it is a significant autoantigen and plays a key role in blister formation. Autoantibodies from BP patients react with BP180, which leads to its degradation and this has been regarded as the central event in BP pathogenesis. The consequent blister formation involves the activation of complement-dependent or -independent signals, as well as inflammatory pathways induced by BP180/anti-BP180 autoantibody interaction. As a multi-epitope molecule, BP180 can cause dermal–epidermal separation via combining each epitope with specific immunoglobulin, which also facilitates blister formation. In addition, some inflammatory factors can directly deplete BP180, thereby leading to fragility of the dermal–epidermal junction and blister formation. This review summarizes recent investigations on the role of BP180 in BP pathogenesis to determine the potential targets for the treatment of patients with BP. PMID:29276517

YADCLAN: yet another digitally-controlled linear artificial neuron.

PubMed

Frenger, Paul

2003-01-01

This paper updates the author's 1999 RMBS presentation on digitally controlled linear artificial neuron design. Each neuron is based on a standard operational amplifier having excitatory and inhibitory inputs, variable gain, an amplified linear analog output and an adjustable threshold comparator for digital output. This design employs a 1-wire serial network of digitally controlled potentiometers and resistors whose resistance values are set and read back under microprocessor supervision. This system embodies several unique and useful features, including: enhanced neuronal stability, dynamic reconfigurability and network extensibility. This artificial neuronal is being employed for feature extraction and pattern recognition in an advanced robotic application.

Educational inequality as a predictor of rising back pain prevalence in Austria-sex differences.

PubMed

Großschädl, Franziska; Stolz, Erwin; Mayerl, Hannes; Rásky, Éva; Freidl, Wolfgang; Stronegger, Willibald

2016-04-01

Back pain (BP) represents a widespread public health problem in Europe. The morbidity depends on several indicators, which must be investigated to discover risk groups. The examination of trends in socioeconomic developments should ensure a better understanding of the complex link between socioeconomic-status and BP. Therefore, the role of social inequalities for BP has been investigated among Austrian subpopulations over a 24-year period. Self-reported data from nationally representative health surveys (1983-2007) were analyzed and adjusted for self-report bias (N=121 486). Absolute changes (ACs) and aetiologic fractions (AF) were calculated to measure trends. To quantify the extent of social inequality, the relative index of inequality was computed based on educational levels. The prevalence of BP nearly doubled between 1983 and 2007. When investigating educational groups, subjects with low educational level were most prevalent. Obese persons generally showed higher rates of BP than non-obese subjects. Continuously rising trends across the different educational groups were more evident in men. The AC was highest in obese men with high education (+32.9%). Education-related inequalities for BP were more evident in men than women. Educational level is an important social indicator for BP. A gradient for low to high educational level in the trends of BP prevalence was clearly identified and stable only among men. We presume that the association 'education' and 'physical workload leading to BP' is more relevant for men than for women. The implementation of effective approaches to BP, in combination with target group-specific interventions focusing on educational status, is recommended. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Artificial neural network modeling of dissolved oxygen in reservoir.

PubMed

Chen, Wei-Bo; Liu, Wen-Cheng

2014-02-01

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Use of artificial neural network for spatial rainfall analysis

NASA Astrophysics Data System (ADS)

Paraskevas, Tsangaratos; Dimitrios, Rozos; Andreas, Benardos

2014-04-01

In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

Evaluation of factors associated with severe and frequent back pain in high school athletes

PubMed Central

Noll, Matias; Silveira, Erika Aparecida; de Avelar, Ivan Silveira

2017-01-01

Several studies have shown that half of all young athletes experience back pain (BP). However, high intensity and frequency of BP may be harmful, and the factors associated with BP severity have not been investigated in detail. Here, we investigated the factors associated with a high intensity and high frequency of BP in high school athletes. We included 251 athletes (173 boys and 78 girls [14–20 years old]) in this cross-sectional study. The dependent variables were a high frequency and high intensity of BP, and the independent variables were demographic, socioeconomic, psychosocial, hereditary, anthropometric, behavioural, and postural factors and the level of exercise. The effect measure is presented as prevalence ratio (PR) with 95% confidence interval (CI). Of 251 athletes, 104 reported BP; thus, only these athletes were included in the present analysis. Results of multivariable analysis showed an association between high BP intensity and time spent using a computer (PR: 1.15, CI: 1.01–1.33), posture while writing (PR: 1.41, CI: 1.27–1.58), and posture while using a computer (PR: 1.39, CI: 1.26–1.54). Multivariable analysis also revealed an association of high BP frequency with studying in bed (PR: 1.19, CI: 1.01–1.40) and the method of carrying a backpack (PR: 1.19, CI: 1.01–1.40). In conclusion, we found that behavioural and postural factors are associated with a high intensity and frequency of BP. To the best of our knowledge, this study is the first to compare different intensities and frequencies of BP, and our results may help physicians and coaches to better understand BP in high school athletes. PMID:28222141

Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks.

PubMed

Khatri, Krishan L; Tamil, Lakshman S

2018-01-01

Chronic respiratory diseases, mainly asthma and chronic obstructive pulmonary disease (COPD), affect the lives of people by limiting their activities in various aspects. Overcrowding of hospital emergency departments (EDs) due to respiratory diseases in certain weather and environmental pollution conditions results in the degradation of quality of medical care, and even limits its availability. A useful tool for ED managers would be to forecast peak demand days so that they can take steps to improve the availability of medical care. In this paper, we developed an artificial neural network based classifier using multilayer perceptron with back propagation algorithm that predicts peak event (peak demand days) of patients with respiratory diseases, mainly asthma and COPD visiting EDs in Dallas County of Texas in the United States. The precision and recall for peak event class were 77.1% and 78.0%, respectively, and those for nonpeak events were 83.9% and 83.2%, respectively. The overall accuracy of the system is 81.0%.

Prevalence of back pain in the community. A COPCORD-based study in the Mexican population.

PubMed

Peláez-Ballestas, Ingris; Flores-Camacho, Roxanna; Rodriguez-Amado, Jacqueline; Sanin, Luz Helena; Valerio, Jorge Esquivel; Navarro-Zarza, Eduardo; Flores, Diana; Rivas, Lourdes L; Casasola-Vargas, Julio; Burgos-Vargas, Ruben

2011-01-01

Back pain (BP) is frequent in the community; its prevalence in México is 6%. Our objective was to determine the prevalence of BP in Mexican communities and determine its most important characteristics. A cross-sectional study of individuals aged > 18 years was conducted in Mexico City and in urban communities in the state of Nuevo León. Sampling in Mexico City was based on community census and in Nuevo León, on stratified, balanced, and random sampling. Procedures included a door-to-door survey, using the Community Oriented Program for the Control of Rheumatic Diseases, to identify individuals with BP > 1 on a visual analog scale in the last 7 days. General practitioners/rheumatology fellows confirmed and characterized BP symptoms. In all, 8159 individuals (mean age 43.7 yrs, two-thirds female) were surveyed and 1219 had BP. The prevalence of nontraumatic BP in the last 7 days was 8.0% (95% CI 7.5-8.7). The mean age of these individuals was 42.7 years, and 61.9% were female. Thirty-seven percent had inflammatory BP [prevalence of 3.0% (95% CI 2.7-3.4)]. Compared with the state of Nuevo Léon, the characteristics and consequences of BP in Mexico City were more severe. In logistic regression analysis, living in Mexico City, having a paid job, any kind of musculoskeletal pain, high pain intensity, and obesity among other variables were associated with BP. The prevalence of nontraumatic BP in the last 7 days in urban communities in México is 8.0%. However, clinical features and consequences differed among the communities studied, suggesting a role for local factors in BP.

BP180 dysfunction triggers spontaneous skin inflammation in mice.

PubMed

Zhang, Yang; Hwang, Bin-Jin; Liu, Zhen; Li, Ning; Lough, Kendall; Williams, Scott E; Chen, Jinbo; Burette, Susan W; Diaz, Luis A; Su, Maureen A; Xiao, Shengxiang; Liu, Zhi

2018-06-04

BP180, also known as collagen XVII, is a hemidesmosomal component and plays a key role in maintaining skin dermal/epidermal adhesion. Dysfunction of BP180, either through genetic mutations in junctional epidermolysis bullosa (JEB) or autoantibody insult in bullous pemphigoid (BP), leads to subepidermal blistering accompanied by skin inflammation. However, whether BP180 is involved in skin inflammation remains unknown. To address this question, we generated a BP180-dysfunctional mouse strain and found that mice lacking functional BP180 (termed Δ NC16A ) developed spontaneous skin inflammatory disease, characterized by severe itch, defective skin barrier, infiltrating immune cells, elevated serum IgE levels, and increased expression of thymic stromal lymphopoietin (TSLP). Severe itch is independent of adaptive immunity and histamine, but dependent on increased expression of TSLP by keratinocytes. In addition, a high TSLP expression is detected in BP patients. Our data provide direct evidence showing that BP180 regulates skin inflammation independently of adaptive immunity, and BP180 dysfunction leads to a TSLP-mediated itch. The newly developed mouse strain could be a model for elucidation of disease mechanisms and development of novel therapeutic strategies for skin inflammation and BP180-related skin conditions.

Gain assisted coherent control of microwave pulse in a one dimensional array of artificial atoms

NASA Astrophysics Data System (ADS)

Waqas, Mohsin; Ayaz, M. Q.; Waseem, M.; Qamar, Sajid; Qamar, Shahid

2018-06-01

We study the coherent propagation of a microwave pulse through a one-dimensional array of artificial atoms. The scheme is based upon gain assisted propagation of the pulse using two-photon Raman transition in a three-level superconducting artificial atoms (SAAs) coupled to a microwave transmission line. Our results show that the group velocity can be significantly reduced by increasing the Rabi frequency of the pump fields which in turn can lead to an efficient storage of the pulse inside a 1D array of SAAs. Further, the intensity of the transmitted pulse increases with the number of artificial atoms owing to the gain associated with the two-photon Raman transition. Our results also show that the window width decreases for both scattering and negligible scattering cases with the increase in the number of SAAs. The fidelity of the system also remains high even after the passage of the pulse through a large number of SAAs.

Analysis of propagation mechanisms of stimulation-induced fractures in rocks

NASA Astrophysics Data System (ADS)

Krause, Michael; Renner, Joerg

2016-04-01

Effectivity of geothermal energy production depends crucially on the heat exchange between the penetrated hot rock and the circulating water. Hydraulic stimulation of rocks at depth intends to create a network of fractures that constitutes a large area for exchange. Two endmembers of stimulation products are typically considered, tensile hydro-fractures that propagate in direction of the largest principal stress and pre-existing faults that are sheared when fluid pressure reduces the effective normal stress acting on them. The understanding of the propagation mechanisms of fractures under in-situ conditions is still incomplete despite intensive research over the last decades. Wing-cracking has been suggested as a mechanism of fracture extension from pre-existent faults with finite length that are induced to shear. The initiation and extension of the wings is believed to be in tensile mode. Open questions concern the variability of the nominal material property controlling tensile fracture initiation and extension, the mode I facture toughness KIC, with in-situ conditions, e.g., its mean-stress dependence. We investigated the fracture-propagation mechanism in different rocks (sandstones and granites) under varying conditions mimicking those representative for geothermal systems. To determine KIC-values we performed 3-point bending experiments. We varied the confining pressure, the piston velocity, and the position of the chevron notch relative to the loading configuration. Additional triaxial experiments at a range of confining pressures were performed to study wing crack propagation from artificial flaws whose geometrical characteristics, i.e., length, width, and orientation relative to the axial load are varied. We monitored acoustic emissions to constrain the spacio-temporal evolution of the fracturing. We found a significant effect of the length of the artificial flaw and the confining pressure on wing-crack initiation but did not observe a systematic dependence

A novel fiber-optical vibration defending system with on-line intelligent identification function

NASA Astrophysics Data System (ADS)

Wu, Huijuan; Xie, Xin; Li, Hanyu; Li, Xiaoyu; Wu, Yu; Gong, Yuan; Rao, Yunjiang

2013-09-01

Capacity of the sensor network is always a bottleneck problem for the novel FBG-based quasi-distributed fiberoptical defending system. In this paper, a highly sensitive sensing network with FBG vibration sensors is presented to relieve stress of the capacity and the system cost. However, higher sensitivity may cause higher Nuisance Alarm Rates (NARs) in practical uses. It is necessary to further classify the intrusion pattern or threat level and determine the validity of an unexpected event. Then an intelligent identification method is proposed by extracting the statistical features of the vibration signals in the time domain, and inputting them into a 3-layer Back-Propagation(BP) Artificial Neural Network to classify the events of interest. Experiments of both simulation and field tests are carried out to validate its effectiveness. The results show the recognition rate can be achieved up to 100% for the simulation signals and as high as 96.03% in the real tests.

[Study of building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm].

PubMed

Liang, Xue; Ji, Hai-yan; Wang, Peng-xin; Rao, Zhen-hong; Shen, Bing-hui

2010-01-01

Preprocess method of multiplicative scatter correction (MSC) was used to reject noises in the original spectra produced by the environmental physical factor effectively, then the principal components of near-infrared spectroscopy were calculated by nonlinear iterative partial least squares (NIPALS) before building the back propagation artificial neural networks method (BP-ANN), and the numbers of principal components were calculated by the method of cross validation. The calculated principal components were used as the inputs of the artificial neural networks model, and the artificial neural networks model was used to find the relation between chlorophyll in winter wheat and reflective spectrum, which can predict the content of chlorophyll in winter wheat. The correlation coefficient (r) of calibration set was 0.9604, while the standard deviation (SD) and relative standard deviation (RSD) was 0.187 and 5.18% respectively. The correlation coefficient (r) of predicted set was 0.9600, and the standard deviation (SD) and relative standard deviation (RSD) was 0.145 and 4.21% respectively. It means that the MSC-ANN algorithm can reject noises in the original spectra produced by the environmental physical factor effectively and set up an exact model to predict the contents of chlorophyll in living leaves veraciously to replace the classical method and meet the needs of fast analysis of agricultural products.

Propagation of back-arc extension in the arc of the southern New Hebrides Subduction Zone (South West Pacific) and possible relation to subduction initiation.

NASA Astrophysics Data System (ADS)

Fabre, M.; Patriat, M.; Collot, J.; Danyushevsky, L. V.; Meffre, S.; Falloon, T.; Rouillard, P.; Pelletier, B.; Roach, M. J.; Fournier, M.

2015-12-01

Geophysical data acquired during three expeditions of the R/V Southern Surveyor allows us to characterize the deformation of the upper plate at the southern termination of the New Hebrides subduction zone where it bends 90° eastward along the Hunter Ridge. As shown by GPS measurements and earthquake slip vectors systematically orthogonal to the trench, this 90° bend does not mark a transition from subduction to strike slip as usually observed at subduction termination. Here the convergence direction remains continuously orthogonal to the trench notwithstanding its bend. Multibeam bathymetric data acquired in the North Fiji Basin reveals active deformation and fragmentation of the upper plate. It shows the southward propagation of a N-S back-arc spreading ridge into the pre-existing volcanic arc, and the connection of the southern end of the spreading axis with an oblique active rift in the active arc. Ultimately the active arc lithosphere is sheared as spreading progressively supersedes rifting. Consequently to such incursion of back-arc basin extension into the arc, peeled off and drifted pieces of arc crust are progressively isolated into the back-arc basin. Another consequence is that the New Hebrides arc is split in two distinct microplates, which move independently relative to the lower plate, and thereby define two different subduction systems. We suggest arc fragmentation could be a consequence of the incipient collision of the Loyalty Ridge with the New Hebrides Arc. We further speculate that this kinematic change could have resulted, less than two million year ago, in the initiation of a new subduction orthogonal to the New Hebrides Subduction possibly along the paleo STEP fault. In this geodynamic setting, with an oceanic lithosphere subducting beneath a sheared volcanic arc, a particularly wide range of primitive subduction-related magmas have been produced including adakites, island arc tholeiites, back-arc basin basalts, and medium-K subduction

BP Reg Experiment Operations

NASA Image and Video Library

2015-04-07

ISS043E091755 (04/07/2015) --- Expedition 43 Commander Terry Virts is seen here working inside of the Columbus laboratory on the Blood Pressure Regulation (BP Reg) experiment. Astronauts returning from long-duration space flights risk experiencing dizziness or fainting when they stand immediately after returning to Earth. This has an important health risk as it reduces the potential for astronauts to safely escape from an emergency situation. BP Reg will help researchers develop appropriate countermeasures so that astronauts returning from long-duration space flights will have very low risk of experiencing dizziness or fainting when they return to Earth.

BP Reg Experiment Operations

NASA Image and Video Library

2015-04-07

ISS043E091740 (04/07/2015) --- Expedition 43 Commander Terry Virts is seen here working inside of the Columbus laboratory on the Blood Pressure Regulation (BP Reg) experiment. Astronauts returning from long-duration space flights risk experiencing dizziness or fainting when they stand immediately after returning to Earth. This has an important health risk as it reduces the potential for astronauts to safely escape from an emergency situation. BP Reg will help researchers develop appropriate countermeasures so that astronauts returning from long-duration space flights will have very low risk of experiencing dizziness or fainting when they return to Earth.

Systematic Testing of Belief-Propagation Estimates for Absolute Free Energies in Atomistic Peptides and Proteins.

PubMed

Donovan-Maiye, Rory M; Langmead, Christopher J; Zuckerman, Daniel M

2018-01-09

Motivated by the extremely high computing costs associated with estimates of free energies for biological systems using molecular simulations, we further the exploration of existing "belief propagation" (BP) algorithms for fixed-backbone peptide and protein systems. The precalculation of pairwise interactions among discretized libraries of side-chain conformations, along with representation of protein side chains as nodes in a graphical model, enables direct application of the BP approach, which requires only ∼1 s of single-processor run time after the precalculation stage. We use a "loopy BP" algorithm, which can be seen as an approximate generalization of the transfer-matrix approach to highly connected (i.e., loopy) graphs, and it has previously been applied to protein calculations. We examine the application of loopy BP to several peptides as well as the binding site of the T4 lysozyme L99A mutant. The present study reports on (i) the comparison of the approximate BP results with estimates from unbiased estimators based on the Amber99SB force field; (ii) investigation of the effects of varying library size on BP predictions; and (iii) a theoretical discussion of the discretization effects that can arise in BP calculations. The data suggest that, despite their approximate nature, BP free-energy estimates are highly accurate-indeed, they never fall outside confidence intervals from unbiased estimators for the systems where independent results could be obtained. Furthermore, we find that libraries of sufficiently fine discretization (which diminish library-size sensitivity) can be obtained with standard computing resources in most cases. Altogether, the extremely low computing times and accurate results suggest the BP approach warrants further study.

Multielement fingerprinting as a tool in origin authentication of PGI food products: Tropea red onion.

PubMed

Furia, Emilia; Naccarato, Attilio; Sindona, Giovanni; Stabile, Gaetano; Tagarelli, Antonio

2011-08-10

Tropea red onion ( Allium cepa L. var. Tropea) is among the most highly appreciated Italian products. It is cultivated in specific areas of Calabria and, due to its characteristics, was recently awarded with the protected geographical indications (PGI) certification from the European Union. A reliable classification of onion samples in groups corresponding to "Tropea" and "non-Tropea" categories is now available to the producers. This important goal has been achieved through the evaluation of three supervised chemometric approaches. Onion samples with PGI brand (120) and onion samples not cultivated following the production regulations (80) were digested by a closed-vessel microwave oven system. ICP-MS equipped with a dynamic reaction cell was used to determine the concentrations of 25 elements (Al, Ba, Ca, Cd, Ce, Cr, Dy, Eu, Fe, Ga, Gd, Ho, La, Mg, Mn, Na, Nd, Ni, Pr, Rb, Sm, Sr, Tl, Y, and Zn). The multielement fingerprint was processed using linear discriminant analysis (LDA) (standard and stepwise), soft independent modeling of class analogy (SIMCA), and back-propagation artificial neural network (BP-ANN). The cross-validation procedure has shown good results in terms of the prediction ability for all of the chemometric models: standard LDA, 94.0%; stepwise LDA, 94.5%; SIMCA, 95.5%; and BP-ANN, 91.5%.

Fuzzy Emotional Semantic Analysis and Automated Annotation of Scene Images

PubMed Central

Cao, Jianfang; Chen, Lichao

2015-01-01

With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance. PMID:25838818

Pacific lamprey artificial propogation and rearing investigations: Rocky Reach Lamprey Management Plan

USGS Publications Warehouse

,; ,; ,; ,; ,

2011-01-01

The impetus for developing this document is through implementing the Rocky Reach Pacific Lamprey Management Plan (PLMP), a component of the Rocky Reach Comprehensive Settlement Agreement, both of which are discussed more thoroughly in Section 1.2. The ultimate goal of the PLMP is to achieve No Net Impact (NNI) to Pacific lamprey of ongoing operations of the Rocky Reach Hydroelectric Project. Conducting artificial propagation of Pacific lamprey was considered by the state and federal fishery agencies and Tribes that are parties to the Settlement Agreement as a potential Protection, Mitigation, and Enhancement measure (PME) for achieving NNI during the term of the current Rocky Reach license. This document is intended to provide guidance as to the feas ibility of culturing Pacific lamprey, the associated facilities necessary for culture practices, and identifying uncertainties for monitoring culture efficacy and rationale for implementing Pacific lamprey artificial propagation

Forecasting the portuguese stock market time series by using artificial neural networks

NASA Astrophysics Data System (ADS)

Isfan, Monica; Menezes, Rui; Mendes, Diana A.

2010-04-01

In this paper, we show that neural networks can be used to uncover the non-linearity that exists in the financial data. First, we follow a traditional approach by analysing the deterministic/stochastic characteristics of the Portuguese stock market data and some typical features are studied, like the Hurst exponents, among others. We also simulate a BDS test to investigate nonlinearities and the results are as expected: the financial time series do not exhibit linear dependence. Secondly, we trained four types of neural networks for the stock markets and used the models to make forecasts. The artificial neural networks were obtained using a three-layer feed-forward topology and the back-propagation learning algorithm. The quite large number of parameters that must be selected to develop a neural network forecasting model involves some trial and as a consequence the error is not small enough. In order to improve this we use a nonlinear optimization algorithm to minimize the error. Finally, the output of the 4 models is quite similar, leading to a qualitative forecast that we compare with the results of the application of k-nearest-neighbor for the same time series.

Microdeletion/microduplication of proximal 15q11.2 between BP1 and BP2: a susceptibility region for neurological dysfunction including developmental and language delay.

PubMed

Burnside, Rachel D; Pasion, Romela; Mikhail, Fady M; Carroll, Andrew J; Robin, Nathaniel H; Youngs, Erin L; Gadi, Inder K; Keitges, Elizabeth; Jaswaney, Vikram L; Papenhausen, Peter R; Potluri, Venkateswara R; Risheg, Hiba; Rush, Brooke; Smith, Janice L; Schwartz, Stuart; Tepperberg, James H; Butler, Merlin G

2011-10-01

The proximal long arm of chromosome 15 has segmental duplications located at breakpoints BP1-BP5 that mediate the generation of NAHR-related microdeletions and microduplications. The classical Prader-Willi/Angelman syndrome deletion is flanked by either of the proximal BP1 or BP2 breakpoints and the distal BP3 breakpoint. The larger Type I deletions are flanked by BP1 and BP3 in both Prader-Willi and Angelman syndrome subjects. Those with this deletion are reported to have a more severe phenotype than individuals with either Type II deletions (BP2-BP3) or uniparental disomy 15. The BP1-BP2 region spans approximately 500 kb and contains four evolutionarily conserved genes that are not imprinted. Reports of mutations or disturbed expression of these genes appear to impact behavioral and neurological function in affected individuals. Recently, reports of deletions and duplications flanked by BP1 and BP2 suggest an association with speech and motor delays, behavioral problems, seizures, and autism. We present a large cohort of subjects with copy number alteration of BP1 to BP2 with common phenotypic features. These include autism, developmental delay, motor and language delays, and behavioral problems, which were present in both cytogenetic groups. Parental studies demonstrated phenotypically normal carriers in several instances, and mildly affected carriers in others, complicating phenotypic association and/or causality. Possible explanations for these results include reduced penetrance, altered gene dosage on a particular genetic background, or a susceptibility region as reported for other areas of the genome implicated in autism and behavior disturbances.

Profile of NF-κBp(65/NFκBp50) among prostate specific antigen sera levels in prostatic pathologies.

PubMed

Bouraoui, Y; Ben Jemaa, A; Rodriguez, G; Ben Rais, N; Fraile, B; Paniagua, R; Sellemi, S; Royuela, M; Oueslati, R

2012-10-01

The aim of this work was to characterise the immunoexpression of NF-κB (p50/p65) in human prostatic pathologies and to study its profiles of activation among sera prostate specific antigen antigen (PSA) according the three groups: 0-4ng/mL, 4-20ng/mL and >20ng/mL. Twenty-four men with benign prostate hyperplasia (BPH); 19 men with prostate cancer (PC) and five men with normal prostates (NP). Immunohistochemical and western blot analysis was performed. Serum levels of PSA were assayed by immulite autoanalyser. In BPH and PC samples, immunoexpressions were observed for NF-κBp65 and NF-κBp50; while in NP samples, only were detected NF-κBp50. PC samples showed immunoreactions to NF-κBp65 and NF-κBp50 more intense (respectively 24.18±0.67 and 28.23±2.01) than that observed in BPH samples (respectively18.46±2.04 and 18.66±1.59) with special localisation in the nucleus. Different profiles of NF-κBp65 immunoexpressions were observed and BPH patients with sera PSA levels between 0-4ng/mL presented a significant weak percentage compared to BPH patients with sera PSA levels between 4-20ng/mL and >20ng/mL. No immunoreactions to NF-κBp65 were observed in PC patients with sera PSA levels between 4-20ng/mL. The sensibility of both NF-κB and PSA to inflammation allowed confirming the relationship between these two molecules and its involvement in prostatic diseases progression (inflammatory and neoplasic). Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Artificial neural network in predicting craniocervical junction injury: an alternative approach to trauma patients.

PubMed

Bektaş, Frat; Eken, Cenker; Soyuncu, Secgin; Kilicaslan, Isa; Cete, Yildiray

2008-12-01

The aim of this study is to determine the efficiency of artificial intelligence in detecting craniocervical junction injuries by using an artificial neural network (ANN) that may be applicable in future studies of different traumatic injuries. Major head trauma patients with Glasgow Coma Scale back propagation ANN and a stepwise forward logistic regression were performed to test the performances of all models. A total of 127 patients fulfilling inclusion criteria were included in the study. The mean age of the study patients was 31+/-17.7, 77.2% (n=98) of them were male, 13.4% of the patients (n=17) had craniocervical junction pathologies. About 64.7% (n=11) of these pathologies were detected only by CT; 23.5% (n=4) of them by both craniocervical CT and cervical plain radiography; and 11.8% (n=2) of them only by cervical plain radiography. A logistic regression model had a sensitivity of 11.8% and specificity of 99.1%. Positive predictive value was 66.7% and negative predictive value was 87.9%. Area under the curve for logistic regression model was 0.794 (P=0.000). ANN had a sensitivity of 82.4% and specificity of 100%. Positive predictive value was 100% and negative predictive value was 97.3%. Area under the curve for ANN model was 0.912 (P=0.000). ANN as an artificial intelligence application seems appropriate for detecting and excluding craniocervical junction injury but it should not replace craniocervical junction CT. However, these findings should lead us to test the applicability of ANN on hard-to-diagnose trauma patients or in constructing clinical decision rules.

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.

Modelling local GPS/levelling geoid undulations using artificial neural networks

NASA Astrophysics Data System (ADS)

Kavzoglu, T.; Saka, M. H.

2005-04-01

The use of GPS for establishing height control in an area where levelling data are available can involve the so-called GPS/levelling technique. Modelling of the GPS/levelling geoid undulations has usually been carried out using polynomial surface fitting, least-squares collocation (LSC) and finite-element methods. Artificial neural networks (ANNs) have recently been used for many investigations, and proven to be effective in solving complex problems represented by noisy and missing data. In this study, a feed-forward ANN structure, learning the characteristics of the training data through the back-propagation algorithm, is employed to model the local GPS/levelling geoid surface. The GPS/levelling geoid undulations for Istanbul, Turkey, were estimated from GPS and precise levelling measurements obtained during a field study in the period 1998-99. The results are compared to those produced by two well-known conventional methods, namely polynomial fitting and LSC, in terms of root mean square error (RMSE) that ranged from 3.97 to 5.73 cm. The results show that ANNs can produce results that are comparable to polynomial fitting and LSC. The main advantage of the ANN-based surfaces seems to be the low deviations from the GPS/levelling data surface, which is particularly important for distorted levelling networks.

Artificial neural network modelling of uncertainty in gamma-ray spectrometry

NASA Astrophysics Data System (ADS)

Dragović, S.; Onjia, A.; Stanković, S.; Aničin, I.; Bačić, G.

2005-03-01

An artificial neural network (ANN) model for the prediction of measuring uncertainties in gamma-ray spectrometry was developed and optimized. A three-layer feed-forward ANN with back-propagation learning algorithm was used to model uncertainties of measurement of activity levels of eight radionuclides ( 226Ra, 238U, 235U, 40K, 232Th, 134Cs, 137Cs and 7Be) in soil samples as a function of measurement time. It was shown that the neural network provides useful data even from small experimental databases. The performance of the optimized neural network was found to be very good, with correlation coefficients ( R2) between measured and predicted uncertainties ranging from 0.9050 to 0.9915. The correlation coefficients did not significantly deteriorate when the network was tested on samples with greatly different uranium-to-thorium ( 238U/ 232Th) ratios. The differences between measured and predicted uncertainties were not influenced by the absolute values of uncertainties of measured radionuclide activities. Once the ANN is trained, it could be employed in analyzing soil samples regardless of the 238U/ 232Th ratio. It was concluded that a considerable saving in time could be obtained using the trained neural network model for predicting the measurement times needed to attain the desired statistical accuracy.

Microscopic artificial swimmers

NASA Astrophysics Data System (ADS)

Dreyfus, Rémi; Baudry, Jean; Roper, Marcus L.; Fermigier, Marc; Stone, Howard A.; Bibette, Jérôme

2005-10-01

Microorganisms such as bacteria and many eukaryotic cells propel themselves with hair-like structures known as flagella, which can exhibit a variety of structures and movement patterns. For example, bacterial flagella are helically shaped and driven at their bases by a reversible rotary engine, which rotates the attached flagellum to give a motion similar to that of a corkscrew. In contrast, eukaryotic cells use flagella that resemble elastic rods and exhibit a beating motion: internally generated stresses give rise to a series of bends that propagate towards the tip. In contrast to this variety of swimming strategies encountered in nature, a controlled swimming motion of artificial micrometre-sized structures has not yet been realized. Here we show that a linear chain of colloidal magnetic particles linked by DNA and attached to a red blood cell can act as a flexible artificial flagellum. The filament aligns with an external uniform magnetic field and is readily actuated by oscillating a transverse field. We find that the actuation induces a beating pattern that propels the structure, and that the external fields can be adjusted to control the velocity and the direction of motion.

The impact of the BP Baker report.

PubMed

Rodríguez, Jennifer M; Payne, Stephanie C; Bergman, Mindy E; Beus, Jeremy M

2011-06-01

This study examined the impact of the British Petroleum (BP) Baker Panel Report, reviewing the March 2005 BP-Texas City explosion, on the field of process safety. Three hundred eighty-four subscribers of a process safety listserv responded to a survey two years after the BP Baker Report was published. Results revealed respondents in the field of process safety are familiar with the BP Baker Report, feel it is important to the future safety of chemical processing, and believe that the findings are generalizable to other plants beyond BP-Texas City. Respondents indicated that few organizations have administered the publicly available BP Process Safety Culture Survey. Our results also showed that perceptions of contractors varied depending on whether respondents were part of processing organizations (internal perspective) or government or consulting agencies (external perspective). This research provides some insight into the beliefs of chemical processing personnel regarding the transportability and generalizability of lessons learned from one organization to another. This study has implications for both organizational scientists and engineers in that it reveals perceptions about the primary mechanism used to share lessons learned within one industry about one major catastrophe (i.e., investigation reports). This study provides preliminary information about the perceived impact of a report such as this one. Copyright © 2011 National Safety Council and Elsevier Ltd. All rights reserved.

Improved belief propagation algorithm finds many Bethe states in the random-field Ising model on random graphs

NASA Astrophysics Data System (ADS)

Perugini, G.; Ricci-Tersenghi, F.

2018-01-01

We first present an empirical study of the Belief Propagation (BP) algorithm, when run on the random field Ising model defined on random regular graphs in the zero temperature limit. We introduce the notion of extremal solutions for the BP equations, and we use them to fix a fraction of spins in their ground state configuration. At the phase transition point the fraction of unconstrained spins percolates and their number diverges with the system size. This in turn makes the associated optimization problem highly non trivial in the critical region. Using the bounds on the BP messages provided by the extremal solutions we design a new and very easy to implement BP scheme which is able to output a large number of stable fixed points. On one hand this new algorithm is able to provide the minimum energy configuration with high probability in a competitive time. On the other hand we found that the number of fixed points of the BP algorithm grows with the system size in the critical region. This unexpected feature poses new relevant questions about the physics of this class of models.

Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia

PubMed Central

Rene, Eldon R.; López, M. Estefanía; Kim, Jung Hoon; Park, Hung Suck

2013-01-01

Lab scale studies were conducted to evaluate the performance of two simultaneously operated immobilized cell biofilters (ICBs) for removing hydrogen sulphide (H2S) and ammonia (NH3) from gas phase. The removal efficiencies (REs) of the biofilter treating H2S varied from 50 to 100% at inlet loading rates (ILRs) varying up to 13 g H2S/m3 ·h, while the NH3 biofilter showed REs ranging from 60 to 100% at ILRs varying between 0.5 and 5.5 g NH3/m3 ·h. An application of the back propagation neural network (BPNN) to predict the performance parameter, namely, RE (%) using this experimental data is presented in this paper. The input parameters to the network were unit flow (per min) and inlet concentrations (ppmv), respectively. The accuracy of BPNN-based model predictions were evaluated by providing the trained network topology with a test dataset and also by calculating the regression coefficient (R 2) values. The results from this predictive modeling work showed that BPNNs were able to predict the RE of both the ICBs efficiently. PMID:24307999

Belief propagation decoding of quantum channels by passing quantum messages

NASA Astrophysics Data System (ADS)

Renes, Joseph M.

2017-07-01

The belief propagation (BP) algorithm is a powerful tool in a wide range of disciplines from statistical physics to machine learning to computational biology, and is ubiquitous in decoding classical error-correcting codes. The algorithm works by passing messages between nodes of the factor graph associated with the code and enables efficient decoding of the channel, in some cases even up to the Shannon capacity. Here we construct the first BP algorithm which passes quantum messages on the factor graph and is capable of decoding the classical-quantum channel with pure state outputs. This gives explicit decoding circuits whose number of gates is quadratic in the code length. We also show that this decoder can be modified to work with polar codes for the pure state channel and as part of a decoder for transmitting quantum information over the amplitude damping channel. These represent the first explicit capacity-achieving decoders for non-Pauli channels.

Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

NASA Astrophysics Data System (ADS)

Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

2018-05-01

The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions

NASA Astrophysics Data System (ADS)

Aksoy, Hafzullah; Dahamsheh, Ahmad

2018-07-01

For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions.

Effects of intraduodenal administration of the artificial sweetener sucralose on blood pressure and superior mesenteric artery blood flow in healthy older subjects.

PubMed

Pham, Hung T; Stevens, Julie E; Rigda, Rachael S; Phillips, Liza K; Wu, Tongzhi; Hausken, Trygve; Soenen, Stijn; Visvanathan, Renuka; Rayner, Christopher K; Horowitz, Michael; Jones, Karen L

2018-06-06

Postprandial hypotension (PPH) occurs frequently, particularly in older people and those with type 2 diabetes, and is associated with increased morbidity and mortality. The magnitude of the decrease in blood pressure (BP) induced by carbohydrate, fat, and protein appears to be comparable and results from the interaction of macronutrients with the small intestine, including an observed stimulation of mesenteric blood flow. It is not known whether artificial sweeteners, such as sucralose, which are widely used, affect BP. The aim of this study was to evaluate the effects of intraduodenal sucralose on BP and superior mesenteric artery (SMA) blood flow, compared with intraduodenal glucose and saline (control), in healthy older subjects. Twelve healthy subjects (6 men, 6 women; aged 66-79 y) were studied on 3 separate occasions in a randomized, double-blind, crossover design. After an overnight fast, subjects had concurrent measurements of BP and heart rate (HR; automated device), SMA blood flow (Doppler ultrasound), and blood glucose (glucometer) during intraduodenal infusion of 1) glucose (25% wt:vol, ∼1400 mOsmol/L), 2) sucralose (4 mmol/L, ∼300 mOsmol/L), or 3) saline (0.9% wt:vol, ∼300 mOsmol/L) at a rate of 3 mL/min for 60 min followed by intraduodenal saline for a further 60 min. There was a decrease in mean arterial BP (P < 0.001) during intraduodenal glucose [baseline (mean ± SEM): 91.7 ± 2.6 mm Hg compared with t = 60 min: 85.9 ± 2.8 mm Hg] but not during intraduodenal saline or intraduodenal sucralose. The HR (P < 0.0001) and SMA blood flow (P < 0.0001) also increased during intraduodenal glucose but not during intraduodenal saline or intraduodenal sucralose. As expected, blood glucose concentrations increased in response to glucose (P < 0.0001) but not saline or sucralose. In healthy older subjects, intraduodenal administration of the artificial sweetener sucralose was not associated with changes in BP or SMA blood flow. Further

Global Discrete Artificial Boundary Conditions for Time-Dependent Wave Propagation

NASA Astrophysics Data System (ADS)

Ryaben'kii, V. S.; Tsynkov, S. V.; Turchaninov, V. I.

2001-12-01

We construct global artificial boundary conditions (ABCs) for the numerical simulation of wave processes on unbounded domains using a special nondeteriorating algorithm that has been developed previously for the long-term computation of wave-radiation solutions. The ABCs are obtained directly for the discrete formulation of the problem; in so doing, neither a rational approximation of “nonreflecting kernels” nor discretization of the continuous boundary conditions is required. The extent of temporal nonlocality of the new ABCs appears fixed and limited; in addition, the ABCs can handle artificial boundaries of irregular shape on regular grids with no fitting/adaptation needed and no accuracy loss induced. The nondeteriorating algorithm, which is the core of the new ABCs, is inherently three-dimensional, it guarantees temporally uniform grid convergence of the solution driven by a continuously operating source on arbitrarily long time intervals and provides unimprovable linear computational complexity with respect to the grid dimension. The algorithm is based on the presence of lacunae, i.e., aft fronts of the waves, in wave-type solutions in odd-dimensional spaces. It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the unmodified scheme. In this paper, we delineate the construction of the global lacunae-based ABCs in the framework of a discretized wave equation. The ABCs are obtained for the most general formulation of the problem that involves radiation of waves by moving sources (e.g., radiation of acoustic waves by a maneuvering aircraft). We also present systematic numerical results that corroborate the theoretical design properties of the ABC algorithm.

Global Discrete Artificial Boundary Conditions for Time-Dependent Wave Propagation

NASA Technical Reports Server (NTRS)

Ryabenkii, V. S.; Tsynkov, S. V.; Turchaninov, V. I.; Bushnell, Dennis M. (Technical Monitor)

2001-01-01

We construct global artificial boundary conditions (ABCs) for the numerical simulation of wave processes on unbounded domains using a special non-deteriorating algorithm that has been developed previously for the long-term computation of wave-radiation solutions. The ABCs are obtained directly for the discrete formulation of the problem; in so doing, neither a rational approximation of 'non-reflecting kernels,' nor discretization of the continuous boundary conditions is required. The extent of temporal nonlocality of the new ABCs appears fixed and limited; in addition, the ABCs can handle artificial boundaries of irregular shape on regular grids with no fitting/adaptation needed and no accuracy loss induced. The non-deteriorating algorithm, which is the core of the new ABCs is inherently three-dimensional, it guarantees temporally uniform grid convergence of the solution driven by a continuously operating source on arbitrarily long time intervals, and provides unimprovable linear computational complexity with respect to the grid dimension. The algorithm is based on the presence of lacunae, i.e., aft fronts of the waves, in wave-type solutions in odd-dimension spaces, It can, in fact, be built as a modification on top of any consistent and stable finite-difference scheme, making its grid convergence uniform in time and at the same time keeping the rate of convergence the same as that of the non-modified scheme. In the paper, we delineate the construction of the global lacunae-based ABCs in the framework of a discretized wave equation. The ABCs are obtained for the most general formulation of the problem that involves radiation of waves by moving sources (e.g., radiation of acoustic waves by a maneuvering aircraft). We also present systematic numerical results that corroborate the theoretical design properties of the ABCs' algorithm.

Numerical study of the generation and propagation of ultralow-frequency waves by artificial ionospheric F region modulation at different latitudes

NASA Astrophysics Data System (ADS)

Xu, Xiang; Zhou, Chen; Shi, Run; Ni, Binbin; Zhao, Zhengyu; Zhang, Yuannong

2016-09-01

Powerful high-frequency (HF) radio waves can be used to efficiently modify the upper-ionospheric plasmas of the F region. The pressure gradient induced by modulated electron heating at ultralow-frequency (ULF) drives a local oscillating diamagnetic ring current source perpendicular to the ambient magnetic field, which can act as an antenna radiating ULF waves. In this paper, utilizing the HF heating model and the model of ULF wave generation and propagation, we investigate the effects of both the background ionospheric profiles at different latitudes in the daytime and nighttime ionosphere and the modulation frequency on the process of the HF modulated heating and the subsequent generation and propagation of artificial ULF waves. Firstly, based on a relation among the radiation efficiency of the ring current source, the size of the spatial distribution of the modulated electron temperature and the wavelength of ULF waves, we discuss the possibility of the effects of the background ionospheric parameters and the modulation frequency. Then the numerical simulations with both models are performed to demonstrate the prediction. Six different background parameters are used in the simulation, and they are from the International Reference Ionosphere (IRI-2012) model and the neutral atmosphere model (NRLMSISE-00), including the High Frequency Active Auroral Research Program (HAARP; 62.39° N, 145.15° W), Wuhan (30.52° N, 114.32° E) and Jicamarca (11.95° S, 76.87° W) at 02:00 and 14:00 LT. A modulation frequency sweep is also used in the simulation. Finally, by analyzing the numerical results, we come to the following conclusions: in the nighttime ionosphere, the size of the spatial distribution of the modulated electron temperature and the ground magnitude of the magnetic field of ULF wave are larger, while the propagation loss due to Joule heating is smaller compared to the daytime ionosphere; the amplitude of the electron temperature oscillation decreases with

The comparison of performance by using alternative refrigerant R152a in automobile climate system with different artificial neural network models

NASA Astrophysics Data System (ADS)

Kalkisim, A. T.; Hasiloglu, A. S.; Bilen, K.

2016-04-01

Due to the refrigerant gas R134a which is used in automobile air conditioning systems and has greater global warming impact will be phased out gradually, an alternative gas is being desired to be used without much change on existing air conditioning systems. It is aimed to obtain the easier solution for intermediate values on the performance by creating a Neural Network Model in case of using a fluid (R152a) in automobile air conditioning systems that has the thermodynamic properties close to each other and near-zero global warming impact. In this instance, a network structure giving the most accurate result has been established by identifying which model provides the best education with which network structure and makes the most accurate predictions in the light of the data obtained after five different ANN models was trained with three different network structures. During training of Artificial Neural Network, Quick Propagation, Quasi-Newton, Levenberg-Marquardt and Conjugate Gradient Descent Batch Back Propagation methodsincluding five inputs and one output were trained with various network structures. Over 1500 iterations have been evaluated and the most appropriate model was identified by determining minimum error rates. The accuracy of the determined ANN model was revealed by comparing with estimates made by the Multi-Regression method.

Geometric Aspects of Artificial Ionospheric Layers Driven by High-Power HF-Heating

NASA Astrophysics Data System (ADS)

Milikh, G. M.; Eliasson, B.; Shao, X.; Djordjevic, B.; Mishin, E. V.; Zawdie, K.; Papadopoulos, K.

2013-12-01

We have generalized earlier developed multi-scale dynamic model for the creation and propagation of artificial plasma layers in the ionosphere [Eliasson et al, 2012] by including two dimensional effects in the horizontal direction. Such layers were observed during high-power high frequency HF heating experiments at HAARP [Pedersen et al., 2010]. We have numerically investigated the importance of different angles of incidence of ordinary mode waves on the Langmuir turbulence and the resulting electron acceleration that leads to the formation of artificial ionospheric layers. It was shown that the most efficient electron acceleration and subsequent ionization is obtained at angles between magnetic zenith and the vertical, where strong Langmuir turbulence dominates over weak turbulence. A role played by the heating wave propagation near caustics was also investigated. Eliasson, B. et al. (2012), J. Geophys. Res. 117, A10321, doi:10.1029/2012JA018105. Pedersen, T., et al. (2010), Geophys. Res. Lett., 37, L02106, doi:10.1029/2009GL041895.

GalaxyDock BP2 score: a hybrid scoring function for accurate protein-ligand docking

NASA Astrophysics Data System (ADS)

Baek, Minkyung; Shin, Woong-Hee; Chung, Hwan Won; Seok, Chaok

2017-07-01

Protein-ligand docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein-ligand docking. Many previously developed docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein-ligand docking program, it outperformed other state-of-the-art docking programs in docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html.

Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network

NASA Astrophysics Data System (ADS)

Qiu, Feng; Dai, Guang; Zhang, Ying

According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status are confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information, and online testing information are established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information is 94 %. The evaluation model can evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.

Biosolar cells: global artificial photosynthesis needs responsive matrices with quantum coherent kinetic control for high yield.

PubMed

Purchase, R L; de Groot, H J M

2015-06-06

This contribution discusses why we should consider developing artificial photosynthesis with the tandem approach followed by the Dutch BioSolar Cells consortium, a current operational paradigm for a global artificial photosynthesis project. We weigh the advantages and disadvantages of a tandem converter against other approaches, including biomass. Owing to the low density of solar energy per unit area, artificial photosynthetic systems must operate at high efficiency to minimize the land (or sea) area required. In particular, tandem converters are a much better option than biomass for densely populated countries and use two photons per electron extracted from water as the raw material into chemical conversion to hydrogen, or carbon-based fuel when CO2 is also used. For the average total light sum of 40 mol m(-2) d(-1) for The Netherlands, the upper limits are many tons of hydrogen or carbon-based fuel per hectare per year. A principal challenge is to forge materials for quantitative conversion of photons to chemical products within the physical limitation of an internal potential of ca 2.9 V. When going from electric charge in the tandem to hydrogen and back to electricity, only the energy equivalent to 1.23 V can be stored in the fuel and regained. A critical step is then to learn from nature how to use the remaining difference of ca 1.7 V effectively by triple use of one overpotential for preventing recombination, kinetic stabilization of catalytic intermediates and finally generating targeted heat for the release of oxygen. Probably the only way to achieve this is by using bioinspired responsive matrices that have quantum-classical pathways for a coherent conversion of photons to fuels, similar to what has been achieved by natural selection in evolution. In appendix A for the expert, we derive a propagator that describes how catalytic reactions can proceed coherently by a convergence of time scales of quantum electron dynamics and classical nuclear dynamics. We

Biosolar cells: global artificial photosynthesis needs responsive matrices with quantum coherent kinetic control for high yield

PubMed Central

Purchase, R. L.; de Groot, H. J. M.

2015-01-01

This contribution discusses why we should consider developing artificial photosynthesis with the tandem approach followed by the Dutch BioSolar Cells consortium, a current operational paradigm for a global artificial photosynthesis project. We weigh the advantages and disadvantages of a tandem converter against other approaches, including biomass. Owing to the low density of solar energy per unit area, artificial photosynthetic systems must operate at high efficiency to minimize the land (or sea) area required. In particular, tandem converters are a much better option than biomass for densely populated countries and use two photons per electron extracted from water as the raw material into chemical conversion to hydrogen, or carbon-based fuel when CO2 is also used. For the average total light sum of 40 mol m−2 d−1 for The Netherlands, the upper limits are many tons of hydrogen or carbon-based fuel per hectare per year. A principal challenge is to forge materials for quantitative conversion of photons to chemical products within the physical limitation of an internal potential of ca 2.9 V. When going from electric charge in the tandem to hydrogen and back to electricity, only the energy equivalent to 1.23 V can be stored in the fuel and regained. A critical step is then to learn from nature how to use the remaining difference of ca 1.7 V effectively by triple use of one overpotential for preventing recombination, kinetic stabilization of catalytic intermediates and finally generating targeted heat for the release of oxygen. Probably the only way to achieve this is by using bioinspired responsive matrices that have quantum–classical pathways for a coherent conversion of photons to fuels, similar to what has been achieved by natural selection in evolution. In appendix A for the expert, we derive a propagator that describes how catalytic reactions can proceed coherently by a convergence of time scales of quantum electron dynamics and classical nuclear dynamics

Prediction of hot deformation behavior of high phosphorus steel using artificial neural network

NASA Astrophysics Data System (ADS)

Singh, Kanchan; Rajput, S. K.; Soota, T.; Verma, Vijay; Singh, Dharmendra

2018-03-01

To predict the hot deformation behavior of high phosphorus steel, the hot compression experiments were performed with the help of thermo-mechanical simulator Gleeble® 3800 in the temperatures ranging from 750 °C to 1050 °C and strain rates of 0.001 s-1, 0.01 s-1, 0.1 s-1, 0.5 s-1, 1.0 s-1 and 10 s-1. The experimental stress-strain data are employed to develop artificial neural network (ANN) model and their predictability. Using different combination of temperature, strain and strain rate as a input parameter and obtained experimental stress as a target, a multi-layer ANN model based on feed-forward back-propagation algorithm is trained, to predict the flow stress for a given processing condition. The relative error between predicted and experimental stress are in the range of ±3.5%, whereas the correlation coefficient (R2) of training and testing data are 0.99986 and 0.99999 respectively. This shows that a well-trained ANN model has excellent capability to predict the hot deformation behavior of materials. Comparative study shows quite good agreement of predicted and experimental values.

Uncertainty Analyses for Back Projection Methods

NASA Astrophysics Data System (ADS)

Zeng, H.; Wei, S.; Wu, W.

2017-12-01

So far few comprehensive error analyses for back projection methods have been conducted, although it is evident that high frequency seismic waves can be easily affected by earthquake depth, focal mechanisms and the Earth's 3D structures. Here we perform 1D and 3D synthetic tests for two back projection methods, MUltiple SIgnal Classification (MUSIC) (Meng et al., 2011) and Compressive Sensing (CS) (Yao et al., 2011). We generate synthetics for both point sources and finite rupture sources with different depths, focal mechanisms, as well as 1D and 3D structures in the source region. The 3D synthetics are generated through a hybrid scheme of Direct Solution Method and Spectral Element Method. Then we back project the synthetic data using MUSIC and CS. The synthetic tests show that the depth phases can be back projected as artificial sources both in space and time. For instance, for a source depth of 10km, back projection gives a strong signal 8km away from the true source. Such bias increases with depth, e.g., the error of horizontal location could be larger than 20km for a depth of 40km. If the array is located around the nodal direction of direct P-waves the teleseismic P-waves are dominated by the depth phases. Therefore, back projections are actually imaging the reflection points of depth phases more than the rupture front. Besides depth phases, the strong and long lasted coda waves due to 3D effects near trench can lead to additional complexities tested here. The strength contrast of different frequency contents in the rupture models also produces some variations to the back projection results. In the synthetic tests, MUSIC and CS derive consistent results. While MUSIC is more computationally efficient, CS works better for sparse arrays. In summary, our analyses indicate that the impact of various factors mentioned above should be taken into consideration when interpreting back projection images, before we can use them to infer the earthquake rupture physics.

Black Phosphorus (BP) Nanodots for Potential Biomedical Applications.

PubMed

Lee, Hyun Uk; Park, So Young; Lee, Soon Chang; Choi, Saehae; Seo, Soonjoo; Kim, Hyeran; Won, Jonghan; Choi, Kyuseok; Kang, Kyoung Suk; Park, Hyun Gyu; Kim, Hee-Sik; An, Ha Rim; Jeong, Kwang-Hun; Lee, Young-Chul; Lee, Jouhahn

2016-01-13

Recently, the appeal of 2D black phosphorus (BP) has been rising due to its unique optical and electronic properties with a tunable band gap (≈0.3-1.5 eV). While numerous research efforts have recently been devoted to nano- and optoelectronic applications of BP, no attention has been paid to promising medical applications. In this article, the preparation of BP-nanodots of a few nm to <20 nm with an average diameter of ≈10 nm and height of ≈8.7 nm is reported by a modified ultrasonication-assisted solution method. Stable formation of nontoxic phosphates and phosphonates from BP crystals with exposure in water or air is observed. As for the BP-nanodot crystals' stability (ionization and persistence of fluorescent intensity) in aqueous solution, after 10 d, ≈80% at 1.5 mg mL(-1) are degraded (i.e., ionized) in phosphate buffered saline. They showed no or little cytotoxic cell-viability effects in vitro involving blue- and green-fluorescence cell imaging. Thus, BP-nanodots can be considered a promising agent for drug delivery or cellular tracking systems. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.

PubMed

Xie, Suchao; Zhou, Hui

2014-01-01

In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times

Modeling the Effects of Cu Content and Deformation Variables on the High-Temperature Flow Behavior of Dilute Al-Fe-Si Alloys Using an Artificial Neural Network.

PubMed

Shakiba, Mohammad; Parson, Nick; Chen, X-Grant

2016-06-30

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002-0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C-550 °C) and strain rates (0.01-10 s -1 ). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress.

Clearance Rate and BP-ANN Model in Paraquat Poisoned Patients Treated with Hemoperfusion

PubMed Central

Hu, Lufeng; Hong, Guangliang; Ma, Jianshe; Wang, Xianqin; Lin, Guanyang; Zhang, Xiuhua; Lu, Zhongqiu

2015-01-01

In order to investigate the effect of hemoperfusion (HP) on the clearance rate of paraquat (PQ) and develop a clearance model, 41 PQ-poisoned patients who acquired acute PQ intoxication received HP treatment. PQ concentrations were determined by high performance liquid chromatography (HPLC). According to initial PQ concentration, study subjects were divided into two groups: Low-PQ group (0.05–1.0 μg/mL) and High-PQ group (1.0–10 μg/mL). After initial HP treatment, PQ concentrations decreased in both groups. However, in the High-PQ group, PQ levels remained in excess of 0.05 μg/mL and increased when the second HP treatment was initiated. Based on the PQ concentrations before and after HP treatment, the mean clearance rate of PQ calculated was 73 ± 15%. We also established a backpropagation artificial neural network (BP-ANN) model, which set PQ concentrations before HP treatment as input data and after HP treatment as output data. When it is used to predict PQ concentration after HP treatment, high prediction accuracy (R = 0.9977) can be obtained in this model. In conclusion, HP is an effective way to clear PQ from the blood, and the PQ concentration after HP treatment can be predicted by BP-ANN model. PMID:25695058

Assembly of human C-terminal binding protein (CtBP) into tetramers.

PubMed

Bellesis, Andrew G; Jecrois, Anne M; Hayes, Janelle A; Schiffer, Celia A; Royer, William E

2018-06-08

C-terminal binding protein 1 (CtBP1) and CtBP2 are transcriptional coregulators that repress numerous cellular processes, such as apoptosis, by binding transcription factors and recruiting chromatin-remodeling enzymes to gene promoters. The NAD(H)-linked oligomerization of human CtBP is coupled to its co-transcriptional activity, which is implicated in cancer progression. However, the biologically relevant level of CtBP assembly has not been firmly established; nor has the stereochemical arrangement of the subunits above that of a dimer. Here, multi-angle light scattering (MALS) data established the NAD + - and NADH-dependent assembly of CtBP1 and CtBP2 into tetramers. An examination of subunit interactions within CtBP1 and CtBP2 crystal lattices revealed that both share a very similar tetrameric arrangement resulting from assembly of two dimeric pairs, with specific interactions probably being sensitive to NAD(H) binding. Creating a series of mutants of both CtBP1 and CtBP2, we tested the hypothesis that the crystallographically observed interdimer pairing stabilizes the solution tetramer. MALS data confirmed that these mutants disrupt both CtBP1 and CtBP2 tetramers, with the dimer generally remaining intact, providing the first stereochemical models for tetrameric assemblies of CtBP1 and CtBP2. The crystal structure of a subtle destabilizing mutant suggested that small structural perturbations of the hinge region linking the substrate- and NAD-binding domains are sufficient to weaken the CtBP1 tetramer. These results strongly suggest that the tetramer is important in CtBP function, and the series of CtBP mutants reported here can be used to investigate the physiological role of the tetramer. © 2018 Bellesis et al.

Risk factors for low back pain and sciatica in elderly men-the MrOS Sweden study.

PubMed

Kherad, Mehrsa; Rosengren, Björn E; Hasserius, Ralph; Nilsson, Jan-Åke; Redlund-Johnell, Inga; Ohlsson, Claes; Mellström, Dan; Lorentzon, Mattiaz; Ljunggren, Östen; Karlsson, Magnus K

2017-01-08

The aim of this study was to identify whether factors beyond anatomical abnormalities are associated with low back pain (LBP) and LBP with sciatica (SCI) in older men. Mister Osteoporosis Sweden includes 3,014 men aged 69–81 years. They answered questionnaires on lifestyle and whether they had experienced LBP and SCI during the preceding 12 months. About 3,007 men answered the back pain (BP) questions, 258 reported BP without specified region. We identified 1,388 with no BP, 1,361 with any LBP (regardless of SCI), 1,074 of those with LBP also indicated if they had experienced LBP (n = 615), LBP+SCI (n = 459). About 49% of those with LBP and 54% of those with LBP+SCI rated their health as poor/very poor (P < 0.001). Men with any LBP to a greater extent than those without BP had poor self-estimated health, depressive symptoms, dizziness, fall tendency, serious comorbidity (diabetes, stroke, coronary heart disease, pulmonary disease and/or cancer) (all P < 0.001), foreign background, were smokers (all P < 0.01), had low physical activity and used walking aids (all P < 0.05). Men with LBP+SCI to a greater extent than those with LBP had lower education, lower self-estimated health, comorbidity, dizziness and used walking aids (all P < 0.001). In older men with LBP and SCI, anatomical abnormalities such as vertebral fractures, metastases, central or lateral spinal stenosis or degenerative conditions may only in part explain prevalent symptoms and disability. Social and lifestyle factors must also be evaluated since they are associated not only with unspecific LBP but also with LBP with SCI.

Around and beyond 53BP1 Nuclear Bodies.

PubMed

Fernandez-Vidal, Anne; Vignard, Julien; Mirey, Gladys

2017-12-05

Within the nucleus, sub-nuclear domains define territories where specific functions occur. Nuclear bodies (NBs) are dynamic structures that concentrate nuclear factors and that can be observed microscopically. Recently, NBs containing the p53 binding protein 1 (53BP1), a key component of the DNA damage response, were defined. Interestingly, 53BP1 NBs are visualized during G1 phase, in daughter cells, while DNA damage was generated in mother cells and not properly processed. Unlike most NBs involved in transcriptional processes, replication has proven to be key for 53BP1 NBs, with replication stress leading to the formation of these large chromatin domains in daughter cells. In this review, we expose the composition and organization of 53BP1 NBs and focus on recent findings regarding their regulation and dynamics. We then concentrate on the importance of the replication stress, examine the relation of 53BP1 NBs with DNA damage and discuss their dysfunction.

Around and beyond 53BP1 Nuclear Bodies

PubMed Central

Fernandez-Vidal, Anne; Vignard, Julien

2017-01-01

Within the nucleus, sub-nuclear domains define territories where specific functions occur. Nuclear bodies (NBs) are dynamic structures that concentrate nuclear factors and that can be observed microscopically. Recently, NBs containing the p53 binding protein 1 (53BP1), a key component of the DNA damage response, were defined. Interestingly, 53BP1 NBs are visualized during G1 phase, in daughter cells, while DNA damage was generated in mother cells and not properly processed. Unlike most NBs involved in transcriptional processes, replication has proven to be key for 53BP1 NBs, with replication stress leading to the formation of these large chromatin domains in daughter cells. In this review, we expose the composition and organization of 53BP1 NBs and focus on recent findings regarding their regulation and dynamics. We then concentrate on the importance of the replication stress, examine the relation of 53BP1 NBs with DNA damage and discuss their dysfunction. PMID:29206178

Modeling of frost crystal growth over a flat plate using artificial neural networks and fractal geometries

NASA Astrophysics Data System (ADS)

Tahavvor, Ali Reza

2017-03-01

In the present study artificial neural network and fractal geometry are used to predict frost thickness and density on a cold flat plate having constant surface temperature under forced convection for different ambient conditions. These methods are very applicable in this area because phase changes such as melting and solidification are simulated by conventional methods but frost formation is a most complicated phase change phenomenon consists of coupled heat and mass transfer. Therefore conventional mathematical techniques cannot capture the effects of all parameters on its growth and development because this process influenced by many factors and it is a time dependent process. Therefore, in this work soft computing method such as artificial neural network and fractal geometry are used to do this manner. The databases for modeling are generated from the experimental measurements. First, multilayer perceptron network is used and it is found that the back-propagation algorithm with Levenberg-Marquardt learning rule is the best choice to estimate frost growth properties due to accurate and faster training procedure. Second, fractal geometry based on the Von-Koch curve is used to model frost growth procedure especially in frost thickness and density. Comparison is performed between experimental measurements and soft computing methods. Results show that soft computing methods can be used more efficiently to determine frost properties over a flat plate. Based on the developed models, wide range of frost formation over flat plates can be determined for various conditions.

Radiowave propagation measurements in Nigeria (preliminary reports)

NASA Astrophysics Data System (ADS)

Falodun, S. E.; Okeke, P. N.

2013-07-01

International conferences on frequency coordination have, in recent years, required new information on radiowave propagation in tropical regions and, in particular, on propagation in Africa. The International Telecommunications Union (ITU-R) initiated `radio-wave propagation measurement campaign' in some African countries some years back. However, none of the ITU-initiated experiments were mounted in Nigeria, and hence, there is lack of adequate understanding of the propagation mechanisms associated with this region of the tropics. The Centre for Basic Space Science (CBSS) of NASRDA has therefore embarked on propagation data collection from the different climatic zones of Nigeria (namely Coastal, Guinea Savannah, Midland, and Sahelian) with the aim of making propagation data available to the ITU, for design and prediction purposes in order to ensure a qualitative and effective communication system in Nigeria. This paper focuses on the current status of propagation data from Nigeria (collected by CBSS), identifying other parameters that still need to be obtained. The centre has deployed weather stations to different locations in the country for refractivity measurements in clear atmosphere, at the ground surface and at an altitude of 100 m, being the average height of communication mast in Nigeria. Other equipments deployed are Micro Rain Radar and Nigerian Environmental and Climatic Observing Program equipments. Some of the locations of the measurement stations are Nsukka (7.4° E, 6.9° N), Akure (5.12° E, 7.15° N), Minna (6.5° E, 9.6° N), Sokoto (5.25° E, 13.08° N), Jos (8.9° E, 9.86° N), and Lagos (3.35° E, 6.6° N). The results obtained from the data analysis have shown that the refractivity values vary with climatic zones and seasons of the year. Also, the occurrence probability of abnormal propagation events, such as super refraction, sub-refraction, and ducting, depends on the location as well as the local time. We have also attempted to identify

Effectiveness and cost-effectiveness of a guided Internet- and mobile-based intervention for the indicated prevention of major depression in patients with chronic back pain-study protocol of the PROD-BP multicenter pragmatic RCT.

PubMed

Sander, L; Paganini, S; Lin, J; Schlicker, S; Ebert, D D; Buntrock, C; Baumeister, H

2017-01-21

Reducing the disease burden of major depressive disorder (MDD) is of major public health relevance. The prevention of depression is regarded as one possible approach to reach this goal. People with multiple risk factors for MDD such as chronic back pain and subthreshold depressive symptoms may benefit most from preventive measures. The Internet as intervention setting allows for scaling up preventive interventions on a public mental health level. This study is a multicenter pragmatic randomized controlled trial (RCT) of parallel design aiming to investigate the (cost-) effectiveness of an Internet- and mobile-based intervention (IMI) for the prevention of depression in chronic back pain patients (PROD-BP) with subthreshold depressive symptoms. eSano BackCare-DP is a guided, chronic back pain-specific depression prevention intervention based on cognitive behavioral therapy (CBT) principles comprising six weekly plus three optional modules and two booster sessions after completion of the intervention. Trained psychologists provide guidance by sending feedback messages after each module. A total of 406 patients with chronic back pain and without a depressive disorder at baseline will be recruited following orthopedic rehabilitation care and allocated to either intervention or treatment-as-usual (TAU). Primary patient-relevant endpoint of the trial is the time to onset of MDD measured by the telephone-administered Structured Clinical Interview for DSM (SCID) at baseline and 1-year post-randomization. Key secondary outcomes are health-related quality of life, depression severity, pain intensity, pain-related disability, ability to work, intervention satisfaction and adherence as well as side effects of the intervention. Online assessments take place at baseline and 9 weeks as well as 6 and 12 months post-randomization. Cox regression survival analysis will be conducted to estimate hazard ratio at 12-month follow-up. Moreover, an economic analysis will be conducted

Influence of Artisan Bakery- or Laboratory-Propagated Sourdoughs on the Diversity of Lactic Acid Bacterium and Yeast Microbiotas

PubMed Central

Minervini, Fabio; Lattanzi, Anna; De Angelis, Maria; Gobbetti, Marco

2012-01-01

Seven mature type I sourdoughs were comparatively back-slopped (80 days) at artisan bakery and laboratory levels under constant technology parameters. The cell density of presumptive lactic acid bacteria and related biochemical features were not affected by the environment of propagation. On the contrary, the number of yeasts markedly decreased from artisan bakery to laboratory propagation. During late laboratory propagation, denaturing gradient gel electrophoresis (DGGE) showed that the DNA band corresponding to Saccharomyces cerevisiae was no longer detectable in several sourdoughs. Twelve species of lactic acid bacteria were variously identified through a culture-dependent approach. All sourdoughs harbored a certain number of species and strains, which were dominant throughout time and, in several cases, varied depending on the environment of propagation. As shown by statistical permutation analysis, the lactic acid bacterium populations differed among sourdoughs propagated at artisan bakery and laboratory levels. Lactobacillus plantarum, Lactobacillus sakei, and Weissella cibaria dominated in only some sourdoughs back-slopped at artisan bakeries, and Leuconostoc citreum seemed to be more persistent under laboratory conditions. Strains of Lactobacillus sanfranciscensis were indifferently found in some sourdoughs. Together with the other stable species and strains, other lactic acid bacteria temporarily contaminated the sourdoughs and largely differed between artisan bakery and laboratory levels. The environment of propagation has an undoubted influence on the composition of sourdough yeast and lactic acid bacterium microbiotas. PMID:22635989

The new world atlas of artificial night sky brightness

PubMed Central

Falchi, Fabio; Cinzano, Pierantonio; Duriscoe, Dan; Kyba, Christopher C. M.; Elvidge, Christopher D.; Baugh, Kimberly; Portnov, Boris A.; Rybnikova, Nataliya A.; Furgoni, Riccardo

2016-01-01

Artificial lights raise night sky luminance, creating the most visible effect of light pollution—artificial skyglow. Despite the increasing interest among scientists in fields such as ecology, astronomy, health care, and land-use planning, light pollution lacks a current quantification of its magnitude on a global scale. To overcome this, we present the world atlas of artificial sky luminance, computed with our light pollution propagation software using new high-resolution satellite data and new precision sky brightness measurements. This atlas shows that more than 80% of the world and more than 99% of the U.S. and European populations live under light-polluted skies. The Milky Way is hidden from more than one-third of humanity, including 60% of Europeans and nearly 80% of North Americans. Moreover, 23% of the world’s land surfaces between 75°N and 60°S, 88% of Europe, and almost half of the United States experience light-polluted nights. PMID:27386582

The new world atlas of artificial night sky brightness.

PubMed

Falchi, Fabio; Cinzano, Pierantonio; Duriscoe, Dan; Kyba, Christopher C M; Elvidge, Christopher D; Baugh, Kimberly; Portnov, Boris A; Rybnikova, Nataliya A; Furgoni, Riccardo

2016-06-01

Artificial lights raise night sky luminance, creating the most visible effect of light pollution-artificial skyglow. Despite the increasing interest among scientists in fields such as ecology, astronomy, health care, and land-use planning, light pollution lacks a current quantification of its magnitude on a global scale. To overcome this, we present the world atlas of artificial sky luminance, computed with our light pollution propagation software using new high-resolution satellite data and new precision sky brightness measurements. This atlas shows that more than 80% of the world and more than 99% of the U.S. and European populations live under light-polluted skies. The Milky Way is hidden from more than one-third of humanity, including 60% of Europeans and nearly 80% of North Americans. Moreover, 23% of the world's land surfaces between 75°N and 60°S, 88% of Europe, and almost half of the United States experience light-polluted nights.

Rafflesia spp.: propagation and conservation.

PubMed

Wicaksono, Adhityo; Mursidawati, Sofi; Sukamto, Lazarus A; Teixeira da Silva, Jaime A

2016-08-01

The propagation of Rafflesia spp. is considered to be important for future development of ornamental and other applications. Thus far, the only successful propagation technique has been grafting. This mini-review succinctly emphasizes what is known about Rafflesia species. Members of the genus Rafflesia (Rafflesiaceae), which are holoparasitic plants known to grow on a host vine, Tetrastigma sp., are widely spread from the Malayan Peninsula to various islands throughout Indonesia. The plant's geographical distribution as well as many other aspects pertaining to the basic biology of this genus have still not been studied. The young flower buds and flowers of wild Rafflesia hasseltii Suringar, Rafflesia keithii Meijer and Rafflesia cantleyi Solms-Laubach are used in local (Malaysia and Indonesia) traditional ethnomedicine as wound-healing agents, but currently no formal published research exists to validate this property. To maintain a balance between its ethnomedicinal and ornamental use, and conservation, Rafflesia spp. must be artificially cultivated to prevent overexploitation. A successful method of vegetative propagation is by host grafting using Rafflesia-impregnated Tetrastigma onto the stem of a normal Tetrastigma plant. Due to difficulties with culture contamination in vitro, callus induction was only accomplished in 2010 for the first time when picloram and 2,4-D were added to a basal Murashige and Skoog medium, and the tissue culture of holoparasitic plants continues to be extremely difficult. Seeds harvested from fertile fruit may serve as a possible method to propagate Rafflesia spp. This paper provides a brief synthesis on what is known about research related to Rafflesia spp. The objective is to further stimulate researchers to examine, through rigorous scientific discovery, the mechanisms underlying the ethnomedicinal properties, the flowering mechanisms, and suitable in vitro regeneration protocols that would allow for the fortification of germplasm

Analytical modeling of flash-back phenomena. [premixed/prevaporized combustion system

NASA Technical Reports Server (NTRS)

Feng, C. C.

1979-01-01

To understand the flame flash-back phenomena more extensively, an analytical model was formed and a numerical program was written and tested to solve the set of differential equations describing the model. Results show that under a given set of conditions flame propagates in the boundary layer on a flat plate when the free stream is at or below 1.8 m/s.

Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data

NASA Astrophysics Data System (ADS)

Arel, Ersin

2012-06-01

The infamous soils of Adapazari, Turkey, that failed extensively during the 46-s long magnitude 7.4 earthquake in 1999 have since been the subject of a research program. Boreholes, piezocone soundings and voluminous laboratory testing have enabled researchers to apply sophisticated methods to determine the soil profiles in the city using the existing database. This paper describes the use of the artificial neural network (ANN) model to predict the complex soil profiles of Adapazari, based on cone penetration test (CPT) results. More than 3236 field CPT readings have been collected from 117 soundings spread over an area of 26 km2. An attempt has been made to develop the ANN model using multilayer perceptrons trained with a feed-forward back-propagation algorithm. The results show that the ANN model is fairly accurate in predicting complex soil profiles. Soil identification using CPT test results has principally been based on the Robertson charts. Applying neural network systems using the chart offers a powerful and rapid route to reliable prediction of the soil profiles.

Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.

PubMed

Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

2015-01-01

Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.

Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

PubMed Central

Liu, Zhijian; Liu, Kejun; Li, Hao; Zhang, Xinyu; Jin, Guangya; Cheng, Kewei

2015-01-01

Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08. PMID:26624613

Thermal Conductivity Prediction of Soil in Complex Plant Soil System using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Wardani, A. K.; Purqon, A.

2016-08-01

Thermal conductivity is one of thermal properties of soil in seed germination and plants growth. Different soil types have different thermal conductivity. One of soft-computing promising method to predict thermal conductivity of soil types is Artificial Neural Network (ANN). In this study, we estimate the thermal conductivity of soil prediction in a soil-plant complex systems using ANN. With a feed-forward multilayer trained with back-propagation with 4, 10 and 1 on the input, hidden and output layers respectively. Our input are heating time, temperature and thermal resistance with thermal conductivity of soil as a target. ANN prediction demonstrates a good agreement with Mean Squared Error-testing (MSEte) of 9.56 x 10-7 for soils with green beans and those of bare soils is 7.00 × 10-7 respectively Green beans grow only on black-clay soil with a thermal conductivity of 0.7 W/m K with a sufficient water content. Our results demonstrate that temperature, moisture content, colour, texture and structure of soil are greatly affect to the thermal conductivity of soil in seed germination and plant growth. In future, it is potentially applied to estimate more complex compositions of plant-soil systems.

Benzo[a]pyrene (BP) DNA adduct formation in DNA repair–deficient p53 haploinsufficient [Xpa(−/−)p53(+/−)] and wild-type mice fed BP and BP plus chlorophyllin for 28 days

PubMed Central

Poirier, Miriam C.

2012-01-01

We have evaluated DNA damage (DNA adduct formation) after feeding benzo[a]pyrene (BP) to wild-type (WT) and cancer-susceptible Xpa(−/−)p53(+/−) mice deficient in nucleotide excision repair and haploinsufficient for the tumor suppressor p53. DNA damage was evaluated by high-performance liquid chromatography/electrospray ionization tandem mass spectrometry (HPLC/ES-MS/MS), which measures r7,t8,t9-trihydroxy-c-10-(N 2-deoxyguanosyl)-7,8,9,10-tetrahydrobenzo[a]pyrene (BPdG), and a chemiluminescence immunoassay (CIA), using anti-r7,t8-dihydroxy-t-9,10-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BPDE)–DNA antiserum, which measures both BPdG and the other stable BP-DNA adducts. When mice were fed 100 ppm BP for 28 days, BP-induced DNA damage measured in esophagus, liver and lung was typically higher in Xpa(−/−)p53(+/−) mice, compared with WT mice. This result is consistent with the previously observed tumor susceptibility of Xpa(−/−)p53(+/−) mice. BPdG, the major DNA adduct associated with tumorigenicity, was the primary DNA adduct formed in esophagus (a target tissue in the mouse), whereas total BP-DNA adducts predominated in higher levels in the liver (a non-target tissue in the mouse). In an attempt to lower BP-induced DNA damage, we fed the WT and Xpa(−/−)p53(+/−) mice 0.3% chlorophyllin (CHL) in the BP-containing diet for 28 days. The addition of CHL resulted in an increase of BP–DNA adducts in esophagus, liver and lung of WT mice, a lowering of BPdG in esophagi of WT mice and livers of Xpa(−/−)p53(+/−) mice and an increase of BPdG in livers of WT mice. Therefore, the addition of CHL to a BP-containing diet showed a lack of consistent chemoprotective effect, indicating that oral CHL administration may not reduce PAH–DNA adduct levels consistently in human organs. PMID:22828138

RanBP9 at the intersection between cofilin and Aβ pathologies: rescue of neurodegenerative changes by RanBP9 reduction.

PubMed

Woo, J A; Boggess, T; Uhlar, C; Wang, X; Khan, H; Cappos, G; Joly-Amado, A; De Narvaez, E; Majid, S; Minamide, L S; Bamburg, J R; Morgan, D; Weeber, E; Kang, D E

2015-03-05

Molecular pathways underlying the neurotoxicity and production of amyloid β protein (Aβ) represent potentially promising therapeutic targets for Alzheimer's disease (AD). We recently found that overexpression of the scaffolding protein RanBP9 increases Aβ production in cell lines and in transgenic mice while promoting cofilin activation and mitochondrial dysfunction. Translocation of cofilin to mitochondria and induction of cofilin-actin pathology require the activation/dephosphorylation of cofilin by Slingshot homolog 1 (SSH1) and cysteine oxidation of cofilin. In this study, we found that endogenous RanBP9 positively regulates SSH1 levels and mediates Aβ-induced translocation of cofilin to mitochondria and induction of cofilin-actin pathology in cultured cells, primary neurons, and in vivo. Endogenous level of RanBP9 was also required for Aβ-induced collapse of growth cones in immature neurons (days in vitro 9 (DIV9)) and depletion of synaptic proteins in mature neurons (DIV21). In vivo, amyloid precursor protein (APP)/presenilin-1 (PS1) mice exhibited 3.5-fold increased RanBP9 levels, and RanBP9 reduction protected against cofilin-actin pathology, synaptic damage, gliosis, and Aβ accumulation associated with APP/PS1 mice. Brains slices derived from APP/PS1 mice showed significantly impaired long-term potentiation (LTP), and RanBP9 reduction significantly enhanced paired pulse facilitation and LTP, as well as partially rescued contextual memory deficits associated with APP/PS1 mice. Therefore, these results underscore the critical importance of endogenous RanBP9 not only in Aβ accumulation but also in mediating the neurotoxic actions of Aβ at the level of synaptic plasticity, mitochondria, and cofilin-actin pathology via control of the SSH1-cofilin pathway in vivo.

Air Monitoring Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Water Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Waste Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Air Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Sediment Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Combined wave propagation analysis of earthquake recordings from borehole and building sensors

NASA Astrophysics Data System (ADS)

Petrovic, B.; Parolai, S.; Dikmen, U.; Safak, E.; Moldobekov, B.; Orunbaev, S.

2015-12-01

In regions highly exposed to natural hazards, Early Warning Systems can play a central role in risk management and mitigation procedures. To improve at a relatively low cost the spatial resolution of regional earthquake early warning (EEW) systems, decentralized onsite EEW and building monitoring, a wireless sensing unit, the Self-Organizing Seismic Early Warning Information Network (SOSEWIN) was developed and further improved to include the multi-parameter acquisition. SOSEWINs working in continuous real time mode are currently tested on various sites. In Bishkek and Istanbul, an instrumented building is located close to a borehole equipped with downhole sensors. The joint data analysis of building and borehole earthquake recordings allows the study of the behavior of the building, characteristics of the soil, and soil-structure interactions. The interferometric approach applied to recordings of the building response is particularly suitable to characterize the wave propagation inside a building, including the propagation velocity of shear waves and attenuation. Applied to borehole sensors, it gives insights into velocity changes in different layers, reflections and mode conversion, and allows the estimation of the quality factor Qs. We used combined building and borehole data from the two test sites: 1) to estimate the characteristics of wave propagation through the building to the soil and back, and 2) to obtain an empirical insight into soil-structure interactions. The two test sites represent two different building and soil types, and soil structure impedance contrasts. The wave propagation through the soil to the building and back is investigated by the joint interferometric approach. The propagation of up and down-going waves through the building and soil is clearly imaged and the reflection of P and S waves from the earth surface and the top of the building identified. An estimate of the reflected and transmitted energy amounts is given, too.

Development of an Efficient Identifier for Nuclear Power Plant Transients Based on Latest Advances of Error Back-Propagation Learning Algorithm

NASA Astrophysics Data System (ADS)

Moshkbar-Bakhshayesh, Khalil; Ghofrani, Mohammad B.

2014-02-01

This study aims to improve the performance of nuclear power plants (NPPs) transients training and identification using the latest advances of error back-propagation (EBP) learning algorithm. To this end, elements of EBP, including input data, initial weights, learning rate, cost function, activation function, and weights updating procedure are investigated and an efficient neural network is developed. Usefulness of modular networks is also examined and appropriate identifiers, one for each transient, are employed. Furthermore, the effect of transient type on transient identifier performance is illustrated. Subsequently, the developed transient identifier is applied to Bushehr nuclear power plant (BNPP). Seven types of the plant events are probed to analyze the ability of the proposed identifier. The results reveal that identification occurs very early with only five plant variables, whilst in the previous studies a larger number of variables (typically 15 to 20) were required. Modular networks facilitated identification due to its sole dependency on the sign of each network output signal. Fast training of input patterns, extendibility for identification of more transients and reduction of false identification are other advantageous of the proposed identifier. Finally, the balance between the correct answer to the trained transients (memorization) and reasonable response to the test transients (generalization) is improved, meeting one of the primary design criteria of identifiers.

Variable Penetrance of the 15q11.2 BP1-BP2 Microduplication in a Family with Cognitive and Language Impairment

PubMed Central

Benítez-Burraco, Antonio; Barcos-Martínez, Montserrat; Espejo-Portero, Isabel; Jiménez-Romero, Salud

2017-01-01

The 15q11.2 BP1-BP2 region is found duplicated or deleted in people with cognitive, language, and behavioral impairment. We report on a family (a father and 3 male twin siblings) that presents with a duplication of the 15q11.2 BP1-BP2 region and a variable phenotype: the father and the fraternal twin are normal carriers, whereas the monozygotic twins exhibit severe language and cognitive delay as well as behavioral disturbances. The genes located within the duplicated region are involved in brain development and function, and some of them are related to language processing. The probands' phenotype may result from changes in the expression level of some of these genes important for cognitive development. PMID:28588435

Propagation of laser beams in scattering media.

PubMed

Zuev, V E; Kabanov, M V; Savelev, B A

1969-01-01

Experimental investigations have been undertaken of some aspects of the propagation of helium-neon gas laser radiation at lambda = 0.63 micro for different scattering media (artificial water fogs, wood smokes, model media). It has been shown that the attenuation coefficients practically coincide when coherent and incoherent radiation is scattered. The applicability limits of Bouguer-Beer's law for describing the attenuation of radiation in scattering media are investigated and the intensity of multiple forward-scattered light for different geometrical parameters of the source and radiation receiver are measured. The applicability of single scattering theory formulas for describing forward-scattered light intensity are discussed.

Injection and waveguiding properties in SU8 nanotubes for sub-wavelength regime propagation and nanophotonics integration

NASA Astrophysics Data System (ADS)

Bigeon, John; Huby, Nolwenn; Duvail, Jean-Luc; Bêche, Bruno

2014-04-01

We report photonic concepts related to injection and sub-wavelength propagation in nanotubes, an unusual but promising geometry for highly integrated photonic devices. Theoretical simulation by the finite domain time-dependent (FDTD) method was first used to determine the features of the direct light injection and sub-wavelength propagation regime within nanotubes. Then, the injection into nanotubes of SU8, a photoresist used for integrated photonics, was successfully achieved by using polymer microlensed fibers with a sub-micronic radius of curvature, as theoretically expected from FDTD simulations. The propagation losses in a single SU8 nanotube were determined by using a comprehensive set-up and a protocol for optical characterization. The attenuation coefficient has been evaluated at 1.25 dB mm-1 by a cut-back method transposed to such nanostructures. The mechanisms responsible for losses in nanotubes were identified with FDTD theoretical support. Both injection and cut-back methods developed here are compatible with any sub-micronic structures. This work on SU8 nanotubes suggests broader perspectives for future nanophotonics.

Injection and waveguiding properties in SU8 nanotubes for sub-wavelength regime propagation and nanophotonics integration.

PubMed

Bigeon, John; Huby, Nolwenn; Duvail, Jean-Luc; Bêche, Bruno

2014-05-21

We report photonic concepts related to injection and sub-wavelength propagation in nanotubes, an unusual but promising geometry for highly integrated photonic devices. Theoretical simulation by the finite domain time-dependent (FDTD) method was first used to determine the features of the direct light injection and sub-wavelength propagation regime within nanotubes. Then, the injection into nanotubes of SU8, a photoresist used for integrated photonics, was successfully achieved by using polymer microlensed fibers with a sub-micronic radius of curvature, as theoretically expected from FDTD simulations. The propagation losses in a single SU8 nanotube were determined by using a comprehensive set-up and a protocol for optical characterization. The attenuation coefficient has been evaluated at 1.25 dB mm(-1) by a cut-back method transposed to such nanostructures. The mechanisms responsible for losses in nanotubes were identified with FDTD theoretical support. Both injection and cut-back methods developed here are compatible with any sub-micronic structures. This work on SU8 nanotubes suggests broader perspectives for future nanophotonics.

Low field domain wall dynamics in artificial spin-ice basis structure

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

Kwon, J.; School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798; Goolaup, S.

2015-10-28

Artificial magnetic spin-ice nanostructures provide an ideal platform for the observation of magnetic monopoles. The formation of a magnetic monopole is governed by the motion of a magnetic charge carrier via the propagation of domain walls (DWs) in a lattice. To date, most experiments have been on the static visualization of DW propagation in the lattice. In this paper, we report on the low field dynamics of DW in a unit spin-ice structure measured by magnetoresistance changes. Our results show that reversible DW propagation can be initiated within the spin-ice basis. The initial magnetization configuration of the unit structure stronglymore » influences the direction of DW motion in the branches. Single or multiple domain wall nucleation can be induced in the respective branches of the unit spin ice by the direction of the applied field.« less

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

NASA Astrophysics Data System (ADS)

Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

2016-11-01

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Evidence that Ribulose 1,5-Bisphosphate (RuBP) Binds to Inactive Sites of RuBP Carboxylase in Vivo and an Estimate of the Rate Constant for Dissociation 1

PubMed Central

Cardon, Zoe G.; Mott, Keith A.

1989-01-01

The binding of ribulose 1,5-bisphosphate (RuBP) to inactive (noncarbamylated) sites of the enzyme RuBP carboxylase in vivo was investigated in Spinacia oleracea and Helianthus annuus. The concentrations of RuBP and inactive sites were determined in leaf tissue as a function of time after a change to darkness. RuBP concentrations fell rapidly after the change to darkness and were approximately equal to the concentration of inactive sites after 60 s. Variations in the concentration of inactive sites, which were induced by differences in the light intensity before the light-dark transition, correlated with the concentration of RuBP between 60 and 120 s after the change to darkness. These data are discussed as evidence that RuBP binds to inactive sites of RuBP carboxylase in vivo. After the concentration of RuBP fell below that of inactive sites (at times longer than 60 s of darkness), the decline in RuBP was logarithmic with time. This would be expected if the dissociation of RuBP from inactive sites controlled the decline in RuBP concentration. These data were used to estimate the rate constant for dissociation of RuBP from inactive sites in vivo. PMID:16666692

Autonomous self-configuration of artificial neural networks for data classification or system control

NASA Astrophysics Data System (ADS)

Fink, Wolfgang

2009-05-01

Artificial neural networks (ANNs) are powerful methods for the classification of multi-dimensional data as well as for the control of dynamic systems. In general terms, ANNs consist of neurons that are, e.g., arranged in layers and interconnected by real-valued or binary neural couplings or weights. ANNs try mimicking the processing taking place in biological brains. The classification and generalization capabilities of ANNs are given by the interconnection architecture and the coupling strengths. To perform a certain classification or control task with a particular ANN architecture (i.e., number of neurons, number of layers, etc.), the inter-neuron couplings and their accordant coupling strengths must be determined (1) either by a priori design (i.e., manually) or (2) using training algorithms such as error back-propagation. The more complex the classification or control task, the less obvious it is how to determine an a priori design of an ANN, and, as a consequence, the architecture choice becomes somewhat arbitrary. Furthermore, rather than being able to determine for a given architecture directly the corresponding coupling strengths necessary to perform the classification or control task, these have to be obtained/learned through training of the ANN on test data. We report on the use of a Stochastic Optimization Framework (SOF; Fink, SPIE 2008) for the autonomous self-configuration of Artificial Neural Networks (i.e., the determination of number of hidden layers, number of neurons per hidden layer, interconnections between neurons, and respective coupling strengths) for performing classification or control tasks. This may provide an approach towards cognizant and self-adapting computing architectures and systems.

Prediction of Weld Penetration in FCAW of HSLA steel using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Asl, Y. Dadgar; Mostafa, N. B.; Panahizadeh R., V.; Seyedkashi, S. M. H.

2011-01-01

Flux-cored arc welding (FCAW) is a semiautomatic or automatic arc welding process that requires a continuously-fed consumable tubular electrode containing a flux. The main FCAW process parameters affecting the depth of penetration are welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed. Shallow depth of penetration may contribute to failure of a welded structure since penetration determines the stress-carrying capacity of a welded joint. To avoid such occurrences; the welding process parameters influencing the weld penetration must be properly selected to obtain an acceptable weld penetration and hence a high quality joint. Artificial neural networks (ANN), also called neural networks (NN), are computational models used to express complex non-linear relationships between input and output data. In this paper, artificial neural network (ANN) method is used to predict the effects of welding current, arc voltage, nozzle-to-work distance, torch angle and welding speed on weld penetration depth in gas shielded FCAW of a grade of high strength low alloy steel. 32 experimental runs were carried out using the bead-on-plate welding technique. Weld penetrations were measured and on the basis of these 32 sets of experimental data, a feed-forward back-propagation neural network was created. 28 sets of the experiments were used as the training data and the remaining 4 sets were used for the testing phase of the network. The ANN has one hidden layer with eight neurons and is trained after 840 iterations. The comparison between the experimental results and ANN results showed that the trained network could predict the effects of the FCAW process parameters on weld penetration adequately.

Numerical analysis of back pressure equal channel angular pressing of an Al-Mg alloy

NASA Astrophysics Data System (ADS)

Comăneci, R.

2017-08-01

Ultrafine grain size provides enhanced mechanical and/or physical properties such as strength and high ductility, superplasticity at relatively low temperatures and high strain rate and better corrosion resistance. Well-known as one of the most promising and effective structure refining method among other severe plastic deformation (SPD) techniques, equal channel angular pressing (ECAP) has been intensively investigated due to spectacular improvements in structure and therefore properties of bulk ultrafine grained/nanostructured materials. A successful ECAP requires surpassing two obstacles: the necessary load level which directly affects tools and a favourable stress distribution so the material withstanding the accumulated strain of repeated deformation. Materials could withstand more passes if a back pressure (BP) is applied. In traditional ECAP, tensile stress along the contact surface between the work piece and the upper wall of the outlet channel leads to crack initiation, while in the presence of BP, a negative (compressive) stress appears during the process balancing the tensile stress. In this study a comparative tridimensional finite element analysis (FEA) is performed to evaluate the flow of an Al-Mg alloy depending on different BP levels and process parameters. The results in terms of load level and strain distribution show the influence of BP on the material behaviour, opening opportunities for industrial applications.

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks.

PubMed

Feng, Lei; Zhu, Susu; Lin, Fucheng; Su, Zhenzhu; Yuan, Kangpei; Zhao, Yiying; He, Yong; Zhang, Chu

2018-06-15

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

CacyBP/SIP promotes the proliferation of colon cancer cells

PubMed Central

Chen, Xiong; Wang, Jun; Lu, Yuanyuan; Zhang, Faming; Liu, Zhengxiong; Lei, Ting; Fan, Daiming

2017-01-01

CacyBP/SIP is a component of the ubiquitin pathway and is overexpressed in several transformed tumor tissues, including colon cancer, which is one of the most common cancers worldwide. It is unknown whether CacyBP/SIP promotes the proliferation of colon cancer cells. This study examined the expression level, subcellular localization, and binding activity of CacyBP/SIP in human colon cancer cells in the presence and absence of the hormone gastrin. We found that CacyBP/SIP was expressed in a high percentage of colon cancer cells, but not in normal colonic surface epithelium. CacyBP/SIP promoted the cell proliferation of colon cancer cells under both basal and gastrin stimulated conditions as shown by knockdown studies. Gastrin stimulation triggered the translocation of CacyBP/SIP to the nucleus, and enhanced interaction between CacyBP/SIP and SKP1, a key component of ubiquitination pathway which further mediated the proteasome-dependent degradation of p27kip1 protein. The gastrin induced reduction in p27kip1 was prevented when cells were treated with the proteasome inhibitor MG132. These results suggest that CacyBP/SIP may be promoting growth of colon cancer cells by enhancing ubiquitin-mediated degradation of p27kip1. PMID:28196083

50 CFR 17.62 - Permits for scientific purposes or for the enhancement of propagation or survival.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., or (ii) the wildlife or plant was bred in captivity, or artificially propagated, or was part of or..., BARTER, EXPORTATION, AND IMPORTATION OF WILDLIFE AND PLANTS (CONTINUED) ENDANGERED AND THREATENED WILDLIFE AND PLANTS Endangered Plants § 17.62 Permits for scientific purposes or for the enhancement of...

50 CFR 17.62 - Permits for scientific purposes or for the enhancement of propagation or survival.

Code of Federal Regulations, 2012 CFR

2012-10-01

..., or (ii) the wildlife or plant was bred in captivity, or artificially propagated, or was part of or..., BARTER, EXPORTATION, AND IMPORTATION OF WILDLIFE AND PLANTS (CONTINUED) ENDANGERED AND THREATENED WILDLIFE AND PLANTS Endangered Plants § 17.62 Permits for scientific purposes or for the enhancement of...

50 CFR 17.62 - Permits for scientific purposes or for the enhancement of propagation or survival.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., or (ii) the wildlife or plant was bred in captivity, or artificially propagated, or was part of or..., BARTER, EXPORTATION, AND IMPORTATION OF WILDLIFE AND PLANTS (CONTINUED) ENDANGERED AND THREATENED WILDLIFE AND PLANTS Endangered Plants § 17.62 Permits for scientific purposes or for the enhancement of...

50 CFR 17.62 - Permits for scientific purposes or for the enhancement of propagation or survival.

Code of Federal Regulations, 2013 CFR

2013-10-01

..., or (ii) the wildlife or plant was bred in captivity, or artificially propagated, or was part of or..., BARTER, EXPORTATION, AND IMPORTATION OF WILDLIFE AND PLANTS (CONTINUED) ENDANGERED AND THREATENED WILDLIFE AND PLANTS Endangered Plants § 17.62 Permits for scientific purposes or for the enhancement of...

Artificial neural networks for defining the water quality determinants of groundwater abstraction in coastal aquifer

NASA Astrophysics Data System (ADS)

Lallahem, S.; Hani, A.

2017-02-01

Water sustainability in the lower Seybouse River basin, eastern Algeria, must take into account the importance of water quantity and quality integration. So, there is a need for a better knowledge and understanding of the water quality determinants of groundwater abstraction to meet the municipal and agricultural uses. In this paper, the artificial neural network (ANN) models were used to model and predict the relationship between groundwater abstraction and water quality determinants in the lower Seybouse River basin. The study area chosen is the lower Seybouse River basin and real data were collected from forty five wells for reference year 2006. Results indicate that the feed-forward multilayer perceptron models with back-propagation are useful tools to define and prioritize the important water quality parameters of groundwater abstraction and use. The model evaluation shows that the correlation coefficients are more than 95% for training, verification and testing data. The model aims to link the water quantity and quality with the objective to strengthen the Integrated Water Resources Management approach. It assists water planners and managers to better assess the water quality parameters and progress towards the provision of appropriate quantities of water of suitable quality.

A Comparison of JPDA and Belief Propagation for Data Association in SSA

NASA Astrophysics Data System (ADS)

Rutten, M.; Williams, J.; Gordon, N.; Jah, M.; Baldwin, J.; Stauch, J.

2014-09-01

The process of initial orbit determination, or catalogue maintenance, using a set of unlabeled observations requires a method of choosing which observation was due to which object. Realities of imperfect sensors mean that the association must be made in the presence of both missed detections and false alarms. Data association is not only essential to processing observations it can also be one of the most significant computational bottlenecks. The constrained admissible region multiple hypothesis filter (CAR-MHF) is an algorithm for initial orbit determination using short-arc observations of space objects. CAR-MHF has used joint probabilistic data association (JPDA), a well-established approach to multi-target data association. A recent development in the target tracking literature is the use of graphical models to formulate data association problems. Using an approximate inference algorithm, belief propagation (BP), on the graphical model results in an algorithm this is both computationally efficient and accurate. This paper compares CAR-MHF using JPDA and CAR-MHF using BP for the problem of initial orbit determination on a set of deep-space objects. The results of the analysis will show that by using the BP algorithm there are significant gains in computational load without any statistically significant loss in overall performance of the orbit determination.

Neural Network Classifier Architectures for Phoneme Recognition. CRC Technical Note No. CRC-TN-92-001.

ERIC Educational Resources Information Center

Treurniet, William

A study applied artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base. A number of proposed network architectures were applied to the phoneme classification task, ranging from the simple feedforward multilayer network to more…

Randomized DNA libraries construction tool: a new 3-bp 'frequent cutter' TthHB27I/sinefungin endonuclease with chemically-induced specificity.

PubMed

Krefft, Daria; Papkov, Aliaksei; Prusinowski, Maciej; Zylicz-Stachula, Agnieszka; Skowron, Piotr M

2018-05-11

Acoustic or hydrodynamic shearing, sonication and enzymatic digestion are used to fragment DNA. However, these methods have several disadvantages, such as DNA damage, difficulties in fragmentation control, irreproducibility and under-representation of some DNA segments. The DNA fragmentation tool would be a gentle enzymatic method, offering cleavage frequency high enough to eliminate DNA fragments distribution bias and allow for easy control of partial digests. Only three such frequently cleaving natural restriction endonucleases (REases) were discovered: CviJI, SetI and FaiI. Therefore, we have previously developed two artificial enzymatic specificities, cleaving DNA approximately every ~ 3-bp: TspGWI/sinefungin (SIN) and TaqII/SIN. In this paper we present the third developed specificity: TthHB27I/SIN(SAM) - a new genomic tool, based on Type IIS/IIC/IIG Thermus-family REases-methyltransferases (MTases). In the presence of dimethyl sulfoxide (DMSO) and S-adenosyl-L-methionine (SAM) or its analogue SIN, the 6-bp cognate TthHB27I recognition sequence 5'-CAARCA-3' is converted into a combined 3.2-3.0-bp 'site' or its statistical equivalent, while a cleavage distance of 11/9 nt is retained. Protocols for various modes of limited DNA digestions were developed. In the presence of DMSO and SAM or SIN, TthHB27I is transformed from rare 6-bp cutter to a very frequent one, approximately 3-bp. Thus, TthHB27I/SIN(SAM) comprises a new tool in the very low-represented segment of such prototype REases specificities. Moreover, this modified TthHB27I enzyme is uniquely suited for controlled DNA fragmentation, due to partial DNA cleavage, which is an inherent feature of the Thermus-family enzymes. Such tool can be used for quasi-random libraries generation as well as for other DNA manipulations, requiring high frequency cleavage and uniform distribution of cuts along DNA.

Neural networks in data analysis and modeling for detecting littoral oil-spills by airborne laser fluorosensor remote sensing

NASA Astrophysics Data System (ADS)

Lin, Bin; An, Jubai; Brown, Carl E.; Chen, Weiwei

2003-05-01

In this paper an artificial neural network (ANN) approach, which is based on flexible nonlinear models for a very broad class of transfer functions, is applied for multi-spectral data analysis and modeling of airborne laser fluorosensor in order to differentiate between classes of oil on water surface. We use three types of algorithm: Perceptron Network, Back-Propagation (B-P) Network and Self-Organizing feature Maps (SOM) Network. Using the data in form of 64-channel spectra as inputs, the ANN presents the analysis and estimation results of the oil type on the basis of the type of background materials as outputs. The ANN is trained and tested using sample data set to the network. The results of the above 3 types of network are compared in this paper. It is proved that the training has developed a network that not only fits the training data, but also fits real-world data that the network will process operationally. The ANN model would play a significant role in the ocean oil-spill identification in the future.

[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.

Core reactivity estimation in space reactors using recurrent dynamic networks

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Tsai, Wei K.

1991-01-01

A recurrent multilayer perceptron network topology is used in the identification of nonlinear dynamic systems from only the input/output measurements. The identification is performed in the discrete time domain, with the learning algorithm being a modified form of the back propagation (BP) rule. The recurrent dynamic network (RDN) developed is applied for the total core reactivity prediction of a spacecraft reactor from only neutronic power level measurements. Results indicate that the RDN can reproduce the nonlinear response of the reactor while keeping the number of nodes roughly equal to the relative order of the system. As accuracy requirements are increased, the number of required nodes also increases, however, the order of the RDN necessary to obtain such results is still in the same order of magnitude as the order of the mathematical model of the system. It is believed that use of the recurrent MLP structure with a variety of different learning algorithms may prove useful in utilizing artificial neural networks for recognition, classification, and prediction of dynamic systems.

Rapid and non-destructive determination of rancidity levels in butter cookies by multi-spectral imaging.

PubMed

Xia, Qing; Liu, Changhong; Liu, Jinxia; Pan, Wenjuan; Lu, Xuzhong; Yang, Jianbo; Chen, Wei; Zheng, Lei

2016-03-30

Rancidity is an important attribute for quality assessment of butter cookies, while traditional methods for rancidity measurement are usually laborious, destructive and prone to operational error. In the present paper, the potential of applying multi-spectral imaging (MSI) technology with 19 wavelengths in the range of 405-970 nm to evaluate the rancidity in butter cookies was investigated. Moisture content, acid value and peroxide value were determined by traditional methods and then related with the spectral information by partial least squares regression (PLSR) and back-propagation artificial neural network (BP-ANN). The optimal models for predicting moisture content, acid value and peroxide value were obtained by PLSR. The correlation coefficient (r) obtained by PLSR models revealed that MSI had a perfect ability to predict moisture content (r = 0.909), acid value (r = 0.944) and peroxide value (r = 0.971). The study demonstrated that the rancidity level of butter cookies can be continuously monitored and evaluated in real-time by the multi-spectral imaging, which is of great significance for developing online food safety monitoring solutions. © 2015 Society of Chemical Industry.

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Cui, Y.; Long, D.; Hong, Y.; Zeng, C.; Han, Z.

2016-12-01

Reconstruction of FY-3B/MWRI soil moisture using an artificial neural network based on reconstructed MODIS optical products over the Tibetan Plateau Yaokui Cui, Di Long, Yang Hong, Chao Zeng, and Zhongying Han State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China Abstract: Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the world's third pole. Large-scale consistent and continuous soil moisture datasets are of importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is one of relatively new passive microwave products. The FY-3B/MWRI soil moisture product is reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo using different gap-filling methods. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and the NDVI, LST, and albedo, but also the relationship between the soil moisture and the four-dimensional variation using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R2 larger than 0.63, and RMSE less than 0.1 cm3 cm-3 and bias less than 0.07 cm3 cm-3 for both frozen and unfrozen periods, compared with in-situ measurements in the central TP. The reconstruction method is subsequently applied to generate spatially consistent and temporally continuous surface soil moisture over the TP. The reconstructed FY-3B/MWRI soil moisture product could be valuable in studying meteorology, hydrology, and agriculture over the TP. Keywords: FY-3B/MWRI; Soil moisture; Reconstruction; Tibetan

76 FR 69712 - Application To Export Electric Energy; BP Energy Company

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-09

... DEPARTMENT OF ENERGY [OE Docket No. EA-315-A] Application To Export Electric Energy; BP Energy.... SUMMARY: BP Energy Company (BP Energy) has applied to renew its authority to transmit electric energy from... BP Energy to transmit electric energy from the United States to Canada as a power marketer for a five...

Experimental study on the pressure wave propagation in the artificial arterial tree in brain

NASA Astrophysics Data System (ADS)

Shimada, Shinya; Tsurusaki, Ryo; Iwase, Fumiaki; Matsukawa, Mami; Lagrée, Pierre-Yves

2018-07-01

A pulse wave measurement is effective for the early detection of arteriosclerosis. The pulse wave consists of incident and reflected waves. The reflected wave of the pulse wave measured at the left common carotid artery seems to originate from the vascular beds in the brain. The aim of this study is to know if the reflected waves from the occlusions in cerebral arteries can affect the pulse waveform. The artificial arterial tree in the brain was therefore fabricated using polyurethane tubes. After investigating the effects of the bifurcation angle on the pulse waveform, we attempted to confirm whether the reflected waves from occlusions in the artificial arterial tree in the brain can be experimentally measured at the left common carotid artery. Results indicate that the bifurcation angle did not affect the pulse waveform, and that the reflected wave from an occlusion with a diameter of more than 1 mm in the brain could be observed.

Wombat reproduction (Marsupialia; Vombatidae): an update and future directions for the development of artificial breeding technology.

PubMed

Hogan, Lindsay A; Janssen, Tina; Johnston, Stephen D

2013-06-01

This review provides an update on what is currently known about wombat reproductive biology and reports on attempts made to manipulate and/or enhance wombat reproduction as part of the development of artificial reproductive technology (ART) in this taxon. Over the last decade, the logistical difficulties associated with monitoring a nocturnal and semi-fossorial species have largely been overcome, enabling new features of wombat physiology and behaviour to be elucidated. Despite this progress, captive propagation rates are still poor and there are areas of wombat reproductive biology that still require attention, e.g. further characterisation of the oestrous cycle and oestrus. Numerous advances in the use of ART have also been recently developed in the Vombatidae but despite this research, practical methods of manipulating wombat reproduction for the purposes of obtaining research material or for artificial breeding are not yet available. Improvement of the propagation, genetic diversity and management of wombat populations requires a thorough understanding of Vombatidae reproduction. While semen collection and cryopreservation in wombats is fairly straightforward there is currently an inability to detect, induce or synchronise oestrus/ovulation and this is an impeding progress in the development of artificial insemination in this taxon.

Habitat relationships of eastern red-backed salamanders (Plethodon cinereus) in Appalachian agroforestry and grazing systems

Treesearch

Breanna L. Riedel; Kevin R. Russell; W. Mark Ford; Katherine P. O' Neill; Harry W. Godwin

2008-01-01

Woodland salamander responses to either traditional grazing or silvopasture systems are virtually unknown. An information-theoretic modelling approach was used to evaluate responses of red-backed salamanders (Plethodon cinereus) to silvopasture and meadow conversions in southern West Virginia. Searches of area-constrained plots and artificial...

Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

PubMed

Eslamizadeh, Gholamhossein; Barati, Ramin

2017-05-01

Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

An Expedient Study on Back-Propagation (BPN) Neural Networks for Modeling Automated Evaluation of the Answers and Progress of Deaf Students' That Possess Basic Knowledge of the English Language and Computer Skills

NASA Astrophysics Data System (ADS)

Vrettaros, John; Vouros, George; Drigas, Athanasios S.

This article studies the expediency of using neural networks technology and the development of back-propagation networks (BPN) models for modeling automated evaluation of the answers and progress of deaf students' that possess basic knowledge of the English language and computer skills, within a virtual e-learning environment. The performance of the developed neural models is evaluated with the correlation factor between the neural networks' response values and the real value data as well as the percentage measurement of the error between the neural networks' estimate values and the real value data during its training process and afterwards with unknown data that weren't used in the training process.

4E-BP1 regulates the differentiation of white adipose tissue.

PubMed

Tsukiyama-Kohara, Kyoko; Katsume, Asao; Kimura, Kazuhiro; Saito, Masayuki; Kohara, Michinori

2013-07-01

4E Binding protein 1 (4E-BP1) suppresses translation initiation. The absence of 4E-BP1 drastically reduces the amount of adipose tissue in mice. To address the role of 4E-BP1 in adipocyte differentiation, we characterized 4E-BP1(-/-) mice in this study. The lack of 4E-BP1 decreased the amount of white adipose tissue and increased the amount of brown adipose tissue. In 4E-BP1(-/-) MEF cells, PPARγ coactivator 1 alpha (PGC-1α) expression increased and exogenous 4E-BP1 expression suppressed PGC-1α expression. The level of 4E-BP1 expression was higher in white adipocytes than in brown adipocytes and showed significantly greater up-regulation in white adipocytes than in brown adipocytes during preadipocyte differentiation into mature adipocytes. The amount of PGC-1α was consistently higher in HB cells (a brown preadipocyte cell line) than in HW cells (a white preadipocyte cell line) during differentiation. Moreover, the ectopic over-expression of 4E-BP1 suppressed PGC-1α expression in white adipocytes, but not in brown adipocytes. Thus, the results of our study indicate that 4E-BP1 may suppress brown adipocyte differentiation and PGC-1α expression in white adipose tissues. © 2013 The Authors Genes to Cells © 2013 by the Molecular Biology Society of Japan and Wiley Publishing Asia Pty Ltd.

50 CFR 23.47 - What are the requirements for export of an Appendix-I plant artificially propagated for...

Code of Federal Regulations, 2010 CFR

2010-10-01

... II. This means that an Appendix-I specimen originating from a commercial nursery that is registered... augmenting a nursery or commercial propagating operation, unless the specimen is pre-Convention (see § 23.45) or was propagated at a nursery that is registered with the CITES Secretariat or a commercial...

Modeling the Effects of Cu Content and Deformation Variables on the High-Temperature Flow Behavior of Dilute Al-Fe-Si Alloys Using an Artificial Neural Network

PubMed Central

Shakiba, Mohammad; Parson, Nick; Chen, X.-Grant

2016-01-01

The hot deformation behavior of Al-0.12Fe-0.1Si alloys with varied amounts of Cu (0.002–0.31 wt %) was investigated by uniaxial compression tests conducted at different temperatures (400 °C–550 °C) and strain rates (0.01–10 s−1). The results demonstrated that flow stress decreased with increasing deformation temperature and decreasing strain rate, while flow stress increased with increasing Cu content for all deformation conditions studied due to the solute drag effect. Based on the experimental data, an artificial neural network (ANN) model was developed to study the relationship between chemical composition, deformation variables and high-temperature flow behavior. A three-layer feed-forward back-propagation artificial neural network with 20 neurons in a hidden layer was established in this study. The input parameters were Cu content, temperature, strain rate and strain, while the flow stress was the output. The performance of the proposed model was evaluated using the K-fold cross-validation method. The results showed excellent generalization capability of the developed model. Sensitivity analysis indicated that the strain rate is the most important parameter, while the Cu content exhibited a modest but significant influence on the flow stress. PMID:28773658

Can Cell to Cell Thermal Runaway Propagation be Prevented in a Li-ion Battery Module?

NASA Technical Reports Server (NTRS)

Jeevarajan, Judith; Lopez, Carlos; Orieukwu, Josephat

2014-01-01

Increasing cell spacing decreased adjacent cell damage center dotElectrically connected adjacent cells drained more than physically adjacent cells center dotRadiant barrier prevents propagation when fully installed between BP cells center dotBP cells vent rapidly and expel contents at 100% SOC -Slower vent with flame/smoke at 50% -Thermal runaway event typically occurs at 160 degC center dotLG cells vent but do not expel contents -Thermal runaway event typically occurs at 200 degC center dotSKC LFP modules did not propagate; fuses on negative terminal of cell may provide a benefit in reducing cell to cell damage propagation. New requirement in NASA-Battery Safety Requirements document: JSC 20793 Rev C 5.1.5.1 Requirements - Thermal Runaway Propagation a. For battery designs greater than a 80-Wh energy employing high specific energy cells (greater than 80 watt-hours/kg, for example, lithium-ion chemistries) with catastrophic failure modes, the battery shall be evaluated to ascertain the severity of a worst-case single-cell thermal runaway event and the propensity of the design to demonstrate cell-to-cell propagation in the intended application and environment. NASA has traditionally addressed the threat of thermal runaway incidents in its battery deployments through comprehensive prevention protocols. This prevention-centered approach has included extensive screening for manufacturing defects, as well as robust battery management controls that prevent abuse-induced runaway even in the face of multiple system failures. This focused strategy has made the likelihood of occurrence of such an event highly improbable. b. The evaluation shall include all necessary analysis and test to quantify the severity (consequence) of the event in the intended application and environment as well as to identify design modifications to the battery or the system that could appreciably reduce that severity. In addition to prevention protocols, programs developing battery designs with

Deepwater BP Oil Spill Natural Resource Damage Assessment Update | NOAA

Science.gov Websites

Publications Press Releases Story Archive Home Deepwater BP Oil Spill Natural Resource Damage Assessment Update Deepwater BP Oil Spill Natural Resource Damage Assessment Update share Posted on July 7, 2011 | Assessment and Early Restoration Restoration Area Title: Deepwater BP Oil Spill Natural Resource Damage

Rehabilitation access and effectiveness for persons with back pain: the protocol of a cohort study (REHAB-BP, DRKS00011554).

PubMed

Bethge, Matthias; Mattukat, Kerstin; Fauser, David; Mau, Wilfried

2017-07-14

Back pain is one of the most common chronic diseases in Germany and has a major impact on work ability and social participation. The German Pension Insurance (GPI) is the main provider of medical rehabilitation to improve work ability and prevent disability pensions in Germany. However, over half of the persons granted a disability pension have never used a medical rehabilitation service. Furthermore, evidence on the effects of medical rehabilitation in Germany is inconclusive. Consequently, this study has two aims: first, to determine barriers to using rehabilitation services, and second, to examine the effectiveness of medical rehabilitation in German residents with chronic back pain. In 2017 a postal questionnaire will be sent to 45,000 persons aged 45 to 59 years whose pension insurance contributions are managed by the GPI North or the GPI Central Germany. In 2019 respondents who report back pain in the first survey (n = 5760 expected) will be sent a second questionnaire. Individuals will be eligible for the first survey if they are employed, have neither used nor applied for a rehabilitation programme during the last 4 years and neither received nor applied for a disability pension. The sample will be drawn randomly from the registers of the GPI North (n = 22,500) and the GPI Central Germany (n = 22,500) and stratified by sex and duration of sickness absence benefits. Barriers to rehabilitation services will be related to socio-demographic and social characteristics, pain and attitudes to pain, health and health behaviour, healthcare utilisation, experiences and cognitions about rehabilitation services and job conditions. Propensity score matched analyses will be used to examine the effectiveness of rehabilitation services. Data on use of medical rehabilitation will be extracted from administrative records. The primary outcome is pain disability. Secondary outcomes are pain intensity and days of disability, pain self-efficacy, fear avoidance beliefs

Estimation of biogas and methane yields in an UASB treating potato starch processing wastewater with backpropagation artificial neural network.

PubMed

Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi

2017-03-01

Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.

A standardized method of propagating the marine fish parasite, Amyloodinium ocellatum.

PubMed

Bower, C E; Turner, D T; Biever, R C

1987-02-01

The peridinian dinoflagellate Amyloodinium ocellatum was propagated by serial passage in clownfish (Amphiprion ocellaris) and hybrid striped bass (Morone chrysops X Morone saxatilis). Each 25-50-mm fish was exposed to 4,000-6,000 dinospores in 400 ml of artificial seawater for 30 min. Two days after exposure, trophonts were harvested by immersing the fishes in fresh water. After encystment, tomonts were axenized by multiple washes with sterile distilled water and sterile artificial seawater containing penicillin and streptomycin, and then incubated in the antibiotic solution. High yields of both tomonts and dinospores of the same sizes and ages were obtained, and host mortalities were eliminated. Microbial growth in incubating cultures was inhibited until after dinospores had emerged from tomonts, and dinospores remained infective for at least 4 days at 26 C.

The potential role of CacyBP/SIP in tumorigenesis.

PubMed

Ning, Xiaoxuan; Chen, Yang; Wang, Xiaosu; Li, Qiaoneng; Sun, Shiren

2016-08-01

Calcyclin-binding protein/Siah-1-interacting protein (CacyBP/SIP) was initially described as a binding partner of S100A6 in the Ehrlich ascites tumor cells and later as a Siah-1-interacting protein. This 30 kDa protein includes three domains and is involved in cell proliferation, differentiation, cytoskeletal rearrangement, and transcriptional regulation via binding to various proteins. Studies have also shown that the CacyBP/SIP is a critical protein in tumorigenesis. But, its promotion or suppression of cancer progression may depend on the cell type. In this review, the biological characteristics and target proteins of CacyBP/SIP have been described. Moreover, the exact role of CacyBP/SIP in various cancers is discussed.

Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor.

PubMed

Pandey, Daya Shankar; Das, Saptarshi; Pan, Indranil; Leahy, James J; Kwapinski, Witold

2016-12-01

In this paper, multi-layer feed forward neural networks are used to predict the lower heating value of gas (LHV), lower heating value of gasification products including tars and entrained char (LHV p ) and syngas yield during gasification of municipal solid waste (MSW) during gasification in a fluidized bed reactor. These artificial neural networks (ANNs) with different architectures are trained using the Levenberg-Marquardt (LM) back-propagation algorithm and a cross validation is also performed to ensure that the results generalise to other unseen datasets. A rigorous study is carried out on optimally choosing the number of hidden layers, number of neurons in the hidden layer and activation function in a network using multiple Monte Carlo runs. Nine input and three output parameters are used to train and test various neural network architectures in both multiple output and single output prediction paradigms using the available experimental datasets. The model selection procedure is carried out to ascertain the best network architecture in terms of predictive accuracy. The simulation results show that the ANN based methodology is a viable alternative which can be used to predict the performance of a fluidized bed gasifier. Copyright © 2016 Elsevier Ltd. All rights reserved.

E258K HCM-causing mutation in cardiac MyBP-C reduces contractile force and accelerates twitch kinetics by disrupting the cMyBP-C and myosin S2 interaction.

PubMed

De Lange, Willem J; Grimes, Adrian C; Hegge, Laura F; Spring, Alexander M; Brost, Taylor M; Ralphe, J Carter

2013-09-01

Mutations in cardiac myosin binding protein C (cMyBP-C) are prevalent causes of hypertrophic cardiomyopathy (HCM). Although HCM-causing truncation mutations in cMyBP-C are well studied, the growing number of disease-related cMyBP-C missense mutations remain poorly understood. Our objective was to define the primary contractile effect and molecular disease mechanisms of the prevalent cMyBP-C E258K HCM-causing mutation in nonremodeled murine engineered cardiac tissue (mECT). Wild-type and human E258K cMyBP-C were expressed in mECT lacking endogenous mouse cMyBP-C through adenoviral-mediated gene transfer. Expression of E258K cMyBP-C did not affect cardiac cell survival and was appropriately incorporated into the cardiac sarcomere. Functionally, expression of E258K cMyBP-C caused accelerated contractile kinetics and severely compromised twitch force amplitude in mECT. Yeast two-hybrid analysis revealed that E258K cMyBP-C abolished interaction between the N terminal of cMyBP-C and myosin heavy chain sub-fragment 2 (S2). Furthermore, this mutation increased the affinity between the N terminal of cMyBP-C and actin. Assessment of phosphorylation of three serine residues in cMyBP-C showed that aberrant phosphorylation of cMyBP-C is unlikely to be responsible for altering these interactions. We show that the E258K mutation in cMyBP-C abolishes interaction between N-terminal cMyBP-C and myosin S2 by directly disrupting the cMyBP-C-S2 interface, independent of cMyBP-C phosphorylation. Similar to cMyBP-C ablation or phosphorylation, abolition of this inhibitory interaction accelerates contractile kinetics. Additionally, the E258K mutation impaired force production of mECT, which suggests that in addition to the loss of physiological function, this mutation disrupts contractility possibly by tethering the thick and thin filament or acting as an internal load.

Surface Water Sampling Data for BP Spill/Deepwater Horizon

EPA Pesticide Factsheets

The Deepwater Horizon oil spill (also referred to as the BP oil spill) began on 20 April 2010 in the Gulf of Mexico on the BP-operated Macondo Prospect. Following the explosion and sinking of the Deepwater Horizon oil rig, a sea-floor oil gusher flowed for 87 days, until it was capped on 15 July 2010.In response to the BP oil spill, EPA sampled air, water, sediment, and waste generated by the cleanup operations.

Microstructure-Tensile Properties Correlation for the Ti-6Al-4V Titanium Alloy

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Zeng, Weidong; Sun, Yu; Han, Yuanfei; Zhao, Yongqing; Guo, Ping

2015-04-01

Finding the quantitative microstructure-tensile properties correlations is the key to achieve performance optimization for various materials. However, it is extremely difficult due to their non-linear and highly interactive interrelations. In the present investigation, the lamellar microstructure features-tensile properties correlations of the Ti-6Al-4V alloy are studied using an error back-propagation artificial neural network (ANN-BP) model. Forty-eight thermomechanical treatments were conducted to prepare the Ti-6Al-4V alloy with different lamellar microstructure features. In the proposed model, the input variables are microstructure features including the α platelet thickness, colony size, and β grain size, which were extracted using Image Pro Plus software. The output variables are the tensile properties, including ultimate tensile strength, yield strength, elongation, and reduction of area. Fourteen hidden-layer neurons which can make ANN-BP model present the most excellent performance were applied. The training results show that all the relative errors between the predicted and experimental values are within 6%, which means that the trained ANN-BP model is capable of providing precise prediction of the tensile properties for Ti-6Al-4V alloy. Based on the corresponding relations between the tensile properties predicted by ANN-BP model and the lamellar microstructure features, it can be found that the yield strength decreases with increasing α platelet thickness continuously. However, the α platelet thickness exerts influence on the elongation in a more complicated way. In addition, for a given α platelet thickness, the yield strength and the elongation both increase with decreasing β grain size and colony size. In general, the β grain size and colony size play a more important role in affecting the tensile properties of Ti-6Al-4V alloy than the α platelet thickness.

A Structure-Adaptive Hybrid RBF-BP Classifier with an Optimized Learning Strategy

PubMed Central

Wen, Hui; Xie, Weixin; Pei, Jihong

2016-01-01

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used for nonlinear classification. The optimized learning strategy is as follows: firstly, a potential function is introduced into training sample space to adaptively determine the number of initial RBF hidden nodes and node parameters, and a form of heterogeneous samples repulsive force is designed to further optimize each generated RBF hidden node parameters, the optimized structure-adaptive RBF network is used for adaptively nonlinear mapping the sample space; then, according to the number of adaptively generated RBF hidden nodes, the number of subsequent BP input nodes can be determined, and the overall SAHRBF-BP classifier is built up; finally, different training sample sets are used to train the BP network parameters in SAHRBF-BP. Compared with other algorithms applied to different data sets, experiments show the superiority of SAHRBF-BP. Especially on most low dimensional and large number of data sets, the classification performance of SAHRBF-BP outperforms other training SLFNs algorithms. PMID:27792737

Artificial plasma jet in the ionosphere

NASA Astrophysics Data System (ADS)

Haerendel, G.; Sagdeev, R. Z.

The dynamics of an artificially injected plasma beam in the near-earth space are analyzed in terms of the beam structure, its propagation across the magnetic field, and the resulting wave phenomena (Porcupine Project, flight 4, March 31, 1979). Out of the four ejectable canisters attached to the main payload, two were instrumented by the U.S., one by the USSR (the Xenon plasma beam experiment), and one by West Germany (carrying a barium ion jet experiment). The propagation of the plasma seems to occur in three stages, with high-frequency broad-band oscillations mainly localized in the 'core' of the jet, while low-frequency oscillations were spatially separated from it. The generation region of LF oscillations was found to be much wider than the jet core. As a result of the interaction between the plasma beam and the ambient medium a heating of electrons, up to energies of about 20 eV, associated with LF noise was observed. The behavior of high-energy ions and the observed HF wave phenomena need further analysis.

Aspect Ratio of Receiver Node Geometry based Indoor WLAN Propagation Model

NASA Astrophysics Data System (ADS)

Naik, Udaykumar; Bapat, Vishram N.

2017-08-01

This paper presents validation of indoor wireless local area network (WLAN) propagation model for varying rectangular receiver node geometry. The rectangular client node configuration is a standard node arrangement in computer laboratories of academic institutes and research organizations. The model assists to install network nodes for the better signal coverage. The proposed model is backed by wide ranging real time received signal strength measurements at 2.4 GHz. The shadow fading component of signal propagation under realistic indoor environment is modelled with the dependency on varying aspect ratio of the client node geometry. The developed new model is useful in predicting indoor path loss for IEEE 802.11b/g WLAN. The new model provides better performance in comparison to well known International Telecommunication Union and free space propagation models. It is shown that the proposed model is simple and can be a useful tool for indoor WLAN node deployment planning and quick method for the best utilisation of the office space.

Optimization of Artificial Propagation in Piracanjuba Fish Brycon orbignyanus Using Cryopreserved Semen.

PubMed

Felizardo, V O; Melo, C C V; Murgas, L D S; Andrade, E S; Navarro, R D; Ftreitas, T F

BACKGROUND: Cryopreserved semen could facilitate procedures during the artificial reproduction in fish. Factors affecting cryopreservation efficiency are important to define efficient protocols. This study investigated the application of cryoprotectants on the quality of piracanjuba fish semen, the sperm concentration required for oocyte fertilization and spermatic activation. We evaluated two intracellular cryoprotectant solutions (DMSO and methanol) and two extracellular cryoprotectant solutions (egg yolk and lactose) to cryopreserved piracanjuba semen. Sperm motility rate, motility duration and spermatic alterations were assessed. The protocol for piracanjuba semen cryopreservation can use solutions including either DMSO or methanol as intracellular cryoprotectant and egg yolk or lactose as extracellular cryoprotectants.

Non-invasive algorithm for bowel motility estimation using a back-propagation neural network model of bowel sounds.

PubMed

Kim, Keo-Sik; Seo, Jeong-Hwan; Song, Chul-Gyu

2011-08-10

Radiological scoring methods such as colon transit time (CTT) have been widely used for the assessment of bowel motility. However, these radiograph-based methods need cumbersome radiological instruments and their frequent exposure to radiation. Therefore, a non-invasive estimation algorithm of bowel motility, based on a back-propagation neural network (BPNN) model of bowel sounds (BS) obtained by an auscultation, was devised. Twelve healthy males (age: 24.8 ± 2.7 years) and 6 patients with spinal cord injury (6 males, age: 55.3 ± 7.1 years) were examined. BS signals generated during the digestive process were recorded from 3 colonic segments (ascending, descending and sigmoid colon), and then, the acoustical features (jitter and shimmer) of the individual BS segment were obtained. Only 6 features (J1, 3, J3, 3, S1, 2, S2, 1, S2, 2, S3, 2), which are highly correlated to the CTTs measured by the conventional method, were used as the features of the input vector for the BPNN. As a results, both the jitters and shimmers of the normal subjects were relatively higher than those of the patients, whereas the CTTs of the normal subjects were relatively lower than those of the patients (p < 0.01). Also, through k-fold cross validation, the correlation coefficient and mean average error between the CTTs measured by a conventional radiograph and the values estimated by our algorithm were 0.89 and 10.6 hours, respectively. The jitter and shimmer of the BS signals generated during the peristalsis could be clinically useful for the discriminative parameters of bowel motility. Also, the devised algorithm showed good potential for the continuous monitoring and estimation of bowel motility, instead of conventional radiography, and thus, it could be used as a complementary tool for the non-invasive measurement of bowel motility.

Remote artificial eyes using micro-optical circuit for long-distance 3D imaging perception.

PubMed

Thammawongsa, Nopparat; Yupapin, Preecha P

2016-01-01

A small-scale optical device incorporated with an optical nano-antenna is designed to operate as the remote artificial eye using a tiny conjugate mirror. A basic device known as a conjugate mirror can be formed using the artificial eye device, the partially reflected light intensities from input source are interfered and the 3D whispering gallery modes formed within the ring centers, which can be modulated and propagated to the object. The image pixel is obtained at the center ring and linked with the optic nerve in the remote area via the nano-antenna, which is useful for blind people.

Avian artificial insemination and semen preservation

USGS Publications Warehouse

Gee, G.F.; Risser, Arthur C.; Todd, Frank S.

1983-01-01

Summary: Artificial insemination is a practical propagation tool that has been successful with a variety of birds. Cooperative, massage, and electroejaculation and modifications of these three basic methods of semen collection are described for a variety of birds. Semen color and consistency and sperm number, moti!ity, and morphology, as discussed, are useful indicators of semen quality, but the most reliable test of semen quality is the production of fertile eggs. Successful cryogenic preservation of avian semen with DMSO or glycerol as the cryoprotectant has been possible. Although the methods for preservation require special equipment, use of frozen semen requires only simple insemination supplies

What Challenges Manual Workers' Ability to Cope with Back Pain at Work, and What Influences Their Decision to Call in Sick?

PubMed

Frederiksen, Pernille; Karsten, Mette Marie V; Indahl, Aage; Bendix, Tom

2015-12-01

Although back pain (BP) is a very common cause for sickness absence, most people stay at work during BP episodes. Existing knowledge on the factors influencing the decision to stay at work despite pain is limited. The aim of this study was to explore challenges for coping with BP at work and decisive factors for work attendance among workers with high physical work demands. Three focus groups (n = 20) were conducted using an explorative inductive method. Participants were public-employed manual workers with high physical work demands. All had personal BP experience. Thematic analysis was used for interpretation. Results were matched with the Flags system framework to guide future recommendations. Workers with BP were challenged by poor physical work conditions and a lack of supervisor support/trust (i.e. lack of adjustment latitude). Organization of workers into teams created close co-worker relationships, which positively affected BP coping. Workers responded to BP by applying helpful individual adjustments to reduce or prevent pain. Traditional ergonomics was considered inconvenient, but nonetheless ideal. When pain was not decisive, the decision to call in sick was mainly governed by workplace factors (i.e. sick absence policies, job strain, and close co-workers relationships) and to a less degree by personal factors. Factors influencing BP coping at work and the decision to report sick was mainly governed by factors concerning general working conditions. Creating a flexible and inclusive working environment guided by the senior management and overall work environment regulations seems favourable.

Application of neural nets in structural optimization

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Hajela, Prabhat

1993-01-01

The biological motivation for Artificial Neural Net developments is briefly discussed, and the most popular paradigm, the feedforward supervised learning net with error back propagation training algorithm, is introduced. Possible approaches for utilization in structural optimization is illustrated through simple examples. Other currently ongoing developments for application in structural mechanics are also mentioned.

Mitochondrial DNA variation in natural populations of endangered Indian feather-back fish, Chitala chitala.

PubMed

Mandal, Anup; Mohindra, Vindhya; Singh, Rajeev Kumar; Punia, Peyush; Singh, Ajay Kumar; Lal, Kuldeep Kumar

2012-02-01

Genetic variation at mitochondrial cytochrome b (cyt b) and D-loop region reveals the evidence of population sub-structuring in Indian populations of highly endangered primitive feather-back fish Chitala chitala. Samples collected through commercial catches from eight riverine populations from different geographical locations of India were analyzed for cyt b region (307 bp) and D-loop region (636-716 bp). The sequences of the both the mitochondrial regions revealed high haplotype diversity and low nucleotide diversity. The patterns of genetic diversity, haplotypes networks clearly indicated two distinct mitochondrial lineages and mismatch distribution strongly suggest a historical influence on the genetic structure of C. chitala populations. The baseline information on genetic variation and the evidence of population sub-structuring generated from this study would be useful for planning effective strategies for conservation and rehabilitation of this highly endangered species.

Avoidable costs of physical treatments for chronic back, neck and shoulder pain within the Spanish National Health Service: a cross-sectional study

PubMed Central

2011-01-01

Background Back, neck and shoulder pain are the most common causes of occupational disability. They reduce health-related quality of life and have a significant economic impact. Many different forms of physical treatment are routinely used. The objective of this study was to estimate the cost of physical treatments which, despite the absence of evidence supporting their effectiveness, were used between 2004 and 2007 for chronic and non-specific neck pain (NP), back pain (BP) and shoulder pain (SP), within the Spanish National Health Service in the Canary Islands (SNHSCI). Methods Chronic patients referred from the SNHSCI to private physical therapy centres for NP, BP or SP, between 2004 and 2007, were identified. The cost of providing physical therapies to these patients was estimated. Systematic reviews (SRs) and clinical practice guidelines (CPGs) for NP, BP and SP available in the same period were searched for and rated according to the Oxman and AGREE criteria, respectively. Those rated positively for ≥70% of the criteria, were used to categorise physical therapies as Effective; Ineffective; Inconclusive; and Insufficiently Assessed. The main outcome was the cost of physical therapies included in each of these categories. Results 8,308 chronic cases of NP, 4,693 of BP and 5,035 of SP, were included in this study. Among prescribed treatments, 39.88% were considered Effective (physical exercise and manual therapy with mobilization); 23.06% Ineffective; 13.38% Inconclusive, and 23.66% Insufficiently Assessed. The total cost of treatments was € 5,107,720. Effective therapies accounted for € 2,069,932. Conclusions Sixty percent of the resources allocated by the SNHSCI to fund physical treatment for NP, BP and SP in private practices are spent on forms of treatment proven to be ineffective, or for which there is no evidence of effectiveness. PMID:22188790

ELECTRONIC STRUCTURE AND LINEAR OPTICAL PROPERTIES OF MIXED ALKALI-METAL BOROPHOSPHATES (LiK2BP2O8, Li3K2BP4O14): A FIRST-PRINCIPLES STUDY

NASA Astrophysics Data System (ADS)

Zhang, Bei; Jing, Qun; Yang, Zhihua; Wang, Ying; Su, Xin; Pan, Shilie; Zhang, Jun

2013-07-01

LiK2BP2O8 and Li3K2BP4O14 are synthesized by high-temperature solution method with the same elements, while contain different fundamental building units. Li3K2BP4O14 is a novel P-O-P linking structure which gives a rare example of violation of Pauling's fourth rule. The electronic structures of LiK2BP2O8 and Li3K2BP4O14 are investigated by density functional calculations. Direct gaps of 5.038 eV (LiK2BP2O8) and 5.487 eV (Li3K2BP4O14) are obtained. By analyzing the density of states (DOS) of LiK2BP2O8 and Li3K2BP4O14, the P-O-P linking in fundamental building units of Li3K2BP4O14 crystal is proved theoretically. Based on the electronic properties, the linear optical information is captured.

Application of Artificial Thunderstorm Cells for the Investigation of Lightning Initiation Problems between a Thundercloud and the Ground

NASA Astrophysics Data System (ADS)

Temnikov, A. G.; Chernensky, L. L.; Orlov, A. V.; Lysov, N. Y.; Zhuravkova, D. S.; Belova, O. S.; Gerastenok, T. K.

2017-12-01

The results of the experimental application of artificial thunderstorm cells of negative and positive polarities for the investigation of the lightning initiation problems between the thundercloud and the ground using model hydrometeor arrays are presented. Possible options of the initiation and development of a discharge between the charged cloud and the ground in the presence of model hydrometeors are established. It is experimentally shown that groups of large hydrometeors of various shapes significantly increase the probability of channel discharge initiation between the artificial thunderstorm cell and the ground, especially in the case of positive polarity of the cloud. The authors assume that large hail arrays in the thundercloud can initiate the preliminary breakdown stage in the lower part of the thundercloud or initiate and stimulate the propagation of positive lightning from its upper part. A significant effect of the shape of model hydrometeors and the way they are grouped on the processes of initiation and stimulation of the channel discharge propagation in the artificial thunderstorm cell of negative or positive polarity-ground gap is experimentally established. It is found that, in the case of negative polarity of a charged cloud, the group of conductive cylindrical hydrometeors connected by a dielectric string more effectively initiates the channel discharge between the artificial thunderstorm cell and the ground. In the case of positive polarity of the artificial thunderstorm cell, the best effect of the channel discharge initiation is achieved for model hydrometeors grouped together by the dielectric tape. The obtained results can be used in the development of the method for the directed artificial lightning initiation between the thundercloud and the ground.

Establishment of turbidity forecasting model and early-warning system for source water turbidity management using back-propagation artificial neural network algorithm and probability analysis.

PubMed

Yang, Tsung-Ming; Fan, Shu-Kai; Fan, Chihhao; Hsu, Nien-Sheng

2014-08-01

The purpose of this study is to establish a turbidity forecasting model as well as an early-warning system for turbidity management using rainfall records as the input variables. The Taipei Water Source Domain was employed as the study area, and ANOVA analysis showed that the accumulative rainfall records of 1-day Ping-lin, 2-day Ping-lin, 2-day Fei-tsui, 2-day Shi-san-gu, 2-day Tai-pin and 2-day Tong-hou were the six most significant parameters for downstream turbidity development. The artificial neural network model was developed and proven capable of predicting the turbidity concentration in the investigated catchment downstream area. The observed and model-calculated turbidity data were applied to developing the turbidity early-warning system. Using a previously determined turbidity as the threshold, the rainfall criterion, above which the downstream turbidity would possibly exceed this respective threshold turbidity, for the investigated rain gauge stations was determined. An exemplary illustration demonstrated the effectiveness of the proposed turbidity early-warning system as a precautionary alarm of possible significant increase of downstream turbidity. This study is the first report of the establishment of the turbidity early-warning system. Hopefully, this system can be applied to source water turbidity forecasting during storm events and provide a useful reference for subsequent adjustment of drinking water treatment operation.

Hindrances to bistable front propagation: application to Wolbachia invasion.

PubMed

Nadin, Grégoire; Strugarek, Martin; Vauchelet, Nicolas

2018-05-01

We study the biological situation when an invading population propagates and replaces an existing population with different characteristics. For instance, this may occur in the presence of a vertically transmitted infection causing a cytoplasmic effect similar to the Allee effect (e.g. Wolbachia in Aedes mosquitoes): the invading dynamics we model is bistable. We aim at quantifying the propagules (what does it take for an invasion to start?) and the invasive power (how far can an invading front go, and what can stop it?). We rigorously show that a heterogeneous environment inducing a strong enough population gradient can stop an invading front, which will converge in this case to a stable front. We characterize the critical population jump, and also prove the existence of unstable fronts above the stable (blocking) fronts. Being above the maximal unstable front enables an invading front to clear the obstacle and propagate further. We are particularly interested in the case of artificial Wolbachia infection, used as a tool to fight arboviruses.

Detection of apnea using a short-window FFT technique and an artificial neural network

NASA Astrophysics Data System (ADS)

Waldemark, Karina E.; Agehed, Kenneth I.; Lindblad, Thomas; Waldemark, Joakim T. A.

1998-03-01

Sleep apnea is characterized by frequent prolonged interruptions of breathing during sleep. This syndrome causes severe sleep disorders and is often responsible for development of other diseases such as heart problems, high blood pressure and daytime fatigue, etc. After diagnosis, sleep apnea is often successfully treated by applying positive air pressure (CPAP) to the mouth and nose. Although effective, the (CPAP) equipment takes up a lot of space and the connected mask causes a lot of inconvenience for the patients. This raised interest in developing new techniques for treatment of sleep apnea syndrome. Several studies have indicated that electrical stimulation of the hypoglossal nerve and muscle in the tongue may be a useful method for treating patients with severe sleep apnea. In order to be able to successfully prevent the occurrence of apnea it is necessary to have some technique for early and fast on-line detection or prediction of the apnea events. This paper suggests using measurements of respiratory airflow (mouth temperature). The signal processing for this task includes the use of a short window FFT technique and uses an artificial back propagation neural net to model or predict the occurrence of apneas. The results show that early detection of respiratory interruption is possible and that the delay time for this is small.

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Occurrence of 4-tert-butylphenol (4-t-BP) biodegradation in an aquatic sample caused by the presence of Spirodela polyrrhiza and isolation of a 4-t-BP-utilizing bacterium.

PubMed

Ogata, Yuka; Toyama, Tadashi; Yu, Ning; Wang, Xuan; Sei, Kazunari; Ike, Michihiko

2013-04-01

Although 4-tert-butylphenol (4-t-BP) is a serious aquatic pollutant, its biodegradation in aquatic environments has not been well documented. In this study, 4-t-BP was obviously and repeatedly removed from water from four different environments in the presence of Spirodela polyrrhiza, giant duckweed, but 4-t-BP persisted in the environmental waters in the absence of S. polyrrhiza. Also, 4-t-BP was not removed from autoclaved pond water with sterilized S. polyrrhiza. These results suggest that the 4-t-BP removal from the environmental waters was caused by biodegradation stimulated by the presence of S. polyrrhiza rather than by uptake by the plant. Moreover, Sphingobium fuliginis OMI capable of utilizing 4-t-BP as a sole carbon and energy source was isolated from the S. polyrrhiza rhizosphere. Strain OMI degraded 4-t-BP via a meta-cleavage pathway, and also degraded a broad range of alkylphenols with linear or branched alkyl side chains containing two to nine carbon atoms. Root exudates of S. polyrrhiza stimulated 4-t-BP degradation and cell growth of strain OMI. Thus, the stimulating effects of S. polyrrhiza root exudates on 4-t-BP-degrading bacteria might have contributed to 4-t-BP removal in the environmental waters with S. polyrrhiza. These results demonstrate that the S. polyrrhiza-bacteria association may be applicable to the removal of highly persistent 4-t-BP from wastewaters or polluted aquatic environments.

Leaders and followers: quantifying consistency in spatio-temporal propagation patterns

NASA Astrophysics Data System (ADS)

Kreuz, Thomas; Satuvuori, Eero; Pofahl, Martin; Mulansky, Mario

2017-04-01

Repetitive spatio-temporal propagation patterns are encountered in fields as wide-ranging as climatology, social communication and network science. In neuroscience, perfectly consistent repetitions of the same global propagation pattern are called a synfire pattern. For any recording of sequences of discrete events (in neuroscience terminology: sets of spike trains) the questions arise how closely it resembles such a synfire pattern and which are the spike trains that lead/follow. Here we address these questions and introduce an algorithm built on two new indicators, termed SPIKE-order and spike train order, that define the synfire indicator value, which allows to sort multiple spike trains from leader to follower and to quantify the consistency of the temporal leader-follower relationships for both the original and the optimized sorting. We demonstrate our new approach using artificially generated datasets before we apply it to analyze the consistency of propagation patterns in two real datasets from neuroscience (giant depolarized potentials in mice slices) and climatology (El Niño sea surface temperature recordings). The new algorithm is distinguished by conceptual and practical simplicity, low computational cost, as well as flexibility and universality.

CacyBP/SIP nuclear translocation induced by gastrin promotes gastric cancer cell proliferation

PubMed Central

Zhai, Hui-Hong; Meng, Juan; Wang, Jing-Bo; Liu, Zhen-Xiong; Li, Yuan-Fei; Feng, Shan-Shan

2014-01-01

AIM: To investigate the role of nuclear translocation of calcyclin binding protein, also called Siah-1 interacting protein (CacyBP/SIP), in gastric carcinogenesis. METHODS: The expression of CacyBP/SIP protein in gastric cancer cell lines was detected by Western blot. Immunofluorescence experiments were performed on gastric cancer cell lines that had been either unstimulated or stimulated with gastrin. To confirm the immunofluorescence findings, the relative abundance of CacyBP/SIP in nuclear and cytoplasmic compartments was assessed by Western blot. The effect of nuclear translocation of CacyBP/SIP on cell proliferation was examined using MTT assay. The colony formation assay was used to measure clonogenic cell survival. The effect of CacyBP/SIP nuclear translocation on cell cycle progression was investigated. Two CacyBP/SIP-specific siRNA vectors were designed and constructed to inhibit CacyBP/SIP expression in order to reduce the nuclear translocation of CacyBP/SIP, and the expression of CacyBP/SIP in stably transfected cells was determined by Western blot. The effect of inhibiting CacyBP/SIP nuclear translocation on cell proliferation was then assessed. RESULTS: CacyBP/SIP protein was present in most of gastric cancer cell lines. In unstimulated cells, CacyBP/SIP was distributed throughout the cytoplasm; while in stimulated cells, CacyBP/SIP was found mainly in the perinuclear region. CacyBP/SIP nuclear translocation generated a growth-stimulatory effect on cells. The number of colonies in the CacyBP/SIP nuclear translocation group was significantly higher than that in the control group. The percentage of stimulated cells in G1 phase was significantly lower than that of control cells (69.70% ± 0.46% and 65.80% ± 0.60%, control cells and gastrin-treated SGC7901 cells, P = 0.008; 72.99% ± 0.46% and 69.36% ± 0.51%, control cells and gastrin-treated MKN45 cells, P = 0.022). CacyBP/SIPsi1 effectively down-regulated the expression of CacyBP/SIP, and cells stably

CacyBP/SIP nuclear translocation induced by gastrin promotes gastric cancer cell proliferation.

PubMed

Zhai, Hui-Hong; Meng, Juan; Wang, Jing-Bo; Liu, Zhen-Xiong; Li, Yuan-Fei; Feng, Shan-Shan

2014-08-07

To investigate the role of nuclear translocation of calcyclin binding protein, also called Siah-1 interacting protein (CacyBP/SIP), in gastric carcinogenesis. The expression of CacyBP/SIP protein in gastric cancer cell lines was detected by Western blot. Immunofluorescence experiments were performed on gastric cancer cell lines that had been either unstimulated or stimulated with gastrin. To confirm the immunofluorescence findings, the relative abundance of CacyBP/SIP in nuclear and cytoplasmic compartments was assessed by Western blot. The effect of nuclear translocation of CacyBP/SIP on cell proliferation was examined using MTT assay. The colony formation assay was used to measure clonogenic cell survival. The effect of CacyBP/SIP nuclear translocation on cell cycle progression was investigated. Two CacyBP/SIP-specific siRNA vectors were designed and constructed to inhibit CacyBP/SIP expression in order to reduce the nuclear translocation of CacyBP/SIP, and the expression of CacyBP/SIP in stably transfected cells was determined by Western blot. The effect of inhibiting CacyBP/SIP nuclear translocation on cell proliferation was then assessed. CacyBP/SIP protein was present in most of gastric cancer cell lines. In unstimulated cells, CacyBP/SIP was distributed throughout the cytoplasm; while in stimulated cells, CacyBP/SIP was found mainly in the perinuclear region. CacyBP/SIP nuclear translocation generated a growth-stimulatory effect on cells. The number of colonies in the CacyBP/SIP nuclear translocation group was significantly higher than that in the control group. The percentage of stimulated cells in G1 phase was significantly lower than that of control cells (69.70% ± 0.46% and 65.80% ± 0.60%, control cells and gastrin-treated SGC7901 cells, P = 0.008; 72.99% ± 0.46% and 69.36% ± 0.51%, control cells and gastrin-treated MKN45 cells, P = 0.022). CacyBP/SIPsi1 effectively down-regulated the expression of CacyBP/SIP, and cells stably transfected by CacyBP

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks

PubMed Central

Ying, Xiaoguo; Liu, Wei; Hui, Guohua; Fu, Jun

2015-01-01

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction. PMID:25714125

Swimming Back and Forth Using Planar Flagellar Propulsion at Low Reynolds Numbers.

PubMed

Khalil, Islam S M; Tabak, Ahmet Fatih; Hamed, Youssef; Mitwally, Mohamed E; Tawakol, Mohamed; Klingner, Anke; Sitti, Metin

2018-02-01

Peritrichously flagellated Escherichia coli swim back and forth by wrapping their flagella together in a helical bundle. However, other monotrichous bacteria cannot swim back and forth with a single flagellum and planar wave propagation. Quantifying this observation, a magnetically driven soft two-tailed microrobot capable of reversing its swimming direction without making a U-turn trajectory or actively modifying the direction of wave propagation is designed and developed. The microrobot contains magnetic microparticles within the polymer matrix of its head and consists of two collinear, unequal, and opposite ultrathin tails. It is driven and steered using a uniform magnetic field along the direction of motion with a sinusoidally varying orthogonal component. Distinct reversal frequencies that enable selective and independent excitation of the first or the second tail of the microrobot based on their tail length ratio are found. While the first tail provides a propulsive force below one of the reversal frequencies, the second is almost passive, and the net propulsive force achieves flagellated motion along one direction. On the other hand, the second tail achieves flagellated propulsion along the opposite direction above the reversal frequency.

Application of Chitosan-Zinc Oxide Nanoparticles for Lead Extraction From Water Samples by Combining Ant Colony Optimization with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Khajeh, M.; Pourkarami, A.; Arefnejad, E.; Bohlooli, M.; Khatibi, A.; Ghaffari-Moghaddam, M.; Zareian-Jahromi, S.

2017-09-01

Chitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multilayer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 μg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.

CacyBP/SIP as a regulator of transcriptional responses in brain cells

PubMed Central

Kilanczyk, Ewa; Filipek, Anna; Hetman, Michal

2014-01-01

Summary The Calcyclin-Binding Protein/Siah-1-Interacting Protein (CacyBP/SIP) is highly expressed in the brain and was shown to regulate the β-catenin-driven transcription in thymocytes. Therefore, it was investigated whether in brain cells CacyBP/SIP might play a role as a transcriptional regulator. In BDNF- or forskolin-stimulated rat primary cortical neurons, overexpression of CacyBP/SIP enhanced transcriptional activity of the cAMP-response element (CRE). In addition, overexpressed CacyBP/SIP enhanced BDNF-mediated activation of the Nuclear Factor of Activated T-cells (NFAT) but not the Serum Response Element (SRE). These stimulatory effects required an intact C-terminal domain of CacyBP/SIP. Moreover, in C6 rat glioma cells, the overexpressed CacyBP/SIP enhanced activation of CRE- or NFAT- following forskolin- or serum stimulation, respectively. Conversely, knockdown of endogenous CacyBP/SIP reduced activation of CRE- and NFAT but not SRE. Taken together, these results indicate that CacyBP/SIP is a novel regulator of CRE- and NFAT-driven transcription. PMID:25163685

The preparation of BP single crystals by high pressure flux method

NASA Technical Reports Server (NTRS)

Kumashiro, Y.; Misawa, S.; Gonda, S.

1984-01-01

Single crystals of BP, a III-V compound semiconductor, were obtained by the high pressure flux method. Cu3P and Ni12P5 powders were used as the flux, and mixed with BP powder. Two kinds of mixtures were prepared: (1) 1.8g (BP) + 35 G (Cu3P) and (2) 1.7 g (BP) + 25 g (Ni12P5). They were compressed into pellets, heated at 1300 C for 24 h in an induction furnace under a pressure of 1 MPa using Ar-P2 gas, and slowly cooled to room temperature. In case (1), BP single crystals grew along the (III) plane, and in case (2) they grew as an aggregate of crystallites. The cathodoluminescence spectra of the synthetic BP crystals showed peaks near 680 nm (1.82 eV) for case (1), and 500 nm (2.47 eV) for case (2). By using the high pressure flux method conventional sized crystals were obtained in a relatively short time.

Vitamin D and ferritin correlation with chronic neck pain using standard statistics and a novel artificial neural network prediction model.

PubMed

Eloqayli, Haytham; Al-Yousef, Ali; Jaradat, Raid

2018-02-15

Despite the high prevalence of chronic neck pain, there is limited consensus about the primary etiology, risk factors, diagnostic criteria and therapeutic outcome. Here, we aimed to determine if Ferritin and Vitamin D are modifiable risk factors with chronic neck pain using slandered statistics and artificial intelligence neural network (ANN). Fifty-four patients with chronic neck pain treated between February 2016 and August 2016 in King Abdullah University Hospital and 54 patients age matched controls undergoing outpatient or minor procedures were enrolled. Patients and control demographic parameters, height, weight and single measurement of serum vitamin D, Vitamin B12, ferritin, calcium, phosphorus, zinc were obtained. An ANN prediction model was developed. The statistical analysis reveals that patients with chronic neck pain have significantly lower serum Vitamin D and Ferritin (p-value <.05). 90% of patients with chronic neck pain were females. Multilayer Feed Forward Neural Network with Back Propagation(MFFNN) prediction model were developed and designed based on vitamin D and ferritin as input variables and CNP as output. The ANN model output results show that, 92 out of 108 samples were correctly classified with 85% classification accuracy. Although Iron and vitamin D deficiency cannot be isolated as the sole risk factors of chronic neck pain, they should be considered as two modifiable risk. The high prevalence of chronic neck pain, hypovitaminosis D and low ferritin amongst women is of concern. Bioinformatics predictions with artificial neural network can be of future benefit in classification and prediction models for chronic neck pain. We hope this initial work will encourage a future larger cohort study addressing vitamin D and iron correction as modifiable factors and the application of artificial intelligence models in clinical practice.

Vibration Propagation in Spider Webs

NASA Astrophysics Data System (ADS)

Hatton, Ross; Otto, Andrew; Elias, Damian

Due to their poor eyesight, spiders rely on web vibrations for situational awareness. Web-borne vibrations are used to determine the location of prey, predators, and potential mates. The influence of web geometry and composition on web vibrations is important for understanding spider's behavior and ecology. Past studies on web vibrations have experimentally measured the frequency response of web geometries by removing threads from existing webs. The full influence of web structure and tension distribution on vibration transmission; however, has not been addressed in prior work. We have constructed physical artificial webs and computer models to better understand the effect of web structure on vibration transmission. These models provide insight into the propagation of vibrations through the webs, the frequency response of the bare web, and the influence of the spider's mass and stiffness on the vibration transmission patterns. Funded by NSF-1504428.

Home and Online Management and Evaluation of Blood Pressure (HOME BP) digital intervention for self-management of uncontrolled, essential hypertension: a protocol for the randomised controlled HOME BP trial

PubMed Central

Morton, Katherine; Stuart, Beth; Raftery, James; Bradbury, Katherine; Yao, Guiqing Lily; Zhu, Shihua; Little, Paul; Yardley, Lucy

2016-01-01

Introduction Self-management of hypertension, including self-monitoring and antihypertensive medication titration, lowers blood pressure (BP) at 1 year compared to usual care. The aim of the current trial is to assess the effectiveness of the Home and Online Management and Evaluation of Blood Pressure (HOME BP) intervention for the self-management of hypertension in primary care. Methods and analysis The HOME BP trial will be a randomised controlled trial comparing BP self-management—consisting of the HOME BP online digital intervention with self-monitoring, lifestyle advice and antihypertensive drug titration—with usual care for people with uncontrolled essential hypertension. Eligible patients will be recruited from primary care and randomised to usual care or to self-management using HOME BP. The primary outcome will be the difference in mean systolic BP (mm Hg) at 12-month follow-up between the intervention and control groups adjusting for baseline BP and covariates. Secondary outcomes (also adjusted for baseline and covariates where appropriate) will be differences in mean BP at 6 months and diastolic BP at 12 months; patient enablement; quality of life, and economic analyses including all key resources associated with the intervention and related services, adopting a broad societal perspective to include NHS, social care and patient costs, considered within trial and modelled with a lifetime horizon. Medication beliefs, adherence and changes; self-efficacy; perceived side effects and lifestyle changes will be measured for process analyses. Qualitative analyses will explore patient and healthcare professional experiences of HOME BP to gain insights into the factors affecting acceptability, feasibility and adherence. Ethics and dissemination This study has received NHS ethical approval (REC reference 15/SC/0082). The findings from HOME BP will be disseminated widely through peer-reviewed publications, scientific conferences and workshops. If

Charcoal and fossil wood from palaeosols, sediments and artificial structures indicating Late Holocene woodland decline in southern Tibet (China)

NASA Astrophysics Data System (ADS)

Kaiser, Knut; Opgenoorth, Lars; Schoch, Werner H.; Miehe, Georg

2009-07-01

Charcoal and fossil wood taken from palaeosols, sediments and artificial structures were analysed in order to evaluate the regional pedoanthracological potential and to obtain information on Holocene environmental changes, particularly on possible past tree occurrences in southern Tibet. This research was initiated by the question to what extent this area is influenced by past human impact. Even recent evaluations have perceived the present treeless desertic environment of southern Tibet as natural, and the previous Holocene palaeoenvironmental changes detected were predominantly interpreted to be climate-determined. The material analysed - comprising a total of 53 botanical spectra and 55 radiocarbon datings from 46 sampling sites (c. 3500-4700 m a.s.l.) - represents the largest systematically obtained data set of charcoal available from Tibet so far. 27 taxa were determined comprising trees, (dwarf-) shrubs and herbs as well as grasses. The predominant tree taxa were Juniperus, Hippophae, Salix and Betula. According to their present-day occurrence in the region, the genera Juniperus and Hippophae can be explicitly attributed to tree species. Further, less frequently detected tree taxa were Populus, Pinus, Quercus, Taxus and Pseudotsuga. Charcoal of Juniperus mainly occurred on southern exposures, whereas Betula was associated with northern exposures. In contrast, the (partly) phreatophytic taxa Hippophae and Salix showed no prevalent orientation. The distribution of radiocarbon ages on charcoal revealed a discontinuous record of burning events cumulating in the Late Holocene (c. 5700-0 cal BP). For southern Tibet, these results indicated a Late Holocene vegetation change from woodlands to the present desertic pastures. As agrarian economies in southern and south-eastern Tibet date back to c. 3700 and 5700 cal BP, respectively, and the present-day climate is suitable for tree growth up to c. 4600 m a.s.l., we concluded that the Late Holocene loss or thinning out

Heterogeneous information-based artificial stock market

NASA Astrophysics Data System (ADS)

Pastore, S.; Ponta, L.; Cincotti, S.

2010-05-01

In this paper, an information-based artificial stock market is considered. The market is populated by heterogeneous agents that are seen as nodes of a sparsely connected graph. Agents trade a risky asset in exchange for cash. Besides the amount of cash and assets owned, each agent is characterized by a sentiment. Moreover, agents share their sentiments by means of interactions that are identified by the graph. Interactions are unidirectional and are supplied with heterogeneous weights. The agent's trading decision is based on sentiment and, consequently, the stock price process depends on the propagation of information among the interacting agents, on budget constraints and on market feedback. A central market maker (clearing house mechanism) determines the price process at the intersection of the demand and supply curves. Both closed- and open-market conditions are considered. The results point out the validity of the proposed model of information exchange among agents and are helpful for understanding the role of information in real markets. Under closed market conditions, the interaction among agents' sentiments yields a price process that reproduces the main stylized facts of real markets, e.g. the fat tails of the returns distributions and the clustering of volatility. Within open-market conditions, i.e. with an external cash inflow that results in asset price inflation, also the unitary root stylized fact is reproduced by the artificial stock market. Finally, the effects of model parameters on the properties of the artificial stock market are also addressed.

Enhanced Expression of IL-18 and IL-18BP in Plasma of Patients with Eczema: Altered Expression of IL-18BP and IL-18 Receptor on Mast Cells.

PubMed

Hu, Yalin; Wang, Junling; Zhang, Huiyun; Xie, Hua; Song, Weiwei; Jiang, Qijun; Zhao, Nan; He, Shaoheng

2017-01-01

IL-18 has been found to be associated with eczema. However, little is known of the role of IL-18 binding protein (BP) and IL-18 receptor (R) in eczema. We therefore investigated the expression of IL-18, IL-18BP, and IL-18R on mast cells by using flow cytometry analysis and mouse eczema model. The results showed that plasma free IL-18 and free IL-18BP levels in eczema patients were higher than those in healthy controls. IL-18 provoked up to 3.1-fold increase in skin mast cells. IL-18 induced also an increase in IL-18BP+ mast cells, but a reduction of IL-18R+ mast cells in mouse eczema skin. It was found that house dust mite allergen Der p1 and egg allergen OVA induced upregulation of the expression of IL-18, IL-18BP, and IL-18R mRNAs in HMC-1 cells following 2 and 16 h incubation. In conclusion, correlation of IL-18 and IL-18BP in eczema plasma suggests an important balance between IL-18 and IL-18BP in eczema. The decrease in molar concentration ratio of plasma IL-18BP/IL-18 and allergen-induced upregulated expression of IL-18 and IL-18R in skin mast cells of the patients with eczema suggests that anti-IL-18 including IL-18BP therapy may be useful for the treatment of eczema.

Enhanced Expression of IL-18 and IL-18BP in Plasma of Patients with Eczema: Altered Expression of IL-18BP and IL-18 Receptor on Mast Cells

PubMed Central

2017-01-01

IL-18 has been found to be associated with eczema. However, little is known of the role of IL-18 binding protein (BP) and IL-18 receptor (R) in eczema. We therefore investigated the expression of IL-18, IL-18BP, and IL-18R on mast cells by using flow cytometry analysis and mouse eczema model. The results showed that plasma free IL-18 and free IL-18BP levels in eczema patients were higher than those in healthy controls. IL-18 provoked up to 3.1-fold increase in skin mast cells. IL-18 induced also an increase in IL-18BP+ mast cells, but a reduction of IL-18R+ mast cells in mouse eczema skin. It was found that house dust mite allergen Der p1 and egg allergen OVA induced upregulation of the expression of IL-18, IL-18BP, and IL-18R mRNAs in HMC-1 cells following 2 and 16 h incubation. In conclusion, correlation of IL-18 and IL-18BP in eczema plasma suggests an important balance between IL-18 and IL-18BP in eczema. The decrease in molar concentration ratio of plasma IL-18BP/IL-18 and allergen-induced upregulated expression of IL-18 and IL-18R in skin mast cells of the patients with eczema suggests that anti-IL-18 including IL-18BP therapy may be useful for the treatment of eczema. PMID:28839348

Reconstitution of the Recombinant RanBP2 SUMO E3 Ligase Complex.

PubMed

Ritterhoff, Tobias; Das, Hrishikesh; Hao, Yuqing; Sakin, Volkan; Flotho, Annette; Werner, Andreas; Melchior, Frauke

2016-01-01

One of the few proteins that have SUMO E3 ligase activity is the 358 kDa nucleoporin RanBP2 (Nup358). While small fragments of RanBP2 can stimulate SUMOylation in vitro, the physiologically relevant E3 ligase is a stable multi-subunit complex comprised of RanBP2, SUMOylated RanGAP1, and Ubc9. Here, we provide a detailed protocol to in vitro reconstitute the RanBP2 SUMO E3 ligase complex. With the exception of RanBP2, reconstitution involves untagged full-length proteins. We describe the bacterial expression and purification of all complex components, namely an 86 kDa His-tagged RanBP2 fragment, the SUMO E2-conjugating enzyme Ubc9, RanGAP1, and SUMO1, and we provide a protocol for quantitative SUMOylation of RanGAP1. Finally, we present details for the assembly and final purification of the catalytically active RanBP2/RanGAP1*SUMO1/Ubc9 complex.

Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

NASA Astrophysics Data System (ADS)

Vellingiri, Govindaraj; Jayabalan, Ramesh

2018-03-01

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.

TopBP1 deficiency impairs V(D)J recombination during lymphocyte development

PubMed Central

Kim, Jieun; Kyu Lee, Sung; Jeon, Yoon; Kim, Yehyun; Lee, Changjin; Ho Jeon, Sung; Shim, Jaegal; Kim, In-Hoo; Hong, Seokmann; Kim, Nayoung; Lee, Ho; Seong, Rho Hyun

2014-01-01

TopBP1 was initially identified as a topoisomerase II-β-binding protein and it plays roles in DNA replication and repair. We found that TopBP1 is expressed at high levels in lymphoid tissues and is essential for early lymphocyte development. Specific abrogation of TopBP1 expression resulted in transitional blocks during early lymphocyte development. These defects were, in major part, due to aberrant V(D)J rearrangements in pro-B cells, double-negative and double-positive thymocytes. We also show that TopBP1 was located at sites of V(D)J rearrangement. In TopBP1-deficient cells, γ-H2AX foci were found to be increased. In addition, greater amount of γ-H2AX product was precipitated from the regions where TopBP1 was localized than from controls, indicating that TopBP1 deficiency results in inefficient DNA double-strand break repair. The developmental defects were rescued by introducing functional TCR αβ transgenes. Our data demonstrate a novel role for TopBP1 as a crucial factor in V(D)J rearrangement during the development of B, T and iNKT cells. PMID:24442639

The MCM-associated protein MCM-BP is important for human nuclear morphology.

PubMed

Jagannathan, Madhav; Sakwe, Amos M; Nguyen, Tin; Frappier, Lori

2012-01-01

Mini-chromosome maintenance complex-binding protein (MCM-BP) was discovered as a protein that is strongly associated with human MCM proteins, known to be crucial for DNA replication in providing DNA helicase activity. The Xenopus MCM-BP homologue appears to play a role in unloading MCM complexes from chromatin after DNA synthesis; however, the importance of MCM-BP and its functional contribution to human cells has been unclear. Here we show that depletion of MCM-BP by sustained expression of short hairpin RNA (shRNA) results in highly abnormal nuclear morphology and centrosome amplification. The abnormal nuclear morphology was not seen with depletion of other MCM proteins and was rescued with shRNA-resistant MCM-BP. MCM-BP depletion was also found to result in transient activation of the G2 checkpoint, slowed progression through G2 and increased replication protein A foci, indicative of replication stress. In addition, MCM-BP depletion led to increased cellular levels of MCM proteins throughout the cell cycle including soluble MCM pools. The results suggest that MCM-BP makes multiple contributions to human cells that are not limited to unloading of the MCM complex.

Coherent manipulation of a solid-state artificial atom with few photons.

PubMed

Giesz, V; Somaschi, N; Hornecker, G; Grange, T; Reznychenko, B; De Santis, L; Demory, J; Gomez, C; Sagnes, I; Lemaître, A; Krebs, O; Lanzillotti-Kimura, N D; Lanco, L; Auffeves, A; Senellart, P

2016-06-17

In a quantum network based on atoms and photons, a single atom should control the photon state and, reciprocally, a single photon should allow the coherent manipulation of the atom. Both operations require controlling the atom environment and developing efficient atom-photon interfaces, for instance by coupling the natural or artificial atom to cavities. So far, much attention has been drown on manipulating the light field with atomic transitions, recently at the few-photon limit. Here we report on the reciprocal operation and demonstrate the coherent manipulation of an artificial atom by few photons. We study a quantum dot-cavity system with a record cooperativity of 13. Incident photons interact with the atom with probability 0.95, which radiates back in the cavity mode with probability 0.96. Inversion of the atomic transition is achieved for 3.8 photons on average, showing that our artificial atom performs as if fully isolated from the solid-state environment.

Field experiments to determine wave propagation principles and mechanical properties of snow

NASA Astrophysics Data System (ADS)

Simioni, Stephan; Gebhard, Felix; Dual, Jürg; Schweizer, Jürg

2017-04-01

To understand the release of snow avalanches by explosions one needs to know how acoustic waves travel above and within the snowpack. Hitherto, wave propagation was investigated in the laboratory with small samples or in the field in the shock wave region. We developed a measurement system and layout to derive wave attenuation in snow, wave speeds and elastic moduli on small-scale (1-2 m) field experiments to close the gap between the lab scale (0.1 m) and the scale of artificial release (10-100 m). We used solid explosives and hammer blows to create the load and accelerometers to measure the resulting wave within the snowpack. The strong attenuation we observed indicates that we measured the second longitudinal wave which propagates through the pore space. The wave speeds, however, corresponded to the speeds of the first longitudinal wave within the ice skeleton. The elastic moduli were high on the order of several tens of MPa for lower densities (150 kg m-3) and agreed well with earlier lab studies, in particular for the higher densities 250-400 kg m-3). However, the scatter was rather large as expected for in-situ experiments in the layered snow cover. In addition, we measured accelerations during propagation saw test experiments. The propagation of cracks during this type of snow instability test has mainly been studied by analysing the bending of the slab (due to the saw cut) using particle tracking velocimetry. We used the accelerometers to measure crack propagation speeds. The wave speeds were slightly higher for most experiments than reported previously. Furthermore, in some experiments, we encountered to different wave types with one propagating at a higher speed. This finding may be interpreted as the actual crack propagation and the settling of the weak layer (collapse wave). Our results show that field measurements of propagation properties are feasible and that crack propagation as observed during propagation saw tests may involve different processes

76 FR 69713 - Application To Export Electric Energy; BP Energy Company

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-09

... DEPARTMENT OF ENERGY [OE Docket No. EA-314-A] Application To Export Electric Energy; BP Energy.... SUMMARY: BP Energy Company (BP Energy) has applied to renew its authority to transmit electric energy from... electric energy from the United States to Mexico as a power marketer for a five-year term using existing...

Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US.

PubMed

Biagiotti, R; Desii, C; Vanzi, E; Gacci, G

1999-02-01

To compare the performance of artificial neural networks (ANNs) with that of multiple logistic regression (MLR) models for predicting ovarian malignancy in patients with adnexal masses by using transvaginal B-mode and color Doppler flow ultrasonography (US). A total of 226 adnexal masses were examined before surgery: Fifty-one were malignant and 175 were benign. The data were divided into training and testing subsets by using a "leave n out method." The training subsets were used to compute the optimum MLR equations and to train the ANNs. The cross-validation subsets were used to estimate the performance of each of the two models in predicting ovarian malignancy. At testing, three-layer back-propagation networks, based on the same input variables selected by using MLR (i.e., women's ages, papillary projections, random echogenicity, peak systolic velocity, and resistance index), had a significantly higher sensitivity than did MLR (96% vs 84%; McNemar test, p = .04). The Brier scores for ANNs were significantly lower than those calculated for MLR (Student t test for paired samples, P = .004). ANNs might have potential for categorizing adnexal masses as either malignant or benign on the basis of multiple variables related to demographic and US features.

Back and neck pain and psychopathology in rural sub-Saharan Africa: evidence from the Gilgel Gibe Growth and Development Study, Ethiopia.

PubMed

El-Sayed, Abdulrahman M; Hadley, Craig; Tessema, Fasil; Tegegn, Ayalew; Cowan, John A; Galea, Sandro

2010-03-15

Community-based cross-sectional analysis of the relation between symptoms of psychopathology and back pain (BP) or neck pain (NP) in rural southwest Ethiopia. Using data from a community-based sample, we assessed the prevalence and psychopathologic correlates of BP or NP in rural sub-Saharan Africa. BP and NP are among the most prevalent pain conditions. Psychopathology has been shown to be associated with both BP and NP in developed and urban developing contexts. Little is known about the relation between psychopathology and BP or NP in the rural, developing context. Data on self-reported BP and NP, symptoms of depression, anxiety, and post-traumatic stress (PTS), gender, age, and socioeconomic status were collected from a representative cohort sample (N = 900) in rural southwest Ethiopia. We calculated univariate statistics to assess the prevalence of BP and NP. We used bivariate χ2 tests and multivariate logistic regression models to assess the relation between psychopathology and BP and NP. The prevalence of BP was 16.7%; that of NP was 5.0%. In χ2 analyses, symptoms of depression, anxiety, and PTS were significantly associated with increased risk for each outcome. In models adjusted for age, household assets, and gender, depression symptomatology was associated with increased risk for BP (OR = 3.44, 95% CI: 2.37-5.00) and NP (OR = 4.92, 95% CI: 2.49-9.74). Anxiety symptomatology was also associated with increased risk for BP (OR = 2.88, 95% CI: 1.98-4.20) and NP (OR = 2.67, 95% CI: 1.41-5.09). PTS symptomatology was associated with increased risk for BP (OR = 2.89, 95% CI: 1.78-4.69). In the first known study about the relation between psychopathologic symptomatology and BP and NP in a rural context in a developing country, the prevalence of BP and NP were comparable to published data in developed and developing countries. Symptoms of depression and anxiety were correlates of BP and NP, and symptoms of PTS were a correlate of BP. Comparative studies about

Volcanism in slab tear faults is larger than in island-arcs and back-arcs.

PubMed

Cocchi, Luca; Passaro, Salvatore; Tontini, Fabio Caratori; Ventura, Guido

2017-11-13

Subduction-transform edge propagators are lithospheric tears bounding slabs and back-arc basins. The volcanism at these edges is enigmatic because it is lacking comprehensive geological and geophysical data. Here we present bathymetric, potential-field data, and direct observations of the seafloor on the 90 km long Palinuro volcanic chain overlapping the E-W striking tear of the roll-backing Ionian slab in Southern Tyrrhenian Sea. The volcanic chain includes arc-type central volcanoes and fissural, spreading-type centers emplaced along second-order shears. The volume of the volcanic chain is larger than that of the neighbor island-arc edifices and back-arc spreading center. Such large volume of magma is associated to an upwelling of the isotherms due to mantle melts upraising from the rear of the slab along the tear fault. The subduction-transform edge volcanism focuses localized spreading processes and its magnitude is underestimated. This volcanism characterizes the subduction settings associated to volcanic arcs and back-arc spreading centers.

Resonant scattering of energetic electrons in the plasmasphere by monotonic whistler-mode waves artificially generated by ionospheric modification

NASA Astrophysics Data System (ADS)

Chang, S. S.; Ni, B. B.; Bortnik, J.; Zhou, C.; Zhao, Z. Y.; Li, J. X.; Gu, X. D.

2014-05-01

Modulated high-frequency (HF) heating of the ionosphere provides a feasible means of artificially generating extremely low-frequency (ELF)/very low-frequency (VLF) whistler waves, which can leak into the inner magnetosphere and contribute to resonant interactions with high-energy electrons in the plasmasphere. By ray tracing the magnetospheric propagation of ELF/VLF emissions artificially generated at low-invariant latitudes, we evaluate the relativistic electron resonant energies along the ray paths and show that propagating artificial ELF/VLF waves can resonate with electrons from ~ 100 keV to ~ 10 MeV. We further implement test particle simulations to investigate the effects of resonant scattering of energetic electrons due to triggered monotonic/single-frequency ELF/VLF waves. The results indicate that within the period of a resonance timescale, changes in electron pitch angle and kinetic energy are stochastic, and the overall effect is cumulative, that is, the changes averaged over all test electrons increase monotonically with time. The localized rates of wave-induced pitch-angle scattering and momentum diffusion in the plasmasphere are analyzed in detail for artificially generated ELF/VLF whistlers with an observable in situ amplitude of ~ 10 pT. While the local momentum diffusion of relativistic electrons is small, with a rate of < 10-7 s-1, the local pitch-angle scattering can be intense near the loss cone with a rate of ~ 10-4 s-1. Our investigation further supports the feasibility of artificial triggering of ELF/VLF whistler waves for removal of high-energy electrons at lower L shells within the plasmasphere. Moreover, our test particle simulation results show quantitatively good agreement with quasi-linear diffusion coefficients, confirming the applicability of both methods to evaluate the resonant diffusion effect of artificial generated ELF/VLF whistlers.

Quantity Effect of Radial Cracks on the Cracking Propagation Behavior and the Crack Morphology

PubMed Central

Chen, Jingjing; Xu, Jun; Liu, Bohan; Yao, Xuefeng; Li, Yibing

2014-01-01

In this letter, the quantity effect of radial cracks on the cracking propagation behavior as well as the circular crack generation on the impacted glass plate within the sandwiched glass sheets are experimentally investigated via high-speed photography system. Results show that the radial crack velocity on the backing glass layer decreases with the crack number under the same impact conditions during large quantities of repeated experiments. Thus, the “energy conversion factor” is suggested to elucidate the physical relation between the cracking number and the crack propagation speed. Besides, the number of radial crack also takes the determinative effect in the crack morphology of the impacted glass plate. This study may shed lights on understanding the cracking and propagation mechanism in laminated glass structures and provide useful tool to explore the impact information on the cracking debris. PMID:25048684

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

Treesearch

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

1998-01-01

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

Electric Power Engineering Cost Predicting Model Based on the PCA-GA-BP

NASA Astrophysics Data System (ADS)

Wen, Lei; Yu, Jiake; Zhao, Xin

2017-10-01

In this paper a hybrid prediction algorithm: PCA-GA-BP model is proposed. PCA algorithm is established to reduce the correlation between indicators of original data and decrease difficulty of BP neural network in complex dimensional calculation. The BP neural network is established to estimate the cost of power transmission project. The results show that PCA-GA-BP algorithm can improve result of prediction of electric power engineering cost.

An in vivo study of low back pain and shock absorption in the human locomotor system.

PubMed

Voloshin, A; Wosk, J

1982-01-01

In this second of three papers, the principles of a non-invasive in vivo method to quantitatively evaluate the shock absorbing capacity of the human musculoskeletal system and the correlation of this shock absorbing capacity with low back pain (LPB) symptoms are presented. The experiments involved patients suffering from low back pain (as well as other degenerative joint diseases) and healthy patients. The obtained results reveal that low back pain correlates with the reduced capacity of the human musculoskeletal system between the femoral condyle and the forehead to attenuate incoming shock waves. Examination of the absolute values of the amplitude of the propagated waves leads to the conclusion that the human locomotor system, which possesses reduced attenuation capacity, tries to prevent overloading of the head from insufficiently attenuated shock waves. Results of the present investigation support the idea that the repetitive loading resulting from gait generates intermittent waves that propagate through the entire human musculoskeletal system from the heel up to the head. These waves are gradually attenuated along this course by the natural shock absorbers (bone and soft tissues). Contemporary methods for examination of the human musculoskeletal system may by improved by using the proposed non-invasive in vivo technique for quantitative characterization of the locomotor system's shock absorbing capacity.

Co-ordinated spatial propagation of blood plasma clotting and fibrinolytic fronts

PubMed Central

Zhalyalov, Ansar S.; Panteleev, Mikhail A.; Gracheva, Marina A.; Ataullakhanov, Fazoil I.

2017-01-01

Fibrinolysis is a cascade of proteolytic reactions occurring in blood and soft tissues, which functions to disintegrate fibrin clots when they are no more needed. In order to elucidate its regulation in space and time, fibrinolysis was investigated using an in vitro reaction-diffusion experimental model of blood clot formation and dissolution. Clotting was activated by a surface with immobilized tissue factor in a thin layer of recalcified blood plasma supplemented with tissue plasminogen activator (TPA), urokinase plasminogen activator or streptokinase. Formation and dissolution of fibrin clot was monitored by videomicroscopy. Computer systems biology model of clot formation and lysis was developed for data analysis and experimental planning. Fibrin clot front propagated in space from tissue factor, followed by a front of clot dissolution propagating from the same source. Velocity of lysis front propagation linearly depended on the velocity clotting front propagation (correlation r2 = 0.91). Computer model revealed that fibrin formation was indeed the rate-limiting step in the fibrinolysis front propagation. The phenomenon of two fronts which switched the state of blood plasma from liquid to solid and then back to liquid did not depend on the fibrinolysis activator. Interestingly, TPA at high concentrations began to increase lysis onset time and to decrease lysis propagation velocity, presumably due to plasminogen depletion. Spatially non-uniform lysis occurred simultaneously with clot formation and detached the clot from the procoagulant surface. These patterns of spatial fibrinolysis provide insights into its regulation and might explain clinical phenomena associated with thrombolytic therapy. PMID:28686711

Laser vibrometry for guided wave propagation phenomena visualisation and damage detection

NASA Astrophysics Data System (ADS)

Malinowski, Pawel; Wandowski, Tomasz; Kudela, Pawel; Ostachowicz, Wieslaw

2010-05-01

This paper presents research on the damage localization method. The method is based on guided wave propagation phenomena. The investigation was focused on application of this method to monitor the condition of structural elements such as aluminium or composite panels. These elements are commonly used in aerospace industry and it is crucial to provide a methodology to determine their condition, in order to prevent from unexpected and dangerous collapse of a structure. Propagating waves interact with cracks, notches, rivets, thickness changes, stiffeners and other discontinuities present in structural elements. It means that registering these waves one can obtain information about the structure condition—whether it is damaged or not. Furthermore these methods can be applied not only to aerospace structures but also to wind turbine blades and pipelines. In reported investigation piezoelectric transducer was used to excite guided waves in considered panel. Measurement of the wave field was realized using laser scanning vibrometer that registered the velocity responses at a defined points belonging to a defined mesh. Mesh spacing was investigated in order to ensure fine wave propagation visualisation. Firstly, wave propagation in pristine specimen was investigated. Secondly, artificial damage was introduced to the specimen. Finally, wave interaction with damage was visualised and conclusions regarding potentials of application of laser vibrometer for damage detection were drawn. All the processing was made with the developed MATLAB procedures.

Intensive Versus Standard Blood Pressure Control in SPRINT-Eligible Participants of ACCORD-BP.

PubMed

Buckley, Leo F; Dixon, Dave L; Wohlford, George F; Wijesinghe, Dayanjan S; Baker, William L; Van Tassell, Benjamin W

2017-12-01

We sought to determine the effect of intensive blood pressure (BP) control on cardiovascular outcomes in participants with type 2 diabetes mellitus (T2DM) and additional risk factors for cardiovascular disease (CVD). This study was a post hoc, multivariate, subgroup analysis of ACCORD-BP (Action to Control Cardiovascular Risk in Diabetes Blood Pressure) participants. Participants were eligible for the analysis if they were in the standard glucose control arm of ACCORD-BP and also had the additional CVD risk factors required for SPRINT (Systolic Blood Pressure Intervention Trial) eligibility. We used a Cox proportional hazards regression model to compare the effect of intensive versus standard BP control on CVD outcomes. The "SPRINT-eligible" ACCORD-BP participants were pooled with SPRINT participants to determine whether the effects of intensive BP control interacted with T2DM. The mean baseline Framingham 10-year CVD risk scores were 14.5% and 14.8%, respectively, in the intensive and standard BP control groups. The mean achieved systolic BP values were 120 and 134 mmHg in the intensive and standard BP control groups ( P < 0.001). Intensive BP control reduced the composite of CVD death, nonfatal myocardial infarction (MI), nonfatal stroke, any revascularization, and heart failure (hazard ratio 0.79; 95% CI 0.65-0.96; P = 0.02). Intensive BP control also reduced CVD death, nonfatal MI, and nonfatal stroke (hazard ratio 0.69; 95% CI 0.51-0.93; P = 0.01). Treatment-related adverse events occurred more frequently in participants receiving intensive BP control (4.1% vs. 2.1%; P = 0.003). The effect of intensive BP control on CVD outcomes did not differ between patients with and without T2DM ( P > 0.62). Intensive BP control reduced CVD outcomes in a cohort of participants with T2DM and additional CVD risk factors. © 2017 by the American Diabetes Association.

Artificial Neural Network and application in calibration transfer of AOTF-based NIR spectrometer

NASA Astrophysics Data System (ADS)

Wang, Wenbo; Jiang, Chengzhi; Xu, Kexin; Wang, Bin

2002-09-01

Chemometrics is widely applied to develop models for quantitative prediction of unknown samples in Near-infrared (NIR) spectroscopy. However, calibrated models generally fail when new instruments are introduced or replacement of the instrument parts occurs. Therefore, calibration transfer becomes necessary to avoid the costly, time-consuming recalibration of models. Piecewise Direct Standardization (PDS) has been proven to be a reference method for standardization. In this paper, Artificial Neural Network (ANN) is employed as an alternative to transfer spectra between instruments. Two Acousto-optic Tunable Filter NIR spectrometers are employed in the experiment. Spectra of glucose solution are collected on the spectrometers through transflectance mode. A Back propagation Network with two layers is employed to simulate the function between instruments piecewisely. Standardization subset is selected by Kennard and Stone (K-S) algorithm in the first two score space of Principal Component Analysis (PCA) of spectra matrix. In current experiment, it is noted that obvious nonlinearity exists between instruments and attempts are made to correct such nonlinear effect. Prediction results before and after successful calibration transfer are compared. Successful transfer can be achieved by adapting window size and training parameters. Final results reveal that ANN is effective in correcting the nonlinear instrumental difference and a only 1.5~2 times larger prediction error is expected after successful transfer.

High reliability linear drive device for artificial hearts

NASA Astrophysics Data System (ADS)

Ji, Jinghua; Zhao, Wenxiang; Liu, Guohai; Shen, Yue; Wang, Fangqun

2012-04-01

In this paper, a new high reliability linear drive device, termed as stator-permanent-magnet tubular oscillating actuator (SPM-TOA), is proposed for artificial hearts (AHs). The key is to incorporate the concept of two independent phases into this linear AH device, hence achieving high reliability operation. The fault-tolerant teeth are employed to provide the desired decoupling phases in magnetic circuit. Also, as the magnets and the coils are located in the stator, the proposed SPM-TOA takes the definite advantages of robust mover and direct-drive capability. By using the time-stepping finite element method, the electromagnetic characteristics of the proposed SPM-TOA are analyzed, including magnetic field distributions, flux linkages, back- electromotive forces (back-EMFs) self- and mutual inductances, as well as cogging and thrust forces. The results confirm that the proposed SPM-TOA meets the dimension, weight, and force requirements of the AH drive device.

ACTS propagation experiment discussion: Ka-band propagation measurements using the ACTS propagation terminal and the CSU-CHILL and Space Communications Technology Center Florida propagation program

NASA Technical Reports Server (NTRS)

Bringi, V. N.; Chandrasekar, V.; Mueller, Eugene A.; Turk, Joseph; Beaver, John; Helmken, Henry F.; Henning, Rudy

1993-01-01

Papers on Ka-band propagation measurements using the ACTS propagation terminal and the Colorado State University CHILL multiparameter radar and on Space Communications Technology Center Florida Propagation Program are discussed. Topics covered include: microwave radiative transfer and propagation models; NASA propagation terminal status; ACTS channel characteristics; FAU receive only terminal; FAU terminal status; and propagation testbed.

Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

PubMed

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-04-21

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

PubMed Central

Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

2015-01-01

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

Visual flight control in naturalistic and artificial environments.

PubMed

Baird, Emily; Dacke, Marie

2012-12-01

Although the visual flight control strategies of flying insects have evolved to cope with the complexity of the natural world, studies investigating this behaviour have typically been performed indoors using simplified two-dimensional artificial visual stimuli. How well do the results from these studies reflect the natural behaviour of flying insects considering the radical differences in contrast, spatial composition, colour and dimensionality between these visual environments? Here, we aim to answer this question by investigating the effect of three- and two-dimensional naturalistic and artificial scenes on bumblebee flight control in an outdoor setting and compare the results with those of similar experiments performed in an indoor setting. In particular, we focus on investigating the effect of axial (front-to-back) visual motion cues on ground speed and centring behaviour. Our results suggest that, in general, ground speed control and centring behaviour in bumblebees is not affected by whether the visual scene is two- or three dimensional, naturalistic or artificial, or whether the experiment is conducted indoors or outdoors. The only effect that we observe between naturalistic and artificial scenes on flight control is that when the visual scene is three-dimensional and the visual information on the floor is minimised, bumblebees fly further from the midline of the tunnel. The findings presented here have implications not only for understanding the mechanisms of visual flight control in bumblebees, but also for the results of past and future investigations into visually guided flight control in other insects.

Gear Fault Diagnosis Based on BP Neural Network

NASA Astrophysics Data System (ADS)

Huang, Yongsheng; Huang, Ruoshi

2018-03-01

Gear transmission is more complex, widely used in machinery fields, which form of fault has some nonlinear characteristics. This paper uses BP neural network to train the gear of four typical failure modes, and achieves satisfactory results. Tested by using test data, test results have an agreement with the actual results. The results show that the BP neural network can effectively solve the complex state of gear fault in the gear fault diagnosis.

[Clinical significance of NS1-BP expression in esophageal squamous cell carcinoma].

PubMed

Ren, K; Qian, D; Wang, Y W; Pang, Q S; Zhang, W C; Yuan, Z Y; Wang, P

2018-01-23

Objective: To investigate the clinical significance of NS1-BP expression in patients with esophageal squamous cell carcinoma (ESCC), and to study the roles of NS1-BP in proliferation and apoptosis of ESCC cells. Methods: A total of 98 tumor tissues and 30 adjacent normal tissues from 98 ESCC patients were used as study group and control group, and these samples were collected in Sun Yat-Sen University Cancer Center between 2002 and 2008. In addition, 46 ESCC tissues which were collected in Cancer Institute and Hospital of Tianjin Medical University were used as validation group. Expression of mucosal NS1-BP was detected by immunohistochemistry. Kaplan-Meier curve and log-rank test were used to analyze the survival rate. Multivariate Cox proportional hazard model was used to analyze the prognostic factors. Furthermore, NS1-BP was over expressed or knocked down in ESCC cells by transient transfection. Protein levels of c-Myc were detected by western blot. Cell viability and apoptosis was analyzed by MTT assay and flow cytometry. Results: Among all of tested samples, NS1-BP were down-regulated in 9 out of 30 non-tumorous normal esophageal tissues (30.0%) and 85 out of 144 ESCC tissues (59.0%), respectively, showing a statistically significant difference ( P =0.012). In the study group, three-year disease-free survival rate of NS1-BP high expression group (53.2%) was significantly higher than that of NS1-BP low expression group (27.6%; P =0.009). In the validation group, the three-year disease-free survival rates were 57.8% and 25.5% in NS1-BP high and low levels groups, respectively, showing a similar results ( P =0.016). Importantly, multivariate analyses showed that low expression of NS1-BP was an independent predictor for chemoradiotherapy sensitivity and shorter disease-free survival time in ESCC patients( P <0.05 for all). Furthermore, overexpressed NS1-BP in TE-1 cells repressed c-Myc expression, inhibited cell proliferation and promoted apoptosis. In contrast

Artificial “ping-pong” cascade of PIWI-interacting RNA in silkworm cells

PubMed Central

Shoji, Keisuke; Suzuki, Yutaka; Sugano, Sumio; Shimada, Toru; Katsuma, Susumu

2017-01-01

PIWI-interacting RNAs (piRNAs) play essential roles in the defense system against selfish elements in animal germline cells by cooperating with PIWI proteins. A subset of piRNAs is predicted to be generated via the “ping-pong” cascade, which is mainly controlled by two different PIWI proteins. Here we established a cell-based artificial piRNA production system using a silkworm ovarian cultured cell line that is believed to possess a complete piRNA pathway. In addition, we took advantage of a unique silkworm sex-determining one-to-one ping-pong piRNA pair, which enabled us to precisely monitor the behavior of individual artificial piRNAs. With this novel strategy, we successfully generated artificial piRNAs against endogenous protein-coding genes via the expected back-and-forth traveling mechanism. Furthermore, we detected “primary” piRNAs from the upstream region of the artificial “ping-pong” site in the endogenous gene. This artificial piRNA production system experimentally confirms the existence of the “ping-pong” cascade of piRNAs. Also, this system will enable us to identify the factors involved in both, or each, of the “ping” and “pong” cascades and the sequence features that are required for efficient piRNA production. PMID:27777367

Displacement-based back-analysis of the model parameters of the Nuozhadu high earth-rockfill dam.

PubMed

Wu, Yongkang; Yuan, Huina; Zhang, Bingyin; Zhang, Zongliang; Yu, Yuzhen

2014-01-01

The parameters of the constitutive model, the creep model, and the wetting model of materials of the Nuozhadu high earth-rockfill dam were back-analyzed together based on field monitoring displacement data by employing an intelligent back-analysis method. In this method, an artificial neural network is used as a substitute for time-consuming finite element analysis, and an evolutionary algorithm is applied for both network training and parameter optimization. To avoid simultaneous back-analysis of many parameters, the model parameters of the three main dam materials are decoupled and back-analyzed separately in a particular order. Displacement back-analyses were performed at different stages of the construction period, with and without considering the creep and wetting deformations. Good agreement between the numerical results and the monitoring data was obtained for most observation points, which implies that the back-analysis method and decoupling method are effective for solving complex problems with multiple models and parameters. The comparison of calculation results based on different sets of back-analyzed model parameters indicates the necessity of taking the effects of creep and wetting into consideration in the numerical analyses of high earth-rockfill dams. With the resulting model parameters, the stress and deformation distributions at completion are predicted and analyzed.

Screen-printed back-to-back electroanalytical sensors.

PubMed

Metters, Jonathan P; Randviir, Edward P; Banks, Craig E

2014-11-07

We introduce the concept of screen-printed back-to-back electroanalytical sensors where in this facile and generic approach, screen-printed electrodes are printed back-to-back with a common electrical connection to the two working electrodes with the counter and reference electrodes for each connected in the same manner as a normal "traditional" screen-printed sensor would be. This approach utilises the usually redundant back of the screen-printed sensor, converting this "dead-space" into a further electrochemical sensor which results in improvements in the analytical performance. In the use of the back-to-back design, the electrode area is consequently doubled with improvements in the analytical performance observed with the analytical sensitivity (gradient of a plot of peak height/analytical signal against concentration) doubling and the corresponding limit-of-detection being reduced. We also demonstrate that through intelligent electrode design, a quadruple in the observed analytical sensitivity can also be realised when double microband electrodes are used in the back-to-back configuration as long as they are placed sufficiently apart such that no diffusional interaction occurs. Such work is generic in nature and can be facilely applied to a plethora of screen-printed (and related) sensors utilising the commonly overlooked redundant back of the electrode providing facile improvements in the electroanalytical performance.

Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.

PubMed