Estimation of the neural drive to the muscle from surface electromyograms

NASA Astrophysics Data System (ADS)

Hofmann, David

Muscle force is highly correlated with the standard deviation of the surface electromyogram (sEMG) produced by the active muscle. Correctly estimating this quantity of non-stationary sEMG and understanding its relation to neural drive and muscle force is of paramount importance. The single constituents of the sEMG are called motor unit action potentials whose biphasic amplitude can interfere (named amplitude cancellation), potentially affecting the standard deviation (Keenan etal. 2005). However, when certain conditions are met the Campbell-Hardy theorem suggests that amplitude cancellation does not affect the standard deviation. By simulation of the sEMG, we verify the applicability of this theorem to myoelectric signals and investigate deviations from its conditions to obtain a more realistic setting. We find no difference in estimated standard deviation with and without interference, standing in stark contrast to previous results (Keenan etal. 2008, Farina etal. 2010). Furthermore, since the theorem provides us with the functional relationship between standard deviation and neural drive we conclude that complex methods based on high density electrode arrays and blind source separation might not bear substantial advantages for neural drive estimation (Farina and Holobar 2016). Funded by NIH Grant Number 1 R01 EB022872 and NSF Grant Number 1208126.

Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

NASA Astrophysics Data System (ADS)

Ghosh, Arpita; Das, Papita; Sinha, Keka

2015-06-01

In the present work, spent tea leaves were modified with Ca(OH)2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

Absolute auditory threshold: testing the absolute.

PubMed

Heil, Peter; Matysiak, Artur

2017-11-02

The mechanisms underlying the detection of sounds in quiet, one of the simplest tasks for auditory systems, are debated. Several models proposed to explain the threshold for sounds in quiet and its dependence on sound parameters include a minimum sound intensity ('hard threshold'), below which sound has no effect on the ear. Also, many models are based on the assumption that threshold is mediated by integration of a neural response proportional to sound intensity. Here, we test these ideas. Using an adaptive forced choice procedure, we obtained thresholds of 95 normal-hearing human ears for 18 tones (3.125 kHz carrier) in quiet, each with a different temporal amplitude envelope. Grand-mean thresholds and standard deviations were well described by a probabilistic model according to which sensory events are generated by a Poisson point process with a low rate in the absence, and higher, time-varying rates in the presence, of stimulation. The subject actively evaluates the process and bases the decision on the number of events observed. The sound-driven rate of events is proportional to the temporal amplitude envelope of the bandpass-filtered sound raised to an exponent. We find no evidence for a hard threshold: When the model is extended to include such a threshold, the fit does not improve. Furthermore, we find an exponent of 3, consistent with our previous studies and further challenging models that are based on the assumption of the integration of a neural response that, at threshold sound levels, is directly proportional to sound amplitude or intensity. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

The neural basis of financial risk taking.

PubMed

Kuhnen, Camelia M; Knutson, Brian

2005-09-01

Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

Validation of Mean Absolute Sea Level of the North Atlantic obtained from Drifter, Altimetry and Wind Data

NASA Technical Reports Server (NTRS)

Maximenko, Nikolai A.

2003-01-01

Mean absolute sea level reflects the deviation of the Ocean surface from geoid due to the ocean currents and is an important characteristic of the dynamical state of the ocean. Values of its spatial variations (order of 1 m) are generally much smaller than deviations of the geoid shape from ellipsoid (order of 100 m) that makes the derivation of the absolute mean sea level a difficult task for gravity and satellite altimetry observations. Technique used by Niiler et al. for computation of the absolute mean sea level in the Kuroshio Extension was then developed into more general method and applied by Niiler et al. (2003b) to the global Ocean. The method is based on the consideration of balance of horizontal momentum.

Forecasting short-term data center network traffic load with convolutional neural networks.

PubMed

Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.

Using statistical and artificial neural network models to forecast potentiometric levels at a deep well in South Texas

NASA Astrophysics Data System (ADS)

Uddameri, V.

2007-01-01

Reliable forecasts of monthly and quarterly fluctuations in groundwater levels are necessary for short- and medium-term planning and management of aquifers to ensure proper service of seasonal demands within a region. Development of physically based transient mathematical models at this time scale poses considerable challenges due to lack of suitable data and other uncertainties. Artificial neural networks (ANN) possess flexible mathematical structures and are capable of mapping highly nonlinear relationships. Feed-forward neural network models were constructed and trained using the back-percolation algorithm to forecast monthly and quarterly time-series water levels at a well that taps into the deeper Evangeline formation of the Gulf Coast aquifer in Victoria, TX. Unlike unconfined formations, no causal relationships exist between water levels and hydro-meteorological variables measured near the vicinity of the well. As such, an endogenous forecasting model using dummy variables to capture short-term seasonal fluctuations and longer-term (decadal) trends was constructed. The root mean square error, mean absolute deviation and correlation coefficient ( R) were noted to be 1.40, 0.33 and 0.77 m, respectively, for an evaluation dataset of quarterly measurements and 1.17, 0.46, and 0.88 m for an evaluative monthly dataset not used to train or test the model. These statistics were better for the ANN model than those developed using statistical regression techniques.

Forecasting short-term data center network traffic load with convolutional neural networks

PubMed Central

Ordozgoiti, Bruno; Gómez-Canaval, Sandra

2018-01-01

Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936

9 CFR 439.20 - Criteria for maintaining accreditation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is...) Variability: The absolute value of the standardized difference between the accredited laboratory's result and... constant, is used in place of the absolute value of the standardized difference to determine the CUSUM-V...

9 CFR 439.20 - Criteria for maintaining accreditation.

Code of Federal Regulations, 2013 CFR

2013-01-01

... deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is...) Variability: The absolute value of the standardized difference between the accredited laboratory's result and... constant, is used in place of the absolute value of the standardized difference to determine the CUSUM-V...

9 CFR 439.20 - Criteria for maintaining accreditation.

Code of Federal Regulations, 2012 CFR

2012-01-01

... deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is...) Variability: The absolute value of the standardized difference between the accredited laboratory's result and... constant, is used in place of the absolute value of the standardized difference to determine the CUSUM-V...

9 CFR 439.20 - Criteria for maintaining accreditation.

Code of Federal Regulations, 2014 CFR

2014-01-01

... deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is...) Variability: The absolute value of the standardized difference between the accredited laboratory's result and... constant, is used in place of the absolute value of the standardized difference to determine the CUSUM-V...

9 CFR 439.20 - Criteria for maintaining accreditation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is...) Variability: The absolute value of the standardized difference between the accredited laboratory's result and... constant, is used in place of the absolute value of the standardized difference to determine the CUSUM-V...

Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

ERIC Educational Resources Information Center

Helmreich, James E.; Krog, K. Peter

2018-01-01

We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

Application of Mean of Absolute Deviation Method for the Selection of Best Nonlinear Component Based on Video Encryption

NASA Astrophysics Data System (ADS)

Anees, Amir; Khan, Waqar Ahmad; Gondal, Muhammad Asif; Hussain, Iqtadar

2013-07-01

The aim of this work is to make use of the mean of absolute deviation (MAD) method for the evaluation process of substitution boxes used in the advanced encryption standard. In this paper, we use the MAD technique to analyze some popular and prevailing substitution boxes used in encryption processes. In particular, MAD is applied to advanced encryption standard (AES), affine power affine (APA), Gray, Lui J., Residue Prime, S8 AES, SKIPJACK, and Xyi substitution boxes.

Database and new models based on a group contribution method to predict the refractive index of ionic liquids.

PubMed

Wang, Xinxin; Lu, Xingmei; Zhou, Qing; Zhao, Yongsheng; Li, Xiaoqian; Zhang, Suojiang

2017-08-02

Refractive index is one of the important physical properties, which is widely used in separation and purification. In this study, the refractive index data of ILs were collected to establish a comprehensive database, which included about 2138 pieces of data from 1996 to 2014. The Group Contribution-Artificial Neural Network (GC-ANN) model and Group Contribution (GC) method were employed to predict the refractive index of ILs at different temperatures from 283.15 K to 368.15 K. Average absolute relative deviations (AARD) of the GC-ANN model and the GC method were 0.179% and 0.628%, respectively. The results showed that a GC-ANN model provided an effective way to estimate the refractive index of ILs, whereas the GC method was simple and extensive. In summary, both of the models were accurate and efficient approaches for estimating refractive indices of ILs.

In vivo dosimetry for external photon treatments of head and neck cancers by diodes and TLDS.

PubMed

Tung, C J; Wang, H C; Lo, S H; Wu, J M; Wang, C J

2004-01-01

In vivo dosimetry was implemented for treatments of head and neck cancers in the large fields. Diode and thermoluminescence dosemeter (TLD) measurements were carried out for the linear accelerators of 6 MV photon beams. ESTRO in vivo dosimetry protocols were followed in the determination of midline doses from measurements of entrance and exit doses. Of the fields monitored by diodes, the maximum absolute deviation of measured midline doses from planned target doses was 8%, with the mean value and the standard deviation of -1.0 and 2.7%. If planned target doses were calculated using radiological water equivalent thicknesses rather than patient geometric thicknesses, the maximum absolute deviation dropped to 4%, with the mean and the standard deviation of 0.7 and 1.8%. For in vivo dosimetry monitored by TLDs, the shift in mean dose remained small but the statistical precision became poor.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models.

PubMed

de Jesus, Karla; Ayala, Helon V H; de Jesus, Kelly; Coelho, Leandro Dos S; Medeiros, Alexandre I A; Abraldes, José A; Vaz, Mário A P; Fernandes, Ricardo J; Vilas-Boas, João Paulo

2018-03-01

Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances.

Interactive Visual Least Absolutes Method: Comparison with the Least Squares and the Median Methods

ERIC Educational Resources Information Center

Kim, Myung-Hoon; Kim, Michelle S.

2016-01-01

A visual regression analysis using the least absolutes method (LAB) was developed, utilizing an interactive approach of visually minimizing the sum of the absolute deviations (SAB) using a bar graph in Excel; the results agree very well with those obtained from nonvisual LAB using a numerical Solver in Excel. These LAB results were compared with…

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Near real-time analysis of extrinsic Fabry-Perot interferometric sensors under damped vibration using artificial neural networks

NASA Astrophysics Data System (ADS)

Dua, Rohit; Watkins, Steve E.

2009-03-01

Strain analysis due to vibration can provide insight into structural health. An Extrinsic Fabry-Perot Interferometric (EFPI) sensor under vibrational strain generates a non-linear modulated output. Advanced signal processing techniques, to extract important information such as absolute strain, are required to demodulate this non-linear output. Past research has employed Artificial Neural Networks (ANN) and Fast Fourier Transforms (FFT) to demodulate the EFPI sensor for limited conditions. These demodulation systems could only handle variations in absolute value of strain and frequency of actuation during a vibration event. This project uses an ANN approach to extend the demodulation system to include the variation in the damping coefficient of the actuating vibration, in a near real-time vibration scenario. A computer simulation provides training and testing data for the theoretical output of the EFPI sensor to demonstrate the approaches. FFT needed to be performed on a window of the EFPI output data. A small window of observation is obtained, while maintaining low absolute-strain prediction errors, heuristically. Results are obtained and compared from employing different ANN architectures including multi-layered feedforward ANN trained using Backpropagation Neural Network (BPNN), and Generalized Regression Neural Networks (GRNN). A two-layered algorithm fusion system is developed and tested that yields better results.

Modelling and Predicting Backstroke Start Performance Using Non-Linear and Linear Models

PubMed Central

de Jesus, Karla; Ayala, Helon V. H.; de Jesus, Kelly; Coelho, Leandro dos S.; Medeiros, Alexandre I.A.; Abraldes, José A.; Vaz, Mário A.P.; Fernandes, Ricardo J.; Vilas-Boas, João Paulo

2018-01-01

Abstract Our aim was to compare non-linear and linear mathematical model responses for backstroke start performance prediction. Ten swimmers randomly completed eight 15 m backstroke starts with feet over the wedge, four with hands on the highest horizontal and four on the vertical handgrip. Swimmers were videotaped using a dual media camera set-up, with the starts being performed over an instrumented block with four force plates. Artificial neural networks were applied to predict 5 m start time using kinematic and kinetic variables and to determine the accuracy of the mean absolute percentage error. Artificial neural networks predicted start time more robustly than the linear model with respect to changing training to the validation dataset for the vertical handgrip (3.95 ± 1.67 vs. 5.92 ± 3.27%). Artificial neural networks obtained a smaller mean absolute percentage error than the linear model in the horizontal (0.43 ± 0.19 vs. 0.98 ± 0.19%) and vertical handgrip (0.45 ± 0.19 vs. 1.38 ± 0.30%) using all input data. The best artificial neural network validation revealed a smaller mean absolute error than the linear model for the horizontal (0.007 vs. 0.04 s) and vertical handgrip (0.01 vs. 0.03 s). Artificial neural networks should be used for backstroke 5 m start time prediction due to the quite small differences among the elite level performances. PMID:29599857

Study of the optimum level of electrode placement for the evaluation of absolute lung resistivity with the Mk3.5 EIT system.

PubMed

Nebuya, S; Noshiro, M; Yonemoto, A; Tateno, S; Brown, B H; Smallwood, R H; Milnes, P

2006-05-01

Inter-subject variability has caused the majority of previous electrical impedance tomography (EIT) techniques to focus on the derivation of relative or difference measures of in vivo tissue resistivity. Implicit in these techniques is the requirement for a reference or previously defined data set. This study assesses the accuracy and optimum electrode placement strategy for a recently developed method which estimates an absolute value of organ resistivity without recourse to a reference data set. Since this measurement of tissue resistivity is absolute, in Ohm metres, it should be possible to use EIT measurements for the objective diagnosis of lung diseases such as pulmonary oedema and emphysema. However, the stability and reproducibility of the method have not yet been investigated fully. To investigate these problems, this study used a Sheffield Mk3.5 system which was configured to operate with eight measurement electrodes. As a result of this study, the absolute resistivity measurement was found to be insensitive to the electrode level between 4 and 5 cm above the xiphoid process. The level of the electrode plane was varied between 2 cm and 7 cm above the xiphoid process. Absolute lung resistivity in 18 normal subjects (age 22.6 +/- 4.9, height 169.1 +/- 5.7 cm, weight 60.6 +/- 4.5 kg, body mass index 21.2 +/- 1.6: mean +/- standard deviation) was measured during both normal and deep breathing for 1 min. Three sets of measurements were made over a period of several days on each of nine of the normal male subjects. No significant differences in absolute lung resistivity were found, either during normal tidal breathing between the electrode levels of 4 and 5 cm (9.3 +/- 2.4 Omega m, 9.6 +/- 1.9 Omega m at 4 and 5 cm, respectively: mean +/- standard deviation) or during deep breathing between the electrode levels of 4 and 5 cm (10.9 +/- 2.9 Omega m and 11.1 +/- 2.3 Omega m, respectively: mean +/- standard deviation). However, the differences in absolute lung resistivity between normal and deep tidal breathing at the same electrode level are significant. No significant difference was found in the coefficient of variation between the electrode levels of 4 and 5 cm (9.5 +/- 3.6%, 8.5 +/- 3.2% at 4 and 5 cm, respectively: mean +/- standard deviation in individual subjects). Therefore, the electrode levels of 4 and 5 cm above the xiphoid process showed reasonable reliability in the measurement of absolute lung resistivity both among individuals and over time.

[Application of wavelet neural networks model to forecast incidence of syphilis].

PubMed

Zhou, Xian-Feng; Feng, Zi-Jian; Yang, Wei-Zhong; Li, Xiao-Song

2011-07-01

To apply Wavelet Neural Networks (WNN) model to forecast incidence of Syphilis. Back Propagation Neural Network (BPNN) and WNN were developed based on the monthly incidence of Syphilis in Sichuan province from 2004 to 2008. The accuracy of forecast was compared between the two models. In the training approximation, the mean absolute error (MAE), rooted mean square error (RMSE) and mean absolute percentage error (MAPE) were 0.0719, 0.0862 and 11.52% respectively for WNN, and 0.0892, 0.1183 and 14.87% respectively for BPNN. The three indexes for generalization of models were 0.0497, 0.0513 and 4.60% for WNN, and 0.0816, 0.1119 and 7.25% for BPNN. WNN is a better model for short-term forecasting of Syphilis.

Comparative study of four time series methods in forecasting typhoid fever incidence in China.

PubMed

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model.

Comparative Study of Four Time Series Methods in Forecasting Typhoid Fever Incidence in China

PubMed Central

Zhang, Xingyu; Liu, Yuanyuan; Yang, Min; Zhang, Tao; Young, Alistair A.; Li, Xiaosong

2013-01-01

Accurate incidence forecasting of infectious disease is critical for early prevention and for better government strategic planning. In this paper, we present a comprehensive study of different forecasting methods based on the monthly incidence of typhoid fever. The seasonal autoregressive integrated moving average (SARIMA) model and three different models inspired by neural networks, namely, back propagation neural networks (BPNN), radial basis function neural networks (RBFNN), and Elman recurrent neural networks (ERNN) were compared. The differences as well as the advantages and disadvantages, among the SARIMA model and the neural networks were summarized and discussed. The data obtained for 2005 to 2009 and for 2010 from the Chinese Center for Disease Control and Prevention were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The results showed that RBFNN obtained the smallest MAE, MAPE and MSE in both the modeling and forecasting processes. The performances of the four models ranked in descending order were: RBFNN, ERNN, BPNN and the SARIMA model. PMID:23650546

Predicting Air Permeability of Handloom Fabrics: A Comparative Analysis of Regression and Artificial Neural Network Models

NASA Astrophysics Data System (ADS)

Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya

2013-03-01

This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.

Estimating Accuracy of Land-Cover Composition From Two-Stage Clustering Sampling

EPA Science Inventory

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), ...

Special electronic distance meter calibration for precise engineering surveying industrial applications

NASA Astrophysics Data System (ADS)

Braun, Jaroslav; Štroner, Martin; Urban, Rudolf

2015-05-01

All surveying instruments and their measurements suffer from some errors. To refine the measurement results, it is necessary to use procedures restricting influence of the instrument errors on the measured values or to implement numerical corrections. In precise engineering surveying industrial applications the accuracy of the distances usually realized on relatively short distance is a key parameter limiting the resulting accuracy of the determined values (coordinates, etc.). To determine the size of systematic and random errors of the measured distances were made test with the idea of the suppression of the random error by the averaging of the repeating measurement, and reducing systematic errors influence of by identifying their absolute size on the absolute baseline realized in geodetic laboratory at the Faculty of Civil Engineering CTU in Prague. The 16 concrete pillars with forced centerings were set up and the absolute distances between the points were determined with a standard deviation of 0.02 millimetre using a Leica Absolute Tracker AT401. For any distance measured by the calibrated instruments (up to the length of the testing baseline, i.e. 38.6 m) can now be determined the size of error correction of the distance meter in two ways: Firstly by the interpolation on the raw data, or secondly using correction function derived by previous FFT transformation usage. The quality of this calibration and correction procedure was tested on three instruments (Trimble S6 HP, Topcon GPT-7501, Trimble M3) experimentally using Leica Absolute Tracker AT401. By the correction procedure was the standard deviation of the measured distances reduced significantly to less than 0.6 mm. In case of Topcon GPT-7501 is the nominal standard deviation 2 mm, achieved (without corrections) 2.8 mm and after corrections 0.55 mm; in case of Trimble M3 is nominal standard deviation 3 mm, achieved (without corrections) 1.1 mm and after corrections 0.58 mm; and finally in case of Trimble S6 is nominal standard deviation 1 mm, achieved (without corrections) 1.2 mm and after corrections 0.51 mm. Proposed procedure of the calibration and correction is in our opinion very suitable for increasing of the accuracy of the electronic distance measurement and allows the use of the common surveying instrument to achieve uncommonly high precision.

Large Deviations for Nonlocal Stochastic Neural Fields

PubMed Central

2014-01-01

We study the effect of additive noise on integro-differential neural field equations. In particular, we analyze an Amari-type model driven by a Q-Wiener process, and focus on noise-induced transitions and escape. We argue that proving a sharp Kramers’ law for neural fields poses substantial difficulties, but that one may transfer techniques from stochastic partial differential equations to establish a large deviation principle (LDP). Then we demonstrate that an efficient finite-dimensional approximation of the stochastic neural field equation can be achieved using a Galerkin method and that the resulting finite-dimensional rate function for the LDP can have a multiscale structure in certain cases. These results form the starting point for an efficient practical computation of the LDP. Our approach also provides the technical basis for further rigorous study of noise-induced transitions in neural fields based on Galerkin approximations. Mathematics Subject Classification (2000): 60F10, 60H15, 65M60, 92C20. PMID:24742297

9 CFR 439.10 - Criteria for obtaining accreditation.

Code of Federal Regulations, 2014 CFR

2014-01-01

... absolute value of the average standardized difference must not exceed the following: (i) For food chemistry... samples must be less than 5.0. A result will have a large deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is less than 2.5 and otherwise a measure equal...

9 CFR 439.10 - Criteria for obtaining accreditation.

Code of Federal Regulations, 2012 CFR

2012-01-01

... absolute value of the average standardized difference must not exceed the following: (i) For food chemistry... samples must be less than 5.0. A result will have a large deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is less than 2.5 and otherwise a measure equal...

9 CFR 439.10 - Criteria for obtaining accreditation.

Code of Federal Regulations, 2013 CFR

2013-01-01

... absolute value of the average standardized difference must not exceed the following: (i) For food chemistry... samples must be less than 5.0. A result will have a large deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is less than 2.5 and otherwise a measure equal...

9 CFR 439.10 - Criteria for obtaining accreditation.

Code of Federal Regulations, 2011 CFR

2011-01-01

... absolute value of the average standardized difference must not exceed the following: (i) For food chemistry... samples must be less than 5.0. A result will have a large deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is less than 2.5 and otherwise a measure equal...

9 CFR 439.10 - Criteria for obtaining accreditation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... absolute value of the average standardized difference must not exceed the following: (i) For food chemistry... samples must be less than 5.0. A result will have a large deviation measure equal to zero when the absolute value of the result's standardized difference, (d), is less than 2.5 and otherwise a measure equal...

On-line determination of transient stability status using multilayer perceptron neural network

NASA Astrophysics Data System (ADS)

Frimpong, Emmanuel Asuming; Okyere, Philip Yaw; Asumadu, Johnson

2018-01-01

A scheme to predict transient stability status following a disturbance is presented. The scheme is activated upon the tripping of a line or bus and operates as follows: Two samples of frequency deviation values at all generator buses are obtained. At each generator bus, the maximum frequency deviation within the two samples is extracted. A vector is then constructed from the extracted maximum frequency deviations. The Euclidean norm of the constructed vector is calculated and then fed as input to a trained multilayer perceptron neural network which predicts the stability status of the system. The scheme was tested using data generated from the New England test system. The scheme successfully predicted the stability status of all two hundred and five disturbance test cases.

Application of Artificial Neural Network to Predict Colour Change, Shrinkage and Texture of Osmotically Dehydrated Pumpkin

NASA Astrophysics Data System (ADS)

Tang, S. Y.; Lee, J. S.; Loh, S. P.; Tham, H. J.

2017-06-01

The objectives of this study were to use Artificial Neural Network (ANN) to predict colour change, shrinkage and texture of osmotically dehydrated pumpkin slices. The effects of process variables such as concentration of osmotic solution, immersion temperature and immersion time on the above mentioned physical properties were studied. The colour of the samples was measured using a colorimeter and the net colour difference changes, ΔE were determined. The texture was measured in terms of hardness by using a Texture Analyzer. As for the shrinkage, displacement of volume method was applied and percentage of shrinkage was obtained in terms of volume changes. A feed-forward backpropagation network with sigmoidal function was developed and best network configuration was chosen based on the highest correlation coefficients between the experimental values versus predicted values. As a comparison, Response Surface Methodology (RSM) statistical analysis was also employed. The performances of both RSM and ANN modelling were evaluated based on absolute average deviation (AAD), correlation of determination (R2) and root mean square error (RMSE). The results showed that ANN has higher prediction capability as compared to RSM. The relative importance of the variables on the physical properties were also determined by using connection weight approach in ANN. It was found that solution concentration showed the highest influence on all three physical properties.

Characterizing Accuracy and Precision of Glucose Sensors and Meters

PubMed Central

2014-01-01

There is need for a method to describe precision and accuracy of glucose measurement as a smooth continuous function of glucose level rather than as a step function for a few discrete ranges of glucose. We propose and illustrate a method to generate a “Glucose Precision Profile” showing absolute relative deviation (ARD) and /or %CV versus glucose level to better characterize measurement errors at any glucose level. We examine the relationship between glucose measured by test and comparator methods using linear regression. We examine bias by plotting deviation = (test – comparator method) versus glucose level. We compute the deviation, absolute deviation (AD), ARD, and standard deviation (SD) for each data pair. We utilize curve smoothing procedures to minimize the effects of random sampling variability to facilitate identification and display of the underlying relationships between ARD or %CV and glucose level. AD, ARD, SD, and %CV display smooth continuous relationships versus glucose level. Estimates of MARD and %CV are subject to relatively large errors in the hypoglycemic range due in part to a markedly nonlinear relationship with glucose level and in part to the limited number of observations in the hypoglycemic range. The curvilinear relationships of ARD and %CV versus glucose level are helpful when characterizing and comparing the precision and accuracy of glucose sensors and meters. PMID:25037194

Comparison between artificial neural network and multilinear regression models in an evaluation of cognitive workload in a flight simulator.

PubMed

Hannula, Manne; Huttunen, Kerttu; Koskelo, Jukka; Laitinen, Tomi; Leino, Tuomo

2008-01-01

In this study, the performances of artificial neural network (ANN) analysis and multilinear regression (MLR) model-based estimation of heart rate were compared in an evaluation of individual cognitive workload. The data comprised electrocardiography (ECG) measurements and an evaluation of cognitive load that induces psychophysiological stress (PPS), collected from 14 interceptor fighter pilots during complex simulated F/A-18 Hornet air battles. In our data, the mean absolute error of the ANN estimate was 11.4 as a visual analog scale score, being 13-23% better than the mean absolute error of the MLR model in the estimation of cognitive workload.

Adaptive Neural Mechanism for Listing’s Law Revealed in Patients with Skew Deviation Caused by Brainstem or Cerebellar Lesion

PubMed Central

Fesharaki, Maryam; Karagiannis, Peter; Tweed, Douglas; Sharpe, James A.; Wong, Agnes M. F.

2016-01-01

Purpose Skew deviation is a vertical strabismus caused by damage to the otolithic–ocular reflex pathway and is associated with abnormal ocular torsion. This study was conducted to determine whether patients with skew deviation show the normal pattern of three-dimensional eye control called Listing’s law, which specifies the eye’s torsional angle as a function of its horizontal and vertical position. Methods Ten patients with skew deviation caused by brain stem or cerebellar lesions and nine normal control subjects were studied. Patients with diplopia and neurologic symptoms less than 1 month in duration were designated as acute (n = 4) and those with longer duration were classified as chronic (n = 10). Serial recordings were made in the four patients with acute skew deviation. With the head immobile, subjects made saccades to a target that moved between straight ahead and eight eccentric positions, while wearing search coils. At each target position, fixation was maintained for 3 seconds before the next saccade. From the eye position data, the plane of best fit, referred to as Listing’s plane, was fitted. Violations of Listing’s law were quantified by computing the “thickness” of this plane, defined as the SD of the distances to the plane from the data points. Results Both the hypertropic and hypotropic eyes in patients with acute skew deviation violated Listing’s and Donders’ laws—that is, the eyes did not show one consistent angle of torsion in any given gaze direction, but rather an abnormally wide range of torsional angles. In contrast, each eye in patients with chronic skew deviation obeyed the laws. However, in chronic skew deviation, Listing’s planes in both eyes had abnormal orientations. Conclusions Patients with acute skew deviation violated Listing’s law, whereas those with chronic skew deviation obeyed it, indicating that despite brain lesions, neural adaptation can restore Listing’s law so that the neural linkage between horizontal, vertical, and torsional eye position remains intact. Violation of Listing’s and Donders’ laws during fixation arises primarily from torsional drifts, indicating that patients with acute skew deviation have unstable torsional gaze holding that is independent of their horizontal–vertical eye positions. PMID:18172094

Comparisons of Upper Tropospheric Humidity Retrievals from TOVS and METEOSAT

NASA Technical Reports Server (NTRS)

Escoffier, C.; Bates, J.; Chedin, A.; Rossow, W. B.; Schmetz, J.

1999-01-01

Two different methods for retrieving Upper Tropospheric Humidities (UTH) from the TOVS (TIROS Operational Vertical Sounder) instruments aboard NOAA polar orbiting satellites are presented and compared. The first one, from the Environmental Technology Laboratory, computed by J. Bates and D. Jackson (hereafter BJ method), estimates UTH from a simplified radiative transfer analysis of the upper tropospheric infrared water vapor channel at wavelength measured by HIRS (6.3 micrometer). The second one results from a neural network analysis of the TOVS (HIRS and MSU) data developed at, the Laboratoire de Meteorologie Dynamique (hereafter the 3I (Improved Initialization Inversion) method). Although the two methods give very similar retrievals in temperate regions (30-60 N and S), an absolute bias up to 16% appears in the convective zone of the tropics. The two datasets have also been compared with UTH retrievals from infrared radiance measurements in the 6.3 micrometer channel from the geostationary satellite METEOSAT (hereafter MET method). The METEOSAT retrievals are systematically drier than the TOVS-based results by an absolute bias between 5 and 25%. Despite the biases, the spatial and temporal correlations are very good. The purpose of this study is to explain the deviations observed between the three datasets. The sensitivity of UTH to air temperature and humidity profiles is analysed as are the clouds effects. Overall, the comparison of the three retrievals gives an assessment of the current uncertainties in water vapor amounts in the upper troposphere as determined from NOAA and METEOSAT satellites.

[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.

Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox

PubMed Central

Marshall, Najja; Timme, Nicholas M.; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M.

2016-01-01

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of “neural avalanches” (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods—power-law fitting, avalanche shape collapse, and neural complexity—have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox. PMID:27445842

Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox.

PubMed

Marshall, Najja; Timme, Nicholas M; Bennett, Nicholas; Ripp, Monica; Lautzenhiser, Edward; Beggs, John M

2016-01-01

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of "neural avalanches" (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods-power-law fitting, avalanche shape collapse, and neural complexity-have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Experimental investigation, model development and sensitivity analysis of rheological behavior of ZnO/10W40 nano-lubricants for automotive applications

NASA Astrophysics Data System (ADS)

Hemmat Esfe, Mohammad; Saedodin, Seyfolah; Rejvani, Mousa; Shahram, Jalal

2017-06-01

In the present study, rheological behavior of ZnO/10W40 nano-lubricant is investigated by an experimental approach. Firstly, ZnO nanoparticles of 10-30 nm were dispersed in 10W40 engine oil with solid volume fractions of 0.25-2%, then the viscosity of the composed nano-lubricant was measured in temperature ranges of 5-55 °C and in various shear rates. From analyzing the results, it was revealed that both of the base oil and nano-lubricants are non-Newtonian fluids which exhibit shear thinning behavior. Sensitivity of viscosity to the solid volume fraction enhancement was calculated by a new correlation which was proposed in terms of solid volume fraction and temperature. In order to attain an accurate model by which experimental data are predicted, an artificial neural network (ANN) with a hidden layer and 5 neurons was designed. This model was considerably accurate in predicting experimental data of dynamic viscosity as R-squared and average absolute relative deviation (AARD %) were respectively 0.9999 and 0.0502.

High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium

NASA Astrophysics Data System (ADS)

Schran, Christoph; Uhl, Felix; Behler, Jörg; Marx, Dominik

2018-03-01

The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol-1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.

High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium.

PubMed

Schran, Christoph; Uhl, Felix; Behler, Jörg; Marx, Dominik

2018-03-14

The design of accurate helium-solute interaction potentials for the simulation of chemically complex molecules solvated in superfluid helium has long been a cumbersome task due to the rather weak but strongly anisotropic nature of the interactions. We show that this challenge can be met by using a combination of an effective pair potential for the He-He interactions and a flexible high-dimensional neural network potential (NNP) for describing the complex interaction between helium and the solute in a pairwise additive manner. This approach yields an excellent agreement with a mean absolute deviation as small as 0.04 kJ mol -1 for the interaction energy between helium and both hydronium and Zundel cations compared with coupled cluster reference calculations with an energetically converged basis set. The construction and improvement of the potential can be performed in a highly automated way, which opens the door for applications to a variety of reactive molecules to study the effect of solvation on the solute as well as the solute-induced structuring of the solvent. Furthermore, we show that this NNP approach yields very convincing agreement with the coupled cluster reference for properties like many-body spatial and radial distribution functions. This holds for the microsolvation of the protonated water monomer and dimer by a few helium atoms up to their solvation in bulk helium as obtained from path integral simulations at about 1 K.

Robust Alternatives to the Standard Deviation in Processing of Physics Experimental Data

NASA Astrophysics Data System (ADS)

Shulenin, V. P.

2016-10-01

Properties of robust estimations of the scale parameter are studied. It is noted that the median of absolute deviations and the modified estimation of the average Gini differences have asymptotically normal distributions and bounded influence functions, are B-robust estimations, and hence, unlike the estimation of the standard deviation, are protected from the presence of outliers in the sample. Results of comparison of estimations of the scale parameter are given for a Gaussian model with contamination. An adaptive variant of the modified estimation of the average Gini differences is considered.

Patient-specific positioning guides for total knee arthroplasty: no significant difference between final component alignment and pre-operative digital plan except for tibial rotation.

PubMed

Boonen, Bert; Schotanus, Martijn G M; Kerens, Bart; Hulsmans, Frans-Jan; Tuinebreijer, Wim E; Kort, Nanne P

2017-09-01

To assess whether there is a significant difference between the alignment of the individual femoral and tibial components (in the frontal, sagittal and horizontal planes) as calculated pre-operatively (digital plan) and the actually achieved alignment in vivo obtained with the use of patient-specific positioning guides (PSPGs) for TKA. It was hypothesised that there would be no difference between post-op implant position and pre-op digital plan. Twenty-six patients were included in this non-inferiority trial. Software permitted matching of the pre-operative MRI scan (and therefore calculated prosthesis position) to a pre-operative CT scan and then to a post-operative full-leg CT scan to determine deviations from pre-op planning in all three anatomical planes. For the femoral component, mean absolute deviations from planning were 1.8° (SD 1.3), 2.5° (SD 1.6) and 1.6° (SD 1.4) in the frontal, sagittal and transverse planes, respectively. For the tibial component, mean absolute deviations from planning were 1.7° (SD 1.2), 1.7° (SD 1.5) and 3.2° (SD 3.6) in the frontal, sagittal and transverse planes, respectively. Absolute mean deviation from planned mechanical axis was 1.9°. The a priori specified null hypothesis for equivalence testing: the difference from planning is >3 or <-3 was rejected for all comparisons except for the tibial transverse plane. PSPG was able to adequately reproduce the pre-op plan in all planes, except for the tibial rotation in the transverse plane. Possible explanations for outliers are discussed and highlight the importance for adequate training surgeons before they start using PSPG in their day-by-day practise. Prospective cohort study, Level II.

Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait.

PubMed

Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J; Murtha, Michael T; Hus, Vanessa; Lowe, Jennifer K; Willsey, A Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E; Ledbetter, David H; Lord, Catherine; Mane, Shrikant M; Lese Martin, Christa; Martin, Donna M; Morrow, Eric M; Walsh, Christopher A; Sutcliffe, James S; State, Matthew W; Devlin, Bernie; Cook, Edwin H; Kim, Soo-Jeong

2013-10-15

Brain development follows a different trajectory in children with autism spectrum disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. Gender, age, height, weight, genetic ancestry, and ASD status were significant predictors of HC (estimate of the ASD effect = .2 cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait, and population norms for HC would be far more accurate if covariates including genetic ancestry, height, and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. © 2013 Society of Biological Psychiatry.

Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait

PubMed Central

Chaste, Pauline; Klei, Lambertus; Sanders, Stephan J.; Murtha, Michael T.; Hus, Vanessa; Lowe, Jennifer K.; Willsey, A. Jeremy; Moreno-De-Luca, Daniel; Yu, Timothy W.; Fombonne, Eric; Geschwind, Daniel; Grice, Dorothy E.; Ledbetter, David H.; Lord, Catherine; Mane, Shrikant M.; Martin, Christa Lese; Martin, Donna M.; Morrow, Eric M.; Walsh, Christopher A.; Sutcliffe, James S.; State, Matthew W.; Devlin, Bernie; Cook, Edwin H.; Kim, Soo-Jeong

2013-01-01

BACKGROUND Brain development follows a different trajectory in children with Autism Spectrum Disorders (ASD) than in typically developing children. A proxy for neurodevelopment could be head circumference (HC), but studies assessing HC and its clinical correlates in ASD have been inconsistent. This study investigates HC and clinical correlates in the Simons Simplex Collection cohort. METHODS We used a mixed linear model to estimate effects of covariates and the deviation from the expected HC given parental HC (genetic deviation). After excluding individuals with incomplete data, 7225 individuals in 1891 families remained for analysis. We examined the relationship between HC/genetic deviation of HC and clinical parameters. RESULTS Gender, age, height, weight, genetic ancestry and ASD status were significant predictors of HC (estimate of the ASD effect=0.2cm). HC was approximately normally distributed in probands and unaffected relatives, with only a few outliers. Genetic deviation of HC was also normally distributed, consistent with a random sampling of parental genes. Whereas larger HC than expected was associated with ASD symptom severity and regression, IQ decreased with the absolute value of the genetic deviation of HC. CONCLUSIONS Measured against expected values derived from covariates of ASD subjects, statistical outliers for HC were uncommon. HC is a strongly heritable trait and population norms for HC would be far more accurate if covariates including genetic ancestry, height and age were taken into account. The association of diminishing IQ with absolute deviation from predicted HC values suggests HC could reflect subtle underlying brain development and warrants further investigation. PMID:23746936

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.

Detection and evaluation of DNA methylation markers found at SCGN and KLF14 loci to estimate human age.

PubMed

Alghanim, Hussain; Antunes, Joana; Silva, Deborah Soares Bispo Santos; Alho, Clarice Sampaio; Balamurugan, Kuppareddi; McCord, Bruce

2017-11-01

Recent developments in the analysis of epigenetic DNA methylation patterns have demonstrated that certain genetic loci show a linear correlation with chronological age. It is the goal of this study to identify a new set of epigenetic methylation markers for the forensic estimation of human age. A total number of 27 CpG sites at three genetic loci, SCGN, DLX5 and KLF14, were examined to evaluate the correlation of their methylation status with age. These sites were evaluated using 72 blood samples and 91 saliva samples collected from volunteers with ages ranging from 5 to 73 years. DNA was bisulfite modified followed by PCR amplification and pyrosequencing to determine the level of DNA methylation at each CpG site. In this study, certain CpG sites in SCGN and KLF14 loci showed methylation levels that were correlated with chronological age, however, the tested CpG sites in DLX5 did not show a correlation with age. Using a 52-saliva sample training set, two age-predictor models were developed by means of a multivariate linear regression analysis for age prediction. The two models performed similarly with a single-locus model explaining 85% of the age variance at a mean absolute deviation of 5.8 years and a dual-locus model explaining 84% of the age variance with a mean absolute deviation of 6.2 years. In the validation set, the mean absolute deviation was measured to be 8.0 years and 7.1 years for the single- and dual-locus model, respectively. Another age predictor model was also developed using a 40-blood sample training set that accounted for 71% of the age variance. This model gave a mean absolute deviation of 6.6 years for the training set and 10.3years for the validation set. The results indicate that specific CpGs in SCGN and KLF14 can be used as potential epigenetic markers to estimate age using saliva and blood specimens. These epigenetic markers could provide important information in cases where the determination of a suspect's age is critical in developing investigative leads. Copyright © 2017. Published by Elsevier B.V.

A critical assessment of two types of personal UV dosimeters.

PubMed

Seckmeyer, Gunther; Klingebiel, Marcus; Riechelmann, Stefan; Lohse, Insa; McKenzie, Richard L; Liley, J Ben; Allen, Martin W; Siani, Anna-Maria; Casale, Giuseppe R

2012-01-01

Doses of erythemally weighted irradiances derived from polysulphone (PS) and electronic ultraviolet (EUV) dosimeters have been compared with measurements obtained using a reference spectroradiometer. PS dosimeters showed mean absolute deviations of 26% with a maximum deviation of 44%, the calibrated EUV dosimeters showed mean absolute deviations of 15% (maximum 33%) around noon during several test days in the northern hemisphere autumn. In the case of EUV dosimeters, measurements with various cut-off filters showed that part of the deviation from the CIE erythema action spectrum was due to a small, but significant sensitivity to visible radiation that varies between devices and which may be avoided by careful preselection. Usually the method of calibrating UV sensors by direct comparison to a reference instrument leads to reliable results. However, in some circumstances the quality of measurements made with simple sensors may be over-estimated. In the extreme case, a simple pyranometer can be used as a UV instrument, providing acceptable results for cloudless skies, but very poor results under cloudy conditions. It is concluded that while UV dosimeters are useful for their design purpose, namely to estimate personal UV exposures, they should not be regarded as an inexpensive replacement for meteorological grade instruments. © 2011 Wiley Periodicals, Inc. Photochemistry and Photobiology © 2011 The American Society of Photobiology.

Radar prediction of absolute rain fade distributions for earth-satellite paths and general methods for extrapolation of fade statistics to other locations

NASA Technical Reports Server (NTRS)

Goldhirsh, J.

1982-01-01

The first absolute rain fade distribution method described establishes absolute fade statistics at a given site by means of a sampled radar data base. The second method extrapolates absolute fade statistics from one location to another, given simultaneously measured fade and rain rate statistics at the former. Both methods employ similar conditional fade statistic concepts and long term rain rate distributions. Probability deviations in the 2-19% range, with an 11% average, were obtained upon comparison of measured and predicted levels at given attenuations. The extrapolation of fade distributions to other locations at 28 GHz showed very good agreement with measured data at three sites located in the continental temperate region.

Ozone indices based on simple meteorological parameters: potentials and limitations of regression and neural network models

NASA Astrophysics Data System (ADS)

Soja, G.; Soja, A.-M.

This study tested the usefulness of extremely simple meteorological models for the prediction of ozone indices. The models were developed with the input parameters of daily maximum temperature and sunshine duration and are based on a data collection period of three years. For a rural environment in eastern Austria, the meteorological and ozone data of three summer periods have been used to develop functions to describe three ozone exposure indices (daily maximum, 7 h mean 9.00-16.00 h, accumulated ozone dose AOT40). Data sets for other years or stations not included in the development of the models were used as test data to validate the performance of the models. Generally, optimized regression models performed better than simplest linear models, especially in the case of AOT40. For the description of the summer period from May to September, the mean absolute daily differences between observed and calculated indices were 8±6 ppb for the maximum half hour mean value, 6±5 ppb for the 7 h mean and 41±40 ppb h for the AOT40. When the parameters were further optimized to describe individual months separately, the mean absolute residuals decreased by ⩽10%. Neural network models did not always perform better than the regression models. This is attributed to the low number of inputs in this comparison and to the simple architecture of these models (2-2-1). Further factorial analyses of those days when the residuals were higher than the mean plus one standard deviation should reveal possible reasons why the models did not perform well on certain days. It was observed that overestimations by the models mainly occurred on days with partly overcast, hazy or very windy conditions. Underestimations more frequently occurred on weekdays than on weekends. It is suggested that the application of this kind of meteorological model will be more successful in topographically homogeneous regions and in rural environments with relatively constant rates of emission and long-range transport of ozone precursors. Under conditions too demanding for advanced physico/chemical models, the presented models may offer useful alternatives to derive ecologically relevant ozone indices directly from meteorological parameters.

Analytical quality goals derived from the total deviation from patients' homeostatic set points, with a margin for analytical errors.

PubMed

Bolann, B J; Asberg, A

2004-01-01

The deviation of test results from patients' homeostatic set points in steady-state conditions may complicate interpretation of the results and the comparison of results with clinical decision limits. In this study the total deviation from the homeostatic set point is defined as the maximum absolute deviation for 95% of measurements, and we present analytical quality requirements that prevent analytical error from increasing this deviation to more than about 12% above the value caused by biology alone. These quality requirements are: 1) The stable systematic error should be approximately 0, and 2) a systematic error that will be detected by the control program with 90% probability, should not be larger than half the value of the combined analytical and intra-individual standard deviation. As a result, when the most common control rules are used, the analytical standard deviation may be up to 0.15 times the intra-individual standard deviation. Analytical improvements beyond these requirements have little impact on the interpretability of measurement results.

Density of Jatropha curcas Seed Oil and its Methyl Esters: Measurement and Estimations

NASA Astrophysics Data System (ADS)

Veny, Harumi; Baroutian, Saeid; Aroua, Mohamed Kheireddine; Hasan, Masitah; Raman, Abdul Aziz; Sulaiman, Nik Meriam Nik

2009-04-01

Density data as a function of temperature have been measured for Jatropha curcas seed oil, as well as biodiesel jatropha methyl esters at temperatures from above their melting points to 90 ° C. The data obtained were used to validate the method proposed by Spencer and Danner using a modified Rackett equation. The experimental and estimated density values using the modified Rackett equation gave almost identical values with average absolute percent deviations less than 0.03% for the jatropha oil and 0.04% for the jatropha methyl esters. The Janarthanan empirical equation was also employed to predict jatropha biodiesel densities. This equation performed equally well with average absolute percent deviations within 0.05%. Two simple linear equations for densities of jatropha oil and its methyl esters are also proposed in this study.

Belief Propagation Algorithm for Portfolio Optimization Problems

PubMed Central

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm. PMID:26305462

Belief Propagation Algorithm for Portfolio Optimization Problems.

PubMed

Shinzato, Takashi; Yasuda, Muneki

2015-01-01

The typical behavior of optimal solutions to portfolio optimization problems with absolute deviation and expected shortfall models using replica analysis was pioneeringly estimated by S. Ciliberti et al. [Eur. Phys. B. 57, 175 (2007)]; however, they have not yet developed an approximate derivation method for finding the optimal portfolio with respect to a given return set. In this study, an approximation algorithm based on belief propagation for the portfolio optimization problem is presented using the Bethe free energy formalism, and the consistency of the numerical experimental results of the proposed algorithm with those of replica analysis is confirmed. Furthermore, the conjecture of H. Konno and H. Yamazaki, that the optimal solutions with the absolute deviation model and with the mean-variance model have the same typical behavior, is verified using replica analysis and the belief propagation algorithm.

Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

NASA Astrophysics Data System (ADS)

Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

2010-10-01

To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

Quantitative Analysis of Ca, Mg, and K in the Roots of Angelica pubescens f. biserrata by Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Wang, J.; Shi, M.; Zheng, P.; Xue, Sh.; Peng, R.

2018-03-01

Laser-induced breakdown spectroscopy has been applied for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens Maxim. f. biserrata Shan et Yuan used in traditional Chinese medicine. Ca II 317.993 nm, Mg I 517.268 nm, and K I 769.896 nm spectral lines have been chosen to set up calibration models for the analysis using the external standard and artificial neural network methods. The linear correlation coefficients of the predicted concentrations versus the standard concentrations of six samples determined by the artificial neural network method are 0.9896, 0.9945, and 0.9911 for Ca, Mg, and K, respectively, which are better than for the external standard method. The artificial neural network method also gives better performance comparing with the external standard method for the average and maximum relative errors, average relative standard deviations, and most maximum relative standard deviations of the predicted concentrations of Ca, Mg, and K in the six samples. Finally, it is proved that the artificial neural network method gives better performance compared to the external standard method for the quantitative analysis of Ca, Mg, and K in the roots of Angelica pubescens.

Absolute Magnitude Calibration for Dwarfs Based on the Colour-Magnitude Diagrams of Galactic Clusters

NASA Astrophysics Data System (ADS)

Karaali, S.; Gökçe, E. Yaz; Bilir, S.; Güçtekin, S. Tunçel

2014-07-01

We present two absolute magnitude calibrations for dwarfs based on colour-magnitude diagrams of Galactic clusters. The combination of the Mg absolute magnitudes of the dwarf fiducial sequences of the clusters M92, M13, M5, NGC 2420, M67, and NGC 6791 with the corresponding metallicities provides absolute magnitude calibration for a given (g - r)0 colour. The calibration is defined in the colour interval 0.25 ≤ (g - r)0 ≤ 1.25 mag and it covers the metallicity interval - 2.15 ≤ [Fe/H] ≤ +0.37 dex. The absolute magnitude residuals obtained by the application of the procedure to another set of Galactic clusters lie in the interval - 0.15 ≤ ΔMg ≤ +0.12 mag. The mean and standard deviation of the residuals are < ΔMg > = - 0.002 and σ = 0.065 mag, respectively. The calibration of the MJ absolute magnitude in terms of metallicity is carried out by using the fiducial sequences of the clusters M92, M13, 47 Tuc, NGC 2158, and NGC 6791. It is defined in the colour interval 0.90 ≤ (V - J)0 ≤ 1.75 mag and it covers the same metallicity interval of the Mg calibration. The absolute magnitude residuals obtained by the application of the procedure to the cluster M5 ([Fe/H] = -1.40 dex) and 46 solar metallicity, - 0.45 ≤ [Fe/H] ≤ +0.35 dex, field stars lie in the interval - 0.29 and + 0.35 mag. However, the range of 87% of them is rather shorter, - 0.20 ≤ ΔMJ ≤ +0.20 mag. The mean and standard deviation of all residuals are < ΔMJ > =0.05 and σ = 0.13 mag, respectively. The derived relations are applicable to stars older than 4 Gyr for the Mg calibration, and older than 2 Gyr for the MJ calibration. The cited limits are the ages of the youngest calibration clusters in the two systems.

Electronic Absolute Cartesian Autocollimator

NASA Technical Reports Server (NTRS)

Leviton, Douglas B.

2006-01-01

An electronic absolute Cartesian autocollimator performs the same basic optical function as does a conventional all-optical or a conventional electronic autocollimator but differs in the nature of its optical target and the manner in which the position of the image of the target is measured. The term absolute in the name of this apparatus reflects the nature of the position measurement, which, unlike in a conventional electronic autocollimator, is based absolutely on the position of the image rather than on an assumed proportionality between the position and the levels of processed analog electronic signals. The term Cartesian in the name of this apparatus reflects the nature of its optical target. Figure 1 depicts the electronic functional blocks of an electronic absolute Cartesian autocollimator along with its basic optical layout, which is the same as that of a conventional autocollimator. Referring first to the optical layout and functions only, this or any autocollimator is used to measure the compound angular deviation of a flat datum mirror with respect to the optical axis of the autocollimator itself. The optical components include an illuminated target, a beam splitter, an objective or collimating lens, and a viewer or detector (described in more detail below) at a viewing plane. The target and the viewing planes are focal planes of the lens. Target light reflected by the datum mirror is imaged on the viewing plane at unit magnification by the collimating lens. If the normal to the datum mirror is parallel to the optical axis of the autocollimator, then the target image is centered on the viewing plane. Any angular deviation of the normal from the optical axis manifests itself as a lateral displacement of the target image from the center. The magnitude of the displacement is proportional to the focal length and to the magnitude (assumed to be small) of the angular deviation. The direction of the displacement is perpendicular to the axis about which the mirror is slightly tilted. Hence, one can determine the amount and direction of tilt from the coordinates of the target image on the viewing plane.

Resting-State Oscillatory Activity in Children Born Small for Gestational Age: An MEG Study

PubMed Central

Boersma, Maria; de Bie, Henrica M. A.; Oostrom, Kim J.; van Dijk, Bob W.; Hillebrand, Arjan; van Wijk, Bernadette C. M.; Delemarre-van de Waal, Henriëtte A.; Stam, Cornelis J.

2013-01-01

Growth restriction in utero during a period that is critical for normal growth of the brain, has previously been associated with deviations in cognitive abilities and brain anatomical and functional changes. We measured magnetoencephalography (MEG) in 4- to 7-year-old children to test if children born small for gestational age (SGA) show deviations in resting-state brain oscillatory activity. Children born SGA with postnatally spontaneous catch-up growth [SGA+; six boys, seven girls; mean age 6.3 year (SD = 0.9)] and children born appropriate for gestational age [AGA; seven boys, three girls; mean age 6.0 year (SD = 1.2)] participated in a resting-state MEG study. We calculated absolute and relative power spectra and used non-parametric statistics to test for group differences. SGA+ and AGA born children showed no significant differences in absolute and relative power except for reduced absolute gamma band power in SGA children. At the time of MEG investigation, SGA+ children showed significantly lower head circumference (HC) and a trend toward lower IQ, however there was no association of HC or IQ with absolute or relative power. Except for reduced absolute gamma band power, our findings suggest normal brain activity patterns at school age in a group of children born SGA in which spontaneous catch-up growth of bodily length after birth occurred. Although previous findings suggest that being born SGA alters brain oscillatory activity early in neonatal life, we show that these neonatal alterations do not persist at early school age when spontaneous postnatal catch-up growth occurs after birth. PMID:24068993

Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles.

PubMed

Martinez-Valdes, Eduardo; Negro, Francesco; Falla, Deborah; De Nunzio, Alessandro Marco; Farina, Dario

2018-04-01

Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to interpret differences in neural drive between muscles.

Adaptive neural coding: from biological to behavioral decision-making

PubMed Central

Louie, Kenway; Glimcher, Paul W.; Webb, Ryan

2015-01-01

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior. PMID:26722666

A suggestion for computing objective function in model calibration

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang

2014-01-01

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).

How well do static electronic dipole polarizabilities from gas-phase experiments compare with density functional and MP2 computations?

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

Thakkar, Ajit J., E-mail: ajit@unb.ca; Wu, Taozhe

2015-10-14

Static electronic dipole polarizabilities for 135 molecules are calculated using second-order Møller-Plesset perturbation theory and six density functionals recently recommended for polarizabilities. Comparison is made with the best gas-phase experimental data. The lowest mean absolute percent deviations from the best experimental values for all 135 molecules are 3.03% and 3.08% for the LC-τHCTH and M11 functionals, respectively. Excluding the eight extreme outliers for which the experimental values are almost certainly in error, the mean absolute percent deviation for the remaining 127 molecules drops to 2.42% and 2.48% for the LC-τHCTH and M11 functionals, respectively. Detailed comparison enables us to identifymore » 32 molecules for which the discrepancy between the calculated and experimental values warrants further investigation.« less

A comparison of portfolio selection models via application on ISE 100 index data

NASA Astrophysics Data System (ADS)

Altun, Emrah; Tatlidil, Hüseyin

2013-10-01

Markowitz Model, a classical approach to portfolio optimization problem, relies on two important assumptions: the expected return is multivariate normally distributed and the investor is risk averter. But this model has not been extensively used in finance. Empirical results show that it is very hard to solve large scale portfolio optimization problems with Mean-Variance (M-V)model. Alternative model, Mean Absolute Deviation (MAD) model which is proposed by Konno and Yamazaki [7] has been used to remove most of difficulties of Markowitz Mean-Variance model. MAD model don't need to assume that the probability of the rates of return is normally distributed and based on Linear Programming. Another alternative portfolio model is Mean-Lower Semi Absolute Deviation (M-LSAD), which is proposed by Speranza [3]. We will compare these models to determine which model gives more appropriate solution to investors.

Size-independent neural networks based first-principles method for accurate prediction of heat of formation of fuels

NASA Astrophysics Data System (ADS)

Yang, GuanYa; Wu, Jiang; Chen, ShuGuang; Zhou, WeiJun; Sun, Jian; Chen, GuanHua

2018-06-01

Neural network-based first-principles method for predicting heat of formation (HOF) was previously demonstrated to be able to achieve chemical accuracy in a broad spectrum of target molecules [L. H. Hu et al., J. Chem. Phys. 119, 11501 (2003)]. However, its accuracy deteriorates with the increase in molecular size. A closer inspection reveals a systematic correlation between the prediction error and the molecular size, which appears correctable by further statistical analysis, calling for a more sophisticated machine learning algorithm. Despite the apparent difference between simple and complex molecules, all the essential physical information is already present in a carefully selected set of small molecule representatives. A model that can capture the fundamental physics would be able to predict large and complex molecules from information extracted only from a small molecules database. To this end, a size-independent, multi-step multi-variable linear regression-neural network-B3LYP method is developed in this work, which successfully improves the overall prediction accuracy by training with smaller molecules only. And in particular, the calculation errors for larger molecules are drastically reduced to the same magnitudes as those of the smaller molecules. Specifically, the method is based on a 164-molecule database that consists of molecules made of hydrogen and carbon elements. 4 molecular descriptors were selected to encode molecule's characteristics, among which raw HOF calculated from B3LYP and the molecular size are also included. Upon the size-independent machine learning correction, the mean absolute deviation (MAD) of the B3LYP/6-311+G(3df,2p)-calculated HOF is reduced from 16.58 to 1.43 kcal/mol and from 17.33 to 1.69 kcal/mol for the training and testing sets (small molecules), respectively. Furthermore, the MAD of the testing set (large molecules) is reduced from 28.75 to 1.67 kcal/mol.

A simple approach for the sonochemical loading of Au, Ag and Pd nanoparticle on functionalized MWCNT and subsequent dispersion studies for removal of organic dyes: Artificial neural network and response surface methodology studies.

PubMed

Moghaddari, Mitra; Yousefi, Fakhri; Ghaedi, Mehrorang; Dashtian, Kheibar

2018-04-01

In this study, the artificial neural network (ANN) and response surface methodology (RSM) based on central composite design (CCD) were applied for modeling and optimization of the simultaneous ultrasound-assisted removal of quinoline yellow (QY) and eosin B (EB). The MWCNT-NH 2 and its composites were prepared by sonochemistry method and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and energy dispersive spectroscopy (EDS) analysis's. Initial dyes concentrations, adsorbent mass, sonication time and pH contribution on QY and EB removal percentage were investigated by CCD and replication of experiments at conditions suggested by model has results which statistically are close to experimented data. The ultrasound irradiation is associated with raising mass transfer of process so that small amount of the adsorbent (0.025 g) is able to remove high percentage (88.00% and 91.00%) of QY and EB, respectively in short time (6.0 min) at pH = 6. Analysis of experimental data by conventional models is good indication of Langmuir efficiency for fitting and explanation of experimented data. The ANN based on the Levenberg-Marquardt algorithm (LMA) combined of linear transfer function at output layer and tangent sigmoid transfer function at hidden layer with 20 hidden neurons supply best operation conditions for good prediction of adsorption data. Accurate and efficient artificial neural network was obtained by changing the number of neurons in the hidden layer, while data was divided into training, test and validation sets which contained 70, 15 and 15% of data points respectively. The Average absolute deviation (AAD)% of a collection of 128 data points for MWCNT-NH 2 and composites is 0.58%.for EB and 0.55 for YQ. Copyright © 2017 Elsevier B.V. All rights reserved.

A promising tool to achieve chemical accuracy for density functional theory calculations on Y-NO homolysis bond dissociation energies.

PubMed

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol(-1)) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol(-1) to 0.15 and 0.18 kcal·mol(-1), respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol(-1). This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules.

A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies

PubMed Central

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol−1) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol−1 to 0.15 and 0.18 kcal·mol−1, respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol−1. This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules. PMID:22942689

Geometric Verification of Dynamic Wave Arc Delivery With the Vero System Using Orthogonal X-ray Fluoroscopic Imaging.

PubMed

Burghelea, Manuela; Verellen, Dirk; Poels, Kenneth; Gevaert, Thierry; Depuydt, Tom; Tournel, Koen; Hung, Cecilia; Simon, Viorica; Hiraoka, Masahiro; de Ridder, Mark

2015-07-15

The purpose of this study was to define an independent verification method based on on-board orthogonal fluoroscopy to determine the geometric accuracy of synchronized gantry-ring (G/R) rotations during dynamic wave arc (DWA) delivery available on the Vero system. A verification method for DWA was developed to calculate O-ring-gantry (G/R) positional information from ball-bearing positions retrieved from fluoroscopic images of a cubic phantom acquired during DWA delivery. Different noncoplanar trajectories were generated in order to investigate the influence of path complexity on delivery accuracy. The G/R positions detected from the fluoroscopy images (DetPositions) were benchmarked against the G/R angulations retrieved from the control points (CP) of the DWA RT plan and the DWA log files recorded by the treatment console during DWA delivery (LogActed). The G/R rotational accuracy was quantified as the mean absolute deviation ± standard deviation. The maximum G/R absolute deviation was calculated as the maximum 3-dimensional distance between the CP and the closest DetPositions. In the CP versus DetPositions comparison, an overall mean G/R deviation of 0.13°/0.16° ± 0.16°/0.16° was obtained, with a maximum G/R deviation of 0.6°/0.2°. For the LogActed versus DetPositions evaluation, the overall mean deviation was 0.08°/0.15° ± 0.10°/0.10° with a maximum G/R of 0.3°/0.4°. The largest decoupled deviations registered for gantry and ring were 0.6° and 0.4° respectively. No directional dependence was observed between clockwise and counterclockwise rotations. Doubling the dose resulted in a double number of detected points around each CP, and an angular deviation reduction in all cases. An independent geometric quality assurance approach was developed for DWA delivery verification and was successfully applied on diverse trajectories. Results showed that the Vero system is capable of following complex G/R trajectories with maximum deviations during DWA below 0.6°. Copyright © 2015 Elsevier Inc. All rights reserved.

Absolutely relative or relatively absolute: violations of value invariance in human decision making.

PubMed

Teodorescu, Andrei R; Moran, Rani; Usher, Marius

2016-02-01

Making decisions based on relative rather than absolute information processing is tied to choice optimality via the accumulation of evidence differences and to canonical neural processing via accumulation of evidence ratios. These theoretical frameworks predict invariance of decision latencies to absolute intensities that maintain differences and ratios, respectively. While information about the absolute values of the choice alternatives is not necessary for choosing the best alternative, it may nevertheless hold valuable information about the context of the decision. To test the sensitivity of human decision making to absolute values, we manipulated the intensities of brightness stimuli pairs while preserving either their differences or their ratios. Although asked to choose the brighter alternative relative to the other, participants responded faster to higher absolute values. Thus, our results provide empirical evidence for human sensitivity to task irrelevant absolute values indicating a hard-wired mechanism that precedes executive control. Computational investigations of several modelling architectures reveal two alternative accounts for this phenomenon, which combine absolute and relative processing. One account involves accumulation of differences with activation dependent processing noise and the other emerges from accumulation of absolute values subject to the temporal dynamics of lateral inhibition. The potential adaptive role of such choice mechanisms is discussed.

Reducing the standard deviation in multiple-assay experiments where the variation matters but the absolute value does not.

PubMed

Echenique-Robba, Pablo; Nelo-Bazán, María Alejandra; Carrodeguas, José A

2013-01-01

When the value of a quantity x for a number of systems (cells, molecules, people, chunks of metal, DNA vectors, so on) is measured and the aim is to replicate the whole set again for different trials or assays, despite the efforts for a near-equal design, scientists might often obtain quite different measurements. As a consequence, some systems' averages present standard deviations that are too large to render statistically significant results. This work presents a novel correction method of a very low mathematical and numerical complexity that can reduce the standard deviation of such results and increase their statistical significance. Two conditions are to be met: the inter-system variations of x matter while its absolute value does not, and a similar tendency in the values of x must be present in the different assays (or in other words, the results corresponding to different assays must present a high linear correlation). We demonstrate the improvements this method offers with a cell biology experiment, but it can definitely be applied to any problem that conforms to the described structure and requirements and in any quantitative scientific field that deals with data subject to uncertainty.

Cohesive energy and structural parameters of binary oxides of groups IIA and IIIB from diffusion quantum Monte Carlo

DOE PAGES

Santana, Juan A.; Krogel, Jaron T.; Kent, Paul R. C.; ...

2016-05-03

We have applied the diffusion quantum Monte Carlo (DMC) method to calculate the cohesive energy and the structural parameters of the binary oxides CaO, SrO, BaO, Sc 2O 3, Y 2O 3 and La 2O 3. The aim of our calculations is to systematically quantify the accuracy of the DMC method to study this type of metal oxides. The DMC results were compared with local and semi-local Density Functional Theory (DFT) approximations as well as with experimental measurements. The DMC method yields cohesive energies for these oxides with a mean absolute deviation from experimental measurements of 0.18(2) eV, while withmore » local and semi-local DFT approximations the deviation is 3.06 and 0.94 eV, respectively. For lattice constants, the mean absolute deviation in DMC, local and semi-local DFT approximations, are 0.017(1), 0.07 and 0.05 , respectively. In conclusion, DMC is highly accurate method, outperforming the local and semi-local DFT approximations in describing the cohesive energies and structural parameters of these binary oxides.« less

A determination of the absolute radiant energy of a Robertson-Berger meter sunburn unit

NASA Astrophysics Data System (ADS)

DeLuisi, John J.; Harris, Joyce M.

Data from a Robertson-Berger (RB) sunburn meter were compared with concurrent measurements obtained with an ultraviolet double monochromator (DM), and the absolute energy of one sunburn unit measured by the RB-meter was determined. It was found that at a solar zenith angle of 30° one sunburn unit (SU) is equivalent to 35 ± 4 mJ cm -2, and at a solar zenith angle of 69°, one SU is equivalent to 20 ± 2 mJ cm -2 (relative to a wavelength of 297 nm), where the rate of change is non-linear. The deviation is due to the different response functions of the RB-meter and the DM system used to simulate the response of human skin to the incident u.v. solar spectrum. The average growth rate of the deviation with increasing solar zenith angle was found to be 1.2% per degree between solar zenith angles 30 and 50° and 2.3% per degree between solar zenith angles 50 and 70°. The deviations of response with solar zenith angle were found to be consistent with reported RB-meter characteristics.

Biosorption of cobalt(II) with sunflower biomass from aqueous solutions in a fixed bed column and neural networks modelling.

PubMed

Oguz, Ensar; Ersoy, Muhammed

2014-01-01

The effects of inlet cobalt(II) concentration (20-60 ppm), feed flow rate (8-19 ml/min) and bed height (5-15 cm), initial solution pH (3-5) and particle size (0.25

A vibration-insensitive optical cavity and absolute determination of its ultrahigh stability.

PubMed

Zhao, Y N; Zhang, J; Stejskal, A; Liu, T; Elman, V; Lu, Z H; Wang, L J

2009-05-25

We use the three-cornered-hat method to evaluate the absolute frequency stabilities of three different ultrastable reference cavities, one of which has a vibration-insensitive design that does not even require vibration isolation. An Nd:YAG laser and a diode laser are implemented as light sources. We observe approximately 1 Hz beat note linewidths between all three cavities. The measurement demonstrates that the vibration-insensitive cavity has a good frequency stability over the entire measurement time from 100 ms to 200 s. An absolute, correlation-removed Allan deviation of 1.4 x 10(-15) at s of this cavity is obtained, giving a frequency uncertainty of only 0.44 Hz.

How do we assign punishment? The impact of minimal and maximal standards on the evaluation of deviants.

PubMed

Kessler, Thomas; Neumann, Jörg; Mummendey, Amélie; Berthold, Anne; Schubert, Thomas; Waldzus, Sven

2010-09-01

To explain the determinants of negative behavior toward deviants (e.g., punishment), this article examines how people evaluate others on the basis of two types of standards: minimal and maximal. Minimal standards focus on an absolute cutoff point for appropriate behavior; accordingly, the evaluation of others varies dichotomously between acceptable or unacceptable. Maximal standards focus on the degree of deviation from that standard; accordingly, the evaluation of others varies gradually from positive to less positive. This framework leads to the prediction that violation of minimal standards should elicit punishment regardless of the degree of deviation, whereas punishment in response to violations of maximal standards should depend on the degree of deviation. Four studies assessed or manipulated the type of standard and degree of deviation displayed by a target. Results consistently showed the expected interaction between type of standard (minimal and maximal) and degree of deviation on punishment behavior.

Simulation and analysis of spectroscopic filter of rotational Raman lidar for absolute measurement of atmospheric temperature

NASA Astrophysics Data System (ADS)

Li, Qimeng; Li, Shichun; Hu, Xianglong; Zhao, Jing; Xin, Wenhui; Song, Yuehui; Hua, Dengxin

2018-01-01

The absolute measurement technique for atmospheric temperature can avoid the calibration process and improve the measurement accuracy. To achieve the rotational Raman temperature lidar of absolute measurement, the two-stage parallel multi-channel spectroscopic filter combined a first-order blazed grating with a fiber Bragg grating is designed and its performance is tested. The parameters and the optical path structure of the core cascaded-device (micron-level fiber array) are optimized, the optical path of the primary spectroscope is simulated and the maximum centrifugal distortion of the rotational Raman spectrum is approximately 0.0031 nm, the centrifugal ratio of 0.69%. The experimental results show that the channel coefficients of the primary spectroscope are 0.67, 0.91, 0.67, 0.75, 0.82, 0.63, 0.87, 0.97, 0.89, 0.87 and 1 by using the twelfth channel as a reference and the average FWHM is about 0.44 nm. The maximum deviation between the experimental wavelength and the theoretical value is approximately 0.0398 nm, with the deviation degree of 8.86%. The effective suppression to elastic scattering signal are 30.6, 35.2, 37.1, 38.4, 36.8, 38.2, 41.0, 44.3, 44.0, 46.7 dB. That means, combined with the second spectroscope, the suppression at least is up to 65 dB. Therefore we can fine extract single rotational Raman line to achieve the absolute measurement technique.

Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network.

PubMed

Wang, K W; Deng, C; Li, J P; Zhang, Y Y; Li, X Y; Wu, M C

2017-04-01

Tuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

Real-Time and Meter-Scale Absolute Distance Measurement by Frequency-Comb-Referenced Multi-Wavelength Interferometry.

PubMed

Wang, Guochao; Tan, Lilong; Yan, Shuhua

2018-02-07

We report on a frequency-comb-referenced absolute interferometer which instantly measures long distance by integrating multi-wavelength interferometry with direct synthetic wavelength interferometry. The reported interferometer utilizes four different wavelengths, simultaneously calibrated to the frequency comb of a femtosecond laser, to implement subwavelength distance measurement, while direct synthetic wavelength interferometry is elaborately introduced by launching a fifth wavelength to extend a non-ambiguous range for meter-scale measurement. A linearity test performed comparatively with a He-Ne laser interferometer shows a residual error of less than 70.8 nm in peak-to-valley over a 3 m distance, and a 10 h distance comparison is demonstrated to gain fractional deviations of ~3 × 10 -8 versus 3 m distance. Test results reveal that the presented absolute interferometer enables precise, stable, and long-term distance measurements and facilitates absolute positioning applications such as large-scale manufacturing and space missions.

Real-Time and Meter-Scale Absolute Distance Measurement by Frequency-Comb-Referenced Multi-Wavelength Interferometry

PubMed Central

Tan, Lilong; Yan, Shuhua

2018-01-01

We report on a frequency-comb-referenced absolute interferometer which instantly measures long distance by integrating multi-wavelength interferometry with direct synthetic wavelength interferometry. The reported interferometer utilizes four different wavelengths, simultaneously calibrated to the frequency comb of a femtosecond laser, to implement subwavelength distance measurement, while direct synthetic wavelength interferometry is elaborately introduced by launching a fifth wavelength to extend a non-ambiguous range for meter-scale measurement. A linearity test performed comparatively with a He–Ne laser interferometer shows a residual error of less than 70.8 nm in peak-to-valley over a 3 m distance, and a 10 h distance comparison is demonstrated to gain fractional deviations of ~3 × 10−8 versus 3 m distance. Test results reveal that the presented absolute interferometer enables precise, stable, and long-term distance measurements and facilitates absolute positioning applications such as large-scale manufacturing and space missions. PMID:29414897

Application of hierarchical dissociated neural network in closed-loop hybrid system integrating biological and mechanical intelligence.

PubMed

Li, Yongcheng; Sun, Rong; Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including 'random' and '4Q' (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the '4Q' cultures presented absolutely different activities, and the robot controlled by the '4Q' network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems.

An oil fraction neural sensor developed using electrical capacitance tomography sensor data.

PubMed

Zainal-Mokhtar, Khursiah; Mohamad-Saleh, Junita

2013-08-26

This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical Capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes.

Application of Hierarchical Dissociated Neural Network in Closed-Loop Hybrid System Integrating Biological and Mechanical Intelligence

PubMed Central

Zhang, Bin; Wang, Yuechao; Li, Hongyi

2015-01-01

Neural networks are considered the origin of intelligence in organisms. In this paper, a new design of an intelligent system merging biological intelligence with artificial intelligence was created. It was based on a neural controller bidirectionally connected to an actual mobile robot to implement a novel vehicle. Two types of experimental preparations were utilized as the neural controller including ‘random’ and ‘4Q’ (cultured neurons artificially divided into four interconnected parts) neural network. Compared to the random cultures, the ‘4Q’ cultures presented absolutely different activities, and the robot controlled by the ‘4Q’ network presented better capabilities in search tasks. Our results showed that neural cultures could be successfully employed to control an artificial agent; the robot performed better and better with the stimulus because of the short-term plasticity. A new framework is provided to investigate the bidirectional biological-artificial interface and develop new strategies for a future intelligent system using these simplified model systems. PMID:25992579

An Oil Fraction Neural Sensor Developed Using Electrical capacitance Tomography Sensor Data

PubMed Central

Zainal-Mokhtar, Khursiah; Mohamad-Saleh, Junita

2013-01-01

This paper presents novel research on the development of a generic intelligent oil fraction sensor based on Electrical capacitance Tomography (ECT) data. An artificial Neural Network (ANN) has been employed as the intelligent system to sense and estimate oil fractions from the cross-sections of two-component flows comprising oil and gas in a pipeline. Previous works only focused on estimating the oil fraction in the pipeline based on fixed ECT sensor parameters. With fixed ECT design sensors, an oil fraction neural sensor can be trained to deal with ECT data based on the particular sensor parameters, hence the neural sensor is not generic. This work focuses on development of a generic neural oil fraction sensor based on training a Multi-Layer Perceptron (MLP) ANN with various ECT sensor parameters. On average, the proposed oil fraction neural sensor has shown to be able to give a mean absolute error of 3.05% for various ECT sensor sizes. PMID:24064598

National Economic Development Procedures Manual - Recreation. Volume 2. A Guide for Using the Contingent Value Methodology in Recreation Studies,

DTIC Science & Technology

1986-03-01

Directly from Sample Bid VI-16 Example 3 VI-16 Determining the Zero Price Qiantity Demanded VI-26 Summary VI -31 CHAPrER VII, THE DETERMINATION OF NED...While the standard deviation and variance are absolute measures of dispersion, a relative measure of dispersion can also be computed. This measure is...refers to the closeness of fit between the estimates obtained from Zli e and the true population value. The only way of being absolutely i: o-.iat the

Retrieval of Aerosol Optical Properties from Ground-Based Remote Sensing Measurements: Aerosol Asymmetry Factor and Single Scattering Albedo

NASA Astrophysics Data System (ADS)

Qie, L.; Li, Z.; Li, L.; Li, K.; Li, D.; Xu, H.

2018-04-01

The Devaux-Vermeulen-Li method (DVL method) is a simple approach to retrieve aerosol optical parameters from the Sun-sky radiance measurements. This study inherited the previous works of retrieving aerosol single scattering albedo (SSA) and scattering phase function, the DVL method was modified to derive aerosol asymmetric factor (g). To assess the algorithm performance at various atmospheric aerosol conditions, retrievals from AERONET observations were implemented, and the results are compared with AERONET official products. The comparison shows that both the DVL SSA and g were well correlated with those of AERONET. The RMSD and the absolute value of MBD deviations between the SSAs are 0.025 and 0.015 respectively, well below the AERONET declared SSA uncertainty of 0.03 for all wavelengths. For asymmetry factor g, the RMSD deviations are smaller than 0.02 and the absolute values of MBDs smaller than 0.01 at 675, 870 and 1020 nm bands. Then, considering several factors probably affecting retrieval quality (i.e. the aerosol optical depth (AOD), the solar zenith angle, and the sky residual error, sphericity proportion and Ångström exponent), the deviations for SSA and g of these two algorithms were calculated at varying value intervals. Both the SSA and g deviations were found decrease with the AOD and the solar zenith angle, and increase with sky residual error. However, the deviations do not show clear sensitivity to the sphericity proportion and Ångström exponent. This indicated that the DVL algorithm is available for both large, non-spherical particles and spherical particles. The DVL results are suitable for the evaluation of aerosol direct radiative effects of different aerosol types.

Inverse Monte Carlo in a multilayered tissue model: merging diffuse reflectance spectroscopy and laser Doppler flowmetry.

PubMed

Fredriksson, Ingemar; Burdakov, Oleg; Larsson, Marcus; Strömberg, Tomas

2013-12-01

The tissue fraction of red blood cells (RBCs) and their oxygenation and speed-resolved perfusion are estimated in absolute units by combining diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF). The DRS spectra (450 to 850 nm) are assessed at two source-detector separations (0.4 and 1.2 mm), allowing for a relative calibration routine, whereas LDF spectra are assessed at 1.2 mm in the same fiber-optic probe. Data are analyzed using nonlinear optimization in an inverse Monte Carlo technique by applying an adaptive multilayered tissue model based on geometrical, scattering, and absorbing properties, as well as RBC flow-speed information. Simulations of 250 tissue-like models including up to 2000 individual blood vessels were used to evaluate the method. The absolute root mean square (RMS) deviation between estimated and true oxygenation was 4.1 percentage units, whereas the relative RMS deviations for the RBC tissue fraction and perfusion were 19% and 23%, respectively. Examples of in vivo measurements on forearm and foot during common provocations are presented. The method offers several advantages such as simultaneous quantification of RBC tissue fraction and oxygenation and perfusion from the same, predictable, sampling volume. The perfusion estimate is speed resolved, absolute (% RBC×mm/s), and more accurate due to the combination with DRS.

Inverse Monte Carlo in a multilayered tissue model: merging diffuse reflectance spectroscopy and laser Doppler flowmetry

NASA Astrophysics Data System (ADS)

Fredriksson, Ingemar; Burdakov, Oleg; Larsson, Marcus; Strömberg, Tomas

2013-12-01

The tissue fraction of red blood cells (RBCs) and their oxygenation and speed-resolved perfusion are estimated in absolute units by combining diffuse reflectance spectroscopy (DRS) and laser Doppler flowmetry (LDF). The DRS spectra (450 to 850 nm) are assessed at two source-detector separations (0.4 and 1.2 mm), allowing for a relative calibration routine, whereas LDF spectra are assessed at 1.2 mm in the same fiber-optic probe. Data are analyzed using nonlinear optimization in an inverse Monte Carlo technique by applying an adaptive multilayered tissue model based on geometrical, scattering, and absorbing properties, as well as RBC flow-speed information. Simulations of 250 tissue-like models including up to 2000 individual blood vessels were used to evaluate the method. The absolute root mean square (RMS) deviation between estimated and true oxygenation was 4.1 percentage units, whereas the relative RMS deviations for the RBC tissue fraction and perfusion were 19% and 23%, respectively. Examples of in vivo measurements on forearm and foot during common provocations are presented. The method offers several advantages such as simultaneous quantification of RBC tissue fraction and oxygenation and perfusion from the same, predictable, sampling volume. The perfusion estimate is speed resolved, absolute (% RBC×mm/s), and more accurate due to the combination with DRS.

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

Morley, Steven

The PyForecastTools package provides Python routines for calculating metrics for model validation, forecast verification and model comparison. For continuous predictands the package provides functions for calculating bias (mean error, mean percentage error, median log accuracy, symmetric signed bias), and for calculating accuracy (mean squared error, mean absolute error, mean absolute scaled error, normalized RMSE, median symmetric accuracy). Convenience routines to calculate the component parts (e.g. forecast error, scaled error) of each metric are also provided. To compare models the package provides: generic skill score; percent better. Robust measures of scale including median absolute deviation, robust standard deviation, robust coefficient ofmore » variation and the Sn estimator are all provided by the package. Finally, the package implements Python classes for NxN contingency tables. In the case of a multi-class prediction, accuracy and skill metrics such as proportion correct and the Heidke and Peirce skill scores are provided as object methods. The special case of a 2x2 contingency table inherits from the NxN class and provides many additional metrics for binary classification: probability of detection, probability of false detection, false alarm ration, threat score, equitable threat score, bias. Confidence intervals for many of these quantities can be calculated using either the Wald method or Agresti-Coull intervals.« less

Single-breath diffusing capacity for carbon monoxide instrument accuracy across 3 health systems.

PubMed

Hegewald, Matthew J; Markewitz, Boaz A; Wilson, Emily L; Gallo, Heather M; Jensen, Robert L

2015-03-01

Measuring diffusing capacity of the lung for carbon monoxide (DLCO) is complex and associated with wide intra- and inter-laboratory variability. Increased D(LCO) variability may have important clinical consequences. The objective of the study was to assess instrument performance across hospital pulmonary function testing laboratories using a D(LCO) simulator that produces precise and repeatable D(LCO) values. D(LCO) instruments were tested with CO gas concentrations representing medium and high range D(LCO) values. The absolute difference between observed and target D(LCO) value was used to determine measurement accuracy; accuracy was defined as an average deviation from the target value of < 2.0 mL/min/mm Hg. Accuracy of inspired volume measurement and gas sensors were also determined. Twenty-three instruments were tested across 3 healthcare systems. The mean absolute deviation from the target value was 1.80 mL/min/mm Hg (range 0.24-4.23) with 10 of 23 instruments (43%) being inaccurate. High volume laboratories performed better than low volume laboratories, although the difference was not significant. There was no significant difference among the instruments by manufacturers. Inspired volume was not accurate in 48% of devices; mean absolute deviation from target value was 3.7%. Instrument gas analyzers performed adequately in all instruments. D(LCO) instrument accuracy was unacceptable in 43% of devices. Instrument inaccuracy can be primarily attributed to errors in inspired volume measurement and not gas analyzer performance. D(LCO) instrument performance may be improved by regular testing with a simulator. Caution should be used when comparing D(LCO) results reported from different laboratories. Copyright © 2015 by Daedalus Enterprises.

Improving Spleen Volume Estimation via Computer Assisted Segmentation on Clinically Acquired CT Scans

PubMed Central

Xu, Zhoubing; Gertz, Adam L.; Burke, Ryan P.; Bansal, Neil; Kang, Hakmook; Landman, Bennett A.; Abramson, Richard G.

2016-01-01

OBJECTIVES Multi-atlas fusion is a promising approach for computer-assisted segmentation of anatomical structures. The purpose of this study was to evaluate the accuracy and time efficiency of multi-atlas segmentation for estimating spleen volumes on clinically-acquired CT scans. MATERIALS AND METHODS Under IRB approval, we obtained 294 deidentified (HIPAA-compliant) abdominal CT scans on 78 subjects from a recent clinical trial. We compared five pipelines for obtaining splenic volumes: Pipeline 1–manual segmentation of all scans, Pipeline 2–automated segmentation of all scans, Pipeline 3–automated segmentation of all scans with manual segmentation for outliers on a rudimentary visual quality check, Pipelines 4 and 5–volumes derived from a unidimensional measurement of craniocaudal spleen length and three-dimensional splenic index measurements, respectively. Using Pipeline 1 results as ground truth, the accuracy of Pipelines 2–5 (Dice similarity coefficient [DSC], Pearson correlation, R-squared, and percent and absolute deviation of volume from ground truth) were compared for point estimates of splenic volume and for change in splenic volume over time. Time cost was also compared for Pipelines 1–5. RESULTS Pipeline 3 was dominant in terms of both accuracy and time cost. With a Pearson correlation coefficient of 0.99, average absolute volume deviation 23.7 cm3, and 1 minute per scan, Pipeline 3 yielded the best results. The second-best approach was Pipeline 5, with a Pearson correlation coefficient 0.98, absolute deviation 46.92 cm3, and 1 minute 30 seconds per scan. Manual segmentation (Pipeline 1) required 11 minutes per scan. CONCLUSION A computer-automated segmentation approach with manual correction of outliers generated accurate splenic volumes with reasonable time efficiency. PMID:27519156

Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar

PubMed Central

Gabella, Marco; Leuenberger, Andreas

2017-01-01

The radio noise that comes from the Sun has been reported in literature as a reference signal to check the quality of dual-polarization weather radar receivers for the S-band and C-band. In most cases, the focus was on relative calibration: horizontal and vertical polarizations were evaluated versus the reference signal mainly in terms of standard deviation of the difference. This means that the investigated radar receivers were able to reproduce the slowly varying component of the microwave signal emitted by the Sun. A novel method, aimed at the absolute calibration of dual-polarization receivers, has recently been presented and applied for the C-band. This method requires the antenna beam axis to be pointed towards the center of the Sun for less than a minute. Standard deviations of the difference as low as 0.1 dB have been found for the Swiss radars. As far as the absolute calibration is concerned, the average differences were of the order of −0.6 dB (after noise subtraction). The method has been implemented on a mobile, X-band radar, and this paper presents the successful results that were obtained during the 2016 field campaign in Payerne (Switzerland). Despite a relatively poor Sun-to-Noise ratio, the “small” (~0.4 dB) amplitude of the slowly varying emission was captured and reproduced; the standard deviation of the difference between the radar and the reference was ~0.2 dB. The absolute calibration of the vertical and horizontal receivers was satisfactory. After the noise subtraction and atmospheric correction a, the mean difference was close to 0 dB. PMID:28531164

Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar.

PubMed

Gabella, Marco; Leuenberger, Andreas

2017-05-22

The radio noise that comes from the Sun has been reported in literature as a reference signal to check the quality of dual-polarization weather radar receivers for the S-band and C-band. In most cases, the focus was on relative calibration: horizontal and vertical polarizations were evaluated versus the reference signal mainly in terms of standard deviation of the difference. This means that the investigated radar receivers were able to reproduce the slowly varying component of the microwave signal emitted by the Sun. A novel method, aimed at the absolute calibration of dual-polarization receivers, has recently been presented and applied for the C-band. This method requires the antenna beam axis to be pointed towards the center of the Sun for less than a minute. Standard deviations of the difference as low as 0.1 dB have been found for the Swiss radars. As far as the absolute calibration is concerned, the average differences were of the order of -0.6 dB (after noise subtraction). The method has been implemented on a mobile, X-band radar, and this paper presents the successful results that were obtained during the 2016 field campaign in Payerne (Switzerland). Despite a relatively poor Sun-to-Noise ratio, the "small" (~0.4 dB) amplitude of the slowly varying emission was captured and reproduced; the standard deviation of the difference between the radar and the reference was ~0.2 dB. The absolute calibration of the vertical and horizontal receivers was satisfactory. After the noise subtraction and atmospheric correction a, the mean difference was close to 0 dB.

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models.

PubMed

Genheden, Samuel

2017-10-01

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Solvation free energies and partition coefficients with the coarse-grained and hybrid all-atom/coarse-grained MARTINI models

NASA Astrophysics Data System (ADS)

Genheden, Samuel

2017-10-01

We present the estimation of solvation free energies of small solutes in water, n-octanol and hexane using molecular dynamics simulations with two MARTINI models at different resolutions, viz. the coarse-grained (CG) and the hybrid all-atom/coarse-grained (AA/CG) models. From these estimates, we also calculate the water/hexane and water/octanol partition coefficients. More than 150 small, organic molecules were selected from the Minnesota solvation database and parameterized in a semi-automatic fashion. Using either the CG or hybrid AA/CG models, we find considerable deviations between the estimated and experimental solvation free energies in all solvents with mean absolute deviations larger than 10 kJ/mol, although the correlation coefficient is between 0.55 and 0.75 and significant. There is also no difference between the results when using the non-polarizable and polarizable water model, although we identify some improvements when using the polarizable model with the AA/CG solutes. In contrast to the estimated solvation energies, the estimated partition coefficients are generally excellent with both the CG and hybrid AA/CG models, giving mean absolute deviations between 0.67 and 0.90 log units and correlation coefficients larger than 0.85. We analyze the error distribution further and suggest avenues for improvements.

Evidence against global attention filters selective for absolute bar-orientation in human vision.

PubMed

Inverso, Matthew; Sun, Peng; Chubb, Charles; Wright, Charles E; Sperling, George

2016-01-01

The finding that an item of type A pops out from an array of distractors of type B typically is taken to support the inference that human vision contains a neural mechanism that is activated by items of type A but not by items of type B. Such a mechanism might be expected to yield a neural image in which items of type A produce high activation and items of type B low (or zero) activation. Access to such a neural image might further be expected to enable accurate estimation of the centroid of an ensemble of items of type A intermixed with to-be-ignored items of type B. Here, it is shown that as the number of items in stimulus displays is increased, performance in estimating the centroids of horizontal (vertical) items amid vertical (horizontal) distractors degrades much more quickly and dramatically than does performance in estimating the centroids of white (black) items among black (white) distractors. Together with previous findings, these results suggest that, although human vision does possess bottom-up neural mechanisms sensitive to abrupt local changes in bar-orientation, and although human vision does possess and utilize top-down global attention filters capable of selecting multiple items of one brightness or of one color from among others, it cannot use a top-down global attention filter capable of selecting multiple bars of a given absolute orientation and filtering bars of the opposite orientation in a centroid task.

Discrete distributed strain sensing of intelligent structures

NASA Technical Reports Server (NTRS)

Anderson, Mark S.; Crawley, Edward F.

1992-01-01

Techniques are developed for the design of discrete highly distributed sensor systems for use in intelligent structures. First the functional requirements for such a system are presented. Discrete spatially averaging strain sensors are then identified as satisfying the functional requirements. A variety of spatial weightings for spatially averaging sensors are examined, and their wave number characteristics are determined. Preferable spatial weightings are identified. Several numerical integration rules used to integrate such sensors in order to determine the global deflection of the structure are discussed. A numerical simulation is conducted using point and rectangular sensors mounted on a cantilevered beam under static loading. Gage factor and sensor position uncertainties are incorporated to assess the absolute error and standard deviation of the error in the estimated tip displacement found by numerically integrating the sensor outputs. An experiment is carried out using a statically loaded cantilevered beam with five point sensors. It is found that in most cases the actual experimental error is within one standard deviation of the absolute error as found in the numerical simulation.

Analytical gradients for subsystem density functional theory within the slater-function-based amsterdam density functional program.

PubMed

Schlüns, Danny; Franchini, Mirko; Götz, Andreas W; Neugebauer, Johannes; Jacob, Christoph R; Visscher, Lucas

2017-02-05

We present a new implementation of analytical gradients for subsystem density-functional theory (sDFT) and frozen-density embedding (FDE) into the Amsterdam Density Functional program (ADF). The underlying theory and necessary expressions for the implementation are derived and discussed in detail for various FDE and sDFT setups. The parallel implementation is numerically verified and geometry optimizations with different functional combinations (LDA/TF and PW91/PW91K) are conducted and compared to reference data. Our results confirm that sDFT-LDA/TF yields good equilibrium distances for the systems studied here (mean absolute deviation: 0.09 Å) compared to reference wave-function theory results. However, sDFT-PW91/PW91k quite consistently yields smaller equilibrium distances (mean absolute deviation: 0.23 Å). The flexibility of our new implementation is demonstrated for an HCN-trimer test system, for which several different setups are applied. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Intracortical myelination in musicians with absolute pitch: Quantitative morphometry using 7‐T MRI

PubMed Central

Knösche, Thomas R.

2016-01-01

Abstract Absolute pitch (AP) is known as the ability to recognize and label the pitch chroma of a given tone without external reference. Known brain structures and functions related to AP are mainly of macroscopic aspects. To shed light on the underlying neural mechanism of AP, we investigated the intracortical myeloarchitecture in musicians with and without AP using the quantitative mapping of the longitudinal relaxation rates with ultra‐high‐field magnetic resonance imaging at 7 T. We found greater intracortical myelination for AP musicians in the anterior region of the supratemporal plane, particularly the medial region of the right planum polare (PP). In the same region of the right PP, we also found a positive correlation with a behavioral index of AP performance. In addition, we found a positive correlation with a frequency discrimination threshold in the anterolateral Heschl's gyrus in the right hemisphere, demonstrating distinctive neural processes of absolute recognition and relative discrimination of pitch. Regarding possible effects of local myelination in the cortex and the known importance of the anterior superior temporal gyrus/sulcus for the identification of auditory objects, we argue that pitch chroma may be processed as an identifiable object property in AP musicians. Hum Brain Mapp 37:3486–3501, 2016. © 2016 Wiley Periodicals, Inc. PMID:27160707

Cirrus cloud retrieval from MSG/SEVIRI during day and night using artificial neural networks

NASA Astrophysics Data System (ADS)

Strandgren, Johan; Bugliaro, Luca

2017-04-01

By covering a large part of the Earth, cirrus clouds play an important role in climate as they reflect incoming solar radiation and absorb outgoing thermal radiation. Nevertheless, the cirrus clouds remain one of the largest uncertainties in atmospheric research and the understanding of the physical processes that govern their life cycle is still poorly understood, as is their representation in climate models. To monitor and better understand the properties and physical processes of cirrus clouds, it's essential that those tenuous clouds can be observed from geostationary spaceborne imagers like SEVIRI (Spinning Enhanced Visible and InfraRed Imager), that possess a high temporal resolution together with a large field of view and play an important role besides in-situ observations for the investigation of cirrus cloud processes. CiPS (Cirrus Properties from Seviri) is a new algorithm targeting thin cirrus clouds. CiPS is an artificial neural network trained with coincident SEVIRI and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations in order to retrieve a cirrus cloud mask along with the cloud top height (CTH), ice optical thickness (IOT) and ice water path (IWP) from SEVIRI. By utilizing only the thermal/IR channels of SEVIRI, CiPS can be used during day and night making it a powerful tool for the cirrus life cycle analysis. Despite the great challenge of detecting thin cirrus clouds and retrieving their properties from a geostationary imager using only the thermal/IR wavelengths, CiPS performs well. Among the cirrus clouds detected by CALIOP, CiPS detects 70 and 95 % of the clouds with an optical thickness of 0.1 and 1.0 respectively. Among the cirrus free pixels, CiPS classify 96 % correctly. For the CTH retrieval, CiPS has a mean absolute percentage error of 10 % or less with respect to CALIOP for cirrus clouds with a CTH greater than 8 km. For the IOT retrieval, CiPS has a mean absolute percentage error of 100 % or less with respect to CALIOP for cirrus clouds with an optical thickness down to 0.07. For such thin cirrus clouds an error of 100 % should be regarded as low from a geostationary imager like SEVIRI. The IWP retrieved by CiPS shows a similar performance, but has larger deviations for the thinner cirrus clouds.

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.

Measuring (subglacial) bedform orientation, length, and longitudinal asymmetry - Method assessment.

PubMed

Jorge, Marco G; Brennand, Tracy A

2017-01-01

Geospatial analysis software provides a range of tools that can be used to measure landform morphometry. Often, a metric can be computed with different techniques that may give different results. This study is an assessment of 5 different methods for measuring longitudinal, or streamlined, subglacial bedform morphometry: orientation, length and longitudinal asymmetry, all of which require defining a longitudinal axis. The methods use the standard deviational ellipse (not previously applied in this context), the longest straight line fitting inside the bedform footprint (2 approaches), the minimum-size footprint-bounding rectangle, and Euler's approximation. We assess how well these methods replicate morphometric data derived from a manually mapped (visually interpreted) longitudinal axis, which, though subjective, is the most typically used reference. A dataset of 100 subglacial bedforms covering the size and shape range of those in the Puget Lowland, Washington, USA is used. For bedforms with elongation > 5, deviations from the reference values are negligible for all methods but Euler's approximation (length). For bedforms with elongation < 5, most methods had small mean absolute error (MAE) and median absolute deviation (MAD) for all morphometrics and thus can be confidently used to characterize the central tendencies of their distributions. However, some methods are better than others. The least precise methods are the ones based on the longest straight line and Euler's approximation; using these for statistical dispersion analysis is discouraged. Because the standard deviational ellipse method is relatively shape invariant and closely replicates the reference values, it is the recommended method. Speculatively, this study may also apply to negative-relief, and fluvial and aeolian bedforms.

Volume Quantification of Acute Infratentorial Hemorrhage with Computed Tomography: Validation of the Formula 1/2ABC and 2/3SH

PubMed Central

Zhang, Yunyun; Yan, Jing; Fu, Yi; Chen, Shengdi

2013-01-01

Objective To compare the accuracy of formula 1/2ABC with 2/3SH on volume estimation for hypertensive infratentorial hematoma. Methods One hundred and forty-seven CT scans diagnosed as hypertensive infratentorial hemorrhage were reviewed. Based on the shape, hematomas were categorized as regular or irregular. Multilobular was defined as a special shape of irregular. Hematoma volume was calculated employing computer-assisted volumetric analysis (CAVA), 1/2ABC and 2/3SH, respectively. Results The correlation coefficients between 1/2ABC (or 2/3SH) and CAVA were greater than 0.900 in all subgroups. There were neither significant differences in absolute values of volume deviation nor percentage deviation between 1/2ABC and 2/3SH for regular hemorrhage (P>0.05). While for cerebellar, brainstem and irregular hemorrhages, the absolute values of volume deviation and percentage deviation by formula 1/2ABC were greater than 2/3SH (P<0.05). 1/2ABC and 2/3SH underestimated hematoma volume each by 10% and 5% for cerebellar hemorrhage, 14% and 9% for brainstem hemorrhage, 19% and 16% for regular hemorrhage, 9% and 3% for irregular hemorrhage, respectively. In addition, for the multilobular hemorrhage, 1/2ABC underestimated the volume by 9% while 2/3SH overestimated it by 2%. Conclusions For regular hemorrhage volume calculation, the accuracy of 2/3SH is similar to 1/2ABC. While for cerebellar, brainstem or irregular hemorrhages (including multilobular), 2/3SH is more accurate than 1/2ABC. PMID:23638025

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Validation of ozone intensities at 10 μm with THz spectrometry

NASA Astrophysics Data System (ADS)

Drouin, Brian J.; Crawford, Timothy J.; Yu, Shanshan

2017-12-01

This manuscript reports an effort to improve the absolute accuracy of ozone intensities in the 10 μm region via a transfer of the precision of the rotational dipole moment onto the infrared measurement. The approach determines the ozone mixing ratio through alternately measuring seven pure rotation ozone lines from 692 to 779 GHz. A multispectrum fitting technique was employed. The results determine the column with absolute accuracy of 1.5% and the intensities of infrared transitions measured at this accuracy reproduce the recommended values to within a standard deviation of 2.8%.

The Central Role of Recognition in Auditory Perception: A Neurobiological Model

ERIC Educational Resources Information Center

McLachlan, Neil; Wilson, Sarah

2010-01-01

The model presents neurobiologically plausible accounts of sound recognition (including absolute pitch), neural plasticity involved in pitch, loudness and location information integration, and streaming and auditory recall. It is proposed that a cortical mechanism for sound identification modulates the spectrotemporal response fields of inferior…

Hierarchical modeling of molecular energies using a deep neural network

NASA Astrophysics Data System (ADS)

Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

2018-06-01

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Generalized regression neural network (GRNN)-based approach for colored dissolved organic matter (CDOM) retrieval: case study of Connecticut River at Middle Haddam Station, USA.

PubMed

Heddam, Salim

2014-11-01

The prediction of colored dissolved organic matter (CDOM) using artificial neural network approaches has received little attention in the past few decades. In this study, colored dissolved organic matter (CDOM) was modeled using generalized regression neural network (GRNN) and multiple linear regression (MLR) models as a function of Water temperature (TE), pH, specific conductance (SC), and turbidity (TU). Evaluation of the prediction accuracy of the models is based on the root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (CC), and Willmott's index of agreement (d). The results indicated that GRNN can be applied successfully for prediction of colored dissolved organic matter (CDOM).

Visual Field Defects and Retinal Ganglion Cell Losses in Human Glaucoma Patients

PubMed Central

Harwerth, Ronald S.; Quigley, Harry A.

2007-01-01

Objective The depth of visual field defects are correlated with retinal ganglion cell densities in experimental glaucoma. This study was to determine whether a similar structure-function relationship holds for human glaucoma. Methods The study was based on retinal ganglion cell densities and visual thresholds of patients with documented glaucoma (Kerrigan-Baumrind, et al.) The data were analyzed by a model that predicted ganglion cell densities from standard clinical perimetry, which were then compared to histologic cell counts. Results The model, without free parameters, produced accurate and relatively precise quantification of ganglion cell densities associated with visual field defects. For 437 sets of data, the unity correlation for predicted vs. measured cell densities had a coefficient of determination of 0.39. The mean absolute deviation of the predicted vs. measured values was 2.59 dB, the mean and SD of the distribution of residual errors of prediction was -0.26 ± 3.22 dB. Conclusions Visual field defects by standard clinical perimetry are proportional to neural losses caused by glaucoma. Clinical Relevance The evidence for quantitative structure-function relationships provides a scientific basis of interpreting glaucomatous neuropathy from visual thresholds and supports the application of standard perimetry to establish the stage of the disease. PMID:16769839

Alpha-actinin-3 (ACTN3) R577X polymorphism influences knee extensor peak power response to strength training in older men and women.

PubMed

Delmonico, Matthew J; Kostek, Matthew C; Doldo, Neil A; Hand, Brian D; Walsh, Sean; Conway, Joan M; Carignan, Craig R; Roth, Stephen M; Hurley, Ben F

2007-02-01

The alpha-actinin-3 (ACTN3) R577X polymorphism has been associated with muscle power performance in cross-sectional studies. We examined baseline knee extensor concentric peak power (PP) and PP change with approximately 10 weeks of unilateral knee extensor strength training (ST) using air-powered resistance machines in 71 older men (65 [standard deviation = 8] years) and 86 older women (64 [standard deviation = 9] years). At baseline in women, the XX genotype group had an absolute (same resistance) PP that was higher than the RR (p =.005) and RX genotype groups (p =.02). The women XX group also had a relative (70% of one-repetition maximum [1-RM]) PP that was higher than that in the RR (p =.002) and RX groups (p =.008). No differences in baseline absolute or relative PP were observed between ACTN3 genotype groups in men. In men, absolute PP change with ST in the RR (n = 16) group approached a significantly higher value than in the XX group (n = 9; p =.07). In women, relative PP change with ST in the RR group (n = 16) was higher than in the XX group (n = 17; p =.02). The results indicate that the ACTN3 R577X polymorphism influences the response of quadriceps muscle power to ST in older adults.

Neural signatures of social conformity: A coordinate-based activation likelihood estimation meta-analysis of functional brain imaging studies.

PubMed

Wu, Haiyan; Luo, Yi; Feng, Chunliang

2016-12-01

People often align their behaviors with group opinions, known as social conformity. Many neuroscience studies have explored the neuropsychological mechanisms underlying social conformity. Here we employed a coordinate-based meta-analysis on neuroimaging studies of social conformity with the purpose to reveal the convergence of the underlying neural architecture. We identified a convergence of reported activation foci in regions associated with normative decision-making, including ventral striatum (VS), dorsal posterior medial frontal cortex (dorsal pMFC), and anterior insula (AI). Specifically, consistent deactivation of VS and activation of dorsal pMFC and AI are identified when people's responses deviate from group opinions. In addition, the deviation-related responses in dorsal pMFC predict people's conforming behavioral adjustments. These are consistent with current models that disagreement with others might evoke "error" signals, cognitive imbalance, and/or aversive feelings, which are plausibly detected in these brain regions as control signals to facilitate subsequent conforming behaviors. Finally, group opinions result in altered neural correlates of valuation, manifested as stronger responses of VS to stimuli endorsed than disliked by others. Copyright © 2016 Elsevier Ltd. All rights reserved.

Note: An absolute X-Y-Θ position sensor using a two-dimensional phase-encoded binary scale

NASA Astrophysics Data System (ADS)

Kim, Jong-Ahn; Kim, Jae Wan; Kang, Chu-Shik; Jin, Jonghan

2018-04-01

This Note presents a new absolute X-Y-Θ position sensor for measuring planar motion of a precision multi-axis stage system. By analyzing the rotated image of a two-dimensional phase-encoded binary scale (2D), the absolute 2D position values at two separated points were obtained and the absolute X-Y-Θ position could be calculated combining these values. The sensor head was constructed using a board-level camera, a light-emitting diode light source, an imaging lens, and a cube beam-splitter. To obtain the uniform intensity profiles from the vignette scale image, we selected the averaging directions deliberately, and higher resolution in the angle measurement could be achieved by increasing the allowable offset size. The performance of a prototype sensor was evaluated in respect of resolution, nonlinearity, and repeatability. The sensor could resolve 25 nm linear and 0.001° angular displacements clearly, and the standard deviations were less than 18 nm when 2D grid positions were measured repeatedly.

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System.

PubMed

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP).

Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PubMed Central

Tang, Jinjun; Zou, Yajie; Ash, John; Zhang, Shen; Liu, Fang; Wang, Yinhai

2016-01-01

Travel time is an important measurement used to evaluate the extent of congestion within road networks. This paper presents a new method to estimate the travel time based on an evolving fuzzy neural inference system. The input variables in the system are traffic flow data (volume, occupancy, and speed) collected from loop detectors located at points both upstream and downstream of a given link, and the output variable is the link travel time. A first order Takagi-Sugeno fuzzy rule set is used to complete the inference. For training the evolving fuzzy neural network (EFNN), two learning processes are proposed: (1) a K-means method is employed to partition input samples into different clusters, and a Gaussian fuzzy membership function is designed for each cluster to measure the membership degree of samples to the cluster centers. As the number of input samples increases, the cluster centers are modified and membership functions are also updated; (2) a weighted recursive least squares estimator is used to optimize the parameters of the linear functions in the Takagi-Sugeno type fuzzy rules. Testing datasets consisting of actual and simulated data are used to test the proposed method. Three common criteria including mean absolute error (MAE), root mean square error (RMSE), and mean absolute relative error (MARE) are utilized to evaluate the estimation performance. Estimation results demonstrate the accuracy and effectiveness of the EFNN method through comparison with existing methods including: multiple linear regression (MLR), instantaneous model (IM), linear model (LM), neural network (NN), and cumulative plots (CP). PMID:26829639

Morphofunctional Experience-Dependent Plasticity in the Honeybee Brain

ERIC Educational Resources Information Center

Andrione, Mara; Timberlake, Benjamin F.; Vallortigara, Giorgio; Antolini, Renzo; Haase, Albrecht

2017-01-01

Repeated or prolonged exposure to an odorant without any positive or negative reinforcement produces experience-dependent plasticity, which results in habituation and latent inhibition. In the honeybee ("Apis mellifera"), it has been demonstrated that, even if the absolute neural representation of an odor in the primary olfactory center,…

Small-Volume Injections: Evaluation of Volume Administration Deviation From Intended Injection Volumes.

PubMed

Muffly, Matthew K; Chen, Michael I; Claure, Rebecca E; Drover, David R; Efron, Bradley; Fitch, William L; Hammer, Gregory B

2017-10-01

In the perioperative period, anesthesiologists and postanesthesia care unit (PACU) nurses routinely prepare and administer small-volume IV injections, yet the accuracy of delivered medication volumes in this setting has not been described. In this ex vivo study, we sought to characterize the degree to which small-volume injections (≤0.5 mL) deviated from the intended injection volumes among a group of pediatric anesthesiologists and pediatric postanesthesia care unit (PACU) nurses. We hypothesized that as the intended injection volumes decreased, the deviation from those intended injection volumes would increase. Ten attending pediatric anesthesiologists and 10 pediatric PACU nurses each performed a series of 10 injections into a simulated patient IV setup. Practitioners used separate 1-mL tuberculin syringes with removable 18-gauge needles (Becton-Dickinson & Company, Franklin Lakes, NJ) to aspirate 5 different volumes (0.025, 0.05, 0.1, 0.25, and 0.5 mL) of 0.25 mM Lucifer Yellow (LY) fluorescent dye constituted in saline (Sigma Aldrich, St. Louis, MO) from a rubber-stoppered vial. Each participant then injected the specified volume of LY fluorescent dye via a 3-way stopcock into IV tubing with free-flowing 0.9% sodium chloride (10 mL/min). The injected volume of LY fluorescent dye and 0.9% sodium chloride then drained into a collection vial for laboratory analysis. Microplate fluorescence wavelength detection (Infinite M1000; Tecan, Mannedorf, Switzerland) was used to measure the fluorescence of the collected fluid. Administered injection volumes were calculated based on the fluorescence of the collected fluid using a calibration curve of known LY volumes and associated fluorescence.To determine whether deviation of the administered volumes from the intended injection volumes increased at lower injection volumes, we compared the proportional injection volume error (loge [administered volume/intended volume]) for each of the 5 injection volumes using a linear regression model. Analysis of variance was used to determine whether the absolute log proportional error differed by the intended injection volume. Interindividual and intraindividual deviation from the intended injection volume was also characterized. As the intended injection volumes decreased, the absolute log proportional injection volume error increased (analysis of variance, P < .0018). The exploratory analysis revealed no significant difference in the standard deviations of the log proportional errors for injection volumes between physicians and pediatric PACU nurses; however, the difference in absolute bias was significantly higher for nurses with a 2-sided significance of P = .03. Clinically significant dose variation occurs when injecting volumes ≤0.5 mL. Administering small volumes of medications may result in unintended medication administration errors.

The Effectiveness of Neural Therapy in Patients With Bell’s Palsy

PubMed Central

Yavuz, Ferdi; Kelle, Bayram; Balaban, Birol

2016-01-01

This report describes the case of a 42-y-old man with a type of facial nerve palsy of the lower motor neurons (LMNs) on the right side, who was treated with neural therapy. After exposure to cold weather, the patient had suddenly developed difficulty in closing his right eye and a deviation to the left in the angle of his mouth. He had no previous medical illness and had no history of trauma, smoking, alcohol intake, or blood transfusion. PMID:27547166

Computational tests of quantum chemical models for excited and ionized states of molecules with phosphorus and sulfur atoms.

PubMed

Hahn, David K; RaghuVeer, Krishans; Ortiz, J V

2014-05-15

Time-dependent density functional theory (TD-DFT) and electron propagator theory (EPT) are used to calculate the electronic transition energies and ionization energies, respectively, of species containing phosphorus or sulfur. The accuracy of TD-DFT and EPT, in conjunction with various basis sets, is assessed with data from gas-phase spectroscopy. TD-DFT is tested using 11 prominent exchange-correlation functionals on a set of 37 vertical and 19 adiabatic transitions. For vertical transitions, TD-CAM-B3LYP calculations performed with the MG3S basis set are lowest in overall error, having a mean absolute deviation from experiment of 0.22 eV, or 0.23 eV over valence transitions and 0.21 eV over Rydberg transitions. Using a larger basis set, aug-pc3, improves accuracy over the valence transitions via hybrid functionals, but improved accuracy over the Rydberg transitions is only obtained via the BMK functional. For adiabatic transitions, all hybrid functionals paired with the MG3S basis set perform well, and B98 is best, with a mean absolute deviation from experiment of 0.09 eV. The testing of EPT used the Outer Valence Green's Function (OVGF) approximation and the Partial Third Order (P3) approximation on 37 vertical first ionization energies. It is found that OVGF outperforms P3 when basis sets of at least triple-ζ quality in the polarization functions are used. The largest basis set used in this study, aug-pc3, obtained the best mean absolute error from both methods -0.08 eV for OVGF and 0.18 eV for P3. The OVGF/6-31+G(2df,p) level of theory is particularly cost-effective, yielding a mean absolute error of 0.11 eV.

A Simple Model Predicting Individual Weight Change in Humans

PubMed Central

Thomas, Diana M.; Martin, Corby K.; Heymsfield, Steven; Redman, Leanne M.; Schoeller, Dale A.; Levine, James A.

2010-01-01

Excessive weight in adults is a national concern with over 2/3 of the US population deemed overweight. Because being overweight has been correlated to numerous diseases such as heart disease and type 2 diabetes, there is a need to understand mechanisms and predict outcomes of weight change and weight maintenance. A simple mathematical model that accurately predicts individual weight change offers opportunities to understand how individuals lose and gain weight and can be used to foster patient adherence to diets in clinical settings. For this purpose, we developed a one dimensional differential equation model of weight change based on the energy balance equation is paired to an algebraic relationship between fat free mass and fat mass derived from a large nationally representative sample of recently released data collected by the Centers for Disease Control. We validate the model's ability to predict individual participants’ weight change by comparing model estimates of final weight data from two recent underfeeding studies and one overfeeding study. Mean absolute error and standard deviation between model predictions and observed measurements of final weights are less than 1.8 ± 1.3 kg for the underfeeding studies and 2.5 ± 1.6 kg for the overfeeding study. Comparison of the model predictions to other one dimensional models of weight change shows improvement in mean absolute error, standard deviation of mean absolute error, and group mean predictions. The maximum absolute individual error decreased by approximately 60% substantiating reliability in individual weight change predictions. The model provides a viable method for estimating individual weight change as a result of changes in intake and determining individual dietary adherence during weight change studies. PMID:24707319

Intracortical myelination in musicians with absolute pitch: Quantitative morphometry using 7-T MRI.

PubMed

Kim, Seung-Goo; Knösche, Thomas R

2016-10-01

Absolute pitch (AP) is known as the ability to recognize and label the pitch chroma of a given tone without external reference. Known brain structures and functions related to AP are mainly of macroscopic aspects. To shed light on the underlying neural mechanism of AP, we investigated the intracortical myeloarchitecture in musicians with and without AP using the quantitative mapping of the longitudinal relaxation rates with ultra-high-field magnetic resonance imaging at 7 T. We found greater intracortical myelination for AP musicians in the anterior region of the supratemporal plane, particularly the medial region of the right planum polare (PP). In the same region of the right PP, we also found a positive correlation with a behavioral index of AP performance. In addition, we found a positive correlation with a frequency discrimination threshold in the anterolateral Heschl's gyrus in the right hemisphere, demonstrating distinctive neural processes of absolute recognition and relative discrimination of pitch. Regarding possible effects of local myelination in the cortex and the known importance of the anterior superior temporal gyrus/sulcus for the identification of auditory objects, we argue that pitch chroma may be processed as an identifiable object property in AP musicians. Hum Brain Mapp 37:3486-3501, 2016. © 2016 Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

The effect of modeled absolute timing variability and relative timing variability on observational learning.

PubMed

Grierson, Lawrence E M; Roberts, James W; Welsher, Arthur M

2017-05-01

There is much evidence to suggest that skill learning is enhanced by skill observation. Recent research on this phenomenon indicates a benefit of observing variable/erred demonstrations. In this study, we explore whether it is variability within the relative organization or absolute parameterization of a movement that facilitates skill learning through observation. To do so, participants were randomly allocated into groups that observed a model with no variability, absolute timing variability, relative timing variability, or variability in both absolute and relative timing. All participants performed a four-segment movement pattern with specific absolute and relative timing goals prior to and following the observational intervention, as well as in a 24h retention test and transfers tests that featured new relative and absolute timing goals. Absolute timing error indicated that all groups initially acquired the absolute timing, maintained their performance at 24h retention, and exhibited performance deterioration in both transfer tests. Relative timing error revealed that the observation of no variability and relative timing variability produced greater performance at the post-test, 24h retention and relative timing transfer tests, but for the no variability group, deteriorated at absolute timing transfer test. The results suggest that the learning of absolute timing following observation unfolds irrespective of model variability. However, the learning of relative timing benefits from holding the absolute features constant, while the observation of no variability partially fails in transfer. We suggest learning by observing no variability and variable/erred models unfolds via similar neural mechanisms, although the latter benefits from the additional coding of information pertaining to movements that require a correction. Copyright © 2017 Elsevier B.V. All rights reserved.

ESTIMATION OF RADIOACTIVE CALCIUM-45 BY LIQUID SCINTILLATION COUNTING

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

Lutwak, L.

1959-03-01

A liquid sclntillation counting method is developed for determining radioactive calcium-45 in biological materials. The calcium-45 is extracted, concentrated, and dissolved in absolute ethyl alcohol to which is added 0.4% diphenyloxazol in toluene. Counting efficiency is about 65 percent with standard deviation of the J-57 engin 7.36 percent. (auth)

A Robust Interpretation of Teaching Evaluation Ratings

ERIC Educational Resources Information Center

Bi, Henry H.

2018-01-01

There are no absolute standards regarding what teaching evaluation ratings are satisfactory. It is also problematic to compare teaching evaluation ratings with the average or with a cutoff number to determine whether they are adequate. In this paper, we use average and standard deviation charts (X[overbar]-S charts), which are based on the theory…

Effects of thermo-order-mechanical coupling on band structures in liquid crystal nematic elastomer porous phononic crystals.

PubMed

Yang, Shuai; Liu, Ying

2018-08-01

Liquid crystal nematic elastomers are one kind of smart anisotropic and viscoelastic solids simultaneously combing the properties of rubber and liquid crystals, which is thermal sensitivity. In this paper, the wave dispersion in a liquid crystal nematic elastomer porous phononic crystal subjected to an external thermal stimulus is theoretically investigated. Firstly, an energy function is proposed to determine thermo-induced deformation in NE periodic structures. Based on this function, thermo-induced band variation in liquid crystal nematic elastomer porous phononic crystals is investigated in detail. The results show that when liquid crystal elastomer changes from nematic state to isotropic state due to the variation of the temperature, the absolute band gaps at different bands are opened or closed. There exists a threshold temperature above which the absolute band gaps are opened or closed. Larger porosity benefits the opening of the absolute band gaps. The deviation of director from the structural symmetry axis is advantageous for the absolute band gap opening in nematic state whist constrains the absolute band gap opening in isotropic state. The combination effect of temperature and director orientation provides an added degree of freedom in the intelligent tuning of the absolute band gaps in phononic crystals. Copyright © 2018 Elsevier B.V. All rights reserved.

Cruise Summary of WHP P6, A10, I3 and I4 Revisits in 2003

NASA Astrophysics Data System (ADS)

Kawano, T.; Uchida, H.; Schneider, W.; Kumamoto, Y.; Nishina, A.; Aoyama, M.; Murata, A.; Sasaki, K.; Yoshikawa, Y.; Watanabe, S.; Fukasawa, M.

2004-12-01

Japan Agency for Marin-Earth Science and Technology (JAMSTEC) conducted a research cruise to round in the southern hemisphere by R/V Mirai. In this presentation, we introduce an outline of the cruise and data quality obtained during the cruise. The cruise started on Aug. 3, 2003 in Brisbane, Australia and sailed eastward until it reached Fremantle, Australia on Feb. 19, 2004. It contained six legs and legs 1, 2, 4 and 5 were revisits of WOCE Hydrographic Program (WHP) sections P6W, P6E, A10 and I3/I4, respectively. The sections consisted of about 500 hydrographic stations in total. On each station, CTD profiles and up to 36 water samples by 12L Niskin-X bottles were taken from the surface to within 10 m of the bottom. Water samples were analyzed at every station for salinity, dissolved oxygen (DO), and nutrients and at alternate stations for concentration of freons, dissolved inorganic carbon (CT), total alkalinity (AT), pH, and so on. Approximately 17,000 samples were obtained for salinity. The standard seawater was measured repeatedly to estimate the uncertainty caused by the setting and stability of the salinometer. The standard deviation of 699 repeated runs of standard seawater was 0.0002 in salinity. Replicate samples, which are a pair of samples drawn from the same Niskin bottle to different sample bottles, were taken to evaluate the overall uncertainty. The standard deviation of absolute differences of 2,769 replicates was also 0.0002 in salinity. For DO, about 13,400 samples were obtained. The analysis was made by a photometric titration technique. The reproducibility estimated from the absolute standard deviation of 1,625 replicates was about 0.09 umol/kg. CTD temperature was calibrated against a deep ocean standards thermometer (SBE35) which was attached to the CTD using a polynomial expression Tcal = T - (a +b*P + c*t), where Tcal is calibrated temperature, T is CTD temperature, P is CTD pressure and t is time. Calibration coefficients, a, b and c, were determined for each station by minimizing the sum of absolute deviation from SBE35 temperature below 2,000dbar. CTD salinity and DO were fitted to values obtained by sampled water analysis using similar polynomials. These corrections yielded deviations of about 0.0002 K in temperature, 0.0003 in salinity and 0.6 umol/kg in DO. Nutrients analyses were accomplished on 16,000 samples using the reference material of nutrients in seawater (RMNS). To establish the traceability and to get higher quality data, 500 bottles of RMNS from the same lot and 150 sets of RMNSs were used. The precisions of phosphate, nitrate and silicate measurements were 0.18 %, 0.17 % and 0.16 % in terms of median of those at 493 stations, respectively. The nutrients concentrations could be expressed with uncertainties explicitly because of the repeated runs of RMNSs. All the analyses for the CO{2}-system parameters in water columns were finished onboard. Analytical precisions of CT, AT and pH were estimated to be \\sim1.0 umol/kg, \\sim2.0 umol/kg, and \\sim7*10-4 pH unit, respectively. Approximately 6,300 samples were obtained for CFC-11 and CFC-12. The concentrations were determined with an electron capture detector - gas chromatograph (ECD-GC) attached the purge and trapping system. The reproducibility estimated from the absolute standard deviation of 365 replicates was less than 1% with respect to the surface concentrations.

Estimation of the lower flammability limit of organic compounds as a function of temperature.

PubMed

Rowley, J R; Rowley, R L; Wilding, W V

2011-02-15

A new method of estimating the lower flammability limit (LFL) of general organic compounds is presented. The LFL is predicted at 298 K for gases and the lower temperature limit for solids and liquids from structural contributions and the ideal gas heat of formation of the fuel. The average absolute deviation from more than 500 experimental data points is 10.7%. In a previous study, the widely used modified Burgess-Wheeler law was shown to underestimate the effect of temperature on the lower flammability limit when determined in a large-diameter vessel. An improved version of the modified Burgess-Wheeler law is presented that represents the temperature dependence of LFL data determined in large-diameter vessels more accurately. When the LFL is estimated at increased temperatures using a combination of this model and the proposed structural-contribution method, an average absolute deviation of 3.3% is returned when compared with 65 data points for 17 organic compounds determined in an ASHRAE-style apparatus. Copyright © 2010 Elsevier B.V. All rights reserved.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

NASA Astrophysics Data System (ADS)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

A new model of strabismic amblyopia: Loss of spatial acuity due to increased temporal dispersion of geniculate X-cell afferents on to cortical neurons.

PubMed

Crewther, D P; Crewther, S G

2015-09-01

Although the neural locus of strabismic amblyopia has been shown to lie at the first site of binocular integration, first in cat and then in primate, an adequate mechanism is still lacking. Here we hypothesise that increased temporal dispersion of LGN X-cell afferents driven by the deviating eye onto single cortical neurons may provide a neural mechanism for strabismic amblyopia. This idea was investigated via single cell extracellular recordings of 93 X and 50 Y type LGN neurons from strabismic and normal cats. Both X and Y neurons driven by the non-deviating eye showed shorter latencies than those driven by either the strabismic or normal eyes. Also the mean latency difference between X and Y neurons was much greater for the strabismic cells compared with the other two groups. The incidence of lagged X-cells driven by the deviating eye of the strabismic cats was higher than that of LGN X-cells from normal animals. Remarkably, none of the cells recorded from the laminae driven by the non-deviating eye were of the lagged class. A simple computational model was constructed in which a mixture of lagged and non-lagged afferents converge on to single cortical neurons. Model cut-off spatial frequencies to a moving grating stimulus were sensitive to the temporal dispersion of the geniculate afferents. Thus strabismic amblyopia could be viewed as a lack of developmental tuning of geniculate lags for neurons driven by the amblyopic eye. Monocular control of fixation by the non-deviating eye is associated with reduced incidence of lagged neurons, suggesting that in normal vision, lagged neurons might play a role in maintaining binocular connections for cortical neurons. Copyright © 2014 Elsevier Ltd. All rights reserved.

Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set.

PubMed

Lenselink, Eelke B; Ten Dijke, Niels; Bongers, Brandon; Papadatos, George; van Vlijmen, Herman W T; Kowalczyk, Wojtek; IJzerman, Adriaan P; van Westen, Gerard J P

2017-08-14

The increase of publicly available bioactivity data in recent years has fueled and catalyzed research in chemogenomics, data mining, and modeling approaches. As a direct result, over the past few years a multitude of different methods have been reported and evaluated, such as target fishing, nearest neighbor similarity-based methods, and Quantitative Structure Activity Relationship (QSAR)-based protocols. However, such studies are typically conducted on different datasets, using different validation strategies, and different metrics. In this study, different methods were compared using one single standardized dataset obtained from ChEMBL, which is made available to the public, using standardized metrics (BEDROC and Matthews Correlation Coefficient). Specifically, the performance of Naïve Bayes, Random Forests, Support Vector Machines, Logistic Regression, and Deep Neural Networks was assessed using QSAR and proteochemometric (PCM) methods. All methods were validated using both a random split validation and a temporal validation, with the latter being a more realistic benchmark of expected prospective execution. Deep Neural Networks are the top performing classifiers, highlighting the added value of Deep Neural Networks over other more conventional methods. Moreover, the best method ('DNN_PCM') performed significantly better at almost one standard deviation higher than the mean performance. Furthermore, Multi-task and PCM implementations were shown to improve performance over single task Deep Neural Networks. Conversely, target prediction performed almost two standard deviations under the mean performance. Random Forests, Support Vector Machines, and Logistic Regression performed around mean performance. Finally, using an ensemble of DNNs, alongside additional tuning, enhanced the relative performance by another 27% (compared with unoptimized 'DNN_PCM'). Here, a standardized set to test and evaluate different machine learning algorithms in the context of multi-task learning is offered by providing the data and the protocols. Graphical Abstract .

Evaluating the accuracy and large inaccuracy of two continuous glucose monitoring systems.

PubMed

Leelarathna, Lalantha; Nodale, Marianna; Allen, Janet M; Elleri, Daniela; Kumareswaran, Kavita; Haidar, Ahmad; Caldwell, Karen; Wilinska, Malgorzata E; Acerini, Carlo L; Evans, Mark L; Murphy, Helen R; Dunger, David B; Hovorka, Roman

2013-02-01

This study evaluated the accuracy and large inaccuracy of the Freestyle Navigator (FSN) (Abbott Diabetes Care, Alameda, CA) and Dexcom SEVEN PLUS (DSP) (Dexcom, Inc., San Diego, CA) continuous glucose monitoring (CGM) systems during closed-loop studies. Paired CGM and plasma glucose values (7,182 data pairs) were collected, every 15-60 min, from 32 adults (36.2±9.3 years) and 20 adolescents (15.3±1.5 years) with type 1 diabetes who participated in closed-loop studies. Levels 1, 2, and 3 of large sensor error with increasing severity were defined according to absolute relative deviation greater than or equal to ±40%, ±50%, and ±60% at a reference glucose level of ≥6 mmol/L or absolute deviation greater than or equal to ±2.4 mmol/L,±3.0 mmol/L, and ±3.6 mmol/L at a reference glucose level of <6 mmol/L. Median absolute relative deviation was 9.9% for FSN and 12.6% for DSP. Proportions of data points in Zones A and B of Clarke error grid analysis were similar (96.4% for FSN vs. 97.8% for DSP). Large sensor over-reading, which increases risk of insulin over-delivery and hypoglycemia, occurred two- to threefold more frequently with DSP than FSN (once every 2.5, 4.6, and 10.7 days of FSN use vs. 1.2, 2.0, and 3.7 days of DSP use for Level 1-3 errors, respectively). At levels 2 and 3, large sensor errors lasting 1 h or longer were absent with FSN but persisted with DSP. FSN and DSP differ substantially in the frequency and duration of large inaccuracy despite only modest differences in conventional measures of numerical and clinical accuracy. Further evaluations are required to confirm that FSN is more suitable for integration into closed-loop delivery systems.

Light localization with low-contrast targets in a patient implanted with a suprachoroidal-transretinal stimulation retinal prosthesis.

PubMed

Endo, Takao; Fujikado, Takashi; Hirota, Masakazu; Kanda, Hiroyuki; Morimoto, Takeshi; Nishida, Kohji

2018-04-20

To evaluate the improvement in targeted reaching movements toward targets of various contrasts in a patient implanted with a suprachoroidal-transretinal stimulation (STS) retinal prosthesis. An STS retinal prosthesis was implanted in the right eye of a 42-year-old man with advanced Stargardt disease (visual acuity: right eye, light perception; left eye, hand motion). In localization tests during the 1-year follow-up period, the patient attempted to touch the center of a white square target (visual angle, 10°; contrast, 96, 85, or 74%) displayed at a random position on a monitor. The distance between the touched point and the center of the target (the absolute deviation) was averaged over 20 trials with the STS system on or off. With the left eye occluded, the absolute deviation was not consistently lower with the system on than off for high-contrast (96%) targets, but was consistently lower with the system on for low-contrast (74%) targets. With both eyes open, the absolute deviation was consistently lower with the system on than off for 85%-contrast targets. With the system on and 96%-contrast targets, we detected a shorter response time while covering the right eye, which was being implanted with the STS, compared to covering the left eye (2.41 ± 2.52 vs 8.45 ± 3.78 s, p < 0.01). Performance of a reaching movement improved in a patient with an STS retinal prosthesis implanted in an eye with residual natural vision. Patients with a retinal prosthesis may be able to improve their visual performance by using both artificial vision and their residual natural vision. Beginning date of the trial: Feb. 20, 2014 Date of registration: Jan. 4, 2014 Trial registration number: UMIN000012754 Registration site: UMIN Clinical Trials Registry (UMIN-CTR) http://www.umin.ac.jp/ctr/index.htm.

Performance evaluations of continuous glucose monitoring systems: precision absolute relative deviation is part of the assessment.

PubMed

Obermaier, Karin; Schmelzeisen-Redeker, Günther; Schoemaker, Michael; Klötzer, Hans-Martin; Kirchsteiger, Harald; Eikmeier, Heino; del Re, Luigi

2013-07-01

Even though a Clinical and Laboratory Standards Institute proposal exists on the design of studies and performance criteria for continuous glucose monitoring (CGM) systems, it has not yet led to a consistent evaluation of different systems, as no consensus has been reached on the reference method to evaluate them or on acceptance levels. As a consequence, performance assessment of CGM systems tends to be inconclusive, and a comparison of the outcome of different studies is difficult. Published information and available data (as presented in this issue of Journal of Diabetes Science and Technology by Freckmann and coauthors) are used to assess the suitability of several frequently used methods [International Organization for Standardization, continuous glucose error grid analysis, mean absolute relative deviation (MARD), precision absolute relative deviation (PARD)] when assessing performance of CGM systems in terms of accuracy and precision. The combined use of MARD and PARD seems to allow for better characterization of sensor performance. The use of different quantities for calibration and evaluation, e.g., capillary blood using a blood glucose (BG) meter versus venous blood using a laboratory measurement, introduces an additional error source. Using BG values measured in more or less large intervals as the only reference leads to a significant loss of information in comparison with the continuous sensor signal and possibly to an erroneous estimation of sensor performance during swings. Both can be improved using data from two identical CGM sensors worn by the same patient in parallel. Evaluation of CGM performance studies should follow an identical study design, including sufficient swings in glycemia. At least a part of the study participants should wear two identical CGM sensors in parallel. All data available should be used for evaluation, both by MARD and PARD, a good PARD value being a precondition to trust a good MARD value. Results should be analyzed and presented separately for clinically different categories, e.g., hypoglycemia, exercise, or night and day. © 2013 Diabetes Technology Society.

Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

NASA Astrophysics Data System (ADS)

Gill, Jatinder; Singh, Jagdev

2018-07-01

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5-16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

The truly remarkable universality of half a standard deviation: confirmation through another look.

PubMed

Norman, Geoffrey R; Sloan, Jeff A; Wyrwich, Kathleen W

2004-10-01

In this issue of Expert Review of Pharmacoeconomics and Outcomes Research, Farivar, Liu, and Hays present their findings in 'Another look at the half standard deviation estimate of the minimally important difference in health-related quality of life scores (hereafter referred to as 'Another look') . These researchers have re-examined the May 2003 Medical Care article 'Interpretation of changes in health-related quality of life: the remarkable universality of half a standard deviation' (hereafter referred to as 'Remarkable') in the hope of supporting their hypothesis that the minimally important difference in health-related quality of life measures is undoubtedly closer to 0.3 standard deviations than 0.5. Nonetheless, despite their extensive wranglings with the exclusion of many articles that we included in our review; the inclusion of articles that we did not include in our review; and the recalculation of effect sizes using the absolute value of the mean differences, in our opinion, the results of the 'Another look' article confirm the same findings in the 'Remarkable' paper.

Activating Endogenous Neural Precursor Cells Using Metformin Leads to Neural Repair and Functional Recovery in a Model of Childhood Brain Injury.

PubMed

Dadwal, Parvati; Mahmud, Neemat; Sinai, Laleh; Azimi, Ashkan; Fatt, Michael; Wondisford, Fredric E; Miller, Freda D; Morshead, Cindi M

2015-08-11

The development of cell replacement strategies to repair the injured brain has gained considerable attention, with a particular interest in mobilizing endogenous neural stem and progenitor cells (known as neural precursor cells [NPCs]) to promote brain repair. Recent work demonstrated metformin, a drug used to manage type II diabetes, promotes neurogenesis. We sought to determine its role in neural repair following brain injury. We find that metformin administration activates endogenous NPCs, expanding the size of the NPC pool and promoting NPC migration and differentiation in the injured neonatal brain in a hypoxia-ischemia (H/I) injury model. Importantly, metformin treatment following H/I restores sensory-motor function. Lineage tracking reveals that metformin treatment following H/I causes an increase in the absolute number of subependyma-derived NPCs relative to untreated H/I controls in areas associated with sensory-motor function. Hence, activation of endogenous NPCs is a promising target for therapeutic intervention in childhood brain injury models. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Rhythmic and melodic deviations in musical sequences recruit different cortical areas for mismatch detection.

PubMed

Lappe, Claudia; Steinsträter, Olaf; Pantev, Christo

2013-01-01

The mismatch negativity (MMN), an event-related potential (ERP) representing the violation of an acoustic regularity, is considered as a pre-attentive change detection mechanism at the sensory level on the one hand and as a prediction error signal on the other hand, suggesting that bottom-up as well as top-down processes are involved in its generation. Rhythmic and melodic deviations within a musical sequence elicit a MMN in musically trained subjects, indicating that acquired musical expertise leads to better discrimination accuracy of musical material and better predictions about upcoming musical events. Expectation violations to musical material could therefore recruit neural generators that reflect top-down processes that are based on musical knowledge. We describe the neural generators of the musical MMN for rhythmic and melodic material after a short-term sensorimotor-auditory (SA) training. We compare the localization of musical MMN data from two previous MEG studies by applying beamformer analysis. One study focused on the melodic harmonic progression whereas the other study focused on rhythmic progression. The MMN to melodic deviations revealed significant right hemispheric neural activation in the superior temporal gyrus (STG), inferior frontal cortex (IFC), and the superior frontal (SFG) and orbitofrontal (OFG) gyri. IFC and SFG activation was also observed in the left hemisphere. In contrast, beamformer analysis of the data from the rhythm study revealed bilateral activation within the vicinity of auditory cortices and in the inferior parietal lobule (IPL), an area that has recently been implied in temporal processing. We conclude that different cortical networks are activated in the analysis of the temporal and the melodic content of musical material, and discuss these networks in the context of the dual-pathway model of auditory processing.

A standardized model for predicting flap failure using indocyanine green dye

NASA Astrophysics Data System (ADS)

Zimmermann, Terence M.; Moore, Lindsay S.; Warram, Jason M.; Greene, Benjamin J.; Nakhmani, Arie; Korb, Melissa L.; Rosenthal, Eben L.

2016-03-01

Techniques that provide a non-invasive method for evaluation of intraoperative skin flap perfusion are currently available but underutilized. We hypothesize that intraoperative vascular imaging can be used to reliably assess skin flap perfusion and elucidate areas of future necrosis by means of a standardized critical perfusion threshold. Five animal groups (negative controls, n=4; positive controls, n=5; chemotherapy group, n=5; radiation group, n=5; chemoradiation group, n=5) underwent pre-flap treatments two weeks prior to undergoing random pattern dorsal fasciocutaneous flaps with a length to width ratio of 2:1 (3 x 1.5 cm). Flap perfusion was assessed via laser-assisted indocyanine green dye angiography and compared to standard clinical assessment for predictive accuracy of flap necrosis. For estimating flap-failure, clinical prediction achieved a sensitivity of 79.3% and a specificity of 90.5%. When average flap perfusion was more than three standard deviations below the average flap perfusion for the negative control group at the time of the flap procedure (144.3+/-17.05 absolute perfusion units), laser-assisted indocyanine green dye angiography achieved a sensitivity of 81.1% and a specificity of 97.3%. When absolute perfusion units were seven standard deviations below the average flap perfusion for the negative control group, specificity of necrosis prediction was 100%. Quantitative absolute perfusion units can improve specificity for intraoperative prediction of viable tissue. Using this strategy, a positive predictive threshold of flap failure can be standardized for clinical use.

Absolute Parameters for the F-type Eclipsing Binary BW Aquarii

NASA Astrophysics Data System (ADS)

Maxted, P. F. L.

2018-05-01

BW Aqr is a bright eclipsing binary star containing a pair of F7V stars. The absolute parameters of this binary (masses, radii, etc.) are known to good precision so they are often used to test stellar models, particularly in studies of convective overshooting. ... Maxted & Hutcheon (2018) analysed the Kepler K2 data for BW Aqr and noted that it shows variability between the eclipses that may be caused by tidally induced pulsations. ... Table 1 shows the absolute parameters for BW Aqr derived from an improved analysis of the Kepler K2 light curve plus the RV measurements from both Imbert (1979) and Lester & Gies (2018). ... The values in Table 1 with their robust error estimates from the standard deviation of the mean are consistent with the values and errors from Maxted & Hutcheon (2018) based on the PPD calculated using emcee for a fit to the entire K2 light curve.

Measurement of the cosmic microwave background spectrum by the COBE FIRAS instrument

NASA Technical Reports Server (NTRS)

Mather, J. C.; Cheng, E. S.; Cottingham, D. A.; Eplee, R. E., Jr.; Fixsen, D. J.; Hewagama, T.; Isaacman, R. B.; Jensen, K. A.; Meyer, S. S.; Noerdlinger, P. D.

1994-01-01

The cosmic microwave background radiation (CMBR) has a blackbody spectrum within 3.4 x 10(exp -8) ergs/sq cm/s/sr cm over the frequency range from 2 to 20/cm (5-0.5 mm). These measurements, derived from the Far-Infrared Absolute Spectrophotomer (FIRAS) instrument on the Cosmic Background Explorer (COBE) satellite, imply stringent limits on energy release in the early universe after t approximately 1 year and redshift z approximately 3 x 10(exp 6). The deviations are less than 0.30% of the peak brightness, with an rms value of 0.01%, and the dimensionless cosmological distortion parameters are limited to the absolute value of y is less than 2.5 x 10(exp -5) and the absolute value of mu is less than 3.3 x 10(exp -4) (95% confidence level). The temperature of the CMBR is 2.726 +/- 0.010 K (95% confidence level systematic).

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.

Money talks: neural substrate of modulation of fairness by monetary incentives

PubMed Central

Zhou, Yuan; Wang, Yun; Rao, Li-Lin; Yang, Liu-Qing; Li, Shu

2014-01-01

A unique feature of the human species is compliance with social norms, e.g., fairness, even though this normative decision means curbing self-interest. However, sometimes people prefer to pursue wealth at the expense of moral goodness. Specifically, deviations from a fairness-related normative choice have been observed in the presence of a high monetary incentive. The neural mechanism underlying this deviation from the fairness-related normative choice has yet to be determined. In order to address this issue, using functional magnetic resonance imaging we employed an ultimatum game (UG) paradigm in which fairness and a proposed monetary amount were orthogonally varied. We found evidence for a significant modulation by the proposed amount on fairness in the right lateral prefrontal cortex (PFC) and the bilateral insular cortices. Additionally, the insular subregions showed dissociable modulation patterns. Inter-individual differences in the modulation effects in the left inferior frontal gyrus (IFG) accounted for inter-individual differences in the behavioral modulation effect as measured by the rejection rate, supporting the concept that the PFC plays a critical role in making fairness-related normative decisions in a social interaction condition. Our findings provide neural evidence for the modulation of fairness by monetary incentives as well as accounting for inter-individual differences. PMID:24834034

A FORMULA FOR HUMAN PAROTID FLUID COLLECTED WITHOUT EXOGENEOUS STIMULATION.

DTIC Science & Technology

Parotid fluid was collected from 4,589 systemically healthy males between 17 and 22 years of age. Collection devices were placed with an absolute...secretion of the parotid gland. For all 4,589 subjects from the 8 experiments the mean rate of flow was 0.040 ml./minute with an average standard deviation of

The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

ERIC Educational Resources Information Center

Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

2007-01-01

Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

40 CFR 1065.1005 - Symbols, abbreviations, acronyms, and units of measure.

Code of Federal Regulations, 2014 CFR

2014-07-01

... of diameters meter per meter m/m 1 b atomic oxygen-to-carbon ratio mole per mole mol/mol 1 C # number... error between a quantity and its reference e brake-specific emission or fuel consumption gram per... standard deviation S Sutherland constant kelvin K K SEE standard estimate of error T absolute temperature...

Water quality management using statistical analysis and time-series prediction model

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River. Model validated using R-squared, root mean square error, mean absolute percentage error, maximum absolute percentage error, mean absolute error, maximum absolute error, normalized Bayesian information criterion, Ljung-Box analysis, predicted value and confidence limits. Using auto regressive integrated moving average model, future water quality parameters values have been estimated. It is observed that predictive model is useful at 95 % confidence limits and curve is platykurtic for potential of hydrogen (pH), free ammonia, total Kjeldahl nitrogen, dissolved oxygen, water temperature (WT); leptokurtic for chemical oxygen demand, biochemical oxygen demand. Also, it is observed that predicted series is close to the original series which provides a perfect fit. All parameters except pH and WT cross the prescribed limits of the World Health Organization /United States Environmental Protection Agency, and thus water is not fit for drinking, agriculture and industrial use.

New calibrators for the Cepheid period-luminosity relation

NASA Technical Reports Server (NTRS)

Evans, Nancy R.

1992-01-01

IUE spectra of six Cepheids have been used to determine their absolute magnitudes from the spectral types of their binary companions. The stars observed are U Aql, V659 Cen, Y Lac, S Nor, V350 Sgr, and V636 Sco. The absolute magnitude for V659 Cen is more uncertain than for the others because its reddening is poorly determined and the spectral type is hotter than those of the others. In addition, a reddening law with extra absorption in the 2200 A region is necessary, although this has a negligible effect on the absolute magnitude. For the other Cepheids, and also Eta Aql and W Sgr, the standard deviation from the Feast and Walker period-luminosity-color (PLC) relation is 0.37 mag, confirming the previously estimated internal uncertainty. The absolute magnitudes for S Nor from the binary companion and from cluster membership are very similar. The preliminary PLC zero point is less than 2 sigma (+0.21 mag) different from that of Feast and Walker. The same narrowing of the instability strip at low luminosities found by Fernie is seen.

Role of dispersion corrected hybrid GGA class in accurately calculating the bond dissociation energy of carbon halogen bond: A benchmark study

NASA Astrophysics Data System (ADS)

Kosar, Naveen; Mahmood, Tariq; Ayub, Khurshid

2017-12-01

Benchmark study has been carried out to find a cost effective and accurate method for bond dissociation energy (BDE) of carbon halogen (Csbnd X) bond. BDE of C-X bond plays a vital role in chemical reactions, particularly for kinetic barrier and thermochemistry etc. The compounds (1-16, Fig. 1) with Csbnd X bond used for current benchmark study are important reactants in organic, inorganic and bioorganic chemistry. Experimental data of Csbnd X bond dissociation energy is compared with theoretical results. The statistical analysis tools such as root mean square deviation (RMSD), standard deviation (SD), Pearson's correlation (R) and mean absolute error (MAE) are used for comparison. Overall, thirty-one density functionals from eight different classes of density functional theory (DFT) along with Pople and Dunning basis sets are evaluated. Among different classes of DFT, the dispersion corrected range separated hybrid GGA class along with 6-31G(d), 6-311G(d), aug-cc-pVDZ and aug-cc-pVTZ basis sets performed best for bond dissociation energy calculation of C-X bond. ωB97XD show the best performance with less deviations (RMSD, SD), mean absolute error (MAE) and a significant Pearson's correlation (R) when compared to experimental data. ωB97XD along with Pople basis set 6-311g(d) has RMSD, SD, R and MAE of 3.14 kcal mol-1, 3.05 kcal mol-1, 0.97 and -1.07 kcal mol-1, respectively.

Application of Artificial Neural Network to Optical Fluid Analyzer

NASA Astrophysics Data System (ADS)

Kimura, Makoto; Nishida, Katsuhiko

1994-04-01

A three-layer artificial neural network has been applied to the presentation of optical fluid analyzer (OFA) raw data, and the accuracy of oil fraction determination has been significantly improved compared to previous approaches. To apply the artificial neural network approach to solving a problem, the first step is training to determine the appropriate weight set for calculating the target values. This involves using a series of data sets (each comprising a set of input values and an associated set of output values that the artificial neural network is required to determine) to tune artificial neural network weighting parameters so that the output of the neural network to the given set of input values is as close as possible to the required output. The physical model used to generate the series of learning data sets was the effective flow stream model, developed for OFA data presentation. The effectiveness of the training was verified by reprocessing the same input data as were used to determine the weighting parameters and then by comparing the results of the artificial neural network to the expected output values. The standard deviation of the expected and obtained values was approximately 10% (two sigma).

Differential processing of melodic, rhythmic and simple tone deviations in musicians--an MEG study.

PubMed

Lappe, Claudia; Lappe, Markus; Pantev, Christo

2016-01-01

Rhythm and melody are two basic characteristics of music. Performing musicians have to pay attention to both, and avoid errors in either aspect of their performance. To investigate the neural processes involved in detecting melodic and rhythmic errors from auditory input we tested musicians on both kinds of deviations in a mismatch negativity (MMN) design. We found that MMN responses to a rhythmic deviation occurred at shorter latencies than MMN responses to a melodic deviation. Beamformer source analysis showed that the melodic deviation activated superior temporal, inferior frontal and superior frontal areas whereas the activation pattern of the rhythmic deviation focused more strongly on inferior and superior parietal areas, in addition to superior temporal cortex. Activation in the supplementary motor area occurred for both types of deviations. We also recorded responses to similar pitch and tempo deviations in a simple, non-musical repetitive tone pattern. In this case, there was no latency difference between the MMNs and cortical activation was smaller and mostly limited to auditory cortex. The results suggest that prediction and error detection of musical stimuli in trained musicians involve a broad cortical network and that rhythmic and melodic errors are processed in partially different cortical streams. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural Correlates of Odor Learning in the Presynaptic Microglomerular Circuitry in the Honeybee Mushroom Body Calyx.

PubMed

Haenicke, Joachim; Yamagata, Nobuhiro; Zwaka, Hanna; Nawrot, Martin; Menzel, Randolf

2018-01-01

The mushroom body (MB) in insects is known as a major center for associative learning and memory, although exact locations for the correlating memory traces remain to be elucidated. Here, we asked whether presynaptic boutons of olfactory projection neurons (PNs) in the main input site of the MB undergo neuronal plasticity during classical odor-reward conditioning and correlate with the conditioned behavior. We simultaneously measured Ca 2+ responses in the boutons and conditioned behavioral responses to learned odors in honeybees. We found that the absolute amount of the neural change for the rewarded but not for the unrewarded odor was correlated with the behavioral learning rate across individuals. The temporal profile of the induced changes matched with odor response dynamics of the MB-associated inhibitory neurons, suggestive of activity modulation of boutons by this neural class. We hypothesize the circuit-specific neural plasticity relates to the learned value of the stimulus and underlies the conditioned behavior of the bees.

Detection of asymmetric blotches (asymmetric structureless areas) in dermoscopy images of malignant melanoma using relative color

PubMed Central

Stoecker, William V.; Gupta, Kapil; Stanley, R. Joe; Moss, Randy H.; Shrestha, Bijaya

2011-01-01

Background Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area). Method Using both relative and absolute colors, blotches are detected in this research automatically by using thresholds in the red and green color planes. Several blotch indices are computed, including the scaled distance between the largest blotch centroid and the lesion centroid, ratio of total blotch areas to lesion area, ratio of largest blotch area to lesion area, total number of blotches, size of largest blotch, and irregularity of largest blotch. Results The effectiveness of the absolute and relative color blotch features was examined for melanoma/benign lesion discrimination over a dermoscopy image set containing 165 melanomas (151 invasive melanomas and 14 melanomas in situ) and 347 benign lesions (124 nevocellular nevi without dysplasia and 223 dysplastic nevi) using a leave-one-out neural network approach. Receiver operating characteristic curve results are shown, highlighting the sensitivity and specificity of melanoma detection. Statistical analysis of the blotch features are also presented. Conclusion Neural network and statistical analysis showed that the blotch detection method was somewhat more effective using relative color than using absolute color. The relative-color blotch detection method gave a diagnostic accuracy of about 77%. PMID:15998328

Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment.

PubMed

da Fonseca Neto, João Viana; Abreu, Ivanildo Silva; da Silva, Fábio Nogueira

2010-04-01

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

Optoelectronic fuzzy associative memory with controllable attraction basin sizes

NASA Astrophysics Data System (ADS)

Wen, Zhiqing; Campbell, Scott; Wu, Weishu; Yeh, Pochi

1995-10-01

We propose and demonstrate a new fuzzy associative memory model that provides an option to control the sizes of the attraction basins in neural networks. In our optoelectronic implementation we use spatial/polarization encoding to represent the fuzzy variables. Shadow casting of the encoded patterns is employed to yield the fuzzy-absolute difference between fuzzy variables.

Stability of discrete time recurrent neural networks and nonlinear optimization problems.

PubMed

Singh, Jayant; Barabanov, Nikita

2016-02-01

We consider the method of Reduction of Dissipativity Domain to prove global Lyapunov stability of Discrete Time Recurrent Neural Networks. The standard and advanced criteria for Absolute Stability of these essentially nonlinear systems produce rather weak results. The method mentioned above is proved to be more powerful. It involves a multi-step procedure with maximization of special nonconvex functions over polytopes on every step. We derive conditions which guarantee an existence of at most one point of local maximum for such functions over every hyperplane. This nontrivial result is valid for wide range of neuron transfer functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Forecasting of Water Consumptions Expenditure Using Holt-Winter’s and ARIMA

NASA Astrophysics Data System (ADS)

Razali, S. N. A. M.; Rusiman, M. S.; Zawawi, N. I.; Arbin, N.

2018-04-01

This study is carried out to forecast water consumption expenditure of Malaysian university specifically at University Tun Hussein Onn Malaysia (UTHM). The proposed Holt-Winter’s and Auto-Regressive Integrated Moving Average (ARIMA) models were applied to forecast the water consumption expenditure in Ringgit Malaysia from year 2006 until year 2014. The two models were compared and performance measurement of the Mean Absolute Percentage Error (MAPE) and Mean Absolute Deviation (MAD) were used. It is found that ARIMA model showed better results regarding the accuracy of forecast with lower values of MAPE and MAD. Analysis showed that ARIMA (2,1,4) model provided a reasonable forecasting tool for university campus water usage.

MetaMQAP: a meta-server for the quality assessment of protein models.

PubMed

Pawlowski, Marcin; Gajda, Michal J; Matlak, Ryszard; Bujnicki, Janusz M

2008-09-29

Computational models of protein structure are usually inaccurate and exhibit significant deviations from the true structure. The utility of models depends on the degree of these deviations. A number of predictive methods have been developed to discriminate between the globally incorrect and approximately correct models. However, only a few methods predict correctness of different parts of computational models. Several Model Quality Assessment Programs (MQAPs) have been developed to detect local inaccuracies in unrefined crystallographic models, but it is not known if they are useful for computational models, which usually exhibit different and much more severe errors. The ability to identify local errors in models was tested for eight MQAPs: VERIFY3D, PROSA, BALA, ANOLEA, PROVE, TUNE, REFINER, PROQRES on 8251 models from the CASP-5 and CASP-6 experiments, by calculating the Spearman's rank correlation coefficients between per-residue scores of these methods and local deviations between C-alpha atoms in the models vs. experimental structures. As a reference, we calculated the value of correlation between the local deviations and trivial features that can be calculated for each residue directly from the models, i.e. solvent accessibility, depth in the structure, and the number of local and non-local neighbours. We found that absolute correlations of scores returned by the MQAPs and local deviations were poor for all methods. In addition, scores of PROQRES and several other MQAPs strongly correlate with 'trivial' features. Therefore, we developed MetaMQAP, a meta-predictor based on a multivariate regression model, which uses scores of the above-mentioned methods, but in which trivial parameters are controlled. MetaMQAP predicts the absolute deviation (in Angströms) of individual C-alpha atoms between the model and the unknown true structure as well as global deviations (expressed as root mean square deviation and GDT_TS scores). Local model accuracy predicted by MetaMQAP shows an impressive correlation coefficient of 0.7 with true deviations from native structures, a significant improvement over all constituent primary MQAP scores. The global MetaMQAP score is correlated with model GDT_TS on the level of 0.89. Finally, we compared our method with the MQAPs that scored best in the 7th edition of CASP, using CASP7 server models (not included in the MetaMQAP training set) as the test data. In our benchmark, MetaMQAP is outperformed only by PCONS6 and method QA_556 - methods that require comparison of multiple alternative models and score each of them depending on its similarity to other models. MetaMQAP is however the best among methods capable of evaluating just single models. We implemented the MetaMQAP as a web server available for free use by all academic users at the URL https://genesilico.pl/toolkit/

Correlation- and covariance-supported normalization method for estimating orthodontic trainer treatment for clenching activity.

PubMed

Akdenur, B; Okkesum, S; Kara, S; Günes, S

2009-11-01

In this study, electromyography signals sampled from children undergoing orthodontic treatment were used to estimate the effect of an orthodontic trainer on the anterior temporal muscle. A novel data normalization method, called the correlation- and covariance-supported normalization method (CCSNM), based on correlation and covariance between features in a data set, is proposed to provide predictive guidance to the orthodontic technique. The method was tested in two stages: first, data normalization using the CCSNM; second, prediction of normalized values of anterior temporal muscles using an artificial neural network (ANN) with a Levenberg-Marquardt learning algorithm. The data set consists of electromyography signals from right anterior temporal muscles, recorded from 20 children aged 8-13 years with class II malocclusion. The signals were recorded at the start and end of a 6-month treatment. In order to train and test the ANN, two-fold cross-validation was used. The CCSNM was compared with four normalization methods: minimum-maximum normalization, z score, decimal scaling, and line base normalization. In order to demonstrate the performance of the proposed method, prevalent performance-measuring methods, and the mean square error and mean absolute error as mathematical methods, the statistical relation factor R2 and the average deviation have been examined. The results show that the CCSNM was the best normalization method among other normalization methods for estimating the effect of the trainer.

Diffuse ultraviolet erythemal irradiance on inclined planes: a comparison of experimental and modeled data.

PubMed

Utrillas, María P; Marín, María J; Esteve, Anna R; Estellés, Victor; Tena, Fernando; Cañada, Javier; Martínez-Lozano, José A

2009-01-01

Values of measured and modeled diffuse UV erythemal irradiance (UVER) for all sky conditions are compared on planes inclined at 40 degrees and oriented north, south, east and west. The models used for simulating diffuse UVER are of the geometric-type, mainly the Isotropic, Klucher, Hay, Muneer, Reindl and Schauberger models. To analyze the precision of the models, some statistical estimators were used such as root mean square deviation, mean absolute deviation and mean bias deviation. It was seen that all the analyzed models reproduce adequately the diffuse UVER on the south-facing plane, with greater discrepancies for the other inclined planes. When the models are applied to cloud-free conditions, the errors obtained are higher because the anisotropy of the sky dome acquires more importance and the models do not provide the estimation of diffuse UVER accurately.

[Influence of human personal features on acoustic correlates of speech emotional intonation characteristics].

PubMed

Dmitrieva, E S; Gel'man, V Ia; Zaĭtseva, K A; Orlov, A M

2009-01-01

Comparative study of acoustic correlates of emotional intonation was conducted on two types of speech material: sensible speech utterances and short meaningless words. The corpus of speech signals of different emotional intonations (happy, angry, frightened, sad and neutral) was created using the actor's method of simulation of emotions. Native Russian 20-70-year-old speakers (both professional actors and non-actors) participated in the study. In the corpus, the following characteristics were analyzed: mean values and standard deviations of the power, fundamental frequency, frequencies of the first and second formants, and utterance duration. Comparison of each emotional intonation with "neutral" utterances showed the greatest deviations of the fundamental frequency and frequencies of the first formant. The direction of these deviations was independent of the semantic content of speech utterance and its duration, age, gender, and being actor or non-actor, though the personal features of the speakers affected the absolute values of these frequencies.

Vapor-liquid equilibria for an R134a/lubricant mixture: Measurements and equation-of-state modeling

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

Huber, M.L.; Holcomb, C.D.; Outcalt, S.L.

2000-07-01

The authors measured bubble point pressures and coexisting liquid densities for two mixtures of R-134a and a polyolester (POE) lubricant. The mass fraction of the lubricant was approximately 9% and 12%, and the temperature ranged from 280 K to 355 K. The authors used the Elliott, Suresh, and Donohue (ESD) equation of state to model the bubble point pressure data. The bubble point pressures were represented with an average absolute deviation of 2.5%. A binary interaction parameter reduced the deviation to 1.4%. The authors also applied the ESD model to other R-134a/POE lubricant data in the literature. As the concentrationmore » of the lubricant increased, the performance of the model deteriorated markedly. However, the use of a single binary interaction parameter reduced the deviations significantly.« less

A Case Study to Improve Emergency Room Patient Flow at Womack Army Medical Center

DTIC Science & Technology

2009-06-01

use just the previous month, moving average 2-month period ( MA2 ) uses the average from the previous two months, moving average 3-month period (MA3...ED prior to discharge by provider) MA2 /MA3/MA4 - moving averages of 2-4 months in length MAD - mean absolute deviation (measure of accuracy for

Observations on the method of determining the velocity of airships

NASA Technical Reports Server (NTRS)

Volterra, Vito

1921-01-01

To obtain the absolute velocity of an airship by knowing the speed at which two routes are covered, we have only to determine the geographical direction of the routes which we locate from a map, and the angles of routes as given by the compass, after correcting for the variation (the algebraical sum of the local magnetic declination and the deviation).

File Carving and Malware Identification Algorithms Applied to Firmware Reverse Engineering

DTIC Science & Technology

2013-03-21

33 3.5 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.6 Experimental...consider a byte value rate-of-change frequency metric [32]. Their system calculates the absolute value of the distance between all consecutive bytes, then...the rate-of-change means and standard deviations. Karresand and Shahmehri use the same distance metric for both byte value frequency and rate-of-change

Development of a Dual-Pump CARS System for Measurements in a Supersonic Combusting Free Jet

NASA Technical Reports Server (NTRS)

Magnotti, Gaetano; Cutler, Andrew D.; Danehy, Paul

2012-01-01

This work describes the development of a dual-pump CARS system for simultaneous measurements of temperature and absolute mole fraction of N2, O2 and H2 in a laboratory scale supersonic combusting free jet. Changes to the experimental set-up and the data analysis to improve the quality of the measurements in this turbulent, high-temperature reacting flow are described. The accuracy and precision of the instrument have been determined using data collected in a Hencken burner flame. For temperature above 800 K, errors in absolute mole fraction are within 1.5, 0.5, and 1% of the total composition for N2, O2 and H2, respectively. Estimated standard deviations based on 500 single shots are between 10 and 65 K for the temperature, between 0.5 and 1.7% of the total composition for O2, and between 1.5 and 3.4% for N2. The standard deviation of H2 is 10% of the average measured mole fraction. Results obtained in the jet with and without combustion are illustrated, and the capabilities and limitations of the dual-pump CARS instrument discussed.

Modelling PET radionuclide production in tissue and external targets using Geant4

NASA Astrophysics Data System (ADS)

Amin, T.; Infantino, A.; Lindsay, C.; Barlow, R.; Hoehr, C.

2017-07-01

The Proton Therapy Facility in TRIUMF provides 74 MeV protons extracted from a 500 MeV H- cyclotron for ocular melanoma treatments. During treatment, positron emitting radionuclides such as 1C, 15O and 13N are produced in patient tissue. Using PET scanners, the isotopic activity distribution can be measured for in-vivo range verification. A second cyclotron, the TR13, provides 13 MeV protons onto liquid targets for the production of PET radionuclides such as 18F, 13N or 68Ga, for medical applications. The aim of this work was to validate Geant4 against FLUKA and experimental measurements for production of the above-mentioned isotopes using the two cyclotrons. The results show variable degrees of agreement. For proton therapy, the proton-range agreement was within 2 mm for 11C activity, whereas 13N disagreed. For liquid targets at the TR13 the average absolute deviation ratio between FLUKA and experiment was 1.9±2.7, whereas the average absolute deviation ratio between Geant4 and experiment was 0. 6±0.4. This is due to the uncertainties present in experimentally determined reaction cross sections.

Patient with oligodontia treated with a miniscrew for unilateral mesial movement of the maxillary molars and alignment of an impacted third molar.

PubMed

Maeda, Aya; Sakoguchi, Yoko; Miyawaki, Shouichi

2013-09-01

This report describes the treatment of a 20-year-old woman with a dental midline deviation and 7 congenitally missing premolars. She had retained a maxillary right deciduous canine and 4 deciduous second molars, and she had an impacted maxillary right third molar. The maxillary right deciduous second molar was extracted, and the space was nearly closed by mesial movement of the maxillary right molars using an edgewise appliance and a miniscrew for absolute anchorage. The miniscrew was removed, and the extraction space of the maxillary right deciduous canine was closed, correcting the dental midline deviation. After the mesial movement of the maxillary right molars, the impacted right third molar was aligned. To prevent root resorption, the retained left deciduous second molars were not aligned by the edgewise appliance. The occlusal contact area and the maximum occlusal force increased over the 2 years of retention. The miniscrew was useful for absolute anchorage for unilateral mesial movement of the maxillary molars and for the creation of eruption space and alignment of the impacted third molar in a patient with oligodontia. Copyright © 2013 American Association of Orthodontists. Published by Mosby, Inc. All rights reserved.

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.

Laser frequency stabilization using a commercial wavelength meter

NASA Astrophysics Data System (ADS)

Couturier, Luc; Nosske, Ingo; Hu, Fachao; Tan, Canzhu; Qiao, Chang; Jiang, Y. H.; Chen, Peng; Weidemüller, Matthias

2018-04-01

We present the characterization of a laser frequency stabilization scheme using a state-of-the-art wavelength meter based on solid Fizeau interferometers. For a frequency-doubled Ti-sapphire laser operated at 461 nm, an absolute Allan deviation below 10-9 with a standard deviation of 1 MHz over 10 h is achieved. Using this laser for cooling and trapping of strontium atoms, the wavemeter scheme provides excellent stability in single-channel operation. Multi-channel operation with a multimode fiber switch results in fluctuations of the atomic fluorescence correlated to residual frequency excursions of the laser. The wavemeter-based frequency stabilization scheme can be applied to a wide range of atoms and molecules for laser spectroscopy, cooling, and trapping.

Estimating error statistics for Chambon-la-Forêt observatory definitive data

NASA Astrophysics Data System (ADS)

Lesur, Vincent; Heumez, Benoît; Telali, Abdelkader; Lalanne, Xavier; Soloviev, Anatoly

2017-08-01

We propose a new algorithm for calibrating definitive observatory data with the goal of providing users with estimates of the data error standard deviations (SDs). The algorithm has been implemented and tested using Chambon-la-Forêt observatory (CLF) data. The calibration process uses all available data. It is set as a large, weakly non-linear, inverse problem that ultimately provides estimates of baseline values in three orthogonal directions, together with their expected standard deviations. For this inverse problem, absolute data error statistics are estimated from two series of absolute measurements made within a day. Similarly, variometer data error statistics are derived by comparing variometer data time series between different pairs of instruments over few years. The comparisons of these time series led us to use an autoregressive process of order 1 (AR1 process) as a prior for the baselines. Therefore the obtained baselines do not vary smoothly in time. They have relatively small SDs, well below 300 pT when absolute data are recorded twice a week - i.e. within the daily to weekly measures recommended by INTERMAGNET. The algorithm was tested against the process traditionally used to derive baselines at CLF observatory, suggesting that statistics are less favourable when this latter process is used. Finally, two sets of definitive data were calibrated using the new algorithm. Their comparison shows that the definitive data SDs are less than 400 pT and may be slightly overestimated by our process: an indication that more work is required to have proper estimates of absolute data error statistics. For magnetic field modelling, the results show that even on isolated sites like CLF observatory, there are very localised signals over a large span of temporal frequencies that can be as large as 1 nT. The SDs reported here encompass signals of a few hundred metres and less than a day wavelengths.

Comparison of absolute gain photometric calibration between Planck/HFI and Herschel/SPIRE at 545 and 857 GHz

NASA Astrophysics Data System (ADS)

Bertincourt, B.; Lagache, G.; Martin, P. G.; Schulz, B.; Conversi, L.; Dassas, K.; Maurin, L.; Abergel, A.; Beelen, A.; Bernard, J.-P.; Crill, B. P.; Dole, H.; Eales, S.; Gudmundsson, J. E.; Lellouch, E.; Moreno, R.; Perdereau, O.

2016-04-01

We compare the absolute gain photometric calibration of the Planck/HFI and Herschel/SPIRE instruments on diffuse emission. The absolute calibration of HFI and SPIRE each relies on planet flux measurements and comparison with theoretical far-infrared emission models of planetary atmospheres. We measure the photometric cross calibration between the instruments at two overlapping bands, 545 GHz/500 μm and 857 GHz/350 μm. The SPIRE maps used have been processed in the Herschel Interactive Processing Environment (Version 12) and the HFI data are from the 2015 Public Data Release 2. For our study we used 15 large fields observed with SPIRE, which cover a total of about 120 deg2. We have selected these fields carefully to provide high signal-to-noise ratio, avoid residual systematics in the SPIRE maps, and span a wide range of surface brightness. The HFI maps are bandpass-corrected to match the emission observed by the SPIRE bandpasses. The SPIRE maps are convolved to match the HFI beam and put on a common pixel grid. We measure the cross-calibration relative gain between the instruments using two methods in each field, pixel-to-pixel correlation and angular power spectrum measurements. The SPIRE/HFI relative gains are 1.047 (±0.0069) and 1.003 (±0.0080) at 545 and 857 GHz, respectively, indicating very good agreement between the instruments. These relative gains deviate from unity by much less than the uncertainty of the absolute extended emission calibration, which is about 6.4% and 9.5% for HFI and SPIRE, respectively, but the deviations are comparable to the values 1.4% and 5.5% for HFI and SPIRE if the uncertainty from models of the common calibrator can be discounted. Of the 5.5% uncertainty for SPIRE, 4% arises from the uncertainty of the effective beam solid angle, which impacts the adopted SPIRE point source to extended source unit conversion factor, highlighting that as a focus for refinement.

Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network.

PubMed

Pan, Hongye; Zhang, Qing; Cui, Keke; Chen, Guoquan; Liu, Xuesong; Wang, Longhu

2017-05-01

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A soft-computing methodology for noninvasive time-spatial temperature estimation.

PubMed

Teixeira, César A; Ruano, Maria Graça; Ruano, António E; Pereira, Wagner C A

2008-02-01

The safe and effective application of thermal therapies is restricted due to lack of reliable noninvasive temperature estimators. In this paper, the temporal echo-shifts of backscattered ultrasound signals, collected from a gel-based phantom, were tracked and assigned with the past temperature values as radial basis functions neural networks input information. The phantom was heated using a piston-like therapeutic ultrasound transducer. The neural models were assigned to estimate the temperature at different intensities and points arranged across the therapeutic transducer radial line (60 mm apart from the transducer face). Model inputs, as well as the number of neurons were selected using the multiobjective genetic algorithm (MOGA). The best attained models present, in average, a maximum absolute error less than 0.5 degrees C, which is pointed as the borderline between a reliable and an unreliable estimator in hyperthermia/diathermia. In order to test the spatial generalization capacity, the best models were tested using spatial points not yet assessed, and some of them presented a maximum absolute error inferior to 0.5 degrees C, being "elected" as the best models. It should be also stressed that these best models present implementational low-complexity, as desired for real-time applications.

Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-11-01

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.

A Reward-Based Behavioral Platform to Measure Neural Activity during Head-Fixed Behavior.

PubMed

Micallef, Andrew H; Takahashi, Naoya; Larkum, Matthew E; Palmer, Lucy M

2017-01-01

Understanding the neural computations that contribute to behavior requires recording from neurons while an animal is behaving. This is not an easy task as most subcellular recording techniques require absolute head stability. The Go/No-Go sensory task is a powerful decision-driven task that enables an animal to report a binary decision during head-fixation. Here we discuss how to set up an Ardunio and Python based platform system to control a Go/No-Go sensory behavior paradigm. Using an Arduino micro-controller and Python-based custom written program, a reward can be delivered to the animal depending on the decision reported. We discuss the various components required to build the behavioral apparatus that can control and report such a sensory stimulus paradigm. This system enables the end user to control the behavioral testing in real-time and therefore it provides a strong custom-made platform for probing the neural basis of behavior.

Response variance in functional maps: neural darwinism revisited.

PubMed

Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei

2013-01-01

The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population.

Response Variance in Functional Maps: Neural Darwinism Revisited

PubMed Central

Takahashi, Hirokazu; Yokota, Ryo; Kanzaki, Ryohei

2013-01-01

The mechanisms by which functional maps and map plasticity contribute to cortical computation remain controversial. Recent studies have revisited the theory of neural Darwinism to interpret the learning-induced map plasticity and neuronal heterogeneity observed in the cortex. Here, we hypothesize that the Darwinian principle provides a substrate to explain the relationship between neuron heterogeneity and cortical functional maps. We demonstrate in the rat auditory cortex that the degree of response variance is closely correlated with the size of its representational area. Further, we show that the response variance within a given population is altered through training. These results suggest that larger representational areas may help to accommodate heterogeneous populations of neurons. Thus, functional maps and map plasticity are likely to play essential roles in Darwinian computation, serving as effective, but not absolutely necessary, structures to generate diverse response properties within a neural population. PMID:23874733

Artificial neural network predictions of lengths of stay on a post-coronary care unit.

PubMed

Mobley, B A; Leasure, R; Davidson, L

1995-01-01

To create and validate a model that predicts length of hospital unit stay. Ex post facto. Seventy-four independent admission variables in 15 general categories were utilized to predict possible stays of 1 to 20 days. Laboratory. Records of patients discharged from a post-coronary care unit in early 1993. An artificial neural network was trained on 629 records and tested on an additional 127 records of patients. The absolute disparity between the actual lengths of stays in the test records and the predictions of the network averaged 1.4 days per record, and the actual length of stay was predicted within 1 day 72% of the time. The artificial neural network demonstrated the capacity to utilize common patient admission characteristics to predict lengths of stay. This technology shows promise in aiding timely initiation of treatment and effective resource planning and cost control.

Peak load demand forecasting using two-level discrete wavelet decomposition and neural network algorithm

NASA Astrophysics Data System (ADS)

Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak

2010-02-01

This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.

LD-SPatt: large deviations statistics for patterns on Markov chains.

PubMed

Nuel, G

2004-01-01

Statistics on Markov chains are widely used for the study of patterns in biological sequences. Statistics on these models can be done through several approaches. Central limit theorem (CLT) producing Gaussian approximations are one of the most popular ones. Unfortunately, in order to find a pattern of interest, these methods have to deal with tail distribution events where CLT is especially bad. In this paper, we propose a new approach based on the large deviations theory to assess pattern statistics. We first recall theoretical results for empiric mean (level 1) as well as empiric distribution (level 2) large deviations on Markov chains. Then, we present the applications of these results focusing on numerical issues. LD-SPatt is the name of GPL software implementing these algorithms. We compare this approach to several existing ones in terms of complexity and reliability and show that the large deviations are more reliable than the Gaussian approximations in absolute values as well as in terms of ranking and are at least as reliable as compound Poisson approximations. We then finally discuss some further possible improvements and applications of this new method.

Linking Comparisons of Absolute Gravimeters: A Proof of Concept for a new Global Absolute Gravity Reference System.

NASA Astrophysics Data System (ADS)

Wziontek, H.; Palinkas, V.; Falk, R.; Vaľko, M.

2016-12-01

Since decades, absolute gravimeters are compared on a regular basis on an international level, starting at the International Bureau for Weights and Measures (BIPM) in 1981. Usually, these comparisons are based on constant reference values deduced from all accepted measurements acquired during the comparison period. Temporal changes between comparison epochs are usually not considered. Resolution No. 2, adopted by IAG during the IUGG General Assembly in Prague 2015, initiates the establishment of a Global Absolute Gravity Reference System based on key comparisons of absolute gravimeters (AG) under the International Committee for Weights and Measures (CIPM) in order to establish a common level in the microGal range. A stable and unique reference frame can only be achieved, if different AG are taking part in different kind of comparisons. Systematic deviations between the respective comparison reference values can be detected, if the AG can be considered stable over time. The continuous operation of superconducting gravimeters (SG) on selected stations further supports the temporal link of comparison reference values by establishing a reference function over time. By a homogenous reprocessing of different comparison epochs and including AG and SG time series at selected stations, links between several comparisons will be established and temporal comparison reference functions will be derived. By this, comparisons on a regional level can be traced to back to the level of key comparisons, providing a reference for other absolute gravimeters. It will be proved and discussed, how such a concept can be used to support the future absolute gravity reference system.

Prediction of forced expiratory volume in pulmonary function test using radial basis neural networks and k-means clustering.

PubMed

Manoharan, Sujatha C; Ramakrishnan, Swaminathan

2009-10-01

In this work, prediction of forced expiratory volume in pulmonary function test, carried out using spirometry and neural networks is presented. The pulmonary function data were recorded from volunteers using commercial available flow volume spirometer in standard acquisition protocol. The Radial Basis Function neural networks were used to predict forced expiratory volume in 1 s (FEV1) from the recorded flow volume curves. The optimal centres of the hidden layer of radial basis function were determined by k-means clustering algorithm. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal, restrictive and obstructive cases. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases. Prediction accuracy was more in obstructive abnormality when compared to restrictive cases. It appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

A function approximation approach to anomaly detection in propulsion system test data

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Hoyt, W. A.

1993-01-01

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance.

PubMed

von Trapp, Gardiner; Buran, Bradley N; Sen, Kamal; Semple, Malcolm N; Sanes, Dan H

2016-10-26

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability of the neural response becomes smaller during task performance, thereby improving neural detection thresholds. Copyright © 2016 the authors 0270-6474/16/3611097-10$15.00/0.

A Decline in Response Variability Improves Neural Signal Detection during Auditory Task Performance

PubMed Central

Buran, Bradley N.; Sen, Kamal; Semple, Malcolm N.; Sanes, Dan H.

2016-01-01

The detection of a sensory stimulus arises from a significant change in neural activity, but a sensory neuron's response is rarely identical to successive presentations of the same stimulus. Large trial-to-trial variability would limit the central nervous system's ability to reliably detect a stimulus, presumably affecting perceptual performance. However, if response variability were to decrease while firing rate remained constant, then neural sensitivity could improve. Here, we asked whether engagement in an auditory detection task can modulate response variability, thereby increasing neural sensitivity. We recorded telemetrically from the core auditory cortex of gerbils, both while they engaged in an amplitude-modulation detection task and while they sat quietly listening to the identical stimuli. Using a signal detection theory framework, we found that neural sensitivity was improved during task performance, and this improvement was closely associated with a decrease in response variability. Moreover, units with the greatest change in response variability had absolute neural thresholds most closely aligned with simultaneously measured perceptual thresholds. Our findings suggest that the limitations imposed by response variability diminish during task performance, thereby improving the sensitivity of neural encoding and potentially leading to better perceptual sensitivity. SIGNIFICANCE STATEMENT The detection of a sensory stimulus arises from a significant change in neural activity. However, trial-to-trial variability of the neural response may limit perceptual performance. If the neural response to a stimulus is quite variable, then the response on a given trial could be confused with the pattern of neural activity generated when the stimulus is absent. Therefore, a neural mechanism that served to reduce response variability would allow for better stimulus detection. By recording from the cortex of freely moving animals engaged in an auditory detection task, we found that variability of the neural response becomes smaller during task performance, thereby improving neural detection thresholds. PMID:27798189

Seismic velocity deviation log: An effective method for evaluating spatial distribution of reservoir pore types

NASA Astrophysics Data System (ADS)

Shirmohamadi, Mohamad; Kadkhodaie, Ali; Rahimpour-Bonab, Hossain; Faraji, Mohammad Ali

2017-04-01

Velocity deviation log (VDL) is a synthetic log used to determine pore types in reservoir rocks based on a combination of the sonic log with neutron-density logs. The current study proposes a two step approach to create a map of porosity and pore types by integrating the results of petrographic studies, well logs and seismic data. In the first step, velocity deviation log was created from the combination of the sonic log with the neutron-density log. The results allowed identifying negative, zero and positive deviations based on the created synthetic velocity log. Negative velocity deviations (below - 500 m/s) indicate connected or interconnected pores and fractures, while positive deviations (above + 500 m/s) are related to isolated pores. Zero deviations in the range of [- 500 m/s, + 500 m/s] are in good agreement with intercrystalline and microporosities. The results of petrographic studies were used to validate the main pore type derived from velocity deviation log. In the next step, velocity deviation log was estimated from seismic data by using a probabilistic neural network model. For this purpose, the inverted acoustic impedance along with the amplitude based seismic attributes were formulated to VDL. The methodology is illustrated by performing a case study from the Hendijan oilfield, northwestern Persian Gulf. The results of this study show that integration of petrographic, well logs and seismic attributes is an instrumental way for understanding the spatial distribution of main reservoir pore types.

Water Level Prediction of Lake Cascade Mahakam Using Adaptive Neural Network Backpropagation (ANNBP)

NASA Astrophysics Data System (ADS)

Mislan; Gaffar, A. F. O.; Haviluddin; Puspitasari, N.

2018-04-01

A natural hazard information and flood events are indispensable as a form of prevention and improvement. One of the causes is flooding in the areas around the lake. Therefore, forecasting the surface of Lake water level to anticipate flooding is required. The purpose of this paper is implemented computational intelligence method namely Adaptive Neural Network Backpropagation (ANNBP) to forecasting the Lake Cascade Mahakam. Based on experiment, performance of ANNBP indicated that Lake water level prediction have been accurate by using mean square error (MSE) and mean absolute percentage error (MAPE). In other words, computational intelligence method can produce good accuracy. A hybrid and optimization of computational intelligence are focus in the future work.

Regional comparison of absolute gravimeters SIM.M.G-K1 key comparison

NASA Astrophysics Data System (ADS)

Newell, D. B.; van Westrum, D.; Francis, O.; Kanney, J.; Liard, J.; Ramirez, A. E.; Lucero, B.; Ellis, B.; Greco, F.; Pistorio, A.; Reudink, R.; Iacovone, D.; Baccaro, F.; Silliker, J.; Wheeler, R. D.; Falk, R.; Ruelke, A.

2017-01-01

Twelve absolute gravimeters were compared during the regional Key Comparison SIM.M.G-K1 of absolute gravimeters. The four gravimeters were from different NMIs and DIs. The comparison was linked to the CCM.G-K2 through EURAMET.M.G-K2 via the DI gravimeter FG5X-216. Overall, the results and uncertainties indicate an excellent agreement among the gravimeters, with a standard deviation of the gravimeters' DoEs better than 1.3 μGal. In the case of the official solution, all the gravimeters are in equivalence well within the declared uncertainties. Main text To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database kcdb.bipm.org/. The final report has been peer-reviewed and approved for publication by the CCM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Absolute distance measurement with correction of air refractive index by using two-color dispersive interferometry.

PubMed

Wu, Hanzhong; Zhang, Fumin; Liu, Tingyang; Li, Jianshuang; Qu, Xinghua

2016-10-17

Two-color interferometry is powerful for the correction of the air refractive index especially in the turbulent air over long distance, since the empirical equations could introduce considerable measurement uncertainty if the environmental parameters cannot be measured with sufficient precision. In this paper, we demonstrate a method for absolute distance measurement with high-accuracy correction of air refractive index using two-color dispersive interferometry. The distances corresponding to the two wavelengths can be measured via the spectrograms captured by a CCD camera pair in real time. In the long-term experiment of the correction of air refractive index, the experimental results show a standard deviation of 3.3 × 10^-8 for 12-h continuous measurement without the precise knowledge of the environmental conditions, while the variation of the air refractive index is about 2 × 10^-6. In the case of absolute distance measurement, the comparison with the fringe counting interferometer shows an agreement within 2.5 μm in 12 m range.

3D shape measurements with a single interferometric sensor for in-situ lathe monitoring

NASA Astrophysics Data System (ADS)

Kuschmierz, R.; Huang, Y.; Czarske, J.; Metschke, S.; Löffler, F.; Fischer, A.

2015-05-01

Temperature drifts, tool deterioration, unknown vibrations as well as spindle play are major effects which decrease the achievable precision of computerized numerically controlled (CNC) lathes and lead to shape deviations between the processed work pieces. Since currently no measurement system exist for fast, precise and in-situ 3d shape monitoring with keyhole access, much effort has to be made to simulate and compensate these effects. Therefore we introduce an optical interferometric sensor for absolute 3d shape measurements, which was integrated into a working lathe. According to the spindle rotational speed, a measurement rate of 2,500 Hz was achieved. In-situ absolute shape, surface profile and vibration measurements are presented. While thermal drifts of the sensor led to errors of several mµm for the absolute shape, reference measurements with a coordinate machine show, that the surface profile could be measured with an uncertainty below one micron. Additionally, the spindle play of 0.8 µm was measured with the sensor.

Motor equivalence during multi-finger accurate force production

PubMed Central

Mattos, Daniela; Schöner, Gregor; Zatsiorsky, Vladimir M.; Latash, Mark L.

2014-01-01

We explored stability of multi-finger cyclical accurate force production action by analysis of responses to small perturbations applied to one of the fingers and inter-cycle analysis of variance. Healthy subjects performed two versions of the cyclical task, with and without an explicit target. The “inverse piano” apparatus was used to lift/lower a finger by 1 cm over 0.5 s; the subjects were always instructed to perform the task as accurate as they could at all times. Deviations in the spaces of finger forces and modes (hypothetical commands to individual fingers) were quantified in directions that did not change total force (motor equivalent) and in directions that changed the total force (non-motor equivalent). Motor equivalent deviations started immediately with the perturbation and increased progressively with time. After a sequence of lifting-lowering perturbations leading to the initial conditions, motor equivalent deviations were dominating. These phenomena were less pronounced for analysis performed with respect to the total moment of force with respect to an axis parallel to the forearm/hand. Analysis of inter-cycle variance showed consistently higher variance in a subspace that did not change the total force as compared to the variance that affected total force. We interpret the results as reflections of task-specific stability of the redundant multi-finger system. Large motor equivalent deviations suggest that reactions of the neuromotor system to a perturbation involve large changes of neural commands that do not affect salient performance variables, even during actions with the purpose to correct those salient variables. Consistency of the analyses of motor equivalence and variance analysis provides additional support for the idea of task-specific stability ensured at a neural level. PMID:25344311

Comparison of Predictive Modeling Methods of Aircraft Landing Speed

NASA Technical Reports Server (NTRS)

Diallo, Ousmane H.

2012-01-01

Expected increases in air traffic demand have stimulated the development of air traffic control tools intended to assist the air traffic controller in accurately and precisely spacing aircraft landing at congested airports. Such tools will require an accurate landing-speed prediction to increase throughput while decreasing necessary controller interventions for avoiding separation violations. There are many practical challenges to developing an accurate landing-speed model that has acceptable prediction errors. This paper discusses the development of a near-term implementation, using readily available information, to estimate/model final approach speed from the top of the descent phase of flight to the landing runway. As a first approach, all variables found to contribute directly to the landing-speed prediction model are used to build a multi-regression technique of the response surface equation (RSE). Data obtained from operations of a major airlines for a passenger transport aircraft type to the Dallas/Fort Worth International Airport are used to predict the landing speed. The approach was promising because it decreased the standard deviation of the landing-speed error prediction by at least 18% from the standard deviation of the baseline error, depending on the gust condition at the airport. However, when the number of variables is reduced to the most likely obtainable at other major airports, the RSE model shows little improvement over the existing methods. Consequently, a neural network that relies on a nonlinear regression technique is utilized as an alternative modeling approach. For the reduced number of variables cases, the standard deviation of the neural network models errors represent over 5% reduction compared to the RSE model errors, and at least 10% reduction over the baseline predicted landing-speed error standard deviation. Overall, the constructed models predict the landing-speed more accurately and precisely than the current state-of-the-art.

Rectangularization of the survival curve in The Netherlands, 1950-1992.

PubMed

Nusselder, W J; Mackenbach, J P

1996-12-01

In this article we determine whether rectangularization of the survival curve occurred in the Netherlands in the period 1950-1992. Rectangularization is defined as a trend toward a more rectangular shape of the survival curve due to increased survival and concentration of deaths around the mean age at death. We distinguish between absolute and relative rectangularization, depending on whether an increase in life expectancy is accompanied by concentration of deaths into a smaller age interval or into a smaller proportion of total life expectancy. We used measures of variability based on Keyfitz' H and the standard deviation, both life table-based. Our results show that absolute and relative rectangularization of the entire survival curve occurred in both sexes and over the complete period (except for the years 1955-1959 and 1965-1969 in men). At older ages, results differ between sexes, periods, and an absolute versus a relative definition of rectangularization. Above age 60 1/2, relative rectangularization occurred in women over the complete period and in men since 1975-1979 only, whereas absolute rectangularization occurred in both sexes since the period of 1980-1984. The implications of the recent rectangularization at older ages for achieving compression of morbidity are discussed.

Partial Adaptation of Obtained and Observed Value Signals Preserves Information about Gains and Losses

PubMed Central

Baddeley, Michelle; Tobler, Philippe N.; Schultz, Wolfram

2016-01-01

Given that the range of rewarding and punishing outcomes of actions is large but neural coding capacity is limited, efficient processing of outcomes by the brain is necessary. One mechanism to increase efficiency is to rescale neural output to the range of outcomes expected in the current context, and process only experienced deviations from this expectation. However, this mechanism comes at the cost of not being able to discriminate between unexpectedly low losses when times are bad versus unexpectedly high gains when times are good. Thus, too much adaptation would result in disregarding information about the nature and absolute magnitude of outcomes, preventing learning about the longer-term value structure of the environment. Here we investigate the degree of adaptation in outcome coding brain regions in humans, for directly experienced outcomes and observed outcomes. We scanned participants while they performed a social learning task in gain and loss blocks. Multivariate pattern analysis showed two distinct networks of brain regions adapt to the most likely outcomes within a block. Frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Critically, in both cases, adaptation was incomplete and information about whether the outcomes arose in a gain block or a loss block was retained. Univariate analysis confirmed incomplete adaptive coding in these regions but also detected nonadapting outcome signals. Thus, although neural areas rescale their responses to outcomes for efficient coding, they adapt incompletely and keep track of the longer-term incentives available in the environment. SIGNIFICANCE STATEMENT Optimal value-based choice requires that the brain precisely and efficiently represents positive and negative outcomes. One way to increase efficiency is to adapt responding to the most likely outcomes in a given context. However, too strong adaptation would result in loss of precise representation (e.g., when the avoidance of a loss in a loss-context is coded the same as receipt of a gain in a gain-context). We investigated an intermediate form of adaptation that is efficient while maintaining information about received gains and avoided losses. We found that frontostriatal areas adapted to directly experienced outcomes, whereas lateral frontal and temporoparietal regions adapted to observed social outcomes. Importantly, adaptation was intermediate, in line with influential models of reference dependence in behavioral economics. PMID:27683899

A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.

PubMed

Zhou, Chunshan; Zhang, Chao; Tian, Di; Wang, Ke; Huang, Mingzhi; Liu, Yanbiao

2018-01-02

In order to manage water resources, a software sensor model was designed to estimate water quality using a hybrid fuzzy neural network (FNN) in Guangzhou section of Pearl River, China. The software sensor system was composed of data storage module, fuzzy decision-making module, neural network module and fuzzy reasoning generator module. Fuzzy subtractive clustering was employed to capture the character of model, and optimize network architecture for enhancing network performance. The results indicate that, on basis of available on-line measured variables, the software sensor model can accurately predict water quality according to the relationship between chemical oxygen demand (COD) and dissolved oxygen (DO), pH and NH 4 + -N. Owing to its ability in recognizing time series patterns and non-linear characteristics, the software sensor-based FNN is obviously superior to the traditional neural network model, and its R (correlation coefficient), MAPE (mean absolute percentage error) and RMSE (root mean square error) are 0.8931, 10.9051 and 0.4634, respectively.

Modeling of surface dust concentrations using neural networks and kriging

NASA Astrophysics Data System (ADS)

Buevich, Alexander G.; Medvedev, Alexander N.; Sergeev, Alexander P.; Tarasov, Dmitry A.; Shichkin, Andrey V.; Sergeeva, Marina V.; Atanasova, T. B.

2016-12-01

Creating models which are able to accurately predict the distribution of pollutants based on a limited set of input data is an important task in environmental studies. In the paper two neural approaches: (multilayer perceptron (MLP)) and generalized regression neural network (GRNN)), and two geostatistical approaches: (kriging and cokriging), are using for modeling and forecasting of dust concentrations in snow cover. The area of study is under the influence of dust emissions from a copper quarry and a several industrial companies. The comparison of two mentioned approaches is conducted. Three indices are used as the indicators of the models accuracy: the mean absolute error (MAE), root mean square error (RMSE) and relative root mean square error (RRMSE). Models based on artificial neural networks (ANN) have shown better accuracy. When considering all indices, the most precision model was the GRNN, which uses as input parameters for modeling the coordinates of sampling points and the distance to the probable emissions source. The results of work confirm that trained ANN may be more suitable tool for modeling of dust concentrations in snow cover.

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging.

PubMed

Gençay, R; Qi, M

2001-01-01

We study the effectiveness of cross validation, Bayesian regularization, early stopping, and bagging to mitigate overfitting and improving generalization for pricing and hedging derivative securities with daily S&P 500 index daily call options from January 1988 to December 1993. Our results indicate that Bayesian regularization can generate significantly smaller pricing and delta-hedging errors than the baseline neural-network (NN) model and the Black-Scholes model for some years. While early stopping does not affect the pricing errors, it significantly reduces the hedging error (HE) in four of the six years we investigated. Although computationally most demanding, bagging seems to provide the most accurate pricing and delta hedging. Furthermore, the standard deviation of the MSPE of bagging is far less than that of the baseline model in all six years, and the standard deviation of the average HE of bagging is far less than that of the baseline model in five out of six years. We conclude that they be used at least in cases when no appropriate hints are available.

An analog neural hardware implementation using charge-injection multipliers and neutron-specific gain control.

PubMed

Massengill, L W; Mundie, D B

1992-01-01

A neural network IC based on a dynamic charge injection is described. The hardware design is space and power efficient, and achieves massive parallelism of analog inner products via charge-based multipliers and spatially distributed summing buses. Basic synaptic cells are constructed of exponential pulse-decay modulation (EPDM) dynamic injection multipliers operating sequentially on propagating signal vectors and locally stored analog weights. Individually adjustable gain controls on each neutron reduce the effects of limited weight dynamic range. A hardware simulator/trainer has been developed which incorporates the physical (nonideal) characteristics of actual circuit components into the training process, thus absorbing nonlinearities and parametric deviations into the macroscopic performance of the network. Results show that charge-based techniques may achieve a high degree of neural density and throughput using standard CMOS processes.

Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis

PubMed Central

Graff, Rebecca E.; Ursin, Giske; dos Santos Silva, Isabel; McCormack, Valerie; Baglietto, Laura; Vachon, Celine; Bakker, Marije F.; Giles, Graham G.; Chia, Kee Seng; Czene, Kamila; Eriksson, Louise; Hall, Per; Hartman, Mikael; Warren, Ruth M. L.; Hislop, Greg; Chiarelli, Anna M.; Hopper, John L.; Krishnan, Kavitha; Li, Jingmei; Li, Qing; Pagano, Ian; Rosner, Bernard A.; Wong, Chia Siong; Scott, Christopher; Stone, Jennifer; Maskarinec, Gertraud; Boyd, Norman F.; van Gils, Carla H.

2014-01-01

Background Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk. Methods We conducted a meta-analysis of 13 case–control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant. Results Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women. Conclusions The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area. PMID:24816206

Directly relating gas-phase cluster measurements to solution-phase hydrolysis, the absolute standard hydrogen electrode potential, and the absolute proton solvation energy.

PubMed

Donald, William A; Leib, Ryan D; O'Brien, Jeremy T; Williams, Evan R

2009-06-08

Solution-phase, half-cell potentials are measured relative to other half-cell potentials, resulting in a thermochemical ladder that is anchored to the standard hydrogen electrode (SHE), which is assigned an arbitrary value of 0 V. A new method for measuring the absolute SHE potential is demonstrated in which gaseous nanodrops containing divalent alkaline-earth or transition-metal ions are reduced by thermally generated electrons. Energies for the reactions 1) M(H(2)O)(24)(2+)(g) + e(-)(g)-->M(H(2)O)(24)(+)(g) and 2) M(H(2)O)(24)(2+)(g) + e(-)(g)-->MOH(H(2)O)(23)(+)(g) + H(g) and the hydrogen atom affinities of MOH(H(2)O)(23)(+)(g) are obtained from the number of water molecules lost through each pathway. From these measurements on clusters containing nine different metal ions and known thermochemical values that include solution hydrolysis energies, an average absolute SHE potential of +4.29 V vs. e(-)(g) (standard deviation of 0.02 V) and a real proton solvation free energy of -265 kcal mol(-1) are obtained. With this method, the absolute SHE potential can be obtained from a one-electron reduction of nanodrops containing divalent ions that are not observed to undergo one-electron reduction in aqueous solution.

Directly Relating Gas-Phase Cluster Measurements to Solution-Phase Hydrolysis, the Absolute Standard Hydrogen Electrode Potential, and the Absolute Proton Solvation Energy

PubMed Central

Donald, William A.; Leib, Ryan D.; O’Brien, Jeremy T.; Williams, Evan R.

2009-01-01

Solution-phase, half-cell potentials are measured relative to other half-cell potentials, resulting in a thermochemical ladder that is anchored to the standard hydrogen electrode (SHE), which is assigned an arbitrary value of 0 V. A new method for measuring the absolute SHE potential is demonstrated in which gaseous nanodrops containing divalent alkaline-earth or transition-metal ions are reduced by thermally generated electrons. Energies for the reactions 1) M-(H2O)242+(g)+e−(g)→M(H2O)24+(g) and 2) M(H2O)242+(g)+e−(g)→MOH(H2O)23+(g)+H(g) and the hydrogen atom affinities of MOH(H2O)23+(g) are obtained from the number of water molecules lost through each pathway. From these measurements on clusters containing nine different metal ions and known thermochemical values that include solution hydrolysis energies, an average absolute SHE potential of +4.29 V vs. e−(g) (standard deviation of 0.02 V) and a real proton solvation free energy of −265 kcal mol−1 are obtained. With this method, the absolute SHE potential can be obtained from a one-electron reduction of nanodrops containing divalent ions that are not observed to undergo one-electron reduction in aqueous solution. PMID:19440999

Intensity stabilisation of optical pulse sequences for coherent control of laser-driven qubits

NASA Astrophysics Data System (ADS)

Thom, Joseph; Yuen, Ben; Wilpers, Guido; Riis, Erling; Sinclair, Alastair G.

2018-05-01

We demonstrate a system for intensity stabilisation of optical pulse sequences used in laser-driven quantum control of trapped ions. Intensity instability is minimised by active stabilisation of the power (over a dynamic range of > 104) and position of the focused beam at the ion. The fractional Allan deviations in power were found to be <2.2 × 10^{-4} for averaging times from 1 to 16,384 s. Over similar times, the absolute Allan deviation of the beam position is <0.1 μm for a 45 {μ }m beam diameter. Using these residual power and position instabilities, we estimate the associated contributions to infidelity in example qubit logic gates to be below 10^{-6} per gate.

Common inputs in subthreshold membrane potential: The role of quiescent states in neuronal activity

NASA Astrophysics Data System (ADS)

Montangie, Lisandro; Montani, Fernando

2018-06-01

Experiments in certain regions of the cerebral cortex suggest that the spiking activity of neuronal populations is regulated by common non-Gaussian inputs across neurons. We model these deviations from random-walk processes with q -Gaussian distributions into simple threshold neurons, and investigate the scaling properties in large neural populations. We show that deviations from the Gaussian statistics provide a natural framework to regulate population statistics such as sparsity, entropy, and specific heat. This type of description allows us to provide an adequate strategy to explain the information encoding in the case of low neuronal activity and its possible implications on information transmission.

Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.

Who's biased? A meta-analysis of buyer-seller differences in the pricing of lotteries.

PubMed

Yechiam, Eldad; Ashby, Nathaniel J S; Pachur, Thorsten

2017-05-01

A large body of empirical research has examined the impact of trading perspective on pricing of consumer products, with the typical finding being that selling prices exceed buying prices (i.e., the endowment effect). Using a meta-analytic approach, we examine to what extent the endowment effect also emerges in the pricing of monetary lotteries. As monetary lotteries have a clearly defined normative value, we also assess whether one trading perspective is more biased than the other. We consider several indicators of bias: absolute deviation from expected values, rank correlation with expected values, overall variance, and per-unit variance. The meta-analysis, which includes 35 articles, indicates that selling prices considerably exceed buying prices (Cohen's d = 0.58). Importantly, we also find that selling prices deviate less from the lotteries' expected values than buying prices, both in absolute and in relative terms. Selling prices also exhibit lower variance per unit. Hierarchical Bayesian modeling with cumulative prospect theory indicates that buyers have lower probability sensitivity and a more pronounced response bias. The finding that selling prices are more in line with normative standards than buying prices challenges the prominent account whereby sellers' valuations are upward biased due to loss aversion, and supports alternative theoretical accounts. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Inter-laboratory validation of bioaccessibility testing for metals.

PubMed

Henderson, Rayetta G; Verougstraete, Violaine; Anderson, Kim; Arbildua, José J; Brock, Thomas O; Brouwers, Tony; Cappellini, Danielle; Delbeke, Katrien; Herting, Gunilla; Hixon, Greg; Odnevall Wallinder, Inger; Rodriguez, Patricio H; Van Assche, Frank; Wilrich, Peter; Oller, Adriana R

2014-10-01

Bioelution assays are fast, simple alternatives to in vivo testing. In this study, the intra- and inter-laboratory variability in bioaccessibility data generated by bioelution tests were evaluated in synthetic fluids relevant to oral, inhalation, and dermal exposure. Using one defined protocol, five laboratories measured metal release from cobalt oxide, cobalt powder, copper concentrate, Inconel alloy, leaded brass alloy, and nickel sulfate hexahydrate. Standard deviations of repeatability (sr) and reproducibility (sR) were used to evaluate the intra- and inter-laboratory variability, respectively. Examination of the sR:sr ratios demonstrated that, while gastric and lysosomal fluids had reasonably good reproducibility, other fluids did not show as good concordance between laboratories. Relative standard deviation (RSD) analysis showed more favorable reproducibility outcomes for some data sets; overall results varied more between- than within-laboratories. RSD analysis of sr showed good within-laboratory variability for all conditions except some metals in interstitial fluid. In general, these findings indicate that absolute bioaccessibility results in some biological fluids may vary between different laboratories. However, for most applications, measures of relative bioaccessibility are needed, diminishing the requirement for high inter-laboratory reproducibility in absolute metal releases. The inter-laboratory exercise suggests that the degrees of freedom within the protocol need to be addressed. Copyright © 2014 Elsevier Inc. All rights reserved.

The laminar organization of the motor cortex in monodactylous mammals: a comparative assessment based on horse, chimpanzee, and macaque.

PubMed

Cozzi, Bruno; De Giorgio, Andrea; Peruffo, A; Montelli, S; Panin, M; Bombardi, C; Grandis, A; Pirone, A; Zambenedetti, P; Corain, L; Granato, Alberto

2017-08-01

The architecture of the neocortex classically consists of six layers, based on cytological criteria and on the layout of intra/interlaminar connections. Yet, the comparison of cortical cytoarchitectonic features across different species proves overwhelmingly difficult, due to the lack of a reliable model to analyze the connection patterns of neuronal ensembles forming the different layers. We first defined a set of suitable morphometric cell features, obtained in digitized Nissl-stained sections of the motor cortex of the horse, chimpanzee, and crab-eating macaque. We then modeled them using a quite general non-parametric data representation model, showing that the assessment of neuronal cell complexity (i.e., how a given cell differs from its neighbors) can be performed using a suitable measure of statistical dispersion such as the mean absolute deviation-mean absolute deviation (MAD). Along with the non-parametric combination and permutation methodology, application of MAD allowed not only to estimate, but also to compare and rank the motor cortical complexity across different species. As to the instances presented in this paper, we show that the pyramidal layers of the motor cortex of the horse are far more irregular than those of primates. This feature could be related to the different organizations of the motor system in monodactylous mammals.

Neural Prediction Errors Distinguish Perception and Misperception of Speech.

PubMed

Blank, Helen; Spangenberg, Marlene; Davis, Matthew H

2018-06-11

Humans use prior expectations to improve perception, especially of sensory signals that are degraded or ambiguous. However, if sensory input deviates from prior expectations, correct perception depends on adjusting or rejecting prior expectations. Failure to adjust or reject the prior leads to perceptual illusions especially if there is partial overlap (hence partial mismatch) between expectations and input. With speech, "Slips of the ear" occur when expectations lead to misperception. For instance, a entomologist, might be more susceptible to hear "The ants are my friends" for "The answer, my friend" (in the Bob Dylan song "Blowing in the Wind"). Here, we contrast two mechanisms by which prior expectations may lead to misperception of degraded speech. Firstly, clear representations of the common sounds in the prior and input (i.e., expected sounds) may lead to incorrect confirmation of the prior. Secondly, insufficient representations of sounds that deviate between prior and input (i.e., prediction errors) could lead to deception. We used cross-modal predictions from written words that partially match degraded speech to compare neural responses when male and female human listeners were deceived into accepting the prior or correctly reject it. Combined behavioural and multivariate representational similarity analysis of functional magnetic resonance imaging data shows that veridical perception of degraded speech is signalled by representations of prediction error in the left superior temporal sulcus. Instead of using top-down processes to support perception of expected sensory input, our findings suggest that the strength of neural prediction error representations distinguishes correct perception and misperception. SIGNIFICANCE STATEMENT Misperceiving spoken words is an everyday experience with outcomes that range from shared amusement to serious miscommunication. For hearing-impaired individuals, frequent misperception can lead to social withdrawal and isolation with severe consequences for well-being. In this work, we specify the neural mechanisms by which prior expectations - which are so often helpful for perception - can lead to misperception of degraded sensory signals. Most descriptive theories of illusory perception explain misperception as arising from a clear sensory representation of features or sounds that are in common between prior expectations and sensory input. Our work instead provides support for a complementary proposal; namely that misperception occurs when there is an insufficient sensory representations of the deviation between expectations and sensory signals. Copyright © 2018 the authors.

Isobaric vapor-liquid equilibria for binary systems α-phenylethylamine + toluene and α-phenylethylamine + cyclohexane at 100 kPa

NASA Astrophysics Data System (ADS)

Wu, Xiaoru; Gao, Yingyu; Ban, Chunlan; Huang, Qiang

2016-09-01

In this paper the results of the vapor-liquid equilibria study at 100 kPa are presented for two binary systems: α-phenylethylamine(1) + toluene (2) and (α-phenylethylamine(1) + cyclohexane(2)). The binary VLE data of the two systems were correlated by the Wilson, NRTL, and UNIQUAC models. For each binary system the deviations between the results of the correlations and the experimental data have been calculated. For the both binary systems the average relative deviations in temperature for the three models were lower than 0.99%. The average absolute deviations in vapour phase composition (mole fractions) and in temperature T were lower than 0.0271 and 1.93 K, respectively. Thermodynamic consistency has been tested for all vapor-liquid equilibrium data by the Herrington method. The values calculated by Wilson and NRTL equations satisfied the thermodynamics consistency test for the both two systems, while the values calculated by UNIQUAC equation didn't.

Allan deviation analysis of financial return series

NASA Astrophysics Data System (ADS)

Hernández-Pérez, R.

2012-05-01

We perform a scaling analysis for the return series of different financial assets applying the Allan deviation (ADEV), which is used in the time and frequency metrology to characterize quantitatively the stability of frequency standards since it has demonstrated to be a robust quantity to analyze fluctuations of non-stationary time series for different observation intervals. The data used are opening price daily series for assets from different markets during a time span of around ten years. We found that the ADEV results for the return series at short scales resemble those expected for an uncorrelated series, consistent with the efficient market hypothesis. On the other hand, the ADEV results for absolute return series for short scales (first one or two decades) decrease following approximately a scaling relation up to a point that is different for almost each asset, after which the ADEV deviates from scaling, which suggests that the presence of clustering, long-range dependence and non-stationarity signatures in the series drive the results for large observation intervals.

A spiking neural integrator model of the adaptive control of action by the medial prefrontal cortex.

PubMed

Bekolay, Trevor; Laubach, Mark; Eliasmith, Chris

2014-01-29

Subjects performing simple reaction-time tasks can improve reaction times by learning the expected timing of action-imperative stimuli and preparing movements in advance. Success or failure on the previous trial is often an important factor for determining whether a subject will attempt to time the stimulus or wait for it to occur before initiating action. The medial prefrontal cortex (mPFC) has been implicated in enabling the top-down control of action depending on the outcome of the previous trial. Analysis of spike activity from the rat mPFC suggests that neural integration is a key mechanism for adaptive control in precisely timed tasks. We show through simulation that a spiking neural network consisting of coupled neural integrators captures the neural dynamics of the experimentally recorded mPFC. Errors lead to deviations in the normal dynamics of the system, a process that could enable learning from past mistakes. We expand on this coupled integrator network to construct a spiking neural network that performs a reaction-time task by following either a cue-response or timing strategy, and show that it performs the task with similar reaction times as experimental subjects while maintaining the same spiking dynamics as the experimentally recorded mPFC.

[Waardenburg's syndrome].

PubMed

Gimñenez, F; Carbonell, R; Pérez, F; Lozano, I

1994-01-01

Reporting one case of this condition type-2 with heterochromia iridis and cochlear deafness. The AA. review the syndrome's components and it nomenclature as well. They discuss about the convenience of including this deviation in the chapter of "diseases of the embryonic neural crest". The specific place of the gene responsibly in the chromosome-2 and the possibilities of genetic counselling are considered.

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.

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.

Simulation and Analysis of Topographic Effect on Land Surface Albedo over Mountainous Areas

NASA Astrophysics Data System (ADS)

Hao, D.; Wen, J.; Xiao, Q.

2017-12-01

Land surface albedo is one of the significant geophysical variables affecting the Earth's climate and controlling the surface radiation budget. Topography leads to the formation of shadows and the redistribution of incident radiation, which complicates the modeling and estimation of the land surface albedo. Some studies show that neglecting the topography effect may lead to significant bias in estimating the land surface albedo for the sloping terrain. However, for the composite sloping terrain, the topographic effects on the albedo remain unclear. Accurately estimating the sub-topographic effect on the land surface albedo over the composite sloping terrain presents a challenge for remote sensing modeling and applications. In our study, we focus on the development of a simplified estimation method for land surface albedo including black-sky albedo (BSA) and white-sky albedo (WSA) of the composite sloping terrain at a kilometer scale based on the fine scale DEM (30m) and quantitatively investigate and understand the topographic effects on the albedo. The albedo is affected by various factors such as solar zenith angle (SZA), solar azimuth angle (SAA), shadows, terrain occlusion, and slope and aspect distribution of the micro-slopes. When SZA is 30°, the absolute and relative deviations between the BSA of flat terrain and that of rugged terrain reaches 0.12 and 50%, respectively. When the mean slope of the terrain is 30.63° and SZA=30°, the absolute deviation of BSA caused by SAA can reach 0.04. The maximal relative and relative deviation between the WSA of flat terrain and that of rugged terrain reaches 0.08 and 50%. These results demonstrate that the topographic effect has to be taken into account in the albedo estimation.

Gödel, Tarski, Turing and the Conundrum of Free Will

NASA Astrophysics Data System (ADS)

Nayakar, Chetan S. Mandayam; Srikanth, R.

2014-07-01

The problem of defining and locating free will (FW) in physics is studied. On the basis of logical paradoxes, we argue that FW has a metatheoretic character, like the concept of truth in Tarski's undefinability theorem. Free will exists relative to a base theory if there is freedom to deviate from the deterministic or indeterministic dynamics in the theory, with the deviations caused by parameters (representing will) in the meta-theory. By contrast, determinism and indeterminism do not require meta-theoretic considerations in their formalization, making FW a fundamentally new causal primitive. FW exists relative to the meta-theory if there is freedom for deviation, due to higher-order causes. Absolute free will, which corresponds to our intuitive introspective notion of free will, exists if this meta-theoretic hierarchy is infinite. We argue that this hierarchy corresponds to higher levels of uncomputability. In other words, at any finitely high order in the hierarchy, there are uncomputable deviations from the law at that order. Applied to the human condition, the hierarchy corresponds to deeper levels of the subconscious or unconscious mind. Possible ramifications of our model for physics, neuroscience and artificial intelligence (AI) are briefly considered.

Decomposition Analyses Applied to a Complex Ultradian Biorhythm: The Oscillating NADH Oxidase Activity of Plasma Membranes Having a Potential Time-Keeping (Clock) Function

PubMed Central

Foster, Ken; Anwar, Nasim; Pogue, Rhea; Morré, Dorothy M.; Keenan, T. W.; Morré, D. James

2003-01-01

Seasonal decomposition analyses were applied to the statistical evaluation of an oscillating activity for a plasma membrane NADH oxidase activity with a temperature compensated period of 24 min. The decomposition fits were used to validate the cyclic oscillatory pattern. Three measured values, average percentage error (MAPE), a measure of the periodic oscillation, mean average deviation (MAD), a measure of the absolute average deviations from the fitted values, and mean standard deviation (MSD), the measure of standard deviation from the fitted values plus R-squared and the Henriksson-Merton p value were used to evaluate accuracy. Decomposition was carried out by fitting a trend line to the data, then detrending the data if necessary, by subtracting the trend component. The data, with or without detrending, were then smoothed by subtracting a centered moving average of length equal to the period length determined by Fourier analysis. Finally, the time series were decomposed into cyclic and error components. The findings not only validate the periodic nature of the major oscillations but suggest, as well, that the minor intervening fluctuations also recur within each period with a reproducible pattern of recurrence. PMID:19330112

What is white?

PubMed Central

Bosten, J. M.; Beer, R. D.; MacLeod, D. I. A.

2015-01-01

To shed light on the perceptual basis of the color white, we measured settings of unique white in a dark surround. We find that settings reliably show more variability in an oblique (blue-yellow) direction in color space than along the cardinal axes of the cone-opponent mechanisms. This is against the idea that white perception arises at the null point of the cone-opponent mechanisms, but one alternative possibility is that it occurs through calibration to the visual environment. We found that the locus of maximum variability in settings lies close to the locus of natural daylights, suggesting that variability may result from uncertainty about the color of the illuminant. We tested this by manipulating uncertainty. First, we altered the extent to which the task was absolute (requiring knowledge of the illumination) or relative. We found no clear effect of this factor on the reduction in sensitivity in the blue-yellow direction. Second, we provided a white surround as a cue to the illumination or left the surround dark. Sensitivity was selectively worse in the blue-yellow direction when the surround was black than when it was white. Our results can be functionally related to the statistics of natural images, where a greater blue-yellow dispersion is characteristic of both reflectances (where anisotropy is weak) and illuminants (where it is very pronounced). Mechanistically, the results could suggest a neural signal responsive to deviations from the blue-yellow locus or an adaptively matched range of contrast response functions for signals that encode different directions in color space. PMID:26641948

Experimental and theoretical investigation of thermal conductivity of ethylene glycol containing functionalized single walled carbon nanotubes

NASA Astrophysics Data System (ADS)

Hemmat Esfe, Mohammad; Firouzi, Masoumeh; Afrand, Masoud

2018-01-01

In this paper, functionalized single walled carbon nanotubes (FSWCNTs) were suspended in Ethylene Glycol (EG) at different volume fractions. A KD2 pro thermal conductivity meter was used to measure the thermal conductivity in the temperature range from 30 to 50 °C. Nanofluids were prepared in solid volume fraction of 0.02, 0.05, 0.075, 0.1, 0.25, 0.5 and, 0.75%. Experimental results revealed that the thermal conductivity of the nanofluid is a non-linear function of temperature and SWCNTs volume fraction in the range of this investigation. Thermal conductivity increases with temperature and nanoparticles volume fraction as usual for this type of nanofluid. Maximum increment in thermal conductivity of the nanofluids was found to be about 45% at 0.75 vol fractions loading at 50 °C. Finally, a new correlation based on artificial neural network (ANN) approach has been proposed for SWCNT-EG thermal conductivity in terms of nanoparticles volume fraction and temperature using the experimental data. Used ANN approach has estimated the experimental values of thermal conductivity with the absolute average relative deviation lower than 0.9%, mean square error of 3.67 × 10-5 and regression coefficient of 0.9989. Comparison between the suggested techniques with various used correlation in the literatures established that the ANN approach is better to other presented methods and therefore can be proposed as a useful means for predicting of the nanofluids thermal conductivity.

Neural and Muscular Contributions to the Age-Related Reductions in Rapid Strength.

PubMed

Gerstner, Gena R; Thompson, Brennan J; Rosenberg, Joseph G; Sobolewski, Eric J; Scharville, Michael J; Ryan, Eric D

2017-07-01

The purposes of this study were to investigate the age-related differences in absolute and normalized plantarflexion rate of torque development (RTD) at early (0-50 ms) and late (100-200 ms) time intervals and to examine specific neural and muscular mechanisms contributing to these differences. Thirty-two young (20.0 ± 2.1 yr) and 20 older (69.5 ± 3.3 yr) recreationally active men performed rapid plantarflexion isometric muscle actions to examine absolute and normalized RTD and muscle activation using EMG at early and late time intervals. Ultrasonography was used to examine medial gastrocnemius muscle size, echo intensity (EI), and muscle architecture (fascicle length [FL] and pennation angle [PA]). The older men were weaker (23.9%, P < 0.001) and had lower later absolute and normalized RTD (P = 0.001-0.034) variables when compared with the young men. The older men also had higher EI (P < 0.001), smaller PA (P = 0.004), and lower later EMG amplitude values (P = 0.009-0.046). However, there were no differences in early RTD and EMG amplitude values, muscle size, or FL between groups (P = 0.097-0.914). Lower late RTD values were related to higher EI, smaller PA, and lower EMG amplitude values (r = -0.28-0.59, P = 0.001-0.044); however, late RTD values were no longer related to PA after normalizing to peak torque. Age-related alterations in muscle quality (EI), architecture, and muscle activation may influence rapid torque production at late time intervals (≥100 ms) from contraction onset. These findings highlight specific neuromuscular factors that influence the age-related reductions in RTD, which has been shown to significantly influence function and performance in older adults.

A Neural Mechanism for Time-Window Separation Resolves Ambiguity of Adaptive Coding

PubMed Central

Hildebrandt, K. Jannis; Ronacher, Bernhard; Hennig, R. Matthias; Benda, Jan

2015-01-01

The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task—namely, the reliable encoding of the pattern of an acoustic signal—but detrimental for another—the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons. PMID:25761097

A neural network controller for automated composite manufacturing

NASA Technical Reports Server (NTRS)

Lichtenwalner, Peter F.

1994-01-01

At McDonnell Douglas Aerospace (MDA), an artificial neural network based control system has been developed and implemented to control laser heating for the fiber placement composite manufacturing process. This neurocontroller learns an approximate inverse model of the process on-line to provide performance that improves with experience and exceeds that of conventional feedback control techniques. When untrained, the control system behaves as a proportional plus integral (PI) controller. However after learning from experience, the neural network feedforward control module provides control signals that greatly improve temperature tracking performance. Faster convergence to new temperature set points and reduced temperature deviation due to changing feed rate have been demonstrated on the machine. A Cerebellar Model Articulation Controller (CMAC) network is used for inverse modeling because of its rapid learning performance. This control system is implemented in an IBM compatible 386 PC with an A/D board interface to the machine.

The endoskeletal origin of the turtle carapace

PubMed Central

Hirasawa, Tatsuya; Nagashima, Hiroshi; Kuratani, Shigeru

2013-01-01

The turtle body plan, with its solid shell, deviates radically from those of other tetrapods. The dorsal part of the turtle shell, or the carapace, consists mainly of costal and neural bony plates, which are continuous with the underlying thoracic ribs and vertebrae, respectively. Because of their superficial position, the evolutionary origins of these costo-neural elements have long remained elusive. Here we show, through comparative morphological and embryological analyses, that the major part of the carapace is derived purely from endoskeletal ribs. We examine turtle embryos and find that the costal and neural plates develop not within the dermis, but within deeper connective tissue where the rib and intercostal muscle anlagen develop. We also examine the fossils of an outgroup of turtles to confirm that the structure equivalent to the turtle carapace developed independently of the true osteoderm. Our results highlight the hitherto unravelled evolutionary course of the turtle shell. PMID:23836118

Binary Associative Memories as a Benchmark for Spiking Neuromorphic Hardware

PubMed Central

Stöckel, Andreas; Jenzen, Christoph; Thies, Michael; Rückert, Ulrich

2017-01-01

Large-scale neuromorphic hardware platforms, specialized computer systems for energy efficient simulation of spiking neural networks, are being developed around the world, for example as part of the European Human Brain Project (HBP). Due to conceptual differences, a universal performance analysis of these systems in terms of runtime, accuracy and energy efficiency is non-trivial, yet indispensable for further hard- and software development. In this paper we describe a scalable benchmark based on a spiking neural network implementation of the binary neural associative memory. We treat neuromorphic hardware and software simulators as black-boxes and execute exactly the same network description across all devices. Experiments on the HBP platforms under varying configurations of the associative memory show that the presented method allows to test the quality of the neuron model implementation, and to explain significant deviations from the expected reference output. PMID:28878642

Evaluation of thermal conductivity of MgO-MWCNTs/EG hybrid nanofluids based on experimental data by selecting optimal artificial neural networks

NASA Astrophysics Data System (ADS)

Vafaei, Masoud; Afrand, Masoud; Sina, Nima; Kalbasi, Rasool; Sourani, Forough; Teimouri, Hamid

2017-01-01

In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.

Quicklook Constituent Abundance and Stretch Parameter Retrieval for the Juno Microwave Radiometer using Neural Networks

NASA Astrophysics Data System (ADS)

Bellotti, A.; Steffes, P. G.

2016-12-01

The Juno Microwave Radiometer (MWR) has six channels ranging from 1.36-50 cm and the ability to peer deep into the Jovian atmosphere. An Artifical Neural Network algorithm has been developed to rapidly perform inversion for the deep abundance of ammonia, the deep abundance of water vapor, and atmospheric "stretch" (a parameter that reflects the deviation from a wet adiabate in the higher atmosphere). This algorithm is "trained" by using simulated emissions at the six wavelengths computed using the Juno atmospheric microwave radiative transfer (JAMRT) model presented by Oyafuso et al. (This meeting). By exploiting the emission measurements conducted at six wavelengths and at various incident angles, the neural network can provide preliminary results to a useful precison in a computational method hundreds of times faster than conventional methods. This can quickly provide important insights into the variability and structure of the Jovian atmosphere.

A Reward-Based Behavioral Platform to Measure Neural Activity during Head-Fixed Behavior

PubMed Central

Micallef, Andrew H.; Takahashi, Naoya; Larkum, Matthew E.; Palmer, Lucy M.

2017-01-01

Understanding the neural computations that contribute to behavior requires recording from neurons while an animal is behaving. This is not an easy task as most subcellular recording techniques require absolute head stability. The Go/No-Go sensory task is a powerful decision-driven task that enables an animal to report a binary decision during head-fixation. Here we discuss how to set up an Ardunio and Python based platform system to control a Go/No-Go sensory behavior paradigm. Using an Arduino micro-controller and Python-based custom written program, a reward can be delivered to the animal depending on the decision reported. We discuss the various components required to build the behavioral apparatus that can control and report such a sensory stimulus paradigm. This system enables the end user to control the behavioral testing in real-time and therefore it provides a strong custom-made platform for probing the neural basis of behavior. PMID:28620282

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

PubMed Central

Yu, Ying; Wang, Yirui; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient. PMID:28246527

The central role of recognition in auditory perception: a neurobiological model.

PubMed

McLachlan, Neil; Wilson, Sarah

2010-01-01

The model presents neurobiologically plausible accounts of sound recognition (including absolute pitch), neural plasticity involved in pitch, loudness and location information integration, and streaming and auditory recall. It is proposed that a cortical mechanism for sound identification modulates the spectrotemporal response fields of inferior colliculus neurons and regulates the encoding of the echoic trace in the thalamus. Identification involves correlation of sequential spectral slices of the stimulus-driven neural activity with stored representations in association with multimodal memories, verbal lexicons, and contextual information. Identities are then consolidated in auditory short-term memory and bound with attribute information (usually pitch, loudness, and direction) that has been integrated according to the identities' spectral properties. Attention to, or recall of, a particular identity will excite a particular sequence in the identification hierarchies and so lead to modulation of thalamus and inferior colliculus neural spectrotemporal response fields. This operates as an adaptive filter for identities, or their attributes, and explains many puzzling human auditory behaviors, such as the cocktail party effect, selective attention, and continuity illusions.

Flexible categorization of relative stimulus strength by the optic tectum

PubMed Central

Mysore, Shreesh P.; Knudsen, Eric I.

2011-01-01

Categorization is the process by which the brain segregates continuously variable stimuli into discrete groups. We report that patterns of neural population activity in the owl optic tectum (OT) categorize stimuli based on their relative strengths into “strongest” versus “other”. The category boundary shifts adaptively to track changes in the absolute strength of the strongest stimulus. This population-wide categorization is mediated by the responses of a small subset of neurons. Our data constitute the first direct demonstration of an explicit categorization of stimuli by a neural network based on relative stimulus strength or salience. The finding of categorization by the population code relaxes constraints on the properties of downstream decoders that might read out the location of the strongest stimulus. These results indicate that the ensemble neural code in the OT could mediate bottom-up stimulus selection for gaze and attention, a form of stimulus categorization in which the category boundary often shifts within hundreds of milliseconds. PMID:21613487

Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

NASA Astrophysics Data System (ADS)

Ghaffari Razin, Mir Reza; Voosoghi, Behzad

2016-08-01

Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

PubMed

Yu, Ying; Wang, Yirui; Gao, Shangce; Tang, Zheng

2017-01-01

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

Modeling and forecasting of KLCI weekly return using WT-ANN integrated model

NASA Astrophysics Data System (ADS)

Liew, Wei-Thong; Liong, Choong-Yeun; Hussain, Saiful Izzuan; Isa, Zaidi

2013-04-01

The forecasting of weekly return is one of the most challenging tasks in investment since the time series are volatile and non-stationary. In this study, an integrated model of wavelet transform and artificial neural network, WT-ANN is studied for modeling and forecasting of KLCI weekly return. First, the WT is applied to decompose the weekly return time series in order to eliminate noise. Then, a mathematical model of the time series is constructed using the ANN. The performance of the suggested model will be evaluated by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE). The result shows that the WT-ANN model can be considered as a feasible and powerful model for time series modeling and prediction.

Results of the first North American comparison of absolute gravimeters, NACAG-2010

USGS Publications Warehouse

Schmerge, David; Francis, Olvier; Henton, J.; Ingles, D.; Jones, D.; Kennedy, Jeffrey R.; Krauterbluth, K.; Liard, J.; Newell, D.; Sands, R.; Schiel, J.; Silliker, J.; van Westrum, D.

2012-01-01

The first North American Comparison of absolute gravimeters (NACAG-2010) was hosted by the National Oceanic and Atmospheric Administration at its newly renovated Table Mountain Geophysical Observatory (TMGO) north of Boulder, Colorado, in October 2010. NACAG-2010 and the renovation of TMGO are part of NGS’s GRAV-D project (Gravity for the Redefinition of the American Vertical Datum). Nine absolute gravimeters from three countries participated in the comparison. Before the comparison, the gravimeter operators agreed to a protocol describing the strategy to measure, calculate, and present the results. Nine sites were used to measure the free-fall acceleration of g. Each gravimeter measured the value of g at a subset of three of the sites, for a total set of 27 g-values for the comparison. The absolute gravimeters agree with one another with a standard deviation of 1.6 µGal (1 Gal = 1 cm s-2). The minimum and maximum offsets are -2.8 and 2.7 µGal. This is an excellent agreement and can be attributed to multiple factors, including gravimeters that were in good working order, good operators, a quiet observatory, and a short duration time for the experiment. These results can be used to standardize gravity surveys internationally.

Effect of helicity on the correlation time of large scales in turbulent flows

NASA Astrophysics Data System (ADS)

Cameron, Alexandre; Alexakis, Alexandros; Brachet, Marc-Étienne

2017-11-01

Solutions of the forced Navier-Stokes equation have been conjectured to thermalize at scales larger than the forcing scale, similar to an absolute equilibrium obtained for the spectrally truncated Euler equation. Using direct numeric simulations of Taylor-Green flows and general-periodic helical flows, we present results on the probability density function, energy spectrum, autocorrelation function, and correlation time that compare the two systems. In the case of highly helical flows, we derive an analytic expression describing the correlation time for the absolute equilibrium of helical flows that is different from the E-1 /2k-1 scaling law of weakly helical flows. This model predicts a new helicity-based scaling law for the correlation time as τ (k ) ˜H-1 /2k-1 /2 . This scaling law is verified in simulations of the truncated Euler equation. In simulations of the Navier-Stokes equations the large-scale modes of forced Taylor-Green symmetric flows (with zero total helicity and large separation of scales) follow the same properties as absolute equilibrium including a τ (k ) ˜E-1 /2k-1 scaling for the correlation time. General-periodic helical flows also show similarities between the two systems; however, the largest scales of the forced flows deviate from the absolute equilibrium solutions.

40 CFR 1065.1005 - Symbols, abbreviations, acronyms, and units of measure.

Code of Federal Regulations, 2012 CFR

2012-07-01

... least squares regression β ratio of diameters meter per meter m/m 1 β atomic oxygen to carbon ratio mole... consumption gram per kilowatt hour g/(kW·hr) g·3.6−1·106·m−2·kg·s2 F F-test statistic f frequency hertz Hz s−1... standard deviation S Sutherland constant kelvin K K SEE standard estimate of error T absolute temperature...

40 CFR 1065.1005 - Symbols, abbreviations, acronyms, and units of measure.

Code of Federal Regulations, 2013 CFR

2013-07-01

... least squares regression β ratio of diameters meter per meter m/m 1 β atomic oxygen to carbon ratio mole... consumption gram per kilowatt hour g/(kW·hr) g·3.6−1·106·m−2·kg·s2 F F-test statistic f frequency hertz Hz s−1... standard deviation S Sutherland constant kelvin K K SEE standard estimate of error T absolute temperature...

High School Students' Accuracy in Estimating the Cost of College: A Proposed Methodological Approach and Differences among Racial/Ethnic Groups and College Financial-Related Factors

ERIC Educational Resources Information Center

Nienhusser, H. Kenny; Oshio, Toko

2017-01-01

High school students' accuracy in estimating the cost of college (AECC) was examined by utilizing a new methodological approach, the absolute-deviation-continuous construct. This study used the High School Longitudinal Study of 2009 (HSLS:09) data and examined 10,530 11th grade students in order to measure their AECC for 4-year public and private…

[Noninvasive total hemoglobin monitoring based on multiwave spectrophotometry in obstetrics and gynecology].

PubMed

Pyregov, A V; Ovechkin, A Iu; Petrov, S V

2012-01-01

Results of prospective randomized comparative research of 2 total hemoglobin estimation methods are presented. There were laboratory tests and continuous noninvasive technique with multiwave spectrophotometry on the Masimo Rainbow SET. Research was carried out in two stages. At the 1st stage (gynecology)--67 patients were included and in second stage (obstetrics)--44 patients during and after Cesarean section. The standard deviation of noninvasive total hemoglobin estimation from absolute values (invasive) was 7.2 and 4.1%, an standard deviation in a sample--5.2 and 2.7 % in gynecologic operations and surgical delivery respectively, that confirms lack of reliable indicators differences. The method of continuous noninvasive total hemoglobin estimation with multiwave spectrophotometry on the Masimo Rainbow SET technology can be recommended for use in obstetrics and gynecology.

Measuring the rate of change of voice fundamental frequency in fluent speech during mental depression.

PubMed

Nilsonne, A; Sundberg, J; Ternström, S; Askenfelt, A

1988-02-01

A method of measuring the rate of change of fundamental frequency has been developed in an effort to find acoustic voice parameters that could be useful in psychiatric research. A minicomputer program was used to extract seven parameters from the fundamental frequency contour of tape-recorded speech samples: (1) the average rate of change of the fundamental frequency and (2) its standard deviation, (3) the absolute rate of fundamental frequency change, (4) the total reading time, (5) the percent pause time of the total reading time, (6) the mean, and (7) the standard deviation of the fundamental frequency distribution. The method is demonstrated on (a) a material consisting of synthetic speech and (b) voice recordings of depressed patients who were examined during depression and after improvement.

Quantification of intensity variations in functional MR images using rotated principal components

NASA Astrophysics Data System (ADS)

Backfrieder, W.; Baumgartner, R.; Sámal, M.; Moser, E.; Bergmann, H.

1996-08-01

In functional MRI (fMRI), the changes in cerebral haemodynamics related to stimulated neural brain activity are measured using standard clinical MR equipment. Small intensity variations in fMRI data have to be detected and distinguished from non-neural effects by careful image analysis. Based on multivariate statistics we describe an algorithm involving oblique rotation of the most significant principal components for an estimation of the temporal and spatial distribution of the stimulated neural activity over the whole image matrix. This algorithm takes advantage of strong local signal variations. A mathematical phantom was designed to generate simulated data for the evaluation of the method. In simulation experiments, the potential of the method to quantify small intensity changes, especially when processing data sets containing multiple sources of signal variations, was demonstrated. In vivo fMRI data collected in both visual and motor stimulation experiments were analysed, showing a proper location of the activated cortical regions within well known neural centres and an accurate extraction of the activation time profile. The suggested method yields accurate absolute quantification of in vivo brain activity without the need of extensive prior knowledge and user interaction.

Aircraft noise-induced awakenings are more reasonably predicted from relative than from absolute sound exposure levels.

PubMed

Fidell, Sanford; Tabachnick, Barbara; Mestre, Vincent; Fidell, Linda

2013-11-01

Assessment of aircraft noise-induced sleep disturbance is problematic for several reasons. Current assessment methods are based on sparse evidence and limited understandings; predictions of awakening prevalence rates based on indoor absolute sound exposure levels (SELs) fail to account for appreciable amounts of variance in dosage-response relationships and are not freely generalizable from airport to airport; and predicted awakening rates do not differ significantly from zero over a wide range of SELs. Even in conjunction with additional predictors, such as time of night and assumed individual differences in "sensitivity to awakening," nominally SEL-based predictions of awakening rates remain of limited utility and are easily misapplied and misinterpreted. Probabilities of awakening are more closely related to SELs scaled in units of standard deviates of local distributions of aircraft SELs, than to absolute sound levels. Self-selection of residential populations for tolerance of nighttime noise and habituation to airport noise environments offer more parsimonious and useful explanations for differences in awakening rates at disparate airports than assumed individual differences in sensitivity to awakening.

Auditory Attentional Capture during Serial Recall: Violations at Encoding of an Algorithm-Based Neural Model?

ERIC Educational Resources Information Center

Hughes, Robert W.; Vachon, Francois; Jones, Dylan M.

2005-01-01

A novel attentional capture effect is reported in which visual-verbal serial recall was disrupted if a single deviation in the interstimulus interval occurred within otherwise regularly presented task-irrelevant spoken items. The degree of disruption was the same whether the temporal deviant was embedded in a sequence made up of a repeating item…

Aftereffects for Face Attributes with Different Natural Variability: Adapter Position Effects and Neural Models

ERIC Educational Resources Information Center

Robbins, Rachel; McKone, Elinor; Edwards, Mark

2007-01-01

Adaptation to distorted faces is commonly interpreted as a shift in the face-space norm for the adapted attribute. This article shows that the size of the aftereffect varies as a function of the distortion level of the adapter. The pattern differed for different facial attributes, increasing with distortion level for symmetric deviations of eye…

Independent coding of absolute duration and distance magnitudes in the prefrontal cortex

PubMed Central

Marcos, Encarni; Tsujimoto, Satoshi

2016-01-01

The estimation of space and time can interfere with each other, and neuroimaging studies have shown overlapping activation in the parietal and prefrontal cortical areas. We used duration and distance discrimination tasks to determine whether space and time share resources in prefrontal cortex (PF) neurons. Monkeys were required to report which of two stimuli, a red circle or blue square, presented sequentially, were longer and farther, respectively, in the duration and distance tasks. In a previous study, we showed that relative duration and distance are coded by different populations of neurons and that the only common representation is related to goal coding. Here, we examined the coding of absolute duration and distance. Our results support a model of independent coding of absolute duration and distance metrics by demonstrating that not only relative magnitude but also absolute magnitude are independently coded in the PF. NEW & NOTEWORTHY Human behavioral studies have shown that spatial and duration judgments can interfere with each other. We investigated the neural representation of such magnitudes in the prefrontal cortex. We found that the two magnitudes are independently coded by prefrontal neurons. We suggest that the interference among magnitude judgments might depend on the goal rather than the perceptual resource sharing. PMID:27760814

Sustained neural activity to gaze and emotion perception in dynamic social scenes

PubMed Central

Ulloa, José Luis; Puce, Aina; Hugueville, Laurent; George, Nathalie

2014-01-01

To understand social interactions, we must decode dynamic social cues from seen faces. Here, we used magnetoencephalography (MEG) to study the neural responses underlying the perception of emotional expressions and gaze direction changes as depicted in an interaction between two agents. Subjects viewed displays of paired faces that first established a social scenario of gazing at each other (mutual attention) or gazing laterally together (deviated group attention) and then dynamically displayed either an angry or happy facial expression. The initial gaze change elicited a significantly larger M170 under the deviated than the mutual attention scenario. At around 400 ms after the dynamic emotion onset, responses at posterior MEG sensors differentiated between emotions, and between 1000 and 2200 ms, left posterior sensors were additionally modulated by social scenario. Moreover, activity on right anterior sensors showed both an early and prolonged interaction between emotion and social scenario. These results suggest that activity in right anterior sensors reflects an early integration of emotion and social attention, while posterior activity first differentiated between emotions only, supporting the view of a dual route for emotion processing. Altogether, our data demonstrate that both transient and sustained neurophysiological responses underlie social processing when observing interactions between others. PMID:23202662

Application of gene expression programming and neural networks to predict adverse events of radical hysterectomy in cervical cancer patients.

PubMed

Kusy, Maciej; Obrzut, Bogdan; Kluska, Jacek

2013-12-01

The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

HYBRID NEURAL NETWORK AND SUPPORT VECTOR MACHINE METHOD FOR OPTIMIZATION

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2005-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

Hybrid Neural Network and Support Vector Machine Method for Optimization

NASA Technical Reports Server (NTRS)

Rai, Man Mohan (Inventor)

2007-01-01

System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.

WE-AB-204-06: Pseudo-CT Generation Using Undersampled, Single-Acquisition UTE-MDixon and Direct-Mapping Artificial Neural Networks for MR-Based Attenuation Correction and Radiation Therapy Planning

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

Su, K; Kuo, J; Department of Radiology, University Hospitals Case Medical Center, Case Western Reserve University, Cleveland, Ohio

Purpose: Emerging technologies such as dedicated PET/MRI and MR-therapy systems require robust and clinically practical methods for determining photon attenuation. Herein, we propose using novel MR acquisition methods and processing for the generation of pseudo-CTs. Methods: A single acquisition, 190-second UTE-mDixon sequence with 25% (angular) sampling density and 3D radial readout was performed on nine volunteers. Three water-filled tubes were placed in the FOV for trajectory-delay correction. The MR data were reconstructed to generate three primitive images acquired at TEs of 0.1, 1.5 and 2.8 ms. In addition, three derived MR images were generated, i.e. two-point Dixon water/fat separation andmore » R2* (1/T2*) map. Furthermore, two spatial features, i.e. local binary pattern (S-1) and relative spatial coordinates (S-2), were incorporated. A direct-mapping operator was generated using Artificial Neural Networks (ANNs) for transforming the MR features to a pseudo-CT. CT images served as the training data and, using a leave-one-out method, for performance evaluation using mean prediction deviation (MPD), mean absolute prediction deviation (MAPD), and correlation coefficient (R). Results: The errors between measured CT and pseudo-CT declined dramatically when the spatial features, i.e. S-1 and S-2, were included. The MPD, MAPD, and R were, respectively, 5±57 HU, 141±41 HU, and 0.815±0.066 for results generated by the ANN trained without the spatial features and were 32±26 HU, 115±18 HU, and 0.869±0.035 with the spatial features. The estimation errors of the pseudo-CT were smaller when both the S-1 and S-2 were used together than when either the S-1 or the S-2 was used. Pseudo-CT generation (256×256×256 voxels) by ANN took < 0.5 s using a computer having an Intel i7 3.4GHz CPU and 16 GB RAM. Conclusion: The proposed direct-mapping ANN approach is a technically accurate, clinically practical method for pseudo-CT generation and can potentially help improve the accuracy of MR-AC and MR-RTP applications. Please note that the project was completed with partial funding from the Ohio Department of Development grant TECH 11-063 and a sponsored research agreement with Philips Healthcare that is managed by Case Western Reserve University. As noted in the affiliations, some of the authors are Philips employees.« less

Assessing microscope image focus quality with deep learning.

PubMed

Yang, Samuel J; Berndl, Marc; Michael Ando, D; Barch, Mariya; Narayanaswamy, Arunachalam; Christiansen, Eric; Hoyer, Stephan; Roat, Chris; Hung, Jane; Rueden, Curtis T; Shankar, Asim; Finkbeiner, Steven; Nelson, Philip

2018-03-15

Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Absolute extinction and the influence of environment - Dark cloud sight lines toward VCT 10, 30, and Walker 67

NASA Technical Reports Server (NTRS)

Cardelli, Jason A.; Clayton, Geoffrey C.

1991-01-01

The range of validity of the average absolute extinction law (AAEL) proposed by Cardelli et al. (1988 and 1989) is investigated, combining published visible and NIR data with IUE UV observations for three lines of sight through dense dark cloud environments with high values of total-to-selective extinction. The characteristics of the data sets and the reduction and parameterization methods applied are described in detail, and the results are presented in extensive tables and graphs. Good agreement with the AAEL is demonstrated for wavelengths from 3.4 microns to 250 nm, but significant deviations are found at shorter wavelengths (where previous studies of lines of sight through bright nebulosity found good agreement with the AAEL). These differences are attributed to the effects of coatings on small-bump and FUV grains.

Dust-Corrected Star Formation Rates in Galaxies with Outer Rings

NASA Astrophysics Data System (ADS)

Kostiuk, I.; Silchenko, O.

2018-03-01

The star formation rates SFR, as well as the SFR surface densities ΣSFR and absolute stellar magnitudes MAB, are determined and corrected for interinsic dust absorption for 34 disk galaxies of early morphological types with an outer ring structure and ultraviolet emission from the ring. These characteristic are determined for the outer ring structures and for the galaxies as a whole. Data from the space telescopes GALEX (in the NUV and FUV ultraviolet ranges) and WISE (in the W4 22 μm infrared band) are used. The average relative deviation in the corrected SFR and ΣSFR derived from the NUV and FUV bands is only 19.0%, so their averaged values are used for statistical consideration. The relations between the dust-corrected SFR characteristics, UV colours, the galaxy morphological type, absolute magnitude are illustrated.

Photon scattering cross sections of H2 and He measured with synchrotron radiation

NASA Technical Reports Server (NTRS)

Ice, G. E.

1977-01-01

Total (elastic + inelastic) differential photon scattering cross sections have been measured for H2 gas and He, using an X-ray beam. Absolute measured cross sections agree with theory within the probable errors. Relative cross sections (normalized to theory at large S) agree to better than one percent with theoretical values calculated from wave functions that include the effect of electron-electron Coulomb correlation, but the data deviate significantly from theoretical independent-particle (e.g., Hartree-Fock) results. The ratios of measured absolute He cross sections to those of H2, at any given S, also agree to better than one percent with theoretical He-to-H2 cross-section ratios computed from correlated wave functions. It appears that photon scattering constitutes a very promising tool for probing electron correlation in light atoms and molecules.

The use of fractional orders in the determination of birefringence of highly dispersive materials by the channelled spectrum method

NASA Astrophysics Data System (ADS)

Nagarajan, K.; Shashidharan Nair, C. K.

2007-07-01

The channelled spectrum employing polarized light interference is a very convenient method for the study of dispersion of birefringence. However, while using this method, the absolute order of the polarized light interference fringes cannot be determined easily. Approximate methods are therefore used to estimate the order. One of the approximations is that the dispersion of birefringence across neighbouring integer order fringes is negligible. In this paper, we show how this approximation can cause errors. A modification is reported whereby the error in the determination of absolute fringe order can be reduced using fractional orders instead of integer orders. The theoretical background for this method supported with computer simulation is presented. An experimental arrangement implementing these modifications is described. This method uses a Constant Deviation Spectrometer (CDS) and a Soleil Babinet Compensator (SBC).

Comparison of the temperature accuracy between smart phone based and high-end thermal cameras using a temperature gradient phantom

NASA Astrophysics Data System (ADS)

Klaessens, John H.; van der Veen, Albert; Verdaasdonk, Rudolf M.

2017-03-01

Recently, low cost smart phone based thermal cameras are being considered to be used in a clinical setting for monitoring physiological temperature responses such as: body temperature change, local inflammations, perfusion changes or (burn) wound healing. These thermal cameras contain uncooled micro-bolometers with an internal calibration check and have a temperature resolution of 0.1 degree. For clinical applications a fast quality measurement before use is required (absolute temperature check) and quality control (stability, repeatability, absolute temperature, absolute temperature differences) should be performed regularly. Therefore, a calibrated temperature phantom has been developed based on thermistor heating on both ends of a black coated metal strip to create a controllable temperature gradient from room temperature 26 °C up to 100 °C. The absolute temperatures on the strip are determined with software controlled 5 PT-1000 sensors using lookup tables. In this study 3 FLIR-ONE cameras and one high end camera were checked with this temperature phantom. The results show a relative good agreement between both low-cost and high-end camera's and the phantom temperature gradient, with temperature differences of 1 degree up to 6 degrees between the camera's and the phantom. The measurements were repeated as to absolute temperature and temperature stability over the sensor area. Both low-cost and high-end thermal cameras measured relative temperature changes with high accuracy and absolute temperatures with constant deviations. Low-cost smart phone based thermal cameras can be a good alternative to high-end thermal cameras for routine clinical measurements, appropriate to the research question, providing regular calibration checks for quality control.

A Markovian event-based framework for stochastic spiking neural networks.

PubMed

Touboul, Jonathan D; Faugeras, Olivier D

2011-11-01

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

Application of Multilayer Feedforward Neural Networks to Precipitation Cell-Top Altitude Estimation

NASA Technical Reports Server (NTRS)

Spina, Michelle S.; Schwartz, Michael J.; Staelin, David H.; Gasiewski, Albin J.

1998-01-01

The use of passive 118-GHz O2 observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feed forward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2 4 km horizontal spatial resolution by using the Millimeter-wave Temperature Sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the COoperative Huntsville Meteorological EXperiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993.

The Resource Consumption Principle: Attention and Memory in Volumes of Neural Tissue

NASA Astrophysics Data System (ADS)

Montague, P. Read

1996-04-01

In the cerebral cortex, the small volume of the extracellular space in relation to the volume enclosed by synapses suggests an important functional role for this relationship. It is well known that there are atoms and molecules in the extracellular space that are absolutely necessary for synapses to function (e.g., calcium). I propose here the hypothesis that the rapid shift of these atoms and molecules from extracellular to intrasynaptic compartments represents the consumption of a shared, limited resource available to local volumes of neural tissue. Such consumption results in a dramatic competition among synapses for resources necessary for their function. In this paper, I explore a theory in which this resource consumption plays a critical role in the way local volumes of neural tissue operate. On short time scales, this principle of resource consumption permits a tissue volume to choose those synapses that function in a particular context and thereby helps to integrate the many neural signals that impinge on a tissue volume at any given moment. On longer time scales, the same principle aids in the stable storage and recall of information. The theory provides one framework for understanding how cerebral cortical tissue volumes integrate, attend to, store, and recall information. In this account, the capacity of neural tissue to attend to stimuli is intimately tied to the way tissue volumes are organized at fine spatial scales.

The statistical properties and possible causes of polar motion prediction errors

NASA Astrophysics Data System (ADS)

Kosek, Wieslaw; Kalarus, Maciej; Wnek, Agnieszka; Zbylut-Gorska, Maria

2015-08-01

The pole coordinate data predictions from different prediction contributors of the Earth Orientation Parameters Combination of Prediction Pilot Project (EOPCPPP) were studied to determine the statistical properties of polar motion forecasts by looking at the time series of differences between them and the future IERS pole coordinates data. The mean absolute errors, standard deviations as well as the skewness and kurtosis of these differences were computed together with their error bars as a function of prediction length. The ensemble predictions show a little smaller mean absolute errors or standard deviations however their skewness and kurtosis values are similar as the for predictions from different contributors. The skewness and kurtosis enable to check whether these prediction differences satisfy normal distribution. The kurtosis values diminish with the prediction length which means that the probability distribution of these prediction differences is becoming more platykurtic than letptokurtic. Non zero skewness values result from oscillating character of these differences for particular prediction lengths which can be due to the irregular change of the annual oscillation phase in the joint fluid (atmospheric + ocean + land hydrology) excitation functions. The variations of the annual oscillation phase computed by the combination of the Fourier transform band pass filter and the Hilbert transform from pole coordinates data as well as from pole coordinates model data obtained from fluid excitations are in a good agreement.

Accuracy of Lagrange-sinc functions as a basis set for electronic structure calculations of atoms and molecules

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

Choi, Sunghwan; Hong, Kwangwoo; Kim, Jaewook

2015-03-07

We developed a self-consistent field program based on Kohn-Sham density functional theory using Lagrange-sinc functions as a basis set and examined its numerical accuracy for atoms and molecules through comparison with the results of Gaussian basis sets. The result of the Kohn-Sham inversion formula from the Lagrange-sinc basis set manifests that the pseudopotential method is essential for cost-effective calculations. The Lagrange-sinc basis set shows faster convergence of the kinetic and correlation energies of benzene as its size increases than the finite difference method does, though both share the same uniform grid. Using a scaling factor smaller than or equal tomore » 0.226 bohr and pseudopotentials with nonlinear core correction, its accuracy for the atomization energies of the G2-1 set is comparable to all-electron complete basis set limits (mean absolute deviation ≤1 kcal/mol). The same basis set also shows small mean absolute deviations in the ionization energies, electron affinities, and static polarizabilities of atoms in the G2-1 set. In particular, the Lagrange-sinc basis set shows high accuracy with rapid convergence in describing density or orbital changes by an external electric field. Moreover, the Lagrange-sinc basis set can readily improve its accuracy toward a complete basis set limit by simply decreasing the scaling factor regardless of systems.« less

[Prediction of schistosomiasis infection rates of population based on ARIMA-NARNN model].

PubMed

Ke-Wei, Wang; Yu, Wu; Jin-Ping, Li; Yu-Yu, Jiang

2016-07-12

To explore the effect of the autoregressive integrated moving average model-nonlinear auto-regressive neural network (ARIMA-NARNN) model on predicting schistosomiasis infection rates of population. The ARIMA model, NARNN model and ARIMA-NARNN model were established based on monthly schistosomiasis infection rates from January 2005 to February 2015 in Jiangsu Province, China. The fitting and prediction performances of the three models were compared. Compared to the ARIMA model and NARNN model, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the ARIMA-NARNN model were the least with the values of 0.011 1, 0.090 0 and 0.282 4, respectively. The ARIMA-NARNN model could effectively fit and predict schistosomiasis infection rates of population, which might have a great application value for the prevention and control of schistosomiasis.

Trait-level temporal lobe hypoactivation to social exclusion in unaffected siblings of children and adolescents with autism spectrum disorders

PubMed Central

Bolling, Danielle Z.; Pelphrey, Kevin A.; Vander Wyk, Brent C.

2015-01-01

Social exclusion elicits powerful feelings of negative affect associated with rejection. Additionally, experiencing social exclusion reliably recruits neural circuitry associated with emotion processing. Recent work has demonstrated abnormal neural responses to social exclusion in children and adolescents with autism spectrum disorders (ASD). However, it remains unknown to what extent these abnormalities are due to atypical social experiences versus genetic predispositions to atypical neural processing. To address this question, the current study investigated brain responses to social exclusion compared to a baseline condition of fair play in unaffected siblings of youth with ASD using functional magnetic resonance imaging. We identified common deviations between unaffected siblings and ASD probands that might represent trait-level abnormalities in processing social exclusion versus fair play, specifically in the right anterior temporoparietal junction extending into posterior superior temporal sulcus. Thus, hypoactivation to social exclusion versus fair play in this region may represent a shared genetic vulnerability to developing autism. In addition, we present evidence supporting the idea that one’s status as an unaffected sibling moderates the relationship between IQ and neural activation to social exclusion versus fair play in anterior cingulate cortex. These results are discussed in the context of previous literature on neural endophenotypes of autism. PMID:26011751

Multi-focus image fusion based on area-based standard deviation in dual tree contourlet transform domain

NASA Astrophysics Data System (ADS)

Dong, Min; Dong, Chenghui; Guo, Miao; Wang, Zhe; Mu, Xiaomin

2018-04-01

Multiresolution-based methods, such as wavelet and Contourlet are usually used to image fusion. This work presents a new image fusion frame-work by utilizing area-based standard deviation in dual tree Contourlet trans-form domain. Firstly, the pre-registered source images are decomposed with dual tree Contourlet transform; low-pass and high-pass coefficients are obtained. Then, the low-pass bands are fused with weighted average based on area standard deviation rather than the simple "averaging" rule. While the high-pass bands are merged with the "max-absolute' fusion rule. Finally, the modified low-pass and high-pass coefficients are used to reconstruct the final fused image. The major advantage of the proposed fusion method over conventional fusion is the approximately shift invariance and multidirectional selectivity of dual tree Contourlet transform. The proposed method is compared with wavelet- , Contourletbased methods and other the state-of-the art methods on common used multi focus images. Experiments demonstrate that the proposed fusion framework is feasible and effective, and it performs better in both subjective and objective evaluation.

Virtual sensors for on-line wheel wear and part roughness measurement in the grinding process.

PubMed

Arriandiaga, Ander; Portillo, Eva; Sánchez, Jose A; Cabanes, Itziar; Pombo, Iñigo

2014-05-19

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations.

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China.

PubMed

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS.

A QSPR model for prediction of diffusion coefficient of non-electrolyte organic compounds in air at ambient condition.

PubMed

Mirkhani, Seyyed Alireza; Gharagheizi, Farhad; Sattari, Mehdi

2012-03-01

Evaluation of diffusion coefficients of pure compounds in air is of great interest for many diverse industrial and air quality control applications. In this communication, a QSPR method is applied to predict the molecular diffusivity of chemical compounds in air at 298.15K and atmospheric pressure. Four thousand five hundred and seventy nine organic compounds from broad spectrum of chemical families have been investigated to propose a comprehensive and predictive model. The final model is derived by Genetic Function Approximation (GFA) and contains five descriptors. Using this dedicated model, we obtain satisfactory results quantified by the following statistical results: Squared Correlation Coefficient=0.9723, Standard Deviation Error=0.003 and Average Absolute Relative Deviation=0.3% for the predicted properties from existing experimental values. Copyright © 2011 Elsevier Ltd. All rights reserved.

Age-specific absolute and relative organ weight distributions for B6C3F1 mice.

PubMed

Marino, Dale J

2012-01-01

The B6C3F1 mouse is the standard mouse strain used in toxicology studies conducted by the National Cancer Institute (NCI) and the National Toxicology Program (NTP). While numerous reports have been published on growth, survival, and tumor incidence, no overall compilation of organ weight data is available. Importantly, organ weight change is an endpoint used by regulatory agencies to develop toxicity reference values (TRVs) for use in human health risk assessments. Furthermore, physiologically based pharmacokinetic (PBPK) models, which utilize relative organ weights, are increasingly being used to develop TRVs. Therefore, all available absolute and relative organ weight data for untreated control B6C3F1 mice were collected from NCI/NTP studies in order to develop age-specific distributions. Results show that organ weights were collected more frequently in NCI/NTP studies at 2-wk (60 studies), 3-mo (147 studies), and 15-mo (40 studies) intervals than at other intervals, and more frequently from feeding and inhalation than drinking water studies. Liver, right kidney, lung, heart, thymus, and brain weights were most frequently collected. From the collected data, the mean and standard deviation for absolute and relative organ weights were calculated. Results show age-related increases in absolute liver, right kidney, lung, and heart weights and relatively stable brain and right testis weights. The results suggest a general variability trend in absolute organ weights of brain < right testis < right kidney < heart < liver < lung < spleen < thymus. This report describes the results of this effort.

Optical track width measurements below 100 nm using artificial neural networks

NASA Astrophysics Data System (ADS)

Smith, R. J.; See, C. W.; Somekh, M. G.; Yacoot, A.; Choi, E.

2005-12-01

This paper discusses the feasibility of using artificial neural networks (ANNs), together with a high precision scanning optical profiler, to measure very fine track widths that are considerably below the conventional diffraction limit of a conventional optical microscope. The ANN is trained using optical profiles obtained from tracks of known widths, the network is then assessed by applying it to test profiles. The optical profiler is an ultra-stable common path scanning interferometer, which provides extremely precise surface measurements. Preliminary results, obtained with a 0.3 NA objective lens and a laser wavelength of 633 nm, show that the system is capable of measuring a 50 nm track width, with a standard deviation less than 4 nm.

Grain-Boundary Resistance in Copper Interconnects: From an Atomistic Model to a Neural Network

NASA Astrophysics Data System (ADS)

Valencia, Daniel; Wilson, Evan; Jiang, Zhengping; Valencia-Zapata, Gustavo A.; Wang, Kuang-Chung; Klimeck, Gerhard; Povolotskyi, Michael

2018-04-01

Orientation effects on the specific resistance of copper grain boundaries are studied systematically with two different atomistic tight-binding methods. A methodology is developed to model the specific resistance of grain boundaries in the ballistic limit using the embedded atom model, tight- binding methods, and nonequilibrium Green's functions. The methodology is validated against first-principles calculations for thin films with a single coincident grain boundary, with 6.4% deviation in the specific resistance. A statistical ensemble of 600 large, random structures with grains is studied. For structures with three grains, it is found that the distribution of specific resistances is close to normal. Finally, a compact model for grain-boundary-specific resistance is constructed based on a neural network.

Reservoir characterization using core, well log, and seismic data and intelligent software

NASA Astrophysics Data System (ADS)

Soto Becerra, Rodolfo

We have developed intelligent software, Oilfield Intelligence (OI), as an engineering tool to improve the characterization of oil and gas reservoirs. OI integrates neural networks and multivariate statistical analysis. It is composed of five main subsystems: data input, preprocessing, architecture design, graphics design, and inference engine modules. More than 1,200 lines of programming code as M-files using the language MATLAB been written. The degree of success of many oil and gas drilling, completion, and production activities depends upon the accuracy of the models used in a reservoir description. Neural networks have been applied for identification of nonlinear systems in almost all scientific fields of humankind. Solving reservoir characterization problems is no exception. Neural networks have a number of attractive features that can help to extract and recognize underlying patterns, structures, and relationships among data. However, before developing a neural network model, we must solve the problem of dimensionality such as determining dominant and irrelevant variables. We can apply principal components and factor analysis to reduce the dimensionality and help the neural networks formulate more realistic models. We validated OI by obtaining confident models in three different oil field problems: (1) A neural network in-situ stress model using lithology and gamma ray logs for the Travis Peak formation of east Texas, (2) A neural network permeability model using porosity and gamma ray and a neural network pseudo-gamma ray log model using 3D seismic attributes for the reservoir VLE 196 Lamar field located in Block V of south-central Lake Maracaibo (Venezuela), and (3) Neural network primary ultimate oil recovery (PRUR), initial waterflooding ultimate oil recovery (IWUR), and infill drilling ultimate oil recovery (IDUR) models using reservoir parameters for San Andres and Clearfork carbonate formations in west Texas. In all cases, we compared the results from the neural network models with the results from regression statistical and non-parametric approach models. The results show that it is possible to obtain the highest cross-correlation coefficient between predicted and actual target variables, and the lowest average absolute errors using the integrated techniques of multivariate statistical analysis and neural networks in our intelligent software.

Solid-gas phase equilibria and thermodynamic properties of cadmium selenide.

NASA Technical Reports Server (NTRS)

Sigai, A. G.; Wiedemeier, H.

1972-01-01

Accurate vapor pressures are determined through direct weight loss measurements using the Knudsen effusion technique. The experimental data are evaluated by establishing the mode of vaporization and determining the heat capacity of cadmium selenide at elevated temperatures. Additional information is obtained through a second- and third-law evaluation of data, namely, the heat of formation and the absolute entropy of cadmium selenide. A preferential loss of selenium during the initial heating of CdSe is observed, which leads to a deviation in stoichiometry.

Robust Programming Problems Based on the Mean-Variance Model Including Uncertainty Factors

NASA Astrophysics Data System (ADS)

Hasuike, Takashi; Ishii, Hiroaki

2009-01-01

This paper considers robust programming problems based on the mean-variance model including uncertainty sets and fuzzy factors. Since these problems are not well-defined problems due to fuzzy factors, it is hard to solve them directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed models are transformed into the deterministic equivalent problems. Furthermore, in order to solve these equivalent problems efficiently, the solution method is constructed introducing the mean-absolute deviation and doing the equivalent transformations.

Thermal sensing of cryogenic wind tunnel model surfaces Evaluation of silicon diodes

NASA Technical Reports Server (NTRS)

Daryabeigi, K.; Ash, R. L.; Dillon-Townes, L. A.

1986-01-01

Different sensors and installation techniques for surface temperature measurement of cryogenic wind tunnel models were investigated. Silicon diodes were selected for further consideration because of their good inherent accuracy. Their average absolute temperature deviation in comparison tests with standard platinum resistance thermometers was found to be 0.2 K in the range from 125 to 273 K. Subsurface temperature measurement was selected as the installation technique in order to minimize aerodynamic interference. Temperature distortion caused by an embedded silicon diode was studied numerically.

Thermal sensing of cryogenic wind tunnel model surfaces - Evaluation of silicon diodes

NASA Technical Reports Server (NTRS)

Daryabeigi, Kamran; Ash, Robert L.; Dillon-Townes, Lawrence A.

1986-01-01

Different sensors and installation techniques for surface temperature measurement of cryogenic wind tunnel models were investigated. Silicon diodes were selected for further consideration because of their good inherent accuracy. Their average absolute temperature deviation in comparison tests with standard platinum resistance thermometers was found to be 0.2 K in the range from 125 to 273 K. Subsurface temperature measurement was selected as the installation technique in order to minimize aerodynamic interference. Temperature distortion caused by an embedded silicon diode was studied numerically.

Checking ozone amounts by measurements of UV-irradiances

NASA Technical Reports Server (NTRS)

Seckmeyer, Gunther; Kettner, Christiane; Thiel, Stephen

1994-01-01

Absolute measurements of UV-irradiances in Germany and New Zealand are used to determine the total amounts of ozone. UV-irradiances measured and calculated for clear skies and for solar zenith angles less than 60 deg generally show a good accordance. The UVB-irradiances, however, show that the actual Dobson values are about 5 percent higher in Germany and about 3 percent higher in New Zealand compared to those obtained by our method. Possible reasons for these deviations are discussed.

Sex Ratios at Birth and Environmental Temperatures

NASA Astrophysics Data System (ADS)

Lerchl, Alexander

The relationship between average monthly air temperature and sex ratios at birth (SRB) was analyzed for children born in Germany during the period 1946-1995. Both the absolute temperature and - more markedly - the monthly temperature deviations from the overall mean were significantly positively correlated with the SRB (P<0.01) when temperatures were time-lagged against the SRB data by -10 or -11months. It is concluded that the sex of the offspring is partially determined by environmental temperatures prior to conception.

[Systemic approach to radiobiological studies].

PubMed

Bulanova, K Ia; Lobanok, L M

2004-01-01

The principles of information theory were applied for analysis of radiobiological effects. The perception of ionizing radiations as a signal enables living organism to discern their benefits or harm, to react to absolute and relatively small deviations, to keep the logic and chronicle of events, to use the former experience for reacting in presence, to forecast consequences. The systemic analysis of organism's response to ionizing radiations allows explaining the peculiarities of effects of different absorbed doses, hormesis, apoptosis, remote consequences and other post-radiation effects.

Robust stability of interval bidirectional associative memory neural network with time delays.

PubMed

Liao, Xiaofeng; Wong, Kwok-wo

2004-04-01

In this paper, the conventional bidirectional associative memory (BAM) neural network with signal transmission delay is intervalized in order to study the bounded effect of deviations in network parameters and external perturbations. The resultant model is referred to as a novel interval dynamic BAM (IDBAM) model. By combining a number of different Lyapunov functionals with the Razumikhin technique, some sufficient conditions for the existence of unique equilibrium and robust stability are derived. These results are fairly general and can be verified easily. To go further, we extend our investigation to the time-varying delay case. Some robust stability criteria for BAM with perturbations of time-varying delays are derived. Besides, our approach for the analysis allows us to consider several different types of activation functions, including piecewise linear sigmoids with bounded activations as well as the usual C1-smooth sigmoids. We believe that the results obtained have leading significance in the design and application of BAM neural networks.

Path synthesis of four-bar mechanisms using synergy of polynomial neural network and Stackelberg game theory

NASA Astrophysics Data System (ADS)

Ahmadi, Bahman; Nariman-zadeh, Nader; Jamali, Ali

2017-06-01

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms.

Learning in Artificial Neural Systems

NASA Technical Reports Server (NTRS)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

Stability Switches, Hopf Bifurcations, and Spatio-temporal Patterns in a Delayed Neural Model with Bidirectional Coupling

NASA Astrophysics Data System (ADS)

Song, Yongli; Zhang, Tonghua; Tadé, Moses O.

2009-12-01

The dynamical behavior of a delayed neural network with bi-directional coupling is investigated by taking the delay as the bifurcating parameter. Some parameter regions are given for conditional/absolute stability and Hopf bifurcations by using the theory of functional differential equations. As the propagation time delay in the coupling varies, stability switches for the trivial solution are found. Conditions ensuring the stability and direction of the Hopf bifurcation are determined by applying the normal form theory and the center manifold theorem. We also discuss the spatio-temporal patterns of bifurcating periodic oscillations by using the symmetric bifurcation theory of delay differential equations combined with representation theory of Lie groups. In particular, we obtain that the spatio-temporal patterns of bifurcating periodic oscillations will alternate according to the change of the propagation time delay in the coupling, i.e., different ranges of delays correspond to different patterns of neural activities. Numerical simulations are given to illustrate the obtained results and show the existence of bursts in some interval of the time for large enough delay.

Normalized value coding explains dynamic adaptation in the human valuation process.

PubMed

Khaw, Mel W; Glimcher, Paul W; Louie, Kenway

2017-11-28

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

Saeed, U.; Ahmad, S.; Alsadi, J.; Ross, D.; Rizvi, G.

2014-05-01

In present paper, the applicability of artificial neural networks (ANN) is investigated for color properties of plastics. The neural networks toolbox of Matlab 6.5 is used to develop and test the ANN model on a personal computer. An optimal design is completed for 10, 12, 14,16,18 & 20 hidden neurons on single hidden layer with five different algorithms: batch gradient descent (GD), batch variable learning rate (GDX), resilient back-propagation (RP), scaled conjugate gradient (SCG), levenberg-marquardt (LM) in the feed forward back-propagation neural network model. The training data for ANN is obtained from experimental measurements. There were twenty two inputs including resins, additives & pigments while three tristimulus color values L*, a* and b* were used as output layer. Statistical analysis in terms of Root-Mean-Squared (RMS), absolute fraction of variance (R squared), as well as mean square error is used to investigate the performance of ANN. LM algorithm with fourteen neurons on hidden layer in Feed Forward Back-Propagation of ANN model has shown best result in the present study. The degree of accuracy of the ANN model in reduction of errors is proven acceptable in all statistical analysis and shown in results. However, it was concluded that ANN provides a feasible method in error reduction in specific color tristimulus values.

Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

NASA Astrophysics Data System (ADS)

Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

2018-04-01

In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

Measurement of the aerothermodynamic state in a high enthalpy plasma wind-tunnel flow

NASA Astrophysics Data System (ADS)

Hermann, Tobias; Löhle, Stefan; Zander, Fabian; Fasoulas, Stefanos

2017-11-01

This paper presents spatially resolved measurements of absolute particle densities of N2, N2+, N, O, N+ , O+ , e- and excitation temperatures of electronic, rotational and vibrational modes of an air plasma free stream. All results are based on optical emission spectroscopy data. The measured parameters are combined to determine the local mass-specific enthalpy of the free stream. The analysis of the radiative transport, relative and absolute intensities, and spectral shape is used to determine various thermochemical parameters. The model uncertainty of each analysis method is assessed. The plasma flow is shown to be close to equilibrium. The strongest deviations from equilibrium occur for N, N+ and N2+ number densities in the free stream. Additional measurements of the local mass-specific enthalpy are conducted using a mass injection probe as well as a heat flux and total pressure probe. The agreement between all methods of enthalpy determination is good.

Forecasting in foodservice: model development, testing, and evaluation.

PubMed

Miller, J L; Thompson, P A; Orabella, M M

1991-05-01

This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.

ORBITAL SOLUTIONS AND ABSOLUTE ELEMENTS OF THE ECLIPSING BINARY EE AQUARII

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

Wronka, Marissa Diehl; Gold, Caitlin; Sowell, James R.

2010-04-15

EE Aqr is a 7.9 mag Algol variable with a 12 hr orbital period. The Wilson-Devinney program is used to simultaneously solve 11 previously published light curves together with two existing radial velocity curves. The resulting masses are M {sub 1} = 2.24 {+-} 0.13 M {sub sun} and M {sub 2} = 0.72 {+-} 0.04 M {sub sun}, and the radii are R {sub 1} = 1.76 {+-} 0.03 R {sub sun} and R {sub 2} = 1.10 {+-} 0.02 R {sub sun}. The system has the lower-mass component completely filling its Roche lobe. Its distance from Hipparcos observationsmore » is 112 {+-} 10 pc. An improved ephemeris is derived, and no deviations in the period over time were seen. Light and velocity curve parameters, orbital elements, and absolute dimensions are presented, plus a comparison is made with previous solutions.« less

Self-mixing instrument for simultaneous distance and speed measurement

NASA Astrophysics Data System (ADS)

Norgia, Michele; Melchionni, Dario; Pesatori, Alessandro

2017-12-01

A novel instrument based on Self-mixing interferometry is proposed to simultaneously measure absolute distance and velocity. The measurement method is designed for working directly on each kind of surface, in industrial environment, overcoming also problems due to speckle pattern effect. The laser pump current is modulated at quite high frequency (40 kHz) and the estimation of the induced fringes frequency allows an almost instantaneous measurement (measurement time equal to 25 μs). A real time digital elaboration processes the measurement data and discards unreliable measurements. The simultaneous measurement reaches a relative standard deviation of about 4·10-4 in absolute distance, and 5·10-3 in velocity measurement. Three different laser sources are tested and compared. The instrument shows good performances also in harsh environment, for example measuring the movement of an opaque iron tube rotating under a running water flow.

Early results from the Far Infrared Absolute Spectrophotometer (FIRAS)

NASA Technical Reports Server (NTRS)

Mather, J. C.; Cheng, E. S.; Shafer, R. A.; Eplee, R. E.; Isaacman, R. B.; Fixsen, D. J.; Read, S. M.; Meyer, S. S.; Weiss, R.; Wright, E. L.

1991-01-01

The Far Infrared Absolute Spectrophotometer (FIRAS) on the Cosmic Background Explorer (COBE) mapped 98 percent of the sky, 60 percent of it twice, before the liquid helium coolant was exhausted. The FIRAS covers the frequency region from 1 to 100/cm with a 7 deg angular resolution. The spectral resolution is 0.2/cm for frequencies less than 20/cm and 0.8/cm for higher frequencies. Preliminary results include: a limit on the deviations from a Planck curve of 1 percent of the peak brightness from 1 to 20/cm, a temperature of 2.735 +/- 0.06 K, a limit on the Comptonization parameter y of 0.001, on the chemical potential parameter mu of 0.01, a strong limit on the existence of a hot smooth intergalactic medium, and a confirmation that the dipole anisotropy spectrum is that of a Doppler shifted blackbody.

Real-Gas Correction Factors for Hypersonic Flow Parameters in Helium

NASA Technical Reports Server (NTRS)

Erickson, Wayne D.

1960-01-01

The real-gas hypersonic flow parameters for helium have been calculated for stagnation temperatures from 0 F to 600 F and stagnation pressures up to 6,000 pounds per square inch absolute. The results of these calculations are presented in the form of simple correction factors which must be applied to the tabulated ideal-gas parameters. It has been shown that the deviations from the ideal-gas law which exist at high pressures may cause a corresponding significant error in the hypersonic flow parameters when calculated as an ideal gas. For example the ratio of the free-stream static to stagnation pressure as calculated from the thermodynamic properties of helium for a stagnation temperature of 80 F and pressure of 4,000 pounds per square inch absolute was found to be approximately 13 percent greater than that determined from the ideal-gas tabulation with a specific heat ratio of 5/3.

The NIST Detector-Based Luminous Intensity Scale

PubMed Central

Cromer, C. L.; Eppeldauer, G.; Hardis, J. E.; Larason, T. C.; Ohno, Y.; Parr, A. C.

1996-01-01

The Système International des Unités (SI) base unit for photometry, the candela, has been realized by using absolute detectors rather than absolute sources. This change in method permits luminous intensity calibrations of standard lamps to be carried out with a relative expanded uncertainty (coverage factor k = 2, and thus a 2 standard deviation estimate) of 0.46 %, almost a factor-of-two improvement. A group of eight reference photometers has been constructed with silicon photodiodes, matched with filters to mimic the spectral luminous efficiency function for photopic vision. The wide dynamic range of the photometers aid in their calibration. The components of the photometers were carefully measured and selected to reduce the sources of error and to provide baseline data for aging studies. Periodic remeasurement of the photometers indicate that a yearly recalibration is required. The design, characterization, calibration, evaluation, and application of the photometers are discussed. PMID:27805119

Absolute S- and P-plane polarization efficiencies for high frequency holographic gratings in the VUV

NASA Technical Reports Server (NTRS)

Caruso, A. J.; Woodgate, B. E.; Mount, G. H.

1981-01-01

High frequency plane gratings (3500 and 3600 gr/mm) have been holographically ruled and blazed for the VUV spectral region. All gratings were coated with 70 nm Al + 25 nm MgF2. Absolute unpolarized and S- and P-plane polarization efficiencies have been measured for the first and second orders in the 120- to 450-nm spectral region at 18.5 and 30 deg angles of deviation. For deep grooves, anomalous features are more pronounced for the P-plane polarization efficiency than for the S-plane polarization efficiency. Holographic gratings can be tailored to produce high polarization or low polarization in the VUV. For comparison, efficiencies and polarization of the best conventional high frequency gratings were also determined. Measurements show that scattered light is significantly lower for holographic gratings in the VUV when compared with the conventional gratings.

Position of the station Borowiec in the Doppler observation campaign WEDOC 80

NASA Astrophysics Data System (ADS)

Pachelski, W.

The position of the Doppler antenna located at the Borowiec Observatory, Poland, is analyzed based on data gathered during the WEDOC 80 study and an earlier study in 1977. Among other findings, it is determined that biases of the reference system origin can be partially eliminated by transforming absolute coordinates of two or more stations into station-to-station vector components, and by determining the vector length while the system scale remains affected by broadcast ephemerides. The standard deviations of absolute coordinates are shown to represent only the internal accuracy of the solution, and are found to depend on the geometrical configuration between the station position and the satellite passes. It is shown that significant correlations between station coordinates in translocation or multilocation are due to the poor conditioning of design matrices with respect to the origin and orientation of the reference system.

Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis.

PubMed

Uzun, Harun; Yıldız, Zeynep; Goldfarb, Jillian L; Ceylan, Selim

2017-06-01

As biomass becomes more integrated into our energy feedstocks, the ability to predict its combustion enthalpies from routine data such as carbon, ash, and moisture content enables rapid decisions about utilization. The present work constructs a novel artificial neural network model with a 3-3-1 tangent sigmoid architecture to predict biomasses' higher heating values from only their proximate analyses, requiring minimal specificity as compared to models based on elemental composition. The model presented has a considerably higher correlation coefficient (0.963) and lower root mean square (0.375), mean absolute (0.328), and mean bias errors (0.010) than other models presented in the literature which, at least when applied to the present data set, tend to under-predict the combustion enthalpy. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

PubMed Central

Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin

2011-01-01

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%. PMID:22164030

Hot Deformation Behavior and Flow Stress Prediction of TC4-DT Alloy in Single-Phase Region and Dual-Phase Regions

NASA Astrophysics Data System (ADS)

Liu, Jianglin; Zeng, Weidong; Zhu, Yanchun; Yu, Hanqing; Zhao, Yongqing

2015-05-01

Isothermal compression tests of TC4-DT titanium alloy at the deformation temperature ranging from 1181 to 1341 K covering α + β phase field and β-phase field, the strain rate ranging from 0.01 to 10.0 s-1 and the height reduction of 70% were conducted on a Gleeble-3500 thermo-mechanical simulator. The experimental true stress-true strain data were employed to develop the strain-compensated Arrhenius-type flow stress model and artificial neural network (ANN) model; the predictability of two models was quantified in terms of correlation coefficient ( R) and average absolute relative error (AARE). The R and AARE for the Arrhenius-type flow stress model were 0.9952 and 5.78%, which were poorer linear relation and more deviation than 0.9997 and 1.04% for the feed-forward back-propagation ANN model, respectively. The results indicated that the trained ANN model was more efficient and accurate in predicting the flow behavior for TC4-DT titanium alloy at elevated temperature deformation than the strain-compensated Arrhenius-type constitutive equations. The constitutive relationship compensating strain could track the experimental data across the whole hot working domain other than that at high strain rates (≥1 s-1). The microstructure analysis illustrated that the deformation mechanisms existed at low strain rates (≤0.1 s-1), where dynamic recrystallization occurred, were far different from that at high strain rates (≥1 s-1) that presented bands of flow localization and cracking along grain boundary.

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

Wen, N; Lu, S; Qin, Y

Purpose: To evaluate the dosimetric uncertainty associated with Gafchromic (EBT3) films and establish an absolute dosimetry protocol for Stereotactic Radiosurgery (SRS) and Stereotactic Body Radiotherapy (SBRT). Methods: EBT3 films were irradiated at each of seven different dose levels between 1 and 15 Gy with open fields, and standard deviations of dose maps were calculated at each color channel for evaluation. A scanner non-uniform response correction map was built by registering and comparing film doses to the reference diode array-based dose map delivered with the same doses. To determine the temporal dependence of EBT3 films, the average correction factors of differentmore » dose levels as a function of time were evaluated up to four days after irradiation. An integrated film dosimetry protocol was developed for dose calibration, calibration curve fitting, dose mapping, and profile/gamma analysis. Patient specific quality assurance (PSQA) was performed for 93 SRS/SBRT treatment plans. Results: The scanner response varied within 1% for the field sizes less than 5 × 5 cm{sup 2}, and up to 5% for the field sizes of 10 × 10 cm{sup 2}. The scanner correction method was able to remove visually evident, irregular detector responses found for larger field sizes. The dose response of the film changed rapidly (∼10%) in the first two hours and plateaued afterwards, ∼3% change between 2 and 24 hours. The mean uncertainties (mean of the standard deviations) were <0.5% over the dose range 1∼15Gy for all color channels for the OD response curves. The percentage of points passing the 3%/1mm gamma criteria based on absolute dose analysis, averaged over all tests, was 95.0 ± 4.2. Conclusion: We have developed an absolute film dose dosimetry protocol using EBT3 films. The overall uncertainty has been established to be approximately 1% for SRS and SBRT PSQA. The work was supported by a Research Scholar Grant, RSG-15-137-01-CCE from the American Cancer Society.« less

Artificial neural networks for modeling ammonia emissions released from sewage sludge composting

NASA Astrophysics Data System (ADS)

Boniecki, P.; Dach, J.; Pilarski, K.; Piekarska-Boniecka, H.

2012-09-01

The project was designed to develop, test and validate an original Neural Model describing ammonia emissions generated in composting sewage sludge. The composting mix was to include the addition of such selected structural ingredients as cereal straw, sawdust and tree bark. All created neural models contain 7 input variables (chemical and physical parameters of composting) and 1 output (ammonia emission). The α data file was subdivided into three subfiles: the learning file (ZU) containing 330 cases, the validation file (ZW) containing 110 cases and the test file (ZT) containing 110 cases. The standard deviation ratios (for all 4 created networks) ranged from 0.193 to 0.218. For all of the selected models, the correlation coefficient reached the high values of 0.972-0.981. The results show that he predictive neural model describing ammonia emissions from composted sewage sludge is well suited for assessing such emissions. The sensitivity analysis of the model for the input of variables of the process in question has shown that the key parameters describing ammonia emissions released in composting sewage sludge are pH and the carbon to nitrogen ratio (C:N).

Shared Neural Mechanisms for the Prediction of Own and Partner Musical Sequences after Short-term Piano Duet Training.

PubMed

Lappe, Claudia; Bodeck, Sabine; Lappe, Markus; Pantev, Christo

2017-01-01

Predictive mechanisms in the human brain can be investigated using markers for prediction violations like the mismatch negativity (MMN). Short-term piano training increases the MMN for melodic and rhythmic deviations in the training material. This increase occurs only when the material is actually played, not when it is only perceived through listening, suggesting that learning predictions about upcoming musical events are derived from motor involvement. However, music is often performed in concert with others. In this case, predictions about upcoming actions from a partner are a crucial part of the performance. In the present experiment, we use magnetoencephalography (MEG) to measure MMNs to deviations in one's own and a partner's musical material after both engaged in musical duet training. Event-related field (ERF) results revealed that the MMN increased significantly for own and partner material suggesting a neural representation of the partner's part in a duet situation. Source analysis using beamforming revealed common activations in auditory, inferior frontal, and parietal areas, similar to previous results for single players, but also a pronounced contribution from the cerebellum. In addition, activation of the precuneus and the medial frontal cortex was observed, presumably related to the need to distinguish between own and partner material.

Arabic morphology in the neural language system.

PubMed

Boudelaa, Sami; Pulvermüller, Friedemann; Hauk, Olaf; Shtyrov, Yury; Marslen-Wilson, William

2010-05-01

There are two views about morphology, the aspect of language concerned with the internal structure of words. One view holds that morphology is a domain of knowledge with a specific type of neurocognitive representation supported by specific brain mechanisms lateralized to left fronto-temporal cortex. The alternate view characterizes morphological effects as being a by-product of the correlation between form and meaning and where no brain area is predicted to subserve morphological processing per se. Here we provided evidence from Arabic that morphemes do have specific memory traces, which differ as a function of their functional properties. In an MMN study, we showed that the abstract consonantal root, which conveys semantic meaning (similarly to monomorphemic content words in English), elicits an MMN starting from 160 msec after the deviation point, whereas the abstract vocalic word pattern, which plays a range of grammatical roles, elicits an MMN response starting from 250 msec after the deviation point. Topographically, the root MMN has a symmetric fronto-central distribution, whereas the word pattern MMN lateralizes significantly to the left, indicating stronger involvement of left peri-sylvian areas. In languages with rich morphologies, morphemic processing seems to be supported by distinct neural networks, thereby providing evidence for a specific neuronal basis for morphology as part of the cerebral language machinery.

The Mismatch Negativity: An Indicator of Perception of Regularities in Music.

PubMed

Yu, Xide; Liu, Tao; Gao, Dingguo

2015-01-01

This paper reviews music research using Mismatch Negativity (MMN). MMN is a deviation-specific component of auditory event-related potential (EPR), which detects a deviation between a sound and an internal representation (e.g., memory trace). Recent studies have expanded the notion and the paradigms of MMN to higher-order music processing such as those involving short melodies, harmony chord, and music syntax. In this vein, we firstly reviewed the evolution of MMN from sound to music and then mainly compared the differences of MMN features between musicians and nonmusicians, followed by the discussion of the potential roles of the training effect and the natural exposure in MMN. Since MMN can serve as an index of neural plasticity, it thus can be widely used in clinical and other applied areas, such as detecting music preference in newborns or assessing wholeness of central auditory system of hearing illness. Finally, we pointed out some open questions and further directions. Current music perception research using MMN has mainly focused on relatively low hierarchical structure of music perception. To fully understand the neural substrates underlying processing of regularities in music, it is important and beneficial to combine MMN with other experimental paradigms such as early right-anterior negativity (ERAN).

The Mismatch Negativity: An Indicator of Perception of Regularities in Music

PubMed Central

Yu, Xide; Liu, Tao; Gao, Dingguo

2015-01-01

This paper reviews music research using Mismatch Negativity (MMN). MMN is a deviation-specific component of auditory event-related potential (EPR), which detects a deviation between a sound and an internal representation (e.g., memory trace). Recent studies have expanded the notion and the paradigms of MMN to higher-order music processing such as those involving short melodies, harmony chord, and music syntax. In this vein, we firstly reviewed the evolution of MMN from sound to music and then mainly compared the differences of MMN features between musicians and nonmusicians, followed by the discussion of the potential roles of the training effect and the natural exposure in MMN. Since MMN can serve as an index of neural plasticity, it thus can be widely used in clinical and other applied areas, such as detecting music preference in newborns or assessing wholeness of central auditory system of hearing illness. Finally, we pointed out some open questions and further directions. Current music perception research using MMN has mainly focused on relatively low hierarchical structure of music perception. To fully understand the neural substrates underlying processing of regularities in music, it is important and beneficial to combine MMN with other experimental paradigms such as early right-anterior negativity (ERAN). PMID:26504352

Absolute backscatter coefficient estimates of tissue-mimicking phantoms in the 5–50 MHz frequency range

PubMed Central

McCormick, Matthew M.; Madsen, Ernest L.; Deaner, Meagan E.; Varghese, Tomy

2011-01-01

Absolute backscatter coefficients in tissue-mimicking phantoms were experimentally determined in the 5–50 MHz frequency range using a broadband technique. A focused broadband transducer from a commercial research system, the VisualSonics Vevo 770, was used with two tissue-mimicking phantoms. The phantoms differed regarding the thin layers covering their surfaces to prevent desiccation and regarding glass bead concentrations and diameter distributions. Ultrasound scanning of these phantoms was performed through the thin layer. To avoid signal saturation, the power spectra obtained from the backscattered radio frequency signals were calibrated by using the signal from a liquid planar reflector, a water-brominated hydrocarbon interface with acoustic impedance close to that of water. Experimental values of absolute backscatter coefficients were compared with those predicted by the Faran scattering model over the frequency range 5–50 MHz. The mean percent difference and standard deviation was 54% ± 45% for the phantom with a mean glass bead diameter of 5.40 μm and was 47% ± 28% for the phantom with 5.16 μm mean diameter beads. PMID:21877789

Cultural influences on neural substrates of attentional control.

PubMed

Hedden, Trey; Ketay, Sarah; Aron, Arthur; Markus, Hazel Rose; Gabrieli, John D E

2008-01-01

Behavioral research has shown that people from Western cultural contexts perform better on tasks emphasizing independent (absolute) dimensions than on tasks emphasizing interdependent (relative) dimensions, whereas the reverse is true for people from East Asian contexts. We assessed functional magnetic resonance imaging responses during performance of simple visuospatial tasks in which participants made absolute judgments (ignoring visual context) or relative judgments (taking visual context into account). In each group, activation in frontal and parietal brain regions known to be associated with attentional control was greater during culturally nonpreferred judgments than during culturally preferred judgments. Also, within each group, activation differences in these regions correlated strongly with scores on questionnaires measuring individual differences in culture-typical identity. Thus, the cultural background of an individual and the degree to which the individual endorses cultural values moderate activation in brain networks engaged during even simple visual and attentional tasks.

l-Theanine and caffeine improve target-specific attention to visual stimuli by decreasing mind wandering: a human functional magnetic resonance imaging study.

PubMed

Kahathuduwa, Chanaka N; Dhanasekara, Chathurika S; Chin, Shao-Hua; Davis, Tyler; Weerasinghe, Vajira S; Dassanayake, Tharaka L; Binks, Martin

2018-01-01

Oral intake of l-theanine and caffeine supplements is known to be associated with faster stimulus discrimination, possibly via improving attention to stimuli. We hypothesized that l-theanine and caffeine may be bringing about this beneficial effect by increasing attention-related neural resource allocation to target stimuli and decreasing deviation of neural resources to distractors. We used functional magnetic resonance imaging (fMRI) to test this hypothesis. Solutions of 200mg of l-theanine, 160mg of caffeine, their combination, or the vehicle (distilled water; placebo) were administered in a randomized 4-way crossover design to 9 healthy adult men. Sixty minutes after administration, a 20-minute fMRI scan was performed while the subjects performed a visual color stimulus discrimination task. l-Theanine and l-theanine-caffeine combination resulted in faster responses to targets compared with placebo (∆=27.8milliseconds, P=.018 and ∆=26.7milliseconds, P=.037, respectively). l-Theanine was associated with decreased fMRI responses to distractor stimuli in brain regions that regulate visual attention, suggesting that l-theanine may be decreasing neural resource allocation to process distractors, thus allowing to attend to targets more efficiently. l-Theanine-caffeine combination was associated with decreased fMRI responses to target stimuli as compared with distractors in several brain regions that typically show increased activation during mind wandering. Factorial analysis suggested that l-theanine and caffeine seem to have a synergistic action in decreasing mind wandering. Therefore, our hypothesis is that l-theanine and caffeine may be decreasing deviation of attention to distractors (including mind wandering); thus, enhancing attention to target stimuli was confirmed. Copyright © 2017 Elsevier Inc. All rights reserved.

High levels of ultraviolet radiation observed by ground-based instruments below the 2011 Arctic ozone hole

NASA Astrophysics Data System (ADS)

Bernhard, G.; Dahlback, A.; Fioletov, V.; Heikkilä, A.; Johnsen, B.; Koskela, T.; Lakkala, K.; Svendby, T.

2013-11-01

Greatly increased levels of ultraviolet (UV) radiation were observed at thirteen Arctic and sub-Arctic ground stations in the spring of 2011, when the ozone abundance in the Arctic stratosphere dropped to the lowest amounts on record. Measurements of the noontime UV Index (UVI) during the low-ozone episode exceeded the climatological mean by up to 77% at locations in the western Arctic (Alaska, Canada, Greenland) and by up to 161% in Scandinavia. The UVI measured at the end of March at the Scandinavian sites was comparable to that typically observed 15-60 days later in the year when solar elevations are much higher. The cumulative UV dose measured during the period of the ozone anomaly exceeded the climatological mean by more than two standard deviations at 11 sites. Enhancements beyond three standard deviations were observed at seven sites and increases beyond four standard deviations at two sites. At the western sites, the episode occurred in March, when the Sun was still low in the sky, limiting absolute UVI anomalies to less than 0.5 UVI units. At the Scandinavian sites, absolute UVI anomalies ranged between 1.0 and 2.2 UVI units. For example, at Finse, Norway, the noontime UVI on 30 March was 4.7, while the climatological UVI is 2.5. Although a UVI of 4.7 is still considered moderate, UV levels of this amount can lead to sunburn and photokeratitis during outdoor activity when radiation is reflected upward by snow towards the face of a person or animal. At the western sites, UV anomalies can be well explained with ozone anomalies of up to 41% below the climatological mean. At the Scandinavian sites, low ozone can only explain a UVI increase of 50-60%. The remaining enhancement was mainly caused by the absence of clouds during the low-ozone period.

High levels of ultraviolet radiation observed by ground-based instruments below the 2011 Arctic ozone hole

NASA Astrophysics Data System (ADS)

Bernhard, G.; Dahlback, A.; Fioletov, V.; Heikkilä, A.; Johnsen, B.; Koskela, T.; Lakkala, K.; Svendby, T. M.

2013-06-01

Greatly increased levels of ultraviolet (UV) radiation were observed at thirteen Arctic and sub-Arctic ground stations in the spring of 2011 when the ozone abundance in the Arctic stratosphere dropped to the lowest amounts on record. Measurements of the noontime UV Index (UVI) during the low-ozone episode exceeded the climatological mean by up to 77% at locations in the western Arctic (Alaska, Canada, Greenland) and by up to 161% in Scandinavia. The UVI measured at the end of March at the Scandinavian sites was comparable to that typically observed 15-60 days later in the year when solar elevations are much higher. The cumulative UV dose measured during the period of the ozone anomaly exceeded the climatological mean by more than two standard deviations at 11 sites. Enhancements beyond three standard deviations were observed at seven sites and increases beyond four standard deviations at two sites. At the western sites, the episode occurred in March when the Sun was still low in the sky, limiting absolute UVI anomalies to less than 0.5 UVI units. At the Scandinavian sites, absolute UVI anomalies ranged between 1.0 and 2.2 UVI units. For example, at Finse, Norway, the noontime UVI on 30 March was 4.7 while the climatological UVI is 2.5. Although a UVI of 4.7 is still considered moderate, UV levels of this amount can lead to sunburn and photokeratitis during outdoor activity when radiation is reflected upward by snow towards the face of a person or animal. At the western sites, UV anomalies can be well explained with ozone anomalies of up to 41% below the climatological mean. At the Scandinavian sites, low ozone can only explain a UVI increase by 50-60%. The remaining enhancement was mainly caused by the absence of clouds during the low-ozone period.

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model.

PubMed

Azeez, Adeboye; Obaromi, Davies; Odeyemi, Akinwumi; Ndege, James; Muntabayi, Ruffin

2016-07-26

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

Color-dependent learning in restrained Africanized honey bees.

PubMed

Jernigan, C M; Roubik, D W; Wcislo, W T; Riveros, A J

2014-02-01

Associative color learning has been demonstrated to be very poor using restrained European honey bees unless the antennae are amputated. Consequently, our understanding of proximate mechanisms in visual information processing is handicapped. Here we test learning performance of Africanized honey bees under restrained conditions with visual and olfactory stimulation using the proboscis extension response (PER) protocol. Restrained individuals were trained to learn an association between a color stimulus and a sugar-water reward. We evaluated performance for 'absolute' learning (learned association between a stimulus and a reward) and 'discriminant' learning (discrimination between two stimuli). Restrained Africanized honey bees (AHBs) readily learned the association of color stimulus for both blue and green LED stimuli in absolute and discriminatory learning tasks within seven presentations, but not with violet as the rewarded color. Additionally, 24-h memory improved considerably during the discrimination task, compared with absolute association (15-55%). We found that antennal amputation was unnecessary and reduced performance in AHBs. Thus color learning can now be studied using the PER protocol with intact AHBs. This finding opens the way towards investigating visual and multimodal learning with application of neural techniques commonly used in restrained honey bees.

Comparison of the Thermal Expansion Behavior of Several Intermetallic Silicide Alloys Between 293 and 1523 K

NASA Technical Reports Server (NTRS)

Raj, Sai V.

2014-01-01

Thermal expansion measurements were conducted on hot-pressed CrSi(sub 2), TiSi(sub 2), W Si(sub 2) and a two-phase Cr-Mo-Si intermetallic alloy between 293 and 1523 K during three heat-cool cycles. The corrected thermal expansion, (L/L(sub 0)(sub thermal), varied with the absolute temperature, T, as (deltaL/L(sub 0)(sub thermal) = A(T-293)(sup 3) + B(T-293)(sup 2) + C(T-293) + D, where A, B, C and D are regression constants. Excellent reproducibility was observed for most of the materials after the first heat-up cycle. In some cases, the data from the first heatup cycle deviated from those determined in the subsequent cycles. This deviation was attributed to the presence of residual stresses developed during processing, which are relieved after the first heat-up cycle.

Trait-level temporal lobe hypoactivation to social exclusion in unaffected siblings of children and adolescents with autism spectrum disorders.

PubMed

Bolling, Danielle Z; Pelphrey, Kevin A; Vander Wyk, Brent C

2015-06-01

Social exclusion elicits powerful feelings of negative affect associated with rejection. Additionally, experiencing social exclusion reliably recruits neural circuitry associated with emotion processing. Recent work has demonstrated abnormal neural responses to social exclusion in children and adolescents with autism spectrum disorders (ASD). However, it remains unknown to what extent these abnormalities are due to atypical social experiences versus genetic predispositions to atypical neural processing. To address this question, the current study investigated brain responses to social exclusion compared to a baseline condition of fair play in unaffected siblings of youth with ASD using functional magnetic resonance imaging. We identified common deviations between unaffected siblings and ASD probands that might represent trait-level abnormalities in processing Social Exclusion vs. Fair Play, specifically in the right anterior temporoparietal junction extending into posterior superior temporal sulcus. Thus, hypoactivation to Social Exclusion vs. Fair Play in this region may represent a shared genetic vulnerability to developing autism. In addition, we present evidence supporting the idea that one's status as an unaffected sibling moderates the relationship between IQ and neural activation to Social Exclusion vs. Fair Play in anterior cingulate cortex. These results are discussed in the context of previous literature on neural endophenotypes of autism. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Engine With Regression and Neural Network Approximators Designed

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Hopkins, Dale A.

2001-01-01

At the NASA Glenn Research Center, the NASA engine performance program (NEPP, ref. 1) and the design optimization testbed COMETBOARDS (ref. 2) with regression and neural network analysis-approximators have been coupled to obtain a preliminary engine design methodology. The solution to a high-bypass-ratio subsonic waverotor-topped turbofan engine, which is shown in the preceding figure, was obtained by the simulation depicted in the following figure. This engine is made of 16 components mounted on two shafts with 21 flow stations. The engine is designed for a flight envelope with 47 operating points. The design optimization utilized both neural network and regression approximations, along with the cascade strategy (ref. 3). The cascade used three algorithms in sequence: the method of feasible directions, the sequence of unconstrained minimizations technique, and sequential quadratic programming. The normalized optimum thrusts obtained by the three methods are shown in the following figure: the cascade algorithm with regression approximation is represented by a triangle, a circle is shown for the neural network solution, and a solid line indicates original NEPP results. The solutions obtained from both approximate methods lie within one standard deviation of the benchmark solution for each operating point. The simulation improved the maximum thrust by 5 percent. The performance of the linear regression and neural network methods as alternate engine analyzers was found to be satisfactory for the analysis and operation optimization of air-breathing propulsion engines (ref. 4).

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching

PubMed Central

2012-01-01

Background The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlapping patterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either ‘noise’ or ‘signal’ lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context. Results We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns. Conclusions We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html). PMID:23137144

Neural correlates of strategic reasoning during competitive games.

PubMed

Seo, Hyojung; Cai, Xinying; Donahue, Christopher H; Lee, Daeyeol

2014-10-17

Although human and animal behaviors are largely shaped by reinforcement and punishment, choices in social settings are also influenced by information about the knowledge and experience of other decision-makers. During competitive games, monkeys increased their payoffs by systematically deviating from a simple heuristic learning algorithm and thereby countering the predictable exploitation by their computer opponent. Neurons in the dorsomedial prefrontal cortex (dmPFC) signaled the animal's recent choice and reward history that reflected the computer's exploitative strategy. The strength of switching signals in the dmPFC also correlated with the animal's tendency to deviate from the heuristic learning algorithm. Therefore, the dmPFC might provide control signals for overriding simple heuristic learning algorithms based on the inferred strategies of the opponent. Copyright © 2014, American Association for the Advancement of Science.

Social deviance activates the brain's error-monitoring system.

PubMed

Kim, Bo-Rin; Liss, Alison; Rao, Monica; Singer, Zachary; Compton, Rebecca J

2012-03-01

Social psychologists have long noted the tendency for human behavior to conform to social group norms. This study examined whether feedback indicating that participants had deviated from group norms would elicit a neural signal previously shown to be elicited by errors and monetary losses. While electroencephalograms were recorded, participants (N = 30) rated the attractiveness of 120 faces and received feedback giving the purported average rating made by a group of peers. The feedback was manipulated so that group ratings either were the same as a participant's rating or deviated by 1, 2, or 3 points. Feedback indicating deviance from the group norm elicited a feedback-related negativity, a brainwave signal known to be elicited by objective performance errors and losses. The results imply that the brain treats deviance from social norms as an error.

Dosimetric verification of lung cancer treatment using the CBCTs estimated from limited-angle on-board projections.

PubMed

Zhang, You; Yin, Fang-Fang; Ren, Lei

2015-08-01

Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the "gold-standard" on-board images (gold-standard doses). The absolute deviations of minimum dose (ΔDmin), maximum dose (ΔDmax), and mean dose (ΔDmean), and the absolute deviations of prescription dose coverage (ΔV100%) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ΔDmin, ΔDmax, ΔDmean, and ΔV100% (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ΔDmin, ΔDmax, ΔDmean, and ΔV100% of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.

Approximations to complete basis set-extrapolated, highly correlated non-covalent interaction energies.

PubMed

Mackie, Iain D; DiLabio, Gino A

2011-10-07

The first-principles calculation of non-covalent (particularly dispersion) interactions between molecules is a considerable challenge. In this work we studied the binding energies for ten small non-covalently bonded dimers with several combinations of correlation methods (MP2, coupled-cluster single double, coupled-cluster single double (triple) (CCSD(T))), correlation-consistent basis sets (aug-cc-pVXZ, X = D, T, Q), two-point complete basis set energy extrapolations, and counterpoise corrections. For this work, complete basis set results were estimated from averaged counterpoise and non-counterpoise-corrected CCSD(T) binding energies obtained from extrapolations with aug-cc-pVQZ and aug-cc-pVTZ basis sets. It is demonstrated that, in almost all cases, binding energies converge more rapidly to the basis set limit by averaging the counterpoise and non-counterpoise corrected values than by using either counterpoise or non-counterpoise methods alone. Examination of the effect of basis set size and electron correlation shows that the triples contribution to the CCSD(T) binding energies is fairly constant with the basis set size, with a slight underestimation with CCSD(T)∕aug-cc-pVDZ compared to the value at the (estimated) complete basis set limit, and that contributions to the binding energies obtained by MP2 generally overestimate the analogous CCSD(T) contributions. Taking these factors together, we conclude that the binding energies for non-covalently bonded systems can be accurately determined using a composite method that combines CCSD(T)∕aug-cc-pVDZ with energy corrections obtained using basis set extrapolated MP2 (utilizing aug-cc-pVQZ and aug-cc-pVTZ basis sets), if all of the components are obtained by averaging the counterpoise and non-counterpoise energies. With such an approach, binding energies for the set of ten dimers are predicted with a mean absolute deviation of 0.02 kcal/mol, a maximum absolute deviation of 0.05 kcal/mol, and a mean percent absolute deviation of only 1.7%, relative to the (estimated) complete basis set CCSD(T) results. Use of this composite approach to an additional set of eight dimers gave binding energies to within 1% of previously published high-level data. It is also shown that binding within parallel and parallel-crossed conformations of naphthalene dimer is predicted by the composite approach to be 9% greater than that previously reported in the literature. The ability of some recently developed dispersion-corrected density-functional theory methods to predict the binding energies of the set of ten small dimers was also examined. © 2011 American Institute of Physics

Deviations of the visual upright in three dimensions in disorders of the brainstem: a clinical exploration.

PubMed

Frisén, Lars

2010-12-01

Deviations of the subjective visual vertical in the roll or fronto-parallel plane occur commonly in disorders of the brainstem and have been extensively explored. In contrast, little is known about deviations in other directions. The present retrospective study focused on deviations in the pitch (sagittal) direction in 176 patients with a wide variety of disorders. The test task was to set a self-illuminated rod in the apparent upright position, in total darkness. Abnormal results (outside ± 4°) were recorded in 58% of the subjects. Negative (top backward) deviations were the most common, particularly with mass lesions in the pineal region, obstructive hydrocephalus, cerebellar lesions and crowding at the craniocervical junction. Positive and negative deviations were about equally common with focal intra-axial lesions. Negative deviations appeared related to dorsal locations of lesions and vice versa. Normal pressure hydrocephalus, Parkinson's disease and progressive supranuclear palsy were associated with smaller deviations, without a clear directional preponderance, and a larger individual variability. Most subjects lacked overt clinical corollaries. The most common ocular signs were aqueduct syndromes (n = 17) and ocular tilt reactions (n = 12), which were associated with deviations in 47 and 92% of instances, respectively. Subjective corollaries of deviation were never reported, not even by those subjects who showed a dramatic improvement upon resolution of the underlying condition. Deviations were also assessed in roll in a subgroup of 40 patients with focal lesions. Thirty subjects returned abnormal results: 13% in roll, 47% in pitch and 40% in pitch and roll. The direction of roll deviation appeared primarily related to laterality, with clockwise deviations with right-sided lesions and vice versa. All subjects with ocular tilt reactions had combined pitch and roll deviations, implying a common neural substrate. Correlation analyses, geometrical modelling and experimental self-observations indicated that deviations in pitch were attributable to cyclotorsional asymmetries between the eyes. The frequent co-existence of abnormal pitch and roll results implies that the true axis of deviation in focal brainstem disorders commonly falls outside traditional reference planes. The term 'visual upright in three dimensions' is suggested to identify unrestricted measurements, preserving the established term 'visual vertical' for measurements confined to the roll plane. Assessment of the visual upright in three dimensions provides a new, quantitative angle on brainstem disorders. The test appears useful for identifying a ubiquitous yet clinically silent feature of brainstem disease and also for monitoring the evolution of underlying conditions. More detailed explorations appear well motivated.

Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity.

PubMed

Snellings, André; Sagher, Oren; Anderson, David J; Aldridge, J Wayne

2009-10-01

The authors developed a wavelet-based measure for quantitative assessment of neural background activity during intraoperative neurophysiological recordings so that the boundaries of the subthalamic nucleus (STN) can be more easily localized for electrode implantation. Neural electrophysiological data were recorded in 14 patients (20 tracks and 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson disease during the target localization portion of deep brain stimulator implantation surgery. During intraoperative recording, the STN was identified based on audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and the known characteristics of the target nucleus. The quantitative wavelet-based measure was applied offline using commercially available software to measure the magnitude of the neural background activity, and the results of this analysis were compared with the intraoperative conclusions. Wavelet-derived estimates were also compared with power spectral density measurements. The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in the surrounding regions (STN, 225 +/- 61 microV; ventral to the STN, 112 +/- 32 microV; and dorsal to the STN, 136 +/- 66 microV). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than measurements with power spectral density. Wavelet-derived background activity can be calculated quickly, does not require spike sorting, and can be used to identify the STN reliably with very little subjective interpretation required. This method may facilitate the rapid intraoperative identification of STN borders.

Identification of the subthalamic nucleus in deep brain stimulation surgery with a novel wavelet-derived measure of neural background activity

PubMed Central

Snellings, André; Sagher, Oren; Anderson, David J.; Aldridge, J. Wayne

2016-01-01

Object A wavelet-based measure was developed to quantitatively assess neural background activity taken during surgical neurophysiological recordings to localize the boundaries of the subthalamic nucleus during target localization for deep brain stimulator implant surgery. Methods Neural electrophysiological data was recorded from 14 patients (20 tracks, n = 275 individual recording sites) with dopamine-sensitive idiopathic Parkinson’s disease during the target localization portion of deep brain stimulator implant surgery. During intraoperative recording the STN was identified based upon audio and visual monitoring of neural firing patterns, kinesthetic tests, and comparisons between neural behavior and known characteristics of the target nucleus. The quantitative wavelet-based measure was applied off-line using MATLAB software to measure the magnitude of the neural background activity, and the results of this analysis were compared to the intraoperative conclusions. Wavelet-derived estimates were compared to power spectral density measures. Results The wavelet-derived background levels were significantly higher in regions encompassed by the clinically estimated boundaries of the STN than in surrounding regions (STN: 225 ± 61 μV vs. ventral to STN: 112 ± 32 μV, and dorsal to STN: 136 ± 66 μV). In every track, the absolute maximum magnitude was found within the clinically identified STN. The wavelet-derived background levels provided a more consistent index with less variability than power spectral density. Conclusions The wavelet-derived background activity assessor can be calculated quickly, requires no spike sorting, and can be reliably used to identify the STN with very little subjective interpretation required. This method may facilitate rapid intraoperative identification of subthalamic nucleus borders. PMID:19344225

Comparison of Two Hybrid Models for Forecasting the Incidence of Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China

PubMed Central

Wu, Wei; Guo, Junqiao; An, Shuyi; Guan, Peng; Ren, Yangwu; Xia, Linzi; Zhou, Baosen

2015-01-01

Background Cases of hemorrhagic fever with renal syndrome (HFRS) are widely distributed in eastern Asia, especially in China, Russia, and Korea. It is proved to be a difficult task to eliminate HFRS completely because of the diverse animal reservoirs and effects of global warming. Reliable forecasting is useful for the prevention and control of HFRS. Methods Two hybrid models, one composed of nonlinear autoregressive neural network (NARNN) and autoregressive integrated moving average (ARIMA) the other composed of generalized regression neural network (GRNN) and ARIMA were constructed to predict the incidence of HFRS in the future one year. Performances of the two hybrid models were compared with ARIMA model. Results The ARIMA, ARIMA-NARNN ARIMA-GRNN model fitted and predicted the seasonal fluctuation well. Among the three models, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of ARIMA-NARNN hybrid model was the lowest both in modeling stage and forecasting stage. As for the ARIMA-GRNN hybrid model, the MSE, MAE and MAPE of modeling performance and the MSE and MAE of forecasting performance were less than the ARIMA model, but the MAPE of forecasting performance did not improve. Conclusion Developing and applying the ARIMA-NARNN hybrid model is an effective method to make us better understand the epidemic characteristics of HFRS and could be helpful to the prevention and control of HFRS. PMID:26270814

Virtual Sensors for On-line Wheel Wear and Part Roughness Measurement in the Grinding Process

PubMed Central

Arriandiaga, Ander; Portillo, Eva; Sánchez, Jose A.; Cabanes, Itziar; Pombo, Iñigo

2014-01-01

Grinding is an advanced machining process for the manufacturing of valuable complex and accurate parts for high added value sectors such as aerospace, wind generation, etc. Due to the extremely severe conditions inside grinding machines, critical process variables such as part surface finish or grinding wheel wear cannot be easily and cheaply measured on-line. In this paper a virtual sensor for on-line monitoring of those variables is presented. The sensor is based on the modelling ability of Artificial Neural Networks (ANNs) for stochastic and non-linear processes such as grinding; the selected architecture is the Layer-Recurrent neural network. The sensor makes use of the relation between the variables to be measured and power consumption in the wheel spindle, which can be easily measured. A sensor calibration methodology is presented, and the levels of error that can be expected are discussed. Validation of the new sensor is carried out by comparing the sensor's results with actual measurements carried out in an industrial grinding machine. Results show excellent estimation performance for both wheel wear and surface roughness. In the case of wheel wear, the absolute error is within the range of microns (average value 32 μm). In the case of surface finish, the absolute error is well below Ra 1 μm (average value 0.32 μm). The present approach can be easily generalized to other grinding operations. PMID:24854055

Intra- and interregional coregulation of opioid genes: broken symmetry in spinal circuits

PubMed Central

Kononenko, Olga; Galatenko, Vladimir; Andersson, Malin; Bazov, Igor; Watanabe, Hiroyuki; Zhou, Xing Wu; Iatsyshyna, Anna; Mityakina, Irina; Yakovleva, Tatiana; Sarkisyan, Daniil; Ponomarev, Igor; Krishtal, Oleg; Marklund, Niklas; Tonevitsky, Alex; Adkins, DeAnna L.; Bakalkin, Georgy

2017-01-01

Regulation of the formation and rewiring of neural circuits by neuropeptides may require coordinated production of these signaling molecules and their receptors that may be established at the transcriptional level. Here, we address this hypothesis by comparing absolute expression levels of opioid peptides with their receptors, the largest neuropeptide family, and by characterizing coexpression (transcriptionally coordinated) patterns of these genes. We demonstrated that expression patterns of opioid genes highly correlate within and across functionally and anatomically different areas. Opioid peptide genes, compared with their receptor genes, are transcribed at much greater absolute levels, which suggests formation of a neuropeptide cloud that covers the receptor-expressed circuits. Surprisingly, we found that both expression levels and the proportion of opioid receptors are strongly lateralized in the spinal cord, interregional coexpression patterns are side specific, and intraregional coexpression profiles are affected differently by left- and right-side unilateral body injury. We propose that opioid genes are regulated as interconnected components of the same molecular system distributed between distinct anatomic regions. The striking feature of this system is its asymmetric coexpression patterns, which suggest side-specific regulation of selective neural circuits by opioid neurohormones.—Kononenko, O., Galatenko, V., Andersson, M., Bazov, I., Watanabe, H., Zhou, X. W., Iatsyshyna, A., Mityakina, I., Yakovleva, T., Sarkisyan, D., Ponomarev, I., Krishtal, O., Marklund, N., Tonevitsky, A., Adkins, D. L., Bakalkin, G. Intra- and interregional coregulation of opioid genes: broken symmetry in spinal circuits. PMID:28122917

Adaptation to stimulus statistics in the perception and neural representation of auditory space.

PubMed

Dahmen, Johannes C; Keating, Peter; Nodal, Fernando R; Schulz, Andreas L; King, Andrew J

2010-06-24

Sensory systems are known to adapt their coding strategies to the statistics of their environment, but little is still known about the perceptual implications of such adjustments. We investigated how auditory spatial processing adapts to stimulus statistics by presenting human listeners and anesthetized ferrets with noise sequences in which interaural level differences (ILD) rapidly fluctuated according to a Gaussian distribution. The mean of the distribution biased the perceived laterality of a subsequent stimulus, whereas the distribution's variance changed the listeners' spatial sensitivity. The responses of neurons in the inferior colliculus changed in line with these perceptual phenomena. Their ILD preference adjusted to match the stimulus distribution mean, resulting in large shifts in rate-ILD functions, while their gain adapted to the stimulus variance, producing pronounced changes in neural sensitivity. Our findings suggest that processing of auditory space is geared toward emphasizing relative spatial differences rather than the accurate representation of absolute position.

Short-term acoustic forecasting via artificial neural networks for neonatal intensive care units.

PubMed

Young, Jason; Macke, Christopher J; Tsoukalas, Lefteri H

2012-11-01

Noise levels in hospitals, especially neonatal intensive care units (NICUs), have become of great concern for hospital designers. This paper details an artificial neural network (ANN) approach to forecasting the sound loads in NICUs. The ANN is used to learn the relationship between past, present, and future noise levels. By training the ANN with data specific to the location and device used to measure the sound, the ANN is able to produce reasonable predictions of noise levels in the NICU. Best case results show average absolute errors of 5.06 ± 4.04% when used to predict the noise levels one hour ahead, which correspond to 2.53 dBA ± 2.02 dBA. The ANN has the tendency to overpredict during periods of stability and underpredict during large transients. This forecasting algorithm could be of use in any application where prediction and prevention of harmful noise levels are of the utmost concern.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A.; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame.

CAD/CAM produces dentures with improved fit.

PubMed

Steinmassl, Otto; Dumfahrt, Herbert; Grunert, Ingrid; Steinmassl, Patricia-Anca

2018-02-22

Resin polymerisation shrinkage reduces the congruence of the denture base with denture-bearing tissues and thereby decreases the retention of conventionally fabricated dentures. CAD/CAM denture manufacturing is a subtractive process, and polymerisation shrinkage is not an issue anymore. Therefore, CAD/CAM dentures are assumed to show a higher denture base congruence than conventionally fabricated dentures. It has been the aim of this study to test this hypothesis. CAD/CAM dentures provided by four different manufacturers (AvaDent, Merz Dental, Whole You, Wieland/Ivoclar) were generated from ten different master casts. Ten conventional dentures (pack and press, long-term heat polymerisation) made from the same master casts served as control group. The master casts and all denture bases were scanned and matched digitally. The absolute incongruences were measured using a 2-mm mesh. Conventionally fabricated dentures showed a mean deviation of 0.105 mm, SD = 0.019 from the master cast. All CAD/CAM dentures showed lower mean incongruences. From all CAD/CAM dentures, AvaDent Digital Dentures showed the highest congruence with the master cast surface with a mean deviation of 0.058 mm, SD = 0.005. Wieland Digital Dentures showed a mean deviation of 0.068 mm, SD = 0.005, Whole You Nexteeth prostheses showed a mean deviation of 0.074 mm, SD = 0.011 and Baltic Denture System prostheses showed a mean deviation of 0.086 mm, SD = 0.012. CAD/CAM produces dentures with better fit than conventional dentures. The present study explains the clinically observed enhanced retention and lower traumatic ulcer-frequency in CAD/CAM dentures.

Cone-Beam Computed Tomography Assessment of Lower Facial Asymmetry in Unilateral Cleft Lip and Palate and Non-Cleft Patients with Class III Skeletal Relationship.

PubMed

Lin, Yifan; Chen, Gui; Fu, Zhen; Ma, Lian; Li, Weiran

2015-01-01

To evaluate, using cone-beam computed tomography (CBCT), both the condylar-fossa relationships and the mandibular and condylar asymmetries between unilateral cleft lip and palate (UCLP) patients and non-cleft patients with class III skeletal relationship, and to investigate the factors of asymmetry contributing to chin deviation. The UCLP and non-cleft groups consisted of 30 and 40 subjects, respectively, in mixed dentition with class III skeletal relationships. Condylar-fossa relationships and the dimensional and positional asymmetries of the condyles and mandibles were examined using CBCT. Intra-group differences were compared between two sides in both groups using a paired t-test. Furthermore, correlations between each measurement and chin deviation were assessed. It was observed that 90% of UCLP and 67.5% of non-cleft subjects had both condyles centered, and no significant asymmetry was found. The axial angle and the condylar center distances to the midsagittal plane were significantly greater on the cleft side than on the non-cleft side (P=0.001 and P=0.028, respectively) and were positively correlated with chin deviation in the UCLP group. Except for a larger gonial angle on the cleft side, the two groups presented with consistent asymmetries showing shorter mandibular bodies and total mandibular lengths on the cleft (deviated) side. The average chin deviation was 1.63 mm to the cleft side, and the average absolute chin deviation was significantly greater in the UCLP group than in the non-cleft group (P=0.037). Compared with non-cleft subjects with similar class III skeletal relationships, the subjects with UCLP showed more severe lower facial asymmetry. The subjects with UCLP presented with more asymmetrical positions and rotations of the condyles on axial slices, which were positively correlated with chin deviation.

A simulation study of nonparametric total deviation index as a measure of agreement based on quantile regression.

PubMed

Lin, Lawrence; Pan, Yi; Hedayat, A S; Barnhart, Huiman X; Haber, Michael

2016-01-01

Total deviation index (TDI) captures a prespecified quantile of the absolute deviation of paired observations from raters, observers, methods, assays, instruments, etc. We compare the performance of TDI using nonparametric quantile regression to the TDI assuming normality (Lin, 2000). This simulation study considers three distributions: normal, Poisson, and uniform at quantile levels of 0.8 and 0.9 for cases with and without contamination. Study endpoints include the bias of TDI estimates (compared with their respective theoretical values), standard error of TDI estimates (compared with their true simulated standard errors), and test size (compared with 0.05), and power. Nonparametric TDI using quantile regression, although it slightly underestimates and delivers slightly less power for data without contamination, works satisfactorily under all simulated cases even for moderate (say, ≥40) sample sizes. The performance of the TDI based on a quantile of 0.8 is in general superior to that of 0.9. The performances of nonparametric and parametric TDI methods are compared with a real data example. Nonparametric TDI can be very useful when the underlying distribution on the difference is not normal, especially when it has a heavy tail.

Comparing airborne and satellite retrievals of cloud optical thickness and particle effective radius using a spectral radiance ratio technique: two case studies for cirrus and deep convective clouds

NASA Astrophysics Data System (ADS)

Krisna, Trismono C.; Wendisch, Manfred; Ehrlich, André; Jäkel, Evelyn; Werner, Frank; Weigel, Ralf; Borrmann, Stephan; Mahnke, Christoph; Pöschl, Ulrich; Andreae, Meinrat O.; Voigt, Christiane; Machado, Luiz A. T.

2018-04-01

Solar radiation reflected by cirrus and deep convective clouds (DCCs) was measured by the Spectral Modular Airborne Radiation Measurement System (SMART) installed on the German High Altitude and Long Range Research Aircraft (HALO) during the Mid-Latitude Cirrus (ML-CIRRUS) and the Aerosol, Cloud, Precipitation, and Radiation Interaction and Dynamic of Convective Clouds System - Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modelling and to the Global Precipitation Measurement (ACRIDICON-CHUVA) campaigns. On particular flights, HALO performed measurements closely collocated with overpasses of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua satellite. A cirrus cloud located above liquid water clouds and a DCC topped by an anvil cirrus are analyzed in this paper. Based on the nadir spectral upward radiance measured above the two clouds, the optical thickness τ and particle effective radius reff of the cirrus and DCC are retrieved using a radiance ratio technique, which considers the cloud thermodynamic phase, the vertical profile of cloud microphysical properties, the presence of multilayer clouds, and the heterogeneity of the surface albedo. For the cirrus case, the comparison of τ and reff retrieved on the basis of SMART and MODIS measurements yields a normalized mean absolute deviation of up to 1.2 % for τ and 2.1 % for reff. For the DCC case, deviations of up to 3.6 % for τ and 6.2 % for reff are obtained. The larger deviations in the DCC case are mainly attributed to the fast cloud evolution and three-dimensional (3-D) radiative effects. Measurements of spectral upward radiance at near-infrared wavelengths are employed to investigate the vertical profile of reff in the cirrus. The retrieved values of reff are compared with corresponding in situ measurements using a vertical weighting method. Compared to the MODIS observations, measurements of SMART provide more information on the vertical distribution of particle sizes, which allow reconstructing the profile of reff close to the cloud top. The comparison between retrieved and in situ reff yields a normalized mean absolute deviation, which ranges between 1.5 and 10.3 %, and a robust correlation coefficient of 0.82.

The influence of cooling forearm/hand and gender on estimation of handgrip strength.

PubMed

Cheng, Chih-Chan; Shih, Yuh-Chuan; Tsai, Yue-Jin; Chi, Chia-Fen

2014-01-01

Handgrip strength is essential in manual operations and activities of daily life, but the influence of forearm/hand skin temperature on estimation of handgrip strength is not well documented. Therefore, the present study intended to investigate the effect of local cooling of the forearm/hand on estimation of handgrip strength at various target force levels (TFLs, in percentage of MVC) for both genders. A cold pressor test was used to lower and maintain the hand skin temperature at 14°C for comparison with the uncooled condition. A total of 10 male and 10 female participants were recruited. The results indicated that females had greater absolute estimation deviations. In addition, both genders had greater absolute deviations in the middle range of TFLs. Cooling caused an underestimation of grip strength. Furthermore, a power function is recommended for establishing the relationship between actual and estimated handgrip force. Statement of relevance: Manipulation with grip strength is essential in daily life and the workplace, so it is important to understand the influence of lowering the forearm/hand skin temperature on grip-strength estimation. Females and the middle range of TFL had greater deviations. Cooling the forearm/hand tended to cause underestimation, and a power function is recommended for establishing the relationship between actual and estimated handgrip force. Practitioner Summary: It is important to understand the effect of lowering the forearm/hand skin temperature on grip-strength estimation. A cold pressor was used to cool the hand. The cooling caused underestimation, and a power function is recommended for establishing the relationship between actual and estimated handgrip force. Manipulation with grip strength is essential in daily life and the workplace, so it is important to understand the influence of lowering the forearm/hand skin temperature on grip-strength estimation. Females and the middle range of TFL had greater deviations. Cooling the forearm/hand tended to cause underestimation, and a power function is recommended for establishing the relationship between actual and estimated handgrip force. It is important to understand the effect of lowering the forearm/hand skin temperature on grip-strength estimation. A cold pressor was used to cool the hand. The cooling caused underestimation, and a power function is recommended for establishing the relationship between actual and estimated handgrip force

Interaction of the renin-angiotensin system and the renal nerves in the regulation of rat kidney function.

PubMed Central

Handa, R K; Johns, E J

1985-01-01

Stimulation of the renal sympathetic nerves in pentobarbitone anaesthetized rats achieved a 13% reduction in renal blood flow, did not change glomerular filtration rate, but reduced urine flow by 37%, absolute sodium excretion by 37%, and fractional sodium excretion by 34%. Following inhibition of converting enzyme with captopril (0.38 mmol kg-1 h-1), similar nerve stimulation reduced both renal blood flow and glomerular filtration rate by 16%, and although urine flow and absolute sodium excretion fell by 32 and 31%, respectively, the 18% fall in fractional sodium excretion was significantly less than that observed in the absence of captopril. Renal nerve stimulation at low levels, which did not change either renal blood flow or glomerular filtration rate, reduced urine flow, and absolute and fractional sodium excretions by 25, 26 and 23%, respectively. In animals receiving captopril at 0.38 mmol kg-1 h-1, low-level nerve stimulation caused small increases in glomerular filtration rate of 7% and urine flow of 12%, but did not change either absolute or fractional sodium excretions. At one-fifth the dose of captopril (0.076 mmol kg-1 h-1), low-level nerve stimulation did not change renal haemodynamics but decreased urine flow, and absolute and fractional sodium excretions by 10, 10 and 8%, respectively. These results showed that angiotensin II production was necessary for regulation of glomerular filtration rate in the face of modest neurally induced reductions in renal blood flow and was compatible with an intra-renal site of action of angiotensin II preferentially at the efferent arteriole. They also demonstrated that in the rat the action of the renal nerves to decrease sodium excretion was dependent on angiotensin II. PMID:3005558

Calculation of Crystallographic Texture of BCC Steels During Cold Rolling

NASA Astrophysics Data System (ADS)

Das, Arpan

2017-05-01

BCC alloys commonly tend to develop strong fibre textures and often represent as isointensity diagrams in φ 1 sections or by fibre diagrams. Alpha fibre in bcc steels is generally characterised by <110> crystallographic axis parallel to the rolling direction. The objective of present research is to correlate carbon content, carbide dispersion, rolling reduction, Euler angles (ϕ) (when φ 1 = 0° and φ 2 = 45° along alpha fibre) and the resulting alpha fibre texture orientation intensity. In the present research, Bayesian neural computation has been employed to correlate these and compare with the existing feed-forward neural network model comprehensively. Excellent match to the measured texture data within the bounding box of texture training data set has been already predicted through the feed-forward neural network model by other researchers. Feed-forward neural network prediction outside the bounds of training texture data showed deviations from the expected values. Currently, Bayesian computation has been similarly applied to confirm that the predictions are reasonable in the context of basic metallurgical principles, and matched better outside the bounds of training texture data set than the reported feed-forward neural network. Bayesian computation puts error bars on predicted values and allows significance of each individual parameters to be estimated. Additionally, it is also possible by Bayesian computation to estimate the isolated influence of particular variable such as carbon concentration, which exactly cannot in practice be varied independently. This shows the ability of the Bayesian neural network to examine the new phenomenon in situations where the data cannot be accessed through experiments.

Absolute Power Spectral Density Changes in the Magnetoencephalographic Activity During the Transition from Childhood to Adulthood.

PubMed

Gómez, Carlos M; Rodríguez-Martínez, Elena I; Fernández, Alberto; Maestú, Fernando; Poza, Jesús; Gómez, Carlos

2017-01-01

The aim of this study was to define the pattern of reduction in absolute power spectral density (PSD) of magnetoencephalography (MEG) signals throughout development. Specifically, we wanted to explore whether the human skull's high permeability for electromagnetic fields would allow us to question whether the pattern of absolute PSD reduction observed in the human electroencephalogram is due to an increase in the skull's resistive properties with age. Furthermore, the topography of the MEG signals during maturation was explored, providing additional insights about the areas and brain rhythms related to late maturation in the human brain. To attain these goals, spontaneous MEG activity was recorded from 148 sensors in a sample of 59 subjects divided into three age groups: children/adolescents (7-14 years), young adults (17-20 years) and adults (21-26 years). Statistical testing was carried out by means of an analysis of variance (ANOVA), with "age group" as between-subject factor and "sensor group" as within-subject factor. Additionally, correlations of absolute PSD with age were computed to assess the influence of age on the spectral content of MEG signals. Results showed a broadband PSD decrease in frontal areas, which suggests the late maturation of this region, but also a mild increase in high frequency PSD with age in posterior areas. These findings suggest that the intensity of the neural sources during spontaneous brain activity decreases with age, which may be related to synaptic pruning.

SNPP VIIRS RSB Earth View Reflectance Uncertainty

NASA Technical Reports Server (NTRS)

Lei, Ning; Twedt, Kevin; McIntire, Jeff; Xiong, Xiaoxiong

2017-01-01

The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) satellite uses its 14 reflective solar bands to passively collect solar radiant energy reflected off the Earth. The Level 1 product is the geolocated and radiometrically calibrated top-of- the-atmosphere solar reflectance. The absolute radiometric uncertainty associated with this product includes contributions from the noise associated with measured detector digital counts and the radiometric calibration bias. Here, we provide a detailed algorithm for calculating the estimated standard deviation of the retrieved top-of-the-atmosphere spectral solar radiation reflectance.

High-speed rangefinder for industrial application

NASA Astrophysics Data System (ADS)

Cavedo, Federico; Pesatori, Alessandro; Norgia, Michele

2016-06-01

The proposed work aims to improve one of the most used telemetry techniques to make absolute measurements of distance: the time of flight telemetry. The main limitation of the low-cost implementation of this technique is the low accuracy (some mm) and measurement rate (few measurements per second). In order to overcome these limits we modified the typical setup of this rangefinder exploiting low-cost telecommunication transceivers and radiofrequency synthesizers. The obtained performances are very encouraging, reaching a standard deviation of a few micrometers over the range of some meters.

Hyperfine-resolved transition frequency list of fundamental vibration bands of H35Cl and H37Cl

NASA Astrophysics Data System (ADS)

Iwakuni, Kana; Sera, Hideyuki; Abe, Masashi; Sasada, Hiroyuki

2014-12-01

Sub-Doppler resolution spectroscopy of the fundamental vibration bands of H35Cl and H37Cl has been carried out from 87.1 to 89.9 THz. We have determined the absolute transition frequencies of the hyperfine-resolved R(0) to R(4) transitions with a typical uncertainty of 10 kHz. We have also yielded six molecular constants for each isotopomer in the vibrational excited state, which reproduce the determined frequencies with a standard deviation of about 10 kHz.

Determination of carrier concentration and compensation microprofiles in GaAs

NASA Technical Reports Server (NTRS)

Jastrzebski, L.; Lagowski, J.; Walukiewicz, W.; Gatos, H. C.

1980-01-01

Simultaneous microprofiling of semiconductor free carrier, donor, and acceptor concentrations was achieved for the first time from the absolute value of the free carrier absorption coefficient and its wavelength dependence determined by IR absorption in a scanning mode. Employing Ge- and Si-doped melt-grown GaAs, striking differences were found between the variations of electron concentration and those of ionized impurity concentrations. These results showed clearly that the electronic characteristics of this material are controlled by amphoteric doping and deviations from stoichiometry rather than by impurity segregation.

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

PubMed Central

Slama, Matous; Benes, Peter M.; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time. PMID:25893194

[Prediction of soil nutrients spatial distribution based on neural network model combined with goestatistics].

PubMed

Li, Qi-Quan; Wang, Chang-Quan; Zhang, Wen-Jiang; Yu, Yong; Li, Bing; Yang, Juan; Bai, Gen-Chuan; Cai, Yan

2013-02-01

In this study, a radial basis function neural network model combined with ordinary kriging (RBFNN_OK) was adopted to predict the spatial distribution of soil nutrients (organic matter and total N) in a typical hilly region of Sichuan Basin, Southwest China, and the performance of this method was compared with that of ordinary kriging (OK) and regression kriging (RK). All the three methods produced the similar soil nutrient maps. However, as compared with those obtained by multiple linear regression model, the correlation coefficients between the measured values and the predicted values of soil organic matter and total N obtained by neural network model increased by 12. 3% and 16. 5% , respectively, suggesting that neural network model could more accurately capture the complicated relationships between soil nutrients and quantitative environmental factors. The error analyses of the prediction values of 469 validation points indicated that the mean absolute error (MAE) , mean relative error (MRE), and root mean squared error (RMSE) of RBFNN_OK were 6.9%, 7.4%, and 5. 1% (for soil organic matter), and 4.9%, 6.1% , and 4.6% (for soil total N) smaller than those of OK (P<0.01), and 2.4%, 2.6% , and 1.8% (for soil organic matter), and 2.1%, 2.8%, and 2.2% (for soil total N) smaller than those of RK, respectively (P<0.05).

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

PubMed Central

Zhao, Ningbo; Li, Zhiming

2017-01-01

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

Neural population encoding and decoding of sound source location across sound level in the rabbit inferior colliculus

PubMed Central

Delgutte, Bertrand

2015-01-01

At lower levels of sensory processing, the representation of a stimulus feature in the response of a neural population can vary in complex ways across different stimulus intensities, potentially changing the amount of feature-relevant information in the response. How higher-level neural circuits could implement feature decoding computations that compensate for these intensity-dependent variations remains unclear. Here we focused on neurons in the inferior colliculus (IC) of unanesthetized rabbits, whose firing rates are sensitive to both the azimuthal position of a sound source and its sound level. We found that the azimuth tuning curves of an IC neuron at different sound levels tend to be linear transformations of each other. These transformations could either increase or decrease the mutual information between source azimuth and spike count with increasing level for individual neurons, yet population azimuthal information remained constant across the absolute sound levels tested (35, 50, and 65 dB SPL), as inferred from the performance of a maximum-likelihood neural population decoder. We harnessed evidence of level-dependent linear transformations to reduce the number of free parameters in the creation of an accurate cross-level population decoder of azimuth. Interestingly, this decoder predicts monotonic azimuth tuning curves, broadly sensitive to contralateral azimuths, in neurons at higher levels in the auditory pathway. PMID:26490292

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

PubMed

Zhao, Ningbo; Li, Zhiming

2017-05-19

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

A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications.

PubMed

Bukovsky, Ivo; Homma, Noriyasu; Ichiji, Kei; Cejnek, Matous; Slama, Matous; Benes, Peter M; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

Artificial neural network associated to UV/Vis spectroscopy for monitoring bioreactions in biopharmaceutical processes.

PubMed

Takahashi, Maria Beatriz; Leme, Jaci; Caricati, Celso Pereira; Tonso, Aldo; Fernández Núñez, Eutimio Gustavo; Rocha, José Celso

2015-06-01

Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV-Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV-Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 10(5) ± 1.90 10(5) cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV-VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.

EMG prediction from Motor Cortical Recordings via a Non-Negative Point Process Filter

PubMed Central

Nazarpour, Kianoush; Ethier, Christian; Paninski, Liam; Rebesco, James M.; Miall, R. Chris; Miller, Lee E.

2012-01-01

A constrained point process filtering mechanism for prediction of electromyogram (EMG) signals from multi-channel neural spike recordings is proposed here. Filters from the Kalman family are inherently sub-optimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Gaussianity assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model (GLM) that encapsulates covariates of neural activity, including the neurons’ own spiking history, concurrent ensemble activity, and extrinsic covariates (EMG signals). In order to predict the envelopes of EMGs, we reformulated the Kalman filter (KF) in an optimization framework and utilized a non-negativity constraint. This structure characterizes the non-linear correspondence between neural activity and EMG signals reasonably. The EMGs were recorded from twelve forearm and hand muscles of a behaving monkey during a grip-force task. For the case of limited training data, the constrained point process filter improved the prediction accuracy when compared to a conventional Wiener cascade filter (a linear causal filter followed by a static non-linearity) for different bin sizes and delays between input spikes and EMG output. For longer training data sets, results of the proposed filter and that of the Wiener cascade filter were comparable. PMID:21659018

Fully convolutional neural network for removing background in noisy images of uranium bearing particles

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

Tarolli, Jay G.; Naes, Benjamin E.; Butler, Lamar

A fully convolutional neural network (FCN) was developed to supersede automatic or manual thresholding algorithms used for tabulating SIMS particle search data. The FCN was designed to perform a binary classification of pixels in each image belonging to a particle or not, thereby effectively removing background signal without manually or automatically determining an intensity threshold. Using 8,000 images from 28 different particle screening analyses, the FCN was trained to accurately predict pixels belonging to a particle with near 99% accuracy. Background eliminated images were then segmented using a watershed technique in order to determine isotopic ratios of particles. A comparisonmore » of the isotopic distributions of an independent data set segmented using the neural network, compared to a commercially available automated particle measurement (APM) program developed by CAMECA, highlighted the necessity for effective background removal to ensure that resulting particle identification is not only accurate, but preserves valuable signal that could be lost due to improper segmentation. The FCN approach improves the robustness of current state-of-the-art particle searching algorithms by reducing user input biases, resulting in an improved absolute signal per particle and decreased uncertainty of the determined isotope ratios.« less

Emergent central pattern generator behavior in chemical coupled two-compartment models with time delay

NASA Astrophysics Data System (ADS)

Li, Shanshan; Zhang, Guoshan; Wang, Jiang; Chen, Yingyuan; Deng, Bin

2018-02-01

This paper proposes that modified two-compartment Pinsky-Rinzel (PR) neural model can be used to develop the simple form of central pattern generator (CPG). The CPG is called as 'half-central oscillator', which constructed by two inhibitory chemical coupled PR neurons with time delay. Some key properties of PR neural model related to CPG are studied and proved to meet the requirements of CPG. Using the simple CPG network, we first study the relationship between rhythmical output and key factors, including ambient noise, sensory feedback signals, morphological character of single neuron as well as the coupling delay time. We demonstrate that, appropriate intensity noise can enhance synchronization between two coupled neurons. Different output rhythm of CPG network can be entrained by sensory feedback signals. We also show that the morphology of single neuron has strong effect on the output rhythm. The phase synchronization indexes decrease with the increase of morphology parameter's difference. Through adjusting coupled delay time, we can get absolutely phase synchronization and antiphase state of CPG. Those results of simulation show the feasibility of PR neural model as a valid CPG as well as the emergent behaviors of the particularly CPG.

Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

NASA Astrophysics Data System (ADS)

ElSaid, AbdElRahman Ahmed

This thesis examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The different networks are trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a "memory" of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 1, 5, 10, and 20 seconds in the future, with 2.84% 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

PubMed Central

Vesperini, Fabio; Schuller, Björn

2017-01-01

In the emerging field of acoustic novelty detection, most research efforts are devoted to probabilistic approaches such as mixture models or state-space models. Only recent studies introduced (pseudo-)generative models for acoustic novelty detection with recurrent neural networks in the form of an autoencoder. In these approaches, auditory spectral features of the next short term frame are predicted from the previous frames by means of Long-Short Term Memory recurrent denoising autoencoders. The reconstruction error between the input and the output of the autoencoder is used as activation signal to detect novel events. There is no evidence of studies focused on comparing previous efforts to automatically recognize novel events from audio signals and giving a broad and in depth evaluation of recurrent neural network-based autoencoders. The present contribution aims to consistently evaluate our recent novel approaches to fill this white spot in the literature and provide insight by extensive evaluations carried out on three databases: A3Novelty, PASCAL CHiME, and PROMETHEUS. Besides providing an extensive analysis of novel and state-of-the-art methods, the article shows how RNN-based autoencoders outperform statistical approaches up to an absolute improvement of 16.4% average F-measure over the three databases. PMID:28182121

Neural-Network Approach to Hyperspectral Data Analysis for Volcanic Ash Clouds Monitoring

NASA Astrophysics Data System (ADS)

Piscini, Alessandro; Ventress, Lucy; Carboni, Elisa; Grainger, Roy Gordon; Del Frate, Fabio

2015-11-01

In this study three artificial neural networks (ANN) were implemented in order to emulate a retrieval model and to estimate the ash Aerosol optical Depth (AOD), particle effective radius (reff) and cloud height from volcanic eruption using hyperspectral remotely sensed data. ANNs were trained using a selection of Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding ash parameters retrieved obtained using the Oxford retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajo ̈kull volcano (Iceland) occurred in 2010. The results of validation provided root mean square error (RMSE) values between neural network outputs and targets lower than standard deviation (STD) of corresponding target outputs, therefore demonstrating the feasibility to estimate volcanic ash parameters using an ANN approach, and its importance in near real time monitoring activities, owing to its fast application. A high accuracy has been achieved for reff and cloud height estimation, while a decreasing in accuracy was obtained when applying the NN approach for AOD estimation, in particular for those values not well characterized during NN training phase.

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

PubMed

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl; Mathiassen, Solvejg Kopp; Somerville, Gayle J; Jørgensen, Rasmus Nyholm

2018-05-16

This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions. The overall performance of this approach achieved a maximum accuracy of 78% for identifying Polygonum spp. and a minimum accuracy of 46% for blackgrass. In addition, it achieved an average 70% accuracy rate in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species.

The neural basis of suppression and amblyopia in strabismus.

PubMed

Sengpiel, F; Blakemore, C

1996-01-01

The neurophysiological consequences of artificial strabismus in cats and monkeys have been studied for 30 years. However, until very recently no clear picture has emerged of neural deficits that might account for the powerful interocular suppression that strabismic humans experience, nor for the severe amblyopia that is often associated with convergent strabismus. Here we review the effects of squint on the integrative capacities of the primary visual cortex and propose a hypothesis about the relationship between suppression and amblyopia. Most neurons in the visual cortex of normal cats and monkeys can be excited through either eye and show strong facilitation during binocular stimulation with contours of similar orientation in the two eyes. But in strabismic animals, cortical neurons tend to fall into two populations of monocularly excitable cells and exhibit suppressive binocular interactions that share key properties with perceptual suppression in strabismic humans. Such interocular suppression, if prolonged and asymmetric (with input from the squinting eye habitually suppressed by that from the fixating eye), might lead to neural defects in the representation of the deviating eye and hence to amblyopia.

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 epididymal sperm spiration; RBFN: radical basis function network; SRNN: simple recurrent neural network; SVM: support vector machines; TSE: testicular sperm extraction; WHO: World Health Organization.

Full-Field Calibration of Color Camera Chromatic Aberration using Absolute Phase Maps.

PubMed

Liu, Xiaohong; Huang, Shujun; Zhang, Zonghua; Gao, Feng; Jiang, Xiangqian

2017-05-06

The refractive index of a lens varies for different wavelengths of light, and thus the same incident light with different wavelengths has different outgoing light. This characteristic of lenses causes images captured by a color camera to display chromatic aberration (CA), which seriously reduces image quality. Based on an analysis of the distribution of CA, a full-field calibration method based on absolute phase maps is proposed in this paper. Red, green, and blue closed sinusoidal fringe patterns are generated, consecutively displayed on an LCD (liquid crystal display), and captured by a color camera from the front viewpoint. The phase information of each color fringe is obtained using a four-step phase-shifting algorithm and optimum fringe number selection method. CA causes the unwrapped phase of the three channels to differ. These pixel deviations can be computed by comparing the unwrapped phase data of the red, blue, and green channels in polar coordinates. CA calibration is accomplished in Cartesian coordinates. The systematic errors introduced by the LCD are analyzed and corrected. Simulated results show the validity of the proposed method and experimental results demonstrate that the proposed full-field calibration method based on absolute phase maps will be useful for practical software-based CA calibration.

The European Comparison of Absolute Gravimeters 2011 (ECAG-2011) in Walferdange, Luxembourg: results and recommendations

NASA Astrophysics Data System (ADS)

Francis, Olivier; Baumann, Henri; Volarik, Tomas; Rothleitner, Christian; Klein, Gilbert; Seil, Marc; Dando, Nicolas; Tracey, Ray; Ullrich, Christian; Castelein, Stefaan; Hua, Hu; Kang, Wu; Chongyang, Shen; Songbo, Xuan; Hongbo, Tan; Zhengyuan, Li; Pálinkás, Vojtech; Kostelecký, Jakub; Mäkinen, Jaakko; Näränen, Jyri; Merlet, Sébastien; Farah, Tristan; Guerlin, Christine; Pereira Dos Santos, Franck; Le Moigne, Nicolas; Champollion, Cédric; Deville, Sabrina; Timmen, Ludger; Falk, Reinhard; Wilmes, Herbert; Iacovone, Domenico; Baccaro, Francesco; Germak, Alessandro; Biolcati, Emanuele; Krynski, Jan; Sekowski, Marcin; Olszak, Tomasz; Pachuta, Andrzej; Agren, Jonas; Engfeldt, Andreas; Reudink, René; Inacio, Pedro; McLaughlin, Daniel; Shannon, Geoff; Eckl, Marc; Wilkins, Tim; van Westrum, Derek; Billson, Ryan

2013-06-01

We present the results of the third European Comparison of Absolute Gravimeters held in Walferdange, Grand Duchy of Luxembourg, in November 2011. Twenty-two gravimeters from both metrological and non-metrological institutes are compared. For the first time, corrections for the laser beam diffraction and the self-attraction of the gravimeters are implemented. The gravity observations are also corrected for geophysical gravity changes that occurred during the comparison using the observations of a superconducting gravimeter. We show that these corrections improve the degree of equivalence between the gravimeters. We present the results for two different combinations of data. In the first one, we use only the observations from the metrological institutes. In the second solution, we include all the data from both metrological and non-metrological institutes. Those solutions are then compared with the official result of the comparison published previously and based on the observations of the metrological institutes and the gravity differences at the different sites as measured by non-metrological institutes. Overall, the absolute gravity meters agree with one another with a standard deviation of 3.1 µGal. Finally, the results of this comparison are linked to previous ones. We conclude with some important recommendations for future comparisons.

Auditory attentional capture during serial recall: violations at encoding of an algorithm-based neural model?

PubMed

Hughes, Robert W; Vachon, François; Jones, Dylan M

2005-07-01

A novel attentional capture effect is reported in which visual-verbal serial recall was disrupted if a single deviation in the interstimulus interval occurred within otherwise regularly presented task-irrelevant spoken items. The degree of disruption was the same whether the temporal deviant was embedded in a sequence made up of a repeating item or a sequence of changing items. Moreover, the effect was evident during the presentation of the to-be-remembered sequence but not during rehearsal just prior to recall, suggesting that the encoding of sequences is particularly susceptible. The results suggest that attentional capture is due to a violation of an algorithm rather than an aggregate-based neural model and further undermine an attentional capture-based account of the classical changing-state irrelevant sound effect. ((c) 2005 APA, all rights reserved).

Neural network modelling of thermal stratification in a solar DHW storage

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

Geczy-Vig, P.; Farkas, I.

2010-05-15

In this study an artificial neural network (ANN) model is introduced for modelling the layer temperatures in a storage tank of a solar thermal system. The model is based on the measured data of a domestic hot water system. The temperatures distribution in the storage tank divided in 8 equal parts in vertical direction were calculated every 5 min using the average 5 min data of solar radiation, ambient temperature, mass flow rate of collector loop, load and the temperature of the layers in previous time steps. The introduced ANN model consists of two parts describing the load periods andmore » the periods between the loads. The identified model gives acceptable results inside the training interval as the average deviation was 0.22 C during the training and 0.24 C during the validation. (author)« less

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

Ren, X.; Senftleben, A.; Pflueger, T.

Absolutely normalized (e,2e) measurements for H{sub 2} and He covering the full solid angle of one ejected electron are presented for 16 eV sum energy of both final state continuum electrons. For both targets rich cross-section structures in addition to the binary and recoil lobes are identified and studied as a function of the fixed electron's emission angle and the energy sharing among both electrons. For H{sub 2} their behavior is consistent with multiple scattering of the projectile as discussed before [Al-Hagan et al., Nature Phys. 5, 59 (2009)]. For He the binary and recoil lobes are significantly larger thanmore » for H{sub 2} and partly cover the multiple scattering structures. To highlight these patterns we propose a alternative representation of the triply differential cross section. Nonperturbative calculations are in good agreement with the He results and show discrepancies for H{sub 2} in the recoil peak region. For H{sub 2} a perturbative approach reasonably reproduces the cross-section shape but deviates in absolute magnitude.« less

Frequency comb calibrated frequency-sweeping interferometry for absolute group refractive index measurement of air.

PubMed

Yang, Lijun; Wu, Xuejian; Wei, Haoyun; Li, Yan

2017-04-10

The absolute group refractive index of air at 194061.02 GHz is measured in real time using frequency-sweeping interferometry calibrated by an optical frequency comb. The group refractive index of air is calculated from the calibration peaks of the laser frequency variation and the interference signal of the two beams passing through the inner and outer regions of a vacuum cell when the frequency of a tunable external cavity diode laser is scanned. We continuously measure the refractive index of air for 2 h, which shows that the difference between measured results and Ciddor's equation is less than 9.6×10^-8, and the standard deviation of that difference is 5.9×10^-8. The relative uncertainty of the measured refractive index of air is estimated to be 8.6×10^-8. The data update rate is 0.2 Hz, making it applicable under conditions in which air refractive index fluctuates fast.

Corsica: A Multi-Mission Absolute Calibration Site

NASA Astrophysics Data System (ADS)

Bonnefond, P.; Exertier, P.; Laurain, O.; Guinle, T.; Femenias, P.

2013-09-01

In collaboration with the CNES and NASA oceanographic projects (TOPEX/Poseidon and Jason), the OCA (Observatoire de la Côte d'Azur) developed a verification site in Corsica since 1996, operational since 1998. CALibration/VALidation embraces a wide variety of activities, ranging from the interpretation of information from internal-calibration modes of the sensors to validation of the fully corrected estimates of the reflector heights using in situ data. Now, Corsica is, like the Harvest platform (NASA side) [14], an operating calibration site able to support a continuous monitoring with a high level of accuracy: a 'point calibration' which yields instantaneous bias estimates with a 10-day repeatability of 30 mm (standard deviation) and mean errors of 4 mm (standard error). For a 35-day repeatability (ERS, Envisat), due to a smaller time series, the standard error is about the double ( 7 mm).In this paper, we will present updated results of the absolute Sea Surface Height (SSH) biases for TOPEX/Poseidon (T/P), Jason-1, Jason-2, ERS-2 and Envisat.

Predicting Stability Constants for Uranyl Complexes Using Density Functional Theory

DOE PAGES

Vukovic, Sinisa; Hay, Benjamin P.; Bryantsev, Vyacheslav S.

2015-04-02

The ability to predict the equilibrium constants for the formation of 1:1 uranyl:ligand complexes (log K 1 values) provides the essential foundation for the rational design of ligands with enhanced uranyl affinity and selectivity. We also use density functional theory (B3LYP) and the IEFPCM continuum solvation model to compute aqueous stability constants for UO 2 2+ complexes with 18 donor ligands. Theoretical calculations permit reasonably good estimates of relative binding strengths, while the absolute log K 1 values are significantly overestimated. Accurate predictions of the absolute log K 1 values (root mean square deviation from experiment < 1.0 for logmore » K 1 values ranging from 0 to 16.8) can be obtained by fitting the experimental data for two groups of mono and divalent negative oxygen donor ligands. The utility of correlations is demonstrated for amidoxime and imide dioxime ligands, providing a useful means of screening for new ligands with strong chelate capability to uranyl.« less

The subtype of alpha-adrenoceptor involved in the neural control of renal tubular sodium reabsorption in the rabbit.

PubMed Central

Hesse, I F; Johns, E J

1984-01-01

A study was undertaken in pentobarbitone anaesthetized rabbits, undergoing a saline diuresis, to determine the subtype of alpha-adrenoceptor mediating renal tubular sodium reabsorption. Stimulation of the renal nerves at low rates, to cause an 11% fall in renal blood flow, did not change glomerular filtration rate but significantly reduced urine flow rate, and absolute and fractional sodium excretions by approximately 40%. These responses were reproducible in different groups of animals and with time. Renal nerve stimulation during an intra-renal arterial infusion of prazosin, to block alpha 1-adrenoceptors, had no effect on the renal haemodynamic response but completely abolished the reductions in urine flow rate, and absolute and fractional sodium excretion. During intra-renal arterial infusion of yohimbine, to block renal alpha 2-adrenoceptors, stimulation of the renal nerves to cause similar renal haemodynamic changes resulted in significantly larger reductions in urine flow rate, and absolute and fractional sodium excretion of about 52-58%. These results indicate that in the rabbit alpha 1-adrenoceptors are present on the renal tubules, which mediate the increase in sodium reabsorption caused by renal nerve stimulation. They further suggest the presence of presynaptic alpha 2-adrenoceptors on those nerves innervating the renal tubules. PMID:6086915

Estimation of Local Bone Loads for the Volume of Interest.

PubMed

Kim, Jung Jin; Kim, Youkyung; Jang, In Gwun

2016-07-01

Computational bone remodeling simulations have recently received significant attention with the aid of state-of-the-art high-resolution imaging modalities. They have been performed using localized finite element (FE) models rather than full FE models due to the excessive computational costs of full FE models. However, these localized bone remodeling simulations remain to be investigated in more depth. In particular, applying simplified loading conditions (e.g., uniform and unidirectional loads) to localized FE models have a severe limitation in a reliable subject-specific assessment. In order to effectively determine the physiological local bone loads for the volume of interest (VOI), this paper proposes a novel method of estimating the local loads when the global musculoskeletal loads are given. The proposed method is verified for the three VOI in a proximal femur in terms of force equilibrium, displacement field, and strain energy density (SED) distribution. The effect of the global load deviation on the local load estimation is also investigated by perturbing a hip joint contact force (HCF) in the femoral head. Deviation in force magnitude exhibits the greatest absolute changes in a SED distribution due to its own greatest deviation, whereas angular deviation perpendicular to a HCF provides the greatest relative change. With further in vivo force measurements and high-resolution clinical imaging modalities, the proposed method will contribute to the development of reliable patient-specific localized FE models, which can provide enhanced computational efficiency for iterative computing processes such as bone remodeling simulations.

Regional variations in cancer survival: Impact of tumour stage, socioeconomic status, comorbidity and type of treatment in Norway.

PubMed

Skyrud, Katrine Damgaard; Bray, Freddie; Eriksen, Morten Tandberg; Nilssen, Yngvar; Møller, Bjørn

2016-05-01

Cancer survival varies by place of residence, but it remains uncertain whether this reflects differences in tumour, patient and treatment characteristics (including tumour stage, indicators of socioeconomic status (SES), comorbidity and information on received surgery and radiotherapy) or possibly regional differences in the quality of delivered health care. National population-based data from the Cancer Registry of Norway were used to identify cancer patients diagnosed in 2002-2011 (n = 258,675). We investigated survival from any type of cancer (all cancer sites combined), as well as for the six most common cancers. The effect of adjusting for prognostic factors on regional variations in cancer survival was examined by calculating the mean deviation, defined by the mean absolute deviation of the relative excess risks across health services regions. For prostate cancer, the mean deviation across regions was 1.78 when adjusting for age and sex only, but decreased to 1.27 after further adjustment for tumour stage. For breast cancer, the corresponding mean deviations were 1.34 and 1.27. Additional adjustment for other prognostic factors did not materially change the regional variation in any of the other sites. Adjustment for tumour stage explained most of the regional variations in prostate cancer survival, but had little impact for other sites. Unexplained regional variations after adjusting for tumour stage, SES indicators, comorbidity and type of treatment in Norway may be related to regional inequalities in the quality of cancer care. © 2015 UICC.

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

PubMed Central

Lowet, Eric; Roberts, Mark J.; Bonizzi, Pietro; Karel, Joël; De Weerd, Peter

2016-01-01

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information flow among networks. PMID:26745498

Forecasting volcanic air pollution in Hawaii: Tests of time series models

NASA Astrophysics Data System (ADS)

Reikard, Gordon

2012-12-01

Volcanic air pollution, known as vog (volcanic smog) has recently become a major issue in the Hawaiian islands. Vog is caused when volcanic gases react with oxygen and water vapor. It consists of a mixture of gases and aerosols, which include sulfur dioxide and other sulfates. The source of the volcanic gases is the continuing eruption of Mount Kilauea. This paper studies predicting vog using statistical methods. The data sets include time series for SO2 and SO4, over locations spanning the west, south and southeast coasts of Hawaii, and the city of Hilo. The forecasting models include regressions and neural networks, and a frequency domain algorithm. The most typical pattern for the SO2 data is for the frequency domain method to yield the most accurate forecasts over the first few hours, and at the 24 h horizon. The neural net places second. For the SO4 data, the results are less consistent. At two sites, the neural net generally yields the most accurate forecasts, except at the 1 and 24 h horizons, where the frequency domain technique wins narrowly. At one site, the neural net and the frequency domain algorithm yield comparable errors over the first 5 h, after which the neural net dominates. At the remaining site, the frequency domain method is more accurate over the first 4 h, after which the neural net achieves smaller errors. For all the series, the average errors are well within one standard deviation of the actual data at all the horizons. However, the errors also show irregular outliers. In essence, the models capture the central tendency of the data, but are less effective in predicting the extreme events.

The impact of water temperature on the measurement of absolute dose

NASA Astrophysics Data System (ADS)

Islam, Naveed Mehdi

To standardize reference dosimetry in radiation therapy, Task Group 51 (TG 51) of American Association of Physicist's in Medicine (AAPM) recommends that dose calibration measurements be made in a water tank at a depth of 10 cm and at a reference geometry. Methodologies are provided for calculating various correction factors to be applied in calculating the absolute dose. However the protocol does not specify the water temperature to be used. In practice, the temperature of water during dosimetry may vary considerably between independent sessions and different centers. In this work the effect of water temperature on absolute dosimetry has been investigated. Density of water varies with temperature, which in turn may impact the beam attenuation and scatter properties. Furthermore, due to thermal expansion or contraction air volume inside the chamber may change. All of these effects can result in a change in the measurement. Dosimetric measurements were made using a Farmer type ion chamber on a Varian Linear Accelerator for 6 MV and 23 MV photon energies for temperatures ranging from 10 to 40 °C. A thermal insulation was designed for the water tank in order to maintain relatively stable temperature over the duration of the experiment. Dose measured at higher temperatures were found to be consistently higher by a very small magnitude. Although the differences in dose were less than the uncertainty in each measurement, a linear regression of the data suggests that the trend is statistically significant with p-values of 0.002 and 0.013 for 6 and 23 MV beams respectively. For a 10 degree difference in water phantom temperatures, which is a realistic deviation across clinics, the final calculated reference dose can differ by 0.24% or more. To address this effect, first a reference temperature (e.g.22 °C) can be set as the standard; subsequently a correction factor can be implemented for deviations from this reference. Such a correction factor is expected to be of similar magnitude as existing TG 51 recommended correction factors.

Impact of the radiotherapy technique on the correlation between dose-volume histograms of the bladder wall defined on MRI imaging and dose-volume/surface histograms in prostate cancer patients

NASA Astrophysics Data System (ADS)

Maggio, Angelo; Carillo, Viviana; Cozzarini, Cesare; Perna, Lucia; Rancati, Tiziana; Valdagni, Riccardo; Gabriele, Pietro; Fiorino, Claudio

2013-04-01

The aim of this study was to evaluate the correlation between the ‘true’ absolute and relative dose-volume histograms (DVHs) of the bladder wall, dose-wall histogram (DWH) defined on MRI imaging and other surrogates of bladder dosimetry in prostate cancer patients, planned both with 3D-conformal and intensity-modulated radiation therapy (IMRT) techniques. For 17 prostate cancer patients, previously treated with radical intent, CT and MRI scans were acquired and matched. The contours of bladder walls were drawn by using MRI images. External bladder surfaces were then used to generate artificial bladder walls by performing automatic contractions of 5, 7 and 10 mm. For each patient a 3D conformal radiotherapy (3DCRT) and an IMRT treatment plan was generated with a prescription dose of 77.4 Gy (1.8 Gy/fr) and DVH of the whole bladder of the artificial walls (DVH-5/10) and dose-surface histograms (DSHs) were calculated and compared against the DWH in absolute and relative value, for both treatment planning techniques. A specific software (VODCA v. 4.4.0, MSS Inc.) was used for calculating the dose-volume/surface histogram. Correlation was quantified for selected dose-volume/surface parameters by the Spearman correlation coefficient. The agreement between %DWH and DVH5, DVH7 and DVH10 was found to be very good (maximum average deviations below 2%, SD < 5%): DVH5 showed the best agreement. The correlation was slightly better for absolute (R = 0.80-0.94) compared to relative (R = 0.66-0.92) histograms. The DSH was also found to be highly correlated with the DWH, although slightly higher deviations were generally found. The DVH was not a good surrogate of the DWH (R < 0.7 for most of parameters). When comparing the two treatment techniques, more pronounced differences between relative histograms were seen for IMRT with respect to 3DCRT (p < 0.0001).

Reassessment of carotid intima-media thickness by standard deviation score in children and adolescents after Kawasaki disease.

PubMed

Noto, Nobutaka; Kato, Masataka; Abe, Yuriko; Kamiyama, Hiroshi; Karasawa, Kensuke; Ayusawa, Mamoru; Takahashi, Shori

2015-01-01

Previous studies that used carotid ultrasound have been largely conflicting in regards to whether or not patients after Kawasaki disease (KD) have a greater carotid intima-media thickness (CIMT) than controls. To test the hypothesis that there are significant differences between the values of CIMT expressed as absolute values and standard deviation scores (SDS) in children and adolescents after KD and controls, we reviewed 12 published articles regarding CIMT on KD patients and controls. The mean ± SD of absolute CIMT (mm) in the KD patients and controls obtained from each article was transformed to SDS (CIMT-SDS) using age-specific reference values established by Jourdan et al. (J: n = 247) and our own data (N: n = 175), and the results among these 12 articles were compared between the two groups and the references for comparison of racial disparities. There were no significant differences in mean absolute CIMT and mean CIMT-SDS for J between KD patients and controls (0.46 ± 0.06 mm vs. 0.44 ± 0.04 mm, p = 0.133, and 1.80 ± 0.84 vs. 1.25 ± 0.12, p = 0.159, respectively). However, there were significant differences in mean CIMT-SDS for N between KD patients and controls (0.60 ± 0.71 vs. 0.01 ± 0.65, p = 0.042). When we assessed the nine articles on Asian subjects, the difference of CIMT-SDS between the two groups was invariably significant only for N (p = 0.015). Compared with the reference values, CIMT-SDS of controls was within the normal range at a rate of 41.6 % for J and 91.6 % for N. These results indicate that age- and race-specific reference values for CIMT are mandatory for performing accurate assessment of the vascular status in healthy children and adolescents, particularly in those after KD considered at increased long-term cardiovascular risk.

Reproducibility of visual acuity assessment in normal and low visual acuity.

PubMed

Becker, Ralph; Teichler, Gunnar; Gräf, Michael

2007-01-01

To assess the reproducibility of measurements of visual acuity in both the upper and lower range of visual acuity. The retroilluminated ETDRS 1 and ETDRS 2 charts (Precision Vision) were used for measurement of visual acuity. Both charts use the same letters. The sequence of the charts followed a pseudorandomized protocol. The examination distance was 4.0 m. When the visual acuity was below 0.16 or 0.03, then the examination distance was reduced to 1 m or 0.4 m, respectively, using an appropriate near correction. Visual acuity measurements obtained during the same session with both charts were compared. A total of 100 patients (age 8-90 years; median 60.5) with various eye disorders, including 39 with amblyopia due to strabismus, were tested in addition to 13 healthy volunteers (age 18-33 years; median 24). At least 3 out of 5 optotypes per line had to be correctly identified to pass this line. Wrong answers were monitored. The interpolated logMAR score was calculated. In the patients, the eye with the lower visual acuity was assessed, and for the healthy subjects the right eye. Differences between ETDRS 1 and ETDRS 2-acuity were compared. The mean logMAR values for ETDRS 1 and ETDRS 2 were -0.17 and -0.14 in the healthy eyes and 0.55 and 0.57 in the entire group. The absolute difference between ETDRS 1 and ETDRS 2 was (mean +/- standard deviation) 0.051 +/- 0.04 for the healthy eyes and 0.063 +/- 0.05 in the entire group. In the acuity range below 0.1 (logMAR > 1.0), the absolute difference (mean +/- standard deviation) between ETDRS 1 and ETDRS 2 of 0.072 +/- 0.04 did not significantly exceed the mean absolute difference in healthy eyes (p = 0.17). Regression analysis (|ETDRS 1 - ETDRS 2| vs. ETDRS 1) showed a slight increase of the difference between the two values with lower visual acuity (p = 0.0505; r = 0.18). Assuming correct measurement, the reproducibilty of visual acuity measurements in the lower acuity range is not significantly worse than in normals.

Modeling Heavy/Medium-Duty Fuel Consumption Based on Drive Cycle Properties

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

Wang, Lijuan; Duran, Adam; Gonder, Jeffrey

This paper presents multiple methods for predicting heavy/medium-duty vehicle fuel consumption based on driving cycle information. A polynomial model, a black box artificial neural net model, a polynomial neural network model, and a multivariate adaptive regression splines (MARS) model were developed and verified using data collected from chassis testing performed on a parcel delivery diesel truck operating over the Heavy Heavy-Duty Diesel Truck (HHDDT), City Suburban Heavy Vehicle Cycle (CSHVC), New York Composite Cycle (NYCC), and hydraulic hybrid vehicle (HHV) drive cycles. Each model was trained using one of four drive cycles as a training cycle and the other threemore » as testing cycles. By comparing the training and testing results, a representative training cycle was chosen and used to further tune each method. HHDDT as the training cycle gave the best predictive results, because HHDDT contains a variety of drive characteristics, such as high speed, acceleration, idling, and deceleration. Among the four model approaches, MARS gave the best predictive performance, with an average absolute percent error of -1.84% over the four chassis dynamometer drive cycles. To further evaluate the accuracy of the predictive models, the approaches were first applied to real-world data. MARS outperformed the other three approaches, providing an average absolute percent error of -2.2% of four real-world road segments. The MARS model performance was then compared to HHDDT, CSHVC, NYCC, and HHV drive cycles with the performance from Future Automotive System Technology Simulator (FASTSim). The results indicated that the MARS method achieved a comparative predictive performance with FASTSim.« less

Left-hemisphere activation is associated with enhanced vocal pitch error detection in musicians with absolute pitch

PubMed Central

Behroozmand, Roozbeh; Ibrahim, Nadine; Korzyukov, Oleg; Robin, Donald A.; Larson, Charles R.

2014-01-01

The ability to process auditory feedback for vocal pitch control is crucial during speaking and singing. Previous studies have suggested that musicians with absolute pitch (AP) develop specialized left-hemisphere mechanisms for pitch processing. The present study adopted an auditory feedback pitch perturbation paradigm combined with ERP recordings to test the hypothesis whether the neural mechanisms of the left-hemisphere enhance vocal pitch error detection and control in AP musicians compared with relative pitch (RP) musicians and non-musicians (NM). Results showed a stronger N1 response to pitch-shifted voice feedback in the right-hemisphere for both AP and RP musicians compared with the NM group. However, the left-hemisphere P2 component activation was greater in AP and RP musicians compared with NMs and also for the AP compared with RP musicians. The NM group was slower in generating compensatory vocal reactions to feedback pitch perturbation compared with musicians, and they failed to re-adjust their vocal pitch after the feedback perturbation was removed. These findings suggest that in the earlier stages of cortical neural processing, the right hemisphere is more active in musicians for detecting pitch changes in voice feedback. In the later stages, the left-hemisphere is more active during the processing of auditory feedback for vocal motor control and seems to involve specialized mechanisms that facilitate pitch processing in the AP compared with RP musicians. These findings indicate that the left hemisphere mechanisms of AP ability are associated with improved auditory feedback pitch processing during vocal pitch control in tasks such as speaking or singing. PMID:24355545

Support vector machine for day ahead electricity price forecasting

NASA Astrophysics Data System (ADS)

Razak, Intan Azmira binti Wan Abdul; Abidin, Izham bin Zainal; Siah, Yap Keem; Rahman, Titik Khawa binti Abdul; Lada, M. Y.; Ramani, Anis Niza binti; Nasir, M. N. M.; Ahmad, Arfah binti

2015-05-01

Electricity price forecasting has become an important part of power system operation and planning. In a pool- based electric energy market, producers submit selling bids consisting in energy blocks and their corresponding minimum selling prices to the market operator. Meanwhile, consumers submit buying bids consisting in energy blocks and their corresponding maximum buying prices to the market operator. Hence, both producers and consumers use day ahead price forecasts to derive their respective bidding strategies to the electricity market yet reduce the cost of electricity. However, forecasting electricity prices is a complex task because price series is a non-stationary and highly volatile series. Many factors cause for price spikes such as volatility in load and fuel price as well as power import to and export from outside the market through long term contract. This paper introduces an approach of machine learning algorithm for day ahead electricity price forecasting with Least Square Support Vector Machine (LS-SVM). Previous day data of Hourly Ontario Electricity Price (HOEP), generation's price and demand from Ontario power market are used as the inputs for training data. The simulation is held using LSSVMlab in Matlab with the training and testing data of 2004. SVM that widely used for classification and regression has great generalization ability with structured risk minimization principle rather than empirical risk minimization. Moreover, same parameter settings in trained SVM give same results that absolutely reduce simulation process compared to other techniques such as neural network and time series. The mean absolute percentage error (MAPE) for the proposed model shows that SVM performs well compared to neural network.

Current Global Absolute Plate Velocities Inferred from the Trends of Hotspot Tracks: Implications for Motion between Groups of Hotspots and Comparison and Combination with Absolute Velocities Inferred from the Orientation of Seismic Anisotropy

NASA Astrophysics Data System (ADS)

Wang, C.; Gordon, R. G.; Zheng, L.

2016-12-01

Hotspot tracks are widely used to estimate the absolute velocities of plates, i.e., relative to the lower mantle. Knowledge of current motion between hotspots is important for both plate kinematics and mantle dynamics and informs the discussion on the origin of the Hawaiian-Emperor Bend. Following Morgan & Morgan (2007), we focus only on the trends of young hotspot tracks and omit volcanic propagation rates. The dispersion of the trends can be partitioned into between-plate and within-plate dispersion. Applying the method of Gripp & Gordon (2002) to the hotspot trend data set of Morgan & Morgan (2007) constrained to the MORVEL relative plate angular velocities (DeMets et al., 2010) results in a standard deviation of the 56 hotspot trends of 22°. The largest angular misfits tend to occur on the slowest moving plates. Alternatively, estimation of best-fitting poles to hotspot tracks on the nine individual plates, results in a standard deviation of trends of only 13°, a statistically significant reduction from the introduction of 15 additional adjustable parameters. If all of the between-plate misfit is due to motion of groups of hotspots (beneath different plates), nominal velocities relative to the mean hotspot reference frame range from 1 to 4 mm/yr with the lower bounds ranging from 1 to 3 mm/yr and the greatest upper bound being 8 mm/yr. These are consistent with bounds on motion between Pacific and Indo-Atlantic hotspots over the past ≈50 Ma, which range from zero (lower bound) to 8 to 13 mm/yr (upper bounds) (Koivisto et al., 2014). We also determine HS4-MORVEL, a new global set of plate angular velocities relative to the hotspots constrained to consistency with the MORVEL relative plate angular velocities, using a two-tier analysis similar to that used by Zheng et al. (2014) to estimate the SKS-MORVEL global set of absolute plate velocities fit to the orientation of seismic anisotropy. We find that the 95% confidence limits of HS4-MORVEL and SKS-MORVEL overlap substantially and that the two sets of angular velocities differ insignificantly. Thus we combine the two sets of angular velocities to estimate ABS-MORVEL, an optimal set of global angular velocities consistent with both hotspot tracks and seismic anisotropy. ABS-MORVEL has more compact confidence limits than either SKS-MORVEL or HS4-MORVEL.

G3(MP2)-CEP theory and applications for compounds containing atoms from representative first, second and third row elements of the periodic table.

PubMed

Pereira, Douglas Henrique; Rocha, Carlos Murilo Romero; Morgon, Nelson Henrique; Custodio, Rogério

2015-08-01

The compact effective potential (CEP) pseudopotential was adapted to the G3(MP2) theory, herein referred to as G3(MP2)-CEP, and applied to the calculation of enthalpies of formation, ionization energies, atomization energies, and electron and proton affinities for 446 species containing elements of the 1st, 2nd, and 3rd rows of the periodic table. A total mean absolute deviation of 1.67 kcal mol(-1) was achieved with G3(MP2)-CEP, compared with 1.47 kcal mol(-1) for G3(MP2). Electron affinities and enthalpies of formation are the properties exhibiting the lowest deviations with respect to the original G3(MP2) theory. The use of pseudopotentials and composite theories in the framework of the G3 theory is feasible and compatible with the all electron approach. Graphical Abstract Application of composite methods in high-level ab initio calculations.

Viscosities of aqueous blended amines

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

Hsu, C.H.; Li, M.H.

1997-07-01

Solutions of alkanolamines are an industrially important class of compounds used in the natural gas, oil refineries, petroleum chemical plants, and synthetic ammonia industries for the removal of acidic components like CO{sub 2} and H{sub 2}S from gas streams. The viscosities of aqueous mixtures of diethanolamine (DEA) + N-methyldiethanolamine (MDEA), DEA + 2-amino-2-methyl-1-propanol (AMP), and monoethanolamine (MEA) + 2-piperidineethanol (2-PE) were measured from 30 C to 80 C. A Redlich-Kister equation for the viscosity deviation was applied to represent the viscosity. On the basis of the available viscosity data for five ternary systems, MEA + MDEA + H{sub 2}O, MEAmore » + AMP + H{sub 2}O, DEA + MDEA + H{sub 2}O, DEA + AMP + H{sub 2}O, and MEA + 2-PE + H{sub 2}O, a generalized set of binary parameters were determined. For the viscosity calculation of the systems tested, the overall average absolute percent deviation is about 1.0% for a total of 499 data points.« less

Gravimetric method for the determination of diclofenac in pharmaceutical preparations.

PubMed

Tubino, Matthieu; De Souza, Rafael L

2005-01-01

A gravimetric method for the determination of diclofenac in pharmaceutical preparations was developed. Diclofenac is precipitated from aqueous solution with copper(II) acetate in pH 5.3 (acetic acid/acetate buffer). Sample aliquots had approximately the same quantity of the drug content in tablets (50 mg) or in ampules (75 mg). The observed standard deviation was about +/- 2 mg; therefore, the relative standard deviation (RSD) was approximately 4% for tablet and 3% for ampule preparations. The results were compared with those obtained with the liquid chromatography method recommended in the United States Pharmacopoeia using the statistical Student's t-test. Complete agreement was observed. It is possible to obtain more precise results using higher aliquots, for example 200 mg, in which case the RSD falls to 1%. This gravimetric method, contrary to what is expected for this kind of procedure, is relatively fast and simple to perform. The main advantage is the absolute character of the gravimetric analysis.

Estimating accuracy of land-cover composition from two-stage cluster sampling

USGS Publications Warehouse

Stehman, S.V.; Wickham, J.D.; Fattorini, L.; Wade, T.D.; Baffetta, F.; Smith, J.H.

2009-01-01

Land-cover maps are often used to compute land-cover composition (i.e., the proportion or percent of area covered by each class), for each unit in a spatial partition of the region mapped. We derive design-based estimators of mean deviation (MD), mean absolute deviation (MAD), root mean square error (RMSE), and correlation (CORR) to quantify accuracy of land-cover composition for a general two-stage cluster sampling design, and for the special case of simple random sampling without replacement (SRSWOR) at each stage. The bias of the estimators for the two-stage SRSWOR design is evaluated via a simulation study. The estimators of RMSE and CORR have small bias except when sample size is small and the land-cover class is rare. The estimator of MAD is biased for both rare and common land-cover classes except when sample size is large. A general recommendation is that rare land-cover classes require large sample sizes to ensure that the accuracy estimators have small bias. ?? 2009 Elsevier Inc.

Compact silicon-based optrode with integrated laser diode chips, SU-8 waveguides and platinum electrodes for optogenetic applications

NASA Astrophysics Data System (ADS)

Schwaerzle, M.; Paul, O.; Ruther, P.

2017-06-01

We report on a compact optrode, i.e. a MEMS-based, invasive, bidirectional neural interface allowing to control neural activity using light while neural signals are recorded nearby. The optrode consists of a silicon (Si) base carrying two pairs of bare laser diodes (LDs) emitting at 650 nm and of two 8 mm-long, 250 µm-wide and down to 50 µm-thick shanks extending from the base. Each LD is efficiently coupled to one of four 15 or 20 µm-wide and 13 µm-high SU-8 waveguides (WGs) running in pairs along the shanks. In addition, each shank comprises four 20 µm-diameter platinum electrodes for neural recording near the WG end facets. After encapsulation of the LDs with a Si cover chip blocking stray light and protecting the LDs from the harsh environment to which the probe is destined, the compact base measures only 4  ×  4  ×  0.43 mm3. The time averaged radiant emittance at the WG end facet is 96.9 mW mm-2 for an LD current of 35 mA at a duty cycle of 5%. The absolute electrode impedance at 1 kHz is 1.54  ±  0.06 MΩ. Using infrared thermography, the temperature increase of the probe during LD operation was determined to be about 1 K under neuroscientifically relevant operating conditions.

Surfing a spike wave down the ventral stream.

PubMed

VanRullen, Rufin; Thorpe, Simon J

2002-10-01

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Prediction of daily sea surface temperature using efficient neural networks

NASA Astrophysics Data System (ADS)

Patil, Kalpesh; Deo, Makaranad Chintamani

2017-04-01

Short-term prediction of sea surface temperature (SST) is commonly achieved through numerical models. Numerical approaches are more suitable for use over a large spatial domain than in a specific site because of the difficulties involved in resolving various physical sub-processes at local levels. Therefore, for a given location, a data-driven approach such as neural networks may provide a better alternative. The application of neural networks, however, needs a large experimentation in their architecture, training methods, and formation of appropriate input-output pairs. A network trained in this manner can provide more attractive results if the advances in network architecture are additionally considered. With this in mind, we propose the use of wavelet neural networks (WNNs) for prediction of daily SST values. The prediction of daily SST values was carried out using WNN over 5 days into the future at six different locations in the Indian Ocean. First, the accuracy of site-specific SST values predicted by a numerical model, ROMS, was assessed against the in situ records. The result pointed out the necessity for alternative approaches. First, traditional networks were tried and after noticing their poor performance, WNN was used. This approach produced attractive forecasts when judged through various error statistics. When all locations were viewed together, the mean absolute error was within 0.18 to 0.32 °C for a 5-day-ahead forecast. The WNN approach was thus found to add value to the numerical method of SST prediction when location-specific information is desired.

The neural basis of belief updating and rational decision making

PubMed Central

Achtziger, Anja; Hügelschäfer, Sabine; Steinhauser, Marco

2014-01-01

Rational decision making under uncertainty requires forming beliefs that integrate prior and new information through Bayes’ rule. Human decision makers typically deviate from Bayesian updating by either overweighting the prior (conservatism) or overweighting new information (e.g. the representativeness heuristic). We investigated these deviations through measurements of electrocortical activity in the human brain during incentivized probability-updating tasks and found evidence of extremely early commitment to boundedly rational heuristics. Participants who overweight new information display a lower sensibility to conflict detection, captured by an event-related potential (the N2) observed around 260 ms after the presentation of new information. Conservative decision makers (who overweight prior probabilities) make up their mind before new information is presented, as indicated by the lateralized readiness potential in the brain. That is, they do not inhibit the processing of new information but rather immediately rely on the prior for making a decision. PMID:22956673

The neural basis of belief updating and rational decision making.

PubMed

Achtziger, Anja; Alós-Ferrer, Carlos; Hügelschäfer, Sabine; Steinhauser, Marco

2014-01-01

Rational decision making under uncertainty requires forming beliefs that integrate prior and new information through Bayes' rule. Human decision makers typically deviate from Bayesian updating by either overweighting the prior (conservatism) or overweighting new information (e.g. the representativeness heuristic). We investigated these deviations through measurements of electrocortical activity in the human brain during incentivized probability-updating tasks and found evidence of extremely early commitment to boundedly rational heuristics. Participants who overweight new information display a lower sensibility to conflict detection, captured by an event-related potential (the N2) observed around 260 ms after the presentation of new information. Conservative decision makers (who overweight prior probabilities) make up their mind before new information is presented, as indicated by the lateralized readiness potential in the brain. That is, they do not inhibit the processing of new information but rather immediately rely on the prior for making a decision.

Measurement of the single-top-quark production cross section at CDF.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Copic, K; Cordelli, M; Cortiana, G; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kusakabe, Y; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Liss, T M; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlok, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Schall, I; Scheidle, T; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S

2008-12-19

We report a measurement of the single-top-quark production cross section in 2.2 fb;{-1} of pp collision data collected by the Collider Detector at Fermilab at sqrt[s]=1.96 TeV. Candidate events are classified as signal-like by three parallel analyses which use likelihood, matrix element, and neural network discriminants. These results are combined in order to improve the sensitivity. We observe a signal consistent with the standard model prediction, but inconsistent with the background-only model by 3.7 standard deviations with a median expected sensitivity of 4.9 standard deviations. We measure a cross section of 2.2(-0.6)(+0.7)(stat+syst) pb, extract the Cabibbo-Kobayashi-Maskawa matrix-element value |V(tb)|=0.88(-0.12)(+0.13)(stat+syst)+/-0.07(theory), and set the limit |V(tb)|>0.66 at the 95% C.L.

Estimation of heart rate and heart rate variability from pulse oximeter recordings using localized model fitting.

PubMed

Wadehn, Federico; Carnal, David; Loeliger, Hans-Andrea

2015-08-01

Heart rate variability is one of the key parameters for assessing the health status of a subject's cardiovascular system. This paper presents a local model fitting algorithm used for finding single heart beats in photoplethysmogram recordings. The local fit of exponentially decaying cosines of frequencies within the physiological range is used to detect the presence of a heart beat. Using 42 subjects from the CapnoBase database, the average heart rate error was 0.16 BPM and the standard deviation of the absolute estimation error was 0.24 BPM.

Development of Colle-Salvetti type electron-nucleus correlation functional for MC-DFT

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

Udagawa, Taro; Tsuneda, Takao; Tachikawa, Masanori

2015-12-31

A Colle-Salvetti type electron-nucleus correlation functional for multicomponent density-functional theory is proposed. We demonstrate that our correlation functional quantitatively reproduces the quantum nuclear effects of protons; the mean absolute deviation value is 2.8 millihartrees for the optimized structure of hydrogen-containing molecules. We also show other practical calculations with our new electron-deuteron and electron-triton correlation functionals. Since this functional is derived without any unphysical assumption, the strategy taken in this development will be a promising recipe to make new functionals for the potentials of other particles’ interactions.

A new Nawaz-Enscore-Ham-based heuristic for permutation flow-shop problems with bicriteria of makespan and machine idle time

NASA Astrophysics Data System (ADS)

Liu, Weibo; Jin, Yan; Price, Mark

2016-10-01

A new heuristic based on the Nawaz-Enscore-Ham algorithm is proposed in this article for solving a permutation flow-shop scheduling problem. A new priority rule is proposed by accounting for the average, mean absolute deviation, skewness and kurtosis, in order to fully describe the distribution style of processing times. A new tie-breaking rule is also introduced for achieving effective job insertion with the objective of minimizing both makespan and machine idle time. Statistical tests illustrate better solution quality of the proposed algorithm compared to existing benchmark heuristics.

Asymptotics of nonparametric L-1 regression models with dependent data

PubMed Central

ZHAO, ZHIBIAO; WEI, YING; LIN, DENNIS K.J.

2013-01-01

We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric regression models with dependent data from multiple subjects. Under a general dependence structure that allows for longitudinal data and some spatially correlated data, we establish uniform Bahadur representations for the proposed median quantile estimates. The obtained Bahadur representations provide deep insights into the asymptotic behavior of the estimates. Our main theoretical development is based on studying the modulus of continuity of kernel weighted empirical process through a coupling argument. Progesterone data is used for an illustration. PMID:24955016

Predicting Functional Capacity From Measures of Muscle Mass in Postmenopausal Women.

PubMed

Orsatti, Fábio Lera; Nunes, Paulo Ricardo Prado; Souza, Aletéia de Paula; Martins, Fernanda Maria; de Oliveira, Anselmo Alves; Nomelini, Rosekeila Simões; Michelin, Márcia Antoniazi; Murta, Eddie Fernando Cândido

2017-06-01

Menopause increases body fat and decreases muscle mass and strength, which contribute to sarcopenia. The amount of appendicular muscle mass has been frequently used to diagnose sarcopenia. Different measures of appendicular muscle mass have been proposed. However, no studies have compared the most salient measure (appendicular muscle mass corrected by body fat) of the appendicular muscle mass to physical function in postmenopausal women. To examine the association of 3 different measurements of appendicular muscle mass (absolute, corrected by stature, and corrected by body fat) with physical function in postmenopausal women. Cross-sectional descriptive study. Outpatient geriatric and gynecological clinic. Forty-eight postmenopausal women with a mean age (standard deviation [SD]) of 62.1 ± 8.2 years, with mean (SD) length of menopause of 15.7 ± 9.8 years and mean (SD) body fat of 43.6% ± 9.8%. Not applicable. Appendicular muscle mass measure was measured with dual-energy x-ray absorptiometry. Physical function was measured by a functional capacity questionnaire, a short physical performance battery, and a 6 minute-walk test. Muscle quality (leg extensor strength to lower-body mineral-free lean mass ratio) and sum of z scores (sum of each physical function tests z score) were performed to provide a global index of physical function. The regression analysis showed that appendicular muscle mass corrected by body fat was the strongest predictor of physical function. Each increase in the standard deviation of appendicular muscle mass corrected by body fat was associated with a mean sum of z score increase of 59% (standard deviation), whereas each increase in absolute appendicular muscle mass and appendicular muscle mass corrected by stature were associated with a mean sum of z scores decrease of 23% and 36%, respectively. Muscle quality was associated with appendicular muscle mass corrected by body fat. These findings indicate that appendicular muscle mass corrected by body fat is a better predictor of physical function than the other measures of appendicular muscle mass in postmenopausal women. I. Copyright © 2017 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Credit card fraud detection using neural network and geolocation

NASA Astrophysics Data System (ADS)

Gulati, Aman; Dubey, Prakash; MdFuzail, C.; Norman, Jasmine; Mangayarkarasi, R.

2017-11-01

The most acknowledged payment mode is credit card for both disconnected and online mediums in today's day and age. It facilitates cashless shopping everywhere in the world. It is the most widespread and reasonable approach with regards to web based shopping, paying bills, what's more, performing other related errands. Thus danger of fraud exchanges utilizing credit card has likewise been expanding. In the Current Fraud Detection framework, false exchange is recognized after the transaction is completed. As opposed to the current system, the proposed system presents a methodology which facilitates the detection of fraudulent exchanges while they are being processed, this is achieved by means of Behaviour and Locational Analysis(Neural Logic) which considers a cardholder's way of managing money and spending pattern. A deviation from such a pattern will then lead to the system classifying it as suspicious transaction and will then be handled accordingly.

Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

PubMed Central

Li, Zhencai; Wang, Yang; Liu, Zhen

2016-01-01

The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model. PMID:27467703

The neural processing of foreign-accented speech and its relationship to listener bias

PubMed Central

Yi, Han-Gyol; Smiljanic, Rajka; Chandrasekaran, Bharath

2014-01-01

Foreign-accented speech often presents a challenging listening condition. In addition to deviations from the target speech norms related to the inexperience of the nonnative speaker, listener characteristics may play a role in determining intelligibility levels. We have previously shown that an implicit visual bias for associating East Asian faces and foreignness predicts the listeners' perceptual ability to process Korean-accented English audiovisual speech (Yi et al., 2013). Here, we examine the neural mechanism underlying the influence of listener bias to foreign faces on speech perception. In a functional magnetic resonance imaging (fMRI) study, native English speakers listened to native- and Korean-accented English sentences, with or without faces. The participants' Asian-foreign association was measured using an implicit association test (IAT), conducted outside the scanner. We found that foreign-accented speech evoked greater activity in the bilateral primary auditory cortices and the inferior frontal gyri, potentially reflecting greater computational demand. Higher IAT scores, indicating greater bias, were associated with increased BOLD response to foreign-accented speech with faces in the primary auditory cortex, the early node for spectrotemporal analysis. We conclude the following: (1) foreign-accented speech perception places greater demand on the neural systems underlying speech perception; (2) face of the talker can exaggerate the perceived foreignness of foreign-accented speech; (3) implicit Asian-foreign association is associated with decreased neural efficiency in early spectrotemporal processing. PMID:25339883

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, Chi Yung

1998-01-01

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller.

Closed loop adaptive control of spectrum-producing step using neural networks

DOEpatents

Fu, C.Y.

1998-11-24

Characteristics of the plasma in a plasma-based manufacturing process step are monitored directly and in real time by observing the spectrum which it produces. An artificial neural network analyzes the plasma spectrum and generates control signals to control one or more of the process input parameters in response to any deviation of the spectrum beyond a narrow range. In an embodiment, a plasma reaction chamber forms a plasma in response to input parameters such as gas flow, pressure and power. The chamber includes a window through which the electromagnetic spectrum produced by a plasma in the chamber, just above the subject surface, may be viewed. The spectrum is conducted to an optical spectrometer which measures the intensity of the incoming optical spectrum at different wavelengths. The output of optical spectrometer is provided to an analyzer which produces a plurality of error signals, each indicating whether a respective one of the input parameters to the chamber is to be increased or decreased. The microcontroller provides signals to control respective controls, but these lines are intercepted and first added to the error signals, before being provided to the controls for the chamber. The analyzer can include a neural network and an optional spectrum preprocessor to reduce background noise, as well as a comparator which compares the parameter values predicted by the neural network with a set of desired values provided by the microcontroller. 7 figs.

Evidence that the notochord may be pivotal in the development of sacral and anorectal malformations.

PubMed

Qi, Bao Quan; Beasley, Spencer W; Frizelle, Francis A

2003-09-01

The notochord is known to organize normal development of central axial structures, such as the spinal cord, vertebral column, and anorectum, but its role in abnormal development of these organs has not been well documented. The current study has used Ethylenethiourea to induce anorectal malformations in fetal rats, allowing investigation of abnormalities of the notochord and their relationship to the axial structural abnormalities that occur. Timed-mated pregnant rats were fed Ethylenethiourea by gavage on gestational day 10. Their embryos were harvested on gestational days 13 to 16 and sectioned in either the transverse or sagittal plane. Sections were stained with H and E and examined serially. Anorectal malformations were identified in 29 of 34 embryos and neural tube defects in 24, ranging from an accessory neural tube to lumbo-sacral rachischisis. There was no tail or only a rudimentary tail in the majority of embryos. Abnormalities of the notochord in the lumbo-sacral area included ventro-dorsal branching, ventral deviation, and ectopic notochordal tissue. Most abnormal notochord branches and ectopic notochordal tissue were abnormally close to or in contact with the wall of the cloaca or neural tube. Given the known role of the notochord in controlling normal development, this study would suggest that abnormal notochord development may be pivotal in producing neural tube defects and anorectal malformations, possibly by altering sonic hedgehog signalling.

Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective.

PubMed

Bechara, Antoine

2005-11-01

Here I argue that addicted people become unable to make drug-use choices on the basis of long-term outcome, and I propose a neural framework that explains this myopia for future consequences. I suggest that addiction is the product of an imbalance between two separate, but interacting, neural systems that control decision making: an impulsive, amygdala system for signaling pain or pleasure of immediate prospects, and a reflective, prefrontal cortex system for signaling pain or pleasure of future prospects. After an individual learns social rules, the reflective system controls the impulsive system via several mechanisms. However, this control is not absolute; hyperactivity within the impulsive system can override the reflective system. I propose that drugs can trigger bottom-up, involuntary signals originating from the amygdala that modulate, bias or even hijack the goal-driven cognitive resources that are needed for the normal operation of the reflective system and for exercising the willpower to resist drugs.

Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography.

PubMed

Yong, Yan Ling; Tan, Li Kuo; McLaughlin, Robert A; Chee, Kok Han; Liew, Yih Miin

2017-12-01

Intravascular optical coherence tomography (OCT) is an optical imaging modality commonly used in the assessment of coronary artery diseases during percutaneous coronary intervention. Manual segmentation to assess luminal stenosis from OCT pullback scans is challenging and time consuming. We propose a linear-regression convolutional neural network to automatically perform vessel lumen segmentation, parameterized in terms of radial distances from the catheter centroid in polar space. Benchmarked against gold-standard manual segmentation, our proposed algorithm achieves average locational accuracy of the vessel wall of 22 microns, and 0.985 and 0.970 in Dice coefficient and Jaccard similarity index, respectively. The average absolute error of luminal area estimation is 1.38%. The processing rate is 40.6 ms per image, suggesting the potential to be incorporated into a clinical workflow and to provide quantitative assessment of vessel lumen in an intraoperative time frame. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems

PubMed Central

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S.; Agarwal, Dev P.

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data. PMID:26366169

A Brain-wide Circuit Model of Heat-Evoked Swimming Behavior in Larval Zebrafish.

PubMed

Haesemeyer, Martin; Robson, Drew N; Li, Jennifer M; Schier, Alexander F; Engert, Florian

2018-05-16

Thermosensation provides crucial information, but how temperature representation is transformed from sensation to behavior is poorly understood. Here, we report a preparation that allows control of heat delivery to zebrafish larvae while monitoring motor output and imaging whole-brain calcium signals, thereby uncovering algorithmic and computational rules that couple dynamics of heat modulation, neural activity and swimming behavior. This approach identifies a critical step in the transformation of temperature representation between the sensory trigeminal ganglia and the hindbrain: A simple sustained trigeminal stimulus representation is transformed into a representation of absolute temperature as well as temperature changes in the hindbrain that explains the observed motor output. An activity constrained dynamic circuit model captures the most prominent aspects of these sensori-motor transformations and predicts both behavior and neural activity in response to novel heat stimuli. These findings provide the first algorithmic description of heat processing from sensory input to behavioral output. Copyright © 2018 Elsevier Inc. All rights reserved.

Reciprocal inhibition of inhibition: A circuit motif for flexible categorization in stimulus selection

PubMed Central

Knudsen, Eric I.

2011-01-01

As a precursor to the selection of a stimulus for gaze and attention, a midbrain network categorizes stimuli into “strongest” and “others.” The categorization tracks flexibly, in real-time, the absolute strength of the strongest stimulus. In this study, we take a first principles approach to computations that are essential for such categorization. We demonstrate that classical feedforward lateral inhibition cannot produce flexible categorization. However, circuits in which the strength of lateral inhibition varies with the relative strength of competing stimuli categorize successfully. One particular implementation - reciprocal inhibition of feedforward lateral inhibition – is structurally the simplest, and it outperforms others in flexibly categorizing rapidly and reliably. Strong predictions of this anatomically supported circuit model are validated by neural responses measured in the owl midbrain. The results demonstrate the extraordinary power of a remarkably simple, neurally grounded circuit motif in producing flexible categorization, a computation fundamental to attention, perception, and decision-making. PMID:22243757

Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.

PubMed

Sakhre, Vandana; Jain, Sanjeev; Sapkal, Vilas S; Agarwal, Dev P

2015-01-01

Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems. In this process, the weight connecting between the instar and outstar, that is, input-hidden and hidden-output layer, respectively, is adjusted by using Fuzzy Competitive Learning (FCL). FCL paradigm adopts the principle of learning, which is used to calculate Best Matched Node (BMN) which is proposed. This strategy offers a robust control of nonlinear dynamical systems. FCPN is compared with the existing network like Dynamic Network (DN) and Back Propagation Network (BPN) on the basis of Mean Absolute Error (MAE), Mean Square Error (MSE), Best Fit Rate (BFR), and so forth. It envisages that the proposed FCPN gives better results than DN and BPN. The effectiveness of the proposed FCPN algorithms is demonstrated through simulations of four nonlinear dynamical systems and multiple input and single output (MISO) and a single input and single output (SISO) gas furnace Box-Jenkins time series data.

Body weight-supported training in Becker and limb girdle 2I muscular dystrophy.

PubMed

Jensen, Bente R; Berthelsen, Martin P; Husu, Edith; Christensen, Sofie B; Prahm, Kira P; Vissing, John

2016-08-01

We studied the functional effects of combined strength and aerobic anti-gravity training in severely affected patients with Becker and Limb-Girdle muscular dystrophies. Eight patients performed 10-week progressive combined strength (squats, calf raises, lunges) and aerobic (walk/run, jogging in place or high knee-lift) training 3 times/week in a lower-body positive pressure environment. Closed-kinetic-chain leg muscle strength, isometric knee strength, rate of force development (RFD), and reaction time were evaluated. Baseline data indicated an intact neural activation pattern but showed compromised muscle contractile properties. Training (compliance 91%) improved functional leg muscle strength. Squat series performance increased 30%, calf raises 45%, and lunges 23%. Anti-gravity training improved closed-kinetic-chain leg muscle strength despite no changes in isometric knee extension strength and absolute RFD. The improved closed-kinetic-chain performance may relate to neural adaptation involving motor learning and/or improved muscle strength of other muscles than the weak knee extensors. Muscle Nerve 54: 239-243, 2016. © 2016 Wiley Periodicals, Inc.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

PubMed

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications

PubMed Central

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-01-01

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively. PMID:27509495

Development and evaluation of a prototype tracking system using the treatment couch

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

Lang, Stephanie, E-mail: stephanie.lang@usz.ch; Riesterer, Oliver; Klöck, Stephan

2014-02-15

Purpose: Tumor motion increases safety margins around the clinical target volume and leads to an increased dose to the surrounding healthy tissue. The authors have developed and evaluated a one-dimensional treatment couch tracking system to counter steer respiratory tumor motion. Three different motion detection sensors with different lag times were evaluated. Methods: The couch tracking system consists of a motion detection sensor, which can be the topometrical system Topos (Cyber Technologies, Germany), the respiratory gating system RPM (Varian Medical Systems) or a laser triangulation system (Micro Epsilon), and the Protura treatment couch (Civco Medical Systems). The control of the treatmentmore » couch was implemented in the block diagram environment Simulink (MathWorks). To achieve real time performance, the Simulink models were executed on a real time engine, provided by Real-Time Windows Target (MathWorks). A proportional-integral control system was implemented. The lag time of the couch tracking system using the three different motion detection sensors was measured. The geometrical accuracy of the system was evaluated by measuring the mean absolute deviation from the reference (static position) during motion tracking. This deviation was compared to the mean absolute deviation without tracking and a reduction factor was defined. A hexapod system was moving according to seven respiration patterns previously acquired with the RPM system as well as according to a sin{sup 6} function with two different frequencies (0.33 and 0.17 Hz) and the treatment table compensated the motion. Results: A prototype system for treatment couch tracking of respiratory motion was developed. The laser based tracking system with a small lag time of 57 ms reduced the residual motion by a factor of 11.9 ± 5.5 (mean value ± standard deviation). An increase in delay time from 57 to 130 ms (RPM based system) resulted in a reduction by a factor of 4.7 ± 2.6. The Topos based tracking system with the largest lag time of 300 ms achieved a mean reduction by a factor of 3.4 ± 2.3. The increase in the penumbra of a profile (1 × 1 cm{sup 2}) for a motion of 6 mm was 1.4 mm. With tracking applied there was no increase in the penumbra. Conclusions: Couch tracking with the Protura treatment couch is achievable. To reliably track all possible respiration patterns without prediction filters a short lag time below 100 ms is needed. More scientific work is necessary to extend our prototype to tracking of internal motion.« less

Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas

USGS Publications Warehouse

Qi, L.; Carr, T.R.

2006-01-01

In the Hugoton Embayment of southwestern Kansas, St. Louis Limestone reservoirs have relatively low recovery efficiencies, attributed to the heterogeneous nature of the oolitic deposits. This study establishes quantitative relationships between digital well logs and core description data, and applies these relationships in a probabilistic sense to predict lithofacies in 90 uncored wells across the Big Bow and Sand Arroyo Creek fields. In 10 wells, a single hidden-layer neural network based on digital well logs and core described lithofacies of the limestone depositional texture was used to train and establish a non-linear relationship between lithofacies assignments from detailed core descriptions and selected log curves. Neural network models were optimized by selecting six predictor variables and automated cross-validation with neural network parameters and then used to predict lithofacies on the whole data set of the 2023 half-foot intervals from the 10 cored wells with the selected network size of 35 and a damping parameter of 0.01. Predicted lithofacies results compared to actual lithofacies displays absolute accuracies of 70.37-90.82%. Incorporating adjoining lithofacies, within-one lithofacies improves accuracy slightly (93.72%). Digital logs from uncored wells were batch processed to predict lithofacies and probabilities related to each lithofacies at half-foot resolution corresponding to log units. The results were used to construct interpolated cross-sections and useful depositional patterns of St. Louis lithofacies were illustrated, e.g., the concentration of oolitic deposits (including lithofacies 5 and 6) along local highs and the relative dominance of quartz-rich carbonate grainstone (lithofacies 1) in the zones A and B of the St. Louis Limestone. Neural network techniques are applicable to other complex reservoirs, in which facies geometry and distribution are the key factors controlling heterogeneity and distribution of rock properties. Future work involves extension of the neural network to predict reservoir properties, and construction of three-dimensional geo-models. ?? 2005 Elsevier Ltd. All rights reserved.

Ion track based tunable device as humidity sensor: a neural network approach

NASA Astrophysics Data System (ADS)

Sharma, Mamta; Sharma, Anuradha; Bhattacherjee, Vandana

2013-01-01

Artificial Neural Network (ANN) has been applied in statistical model development, adaptive control system, pattern recognition in data mining, and decision making under uncertainty. The nonlinear dependence of any sensor output on the input physical variable has been the motivation for many researchers to attempt unconventional modeling techniques such as neural networks and other machine learning approaches. Artificial neural network (ANN) is a computational tool inspired by the network of neurons in biological nervous system. It is a network consisting of arrays of artificial neurons linked together with different weights of connection. The states of the neurons as well as the weights of connections among them evolve according to certain learning rules.. In the present work we focus on the category of sensors which respond to electrical property changes such as impedance or capacitance. Recently, sensor materials have been embedded in etched tracks due to their nanometric dimensions and high aspect ratio which give high surface area available for exposure to sensing material. Various materials can be used for this purpose to probe physical (light intensity, temperature etc.), chemical (humidity, ammonia gas, alcohol etc.) or biological (germs, hormones etc.) parameters. The present work involves the application of TEMPOS structures as humidity sensors. The sample to be studied was prepared using the polymer electrolyte (PEO/NH4ClO4) with CdS nano-particles dispersed in the polymer electrolyte. In the present research we have attempted to correlate the combined effects of voltage and frequency on impedance of humidity sensors using a neural network model and results have indicated that the mean absolute error of the ANN Model for the training data was 3.95% while for the validation data it was 4.65%. The corresponding values for the LR model were 8.28% and 8.35% respectively. It was also demonstrated the percentage improvement of the ANN Model with respect to the linear regression model. This demonstrates the suitability of neural networks to perform such modeling.

Direct process estimation from tomographic data using artificial neural systems

NASA Astrophysics Data System (ADS)

Mohamad-Saleh, Junita; Hoyle, Brian S.; Podd, Frank J.; Spink, D. M.

2001-07-01

The paper deals with the goal of component fraction estimation in multicomponent flows, a critical measurement in many processes. Electrical capacitance tomography (ECT) is a well-researched sensing technique for this task, due to its low-cost, non-intrusion, and fast response. However, typical systems, which include practicable real-time reconstruction algorithms, give inaccurate results, and existing approaches to direct component fraction measurement are flow-regime dependent. In the investigation described, an artificial neural network approach is used to directly estimate the component fractions in gas-oil, gas-water, and gas-oil-water flows from ECT measurements. A 2D finite- element electric field model of a 12-electrode ECT sensor is used to simulate ECT measurements of various flow conditions. The raw measurements are reduced to a mutually independent set using principal components analysis and used with their corresponding component fractions to train multilayer feed-forward neural networks (MLFFNNs). The trained MLFFNNs are tested with patterns consisting of unlearned ECT simulated and plant measurements. Results included in the paper have a mean absolute error of less than 1% for the estimation of various multicomponent fractions of the permittivity distribution. They are also shown to give improved component fraction estimation compared to a well known direct ECT method.

Visual Saliency Detection Based on Multiscale Deep CNN Features.

PubMed

Guanbin Li; Yizhou Yu

2016-11-01

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

SU-F-E-09: Respiratory Signal Prediction Based On Multi-Layer Perceptron Neural Network Using Adjustable Training Samples

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

Sun, W; Jiang, M; Yin, F

Purpose: Dynamic tracking of moving organs, such as lung and liver tumors, under radiation therapy requires prediction of organ motions prior to delivery. The shift of moving organ may change a lot due to huge transform of respiration at different periods. This study aims to reduce the influence of that changes using adjustable training signals and multi-layer perceptron neural network (ASMLP). Methods: Respiratory signals obtained using a Real-time Position Management(RPM) device were used for this study. The ASMLP uses two multi-layer perceptron neural networks(MLPs) to infer respiration position alternately and the training sample will be updated with time. Firstly, amore » Savitzky-Golay finite impulse response smoothing filter was established to smooth the respiratory signal. Secondly, two same MLPs were developed to estimate respiratory position from its previous positions separately. Weights and thresholds were updated to minimize network errors according to Leverberg-Marquart optimization algorithm through backward propagation method. Finally, MLP 1 was used to predict 120∼150s respiration position using 0∼120s training signals. At the same time, MLP 2 was trained using 30∼150s training signals. Then MLP is used to predict 150∼180s training signals according to 30∼150s training signals. The respiration position is predicted as this way until it was finished. Results: In this experiment, the two methods were used to predict 2.5 minute respiratory signals. For predicting 1s ahead of response time, correlation coefficient was improved from 0.8250(MLP method) to 0.8856(ASMLP method). Besides, a 30% improvement of mean absolute error between MLP(0.1798 on average) and ASMLP(0.1267 on average) was achieved. For predicting 2s ahead of response time, correlation coefficient was improved from 0.61415 to 0.7098.Mean absolute error of MLP method(0.3111 on average) was reduced by 35% using ASMLP method(0.2020 on average). Conclusion: The preliminary results demonstrate that the ASMLP respiratory prediction method is more accurate than MLP method and can improve the respiration forecast accuracy.« less

Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

NASA Astrophysics Data System (ADS)

Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

2012-01-01

This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

A non-interventional comparative study of the 20:1 combination of cafedrine/theodrenaline versus ephedrine for the treatment of intra-operative arterial hypotension: the 'HYPOTENS' study design and rationale.

PubMed

Eberhart, Leopold; Geldner, Götz; Huljic, Susanne; Marggraf, Kerstin; Keller, Thomas; Koch, Tilo; Kranke, Peter

2018-06-01

To compare the effectiveness of 20:1 cafedrine/theodrenaline approved for use in Germany to ephedrine in the restoration of arterial blood pressure and on post-operative outcomes in patients with intra-operative arterial hypotension of any origin under standard clinical practice conditions. 'HYPOTENS' is a national, multi-center, prospective, open-label, two-armed, non-interventional study. Effectiveness and post-operative outcome following cafedrine/theodrenaline or ephedrine therapy will be evaluated in two cohorts of hypotensive patients. Cohort A includes patients aged ≥50 years with ASA-classification 2-4 undergoing non-emergency surgical procedures under general anesthesia. Cohort B comprises patients undergoing Cesarean section under spinal anesthesia. Participating surgical departments will be assigned to a treatment arm by routinely used anti-hypotensive agent. To minimize bias, matched department pairs will be compared in a stratified selection process. The composite primary end-point is the lower absolute deviation from individually determined target blood pressure (IDTBP) and the incidence of heart rate ≥100 beats/min in the first 15 min. Secondary end-points include incidence and degree of early post-operative delirium (cohort A), severity of fetal acidosis in the newborn (cohort B), upper absolute deviation from IDTBP, percentage increase in systolic blood pressure, and time to IDTBP. This open-label, non-interventional study design mirrors daily practice in the treatment of patients with intra-operative hypotension and ensures full treatment decision autonomy with respect to each patient's individual condition. Selection of participating sites by a randomization process addresses bias without interfering with the non-interventional nature of the study. First results are expected in 2018. ClinicalTrials.gov identifier: NCT02893241; DRKS identifier: DRKS00010740.

A multilaboratory comparison of calibration accuracy and the performance of external references in analytical ultracentrifugation.

PubMed

Zhao, Huaying; Ghirlando, Rodolfo; Alfonso, Carlos; Arisaka, Fumio; Attali, Ilan; Bain, David L; Bakhtina, Marina M; Becker, Donald F; Bedwell, Gregory J; Bekdemir, Ahmet; Besong, Tabot M D; Birck, Catherine; Brautigam, Chad A; Brennerman, William; Byron, Olwyn; Bzowska, Agnieszka; Chaires, Jonathan B; Chaton, Catherine T; Cölfen, Helmut; Connaghan, Keith D; Crowley, Kimberly A; Curth, Ute; Daviter, Tina; Dean, William L; Díez, Ana I; Ebel, Christine; Eckert, Debra M; Eisele, Leslie E; Eisenstein, Edward; England, Patrick; Escalante, Carlos; Fagan, Jeffrey A; Fairman, Robert; Finn, Ron M; Fischle, Wolfgang; de la Torre, José García; Gor, Jayesh; Gustafsson, Henning; Hall, Damien; Harding, Stephen E; Cifre, José G Hernández; Herr, Andrew B; Howell, Elizabeth E; Isaac, Richard S; Jao, Shu-Chuan; Jose, Davis; Kim, Soon-Jong; Kokona, Bashkim; Kornblatt, Jack A; Kosek, Dalibor; Krayukhina, Elena; Krzizike, Daniel; Kusznir, Eric A; Kwon, Hyewon; Larson, Adam; Laue, Thomas M; Le Roy, Aline; Leech, Andrew P; Lilie, Hauke; Luger, Karolin; Luque-Ortega, Juan R; Ma, Jia; May, Carrie A; Maynard, Ernest L; Modrak-Wojcik, Anna; Mok, Yee-Foong; Mücke, Norbert; Nagel-Steger, Luitgard; Narlikar, Geeta J; Noda, Masanori; Nourse, Amanda; Obsil, Tomas; Park, Chad K; Park, Jin-Ku; Pawelek, Peter D; Perdue, Erby E; Perkins, Stephen J; Perugini, Matthew A; Peterson, Craig L; Peverelli, Martin G; Piszczek, Grzegorz; Prag, Gali; Prevelige, Peter E; Raynal, Bertrand D E; Rezabkova, Lenka; Richter, Klaus; Ringel, Alison E; Rosenberg, Rose; Rowe, Arthur J; Rufer, Arne C; Scott, David J; Seravalli, Javier G; Solovyova, Alexandra S; Song, Renjie; Staunton, David; Stoddard, Caitlin; Stott, Katherine; Strauss, Holger M; Streicher, Werner W; Sumida, John P; Swygert, Sarah G; Szczepanowski, Roman H; Tessmer, Ingrid; Toth, Ronald T; Tripathy, Ashutosh; Uchiyama, Susumu; Uebel, Stephan F W; Unzai, Satoru; Gruber, Anna Vitlin; von Hippel, Peter H; Wandrey, Christine; Wang, Szu-Huan; Weitzel, Steven E; Wielgus-Kutrowska, Beata; Wolberger, Cynthia; Wolff, Martin; Wright, Edward; Wu, Yu-Sung; Wubben, Jacinta M; Schuck, Peter

2015-01-01

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies.

Development of a transformation model to derive general population-based utility: Mapping the pruritus-visual analog scale (VAS) to the EQ-5D utility.

PubMed

Park, Sun-Young; Park, Eun-Ja; Suh, Hae Sun; Ha, Dongmun; Lee, Eui-Kyung

2017-08-01

Although nonpreference-based disease-specific measures are widely used in clinical studies, they cannot generate utilities for economic evaluation. A solution to this problem is to estimate utilities from disease-specific instruments using the mapping function. This study aimed to develop a transformation model for mapping the pruritus-visual analog scale (VAS) to the EuroQol 5-Dimension 3-Level (EQ-5D-3L) utility index in pruritus. A cross-sectional survey was conducted with a sample (n = 268) drawn from the general population of South Korea. Data were randomly divided into 2 groups, one for estimating and the other for validating mapping models. To select the best model, we developed and compared 3 separate models using demographic information and the pruritus-VAS as independent variables. The predictive performance was assessed using the mean absolute deviation and root mean square error in a separate dataset. Among the 3 models, model 2 using age, age squared, sex, and the pruritus-VAS as independent variables had the best performance based on the goodness of fit and model simplicity, with a log likelihood of 187.13. The 3 models had similar precision errors based on mean absolute deviation and root mean square error in the validation dataset. No statistically significant difference was observed between the mean observed and predicted values in all models. In conclusion, model 2 was chosen as the preferred mapping model. Outcomes measured as the pruritus-VAS can be transformed into the EQ-5D-3L utility index using this mapping model, which makes an economic evaluation possible when only pruritus-VAS data are available. © 2017 John Wiley & Sons, Ltd.

G4CEP: A G4 theory modification by including pseudopotential for molecules containing first-, second- and third-row representative elements

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

Silva, Cleuton de Souza; Instituto de Ciências Exatas e Tecnologia, Universidade Federal do Amazonas, Campus de Itacoatiara, 69100-021 Itacoatiara, Amazonas; Pereira, Douglas Henrique

2016-05-28

The G4CEP composite method was developed from the respective G4 all-electron version by considering the implementation of compact effective pseudopotential (CEP). The G3/05 test set was used as reference to benchmark the adaptation by treating in this work atoms and compounds from the first and second periods of the periodic table, as well as representative elements of the third period, comprising 440 thermochemical data. G4CEP has not reached a so high level of accuracy as the G4 all-electron theory. G4CEP presented a mean absolute error around 1.09 kcal mol{sup −1}, while the original method presents a deviation corresponding to 0.83more » kcal mol{sup −1}. The similarity of the optimized molecular geometries between G4 and G4CEP indicates that the core-electron effects and basis set adjustments may be pointed out as a significant factor responsible for the large discrepancies between the pseudopotential results and the experimental data, or even that the all-electron calculations are more efficient either in its formulation or in the cancellation of errors. When the G4CEP mean absolute error (1.09 kcal mol{sup −1}) is compared to 1.29 kcal mol{sup −1} from G3CEP, it does not seem so efficient. However, while the G3CEP uncertainty is ±4.06 kcal mol{sup −1}, the G4CEP deviation is ±2.72 kcal mol{sup −1}. Therefore, the G4CEP theory is considerably more reliable than any previous combination of composite theory and pseudopotential, particularly for enthalpies of formation and electron affinities.« less

Extensive validation of CM SAF surface radiation products over Europe.

PubMed

Urraca, Ruben; Gracia-Amillo, Ana M; Koubli, Elena; Huld, Thomas; Trentmann, Jörg; Riihelä, Aku; Lindfors, Anders V; Palmer, Diane; Gottschalg, Ralph; Antonanzas-Torres, Fernando

2017-09-15

This work presents a validation of three satellite-based radiation products over an extensive network of 313 pyranometers across Europe, from 2005 to 2015. The products used have been developed by the Satellite Application Facility on Climate Monitoring (CM SAF) and are one geostationary climate dataset (SARAH-JRC), one polar-orbiting climate dataset (CLARA-A2) and one geostationary operational product. Further, the ERA-Interim reanalysis is also included in the comparison. The main objective is to determine the quality level of the daily means of CM SAF datasets, identifying their limitations, as well as analyzing the different factors that can interfere in the adequate validation of the products. The quality of the pyranometer was the most critical source of uncertainty identified. In this respect, the use of records from Second Class pyranometers and silicon-based photodiodes increased the absolute error and the bias, as well as the dispersion of both metrics, preventing an adequate validation of the daily means. The best spatial estimates for the three datasets were obtained in Central Europe with a Mean Absolute Deviation (MAD) within 8-13 W/m 2 , whereas the MAD always increased at high-latitudes, snow-covered surfaces, high mountain ranges and coastal areas. Overall, the SARAH-JRC's accuracy was demonstrated over a dense network of stations making it the most consistent dataset for climate monitoring applications. The operational dataset was comparable to SARAH-JRC in Central Europe, but lacked of the temporal stability of climate datasets, while CLARA-A2 did not achieve the same level of accuracy despite predictions obtained showed high uniformity with a small negative bias. The ERA-Interim reanalysis shows the by-far largest deviations from the surface reference measurements.

A Multilaboratory Comparison of Calibration Accuracy and the Performance of External References in Analytical Ultracentrifugation

PubMed Central

Zhao, Huaying; Ghirlando, Rodolfo; Alfonso, Carlos; Arisaka, Fumio; Attali, Ilan; Bain, David L.; Bakhtina, Marina M.; Becker, Donald F.; Bedwell, Gregory J.; Bekdemir, Ahmet; Besong, Tabot M. D.; Birck, Catherine; Brautigam, Chad A.; Brennerman, William; Byron, Olwyn; Bzowska, Agnieszka; Chaires, Jonathan B.; Chaton, Catherine T.; Cölfen, Helmut; Connaghan, Keith D.; Crowley, Kimberly A.; Curth, Ute; Daviter, Tina; Dean, William L.; Díez, Ana I.; Ebel, Christine; Eckert, Debra M.; Eisele, Leslie E.; Eisenstein, Edward; England, Patrick; Escalante, Carlos; Fagan, Jeffrey A.; Fairman, Robert; Finn, Ron M.; Fischle, Wolfgang; de la Torre, José García; Gor, Jayesh; Gustafsson, Henning; Hall, Damien; Harding, Stephen E.; Cifre, José G. Hernández; Herr, Andrew B.; Howell, Elizabeth E.; Isaac, Richard S.; Jao, Shu-Chuan; Jose, Davis; Kim, Soon-Jong; Kokona, Bashkim; Kornblatt, Jack A.; Kosek, Dalibor; Krayukhina, Elena; Krzizike, Daniel; Kusznir, Eric A.; Kwon, Hyewon; Larson, Adam; Laue, Thomas M.; Le Roy, Aline; Leech, Andrew P.; Lilie, Hauke; Luger, Karolin; Luque-Ortega, Juan R.; Ma, Jia; May, Carrie A.; Maynard, Ernest L.; Modrak-Wojcik, Anna; Mok, Yee-Foong; Mücke, Norbert; Nagel-Steger, Luitgard; Narlikar, Geeta J.; Noda, Masanori; Nourse, Amanda; Obsil, Tomas; Park, Chad K.; Park, Jin-Ku; Pawelek, Peter D.; Perdue, Erby E.; Perkins, Stephen J.; Perugini, Matthew A.; Peterson, Craig L.; Peverelli, Martin G.; Piszczek, Grzegorz; Prag, Gali; Prevelige, Peter E.; Raynal, Bertrand D. E.; Rezabkova, Lenka; Richter, Klaus; Ringel, Alison E.; Rosenberg, Rose; Rowe, Arthur J.; Rufer, Arne C.; Scott, David J.; Seravalli, Javier G.; Solovyova, Alexandra S.; Song, Renjie; Staunton, David; Stoddard, Caitlin; Stott, Katherine; Strauss, Holger M.; Streicher, Werner W.; Sumida, John P.; Swygert, Sarah G.; Szczepanowski, Roman H.; Tessmer, Ingrid; Toth, Ronald T.; Tripathy, Ashutosh; Uchiyama, Susumu; Uebel, Stephan F. W.; Unzai, Satoru; Gruber, Anna Vitlin; von Hippel, Peter H.; Wandrey, Christine; Wang, Szu-Huan; Weitzel, Steven E.; Wielgus-Kutrowska, Beata; Wolberger, Cynthia; Wolff, Martin; Wright, Edward; Wu, Yu-Sung; Wubben, Jacinta M.; Schuck, Peter

2015-01-01

Analytical ultracentrifugation (AUC) is a first principles based method to determine absolute sedimentation coefficients and buoyant molar masses of macromolecules and their complexes, reporting on their size and shape in free solution. The purpose of this multi-laboratory study was to establish the precision and accuracy of basic data dimensions in AUC and validate previously proposed calibration techniques. Three kits of AUC cell assemblies containing radial and temperature calibration tools and a bovine serum albumin (BSA) reference sample were shared among 67 laboratories, generating 129 comprehensive data sets. These allowed for an assessment of many parameters of instrument performance, including accuracy of the reported scan time after the start of centrifugation, the accuracy of the temperature calibration, and the accuracy of the radial magnification. The range of sedimentation coefficients obtained for BSA monomer in different instruments and using different optical systems was from 3.655 S to 4.949 S, with a mean and standard deviation of (4.304 ± 0.188) S (4.4%). After the combined application of correction factors derived from the external calibration references for elapsed time, scan velocity, temperature, and radial magnification, the range of s-values was reduced 7-fold with a mean of 4.325 S and a 6-fold reduced standard deviation of ± 0.030 S (0.7%). In addition, the large data set provided an opportunity to determine the instrument-to-instrument variation of the absolute radial positions reported in the scan files, the precision of photometric or refractometric signal magnitudes, and the precision of the calculated apparent molar mass of BSA monomer and the fraction of BSA dimers. These results highlight the necessity and effectiveness of independent calibration of basic AUC data dimensions for reliable quantitative studies. PMID:25997164

Use of consensus development to establish national research priorities in critical care

PubMed Central

Vella, Keryn; Goldfrad, Caroline; Rowan, Kathy; Bion, Julian; Black, Nick

2000-01-01

Objectives To test the feasibility of using a nominal group technique to establish clinical and health services research priorities in critical care and to test the representativeness of the group's views. Design Generation of topics by means of a national survey; a nominal group technique to establish the level of consensus; a survey to test the representativeness of the results. Setting United Kingdom and Republic of Ireland. Subjects Nominal group composed of 10 doctors (8 consultants, 2 trainees) and 2 nurses. Main outcome measure Level of support (median) and level of agreement (mean absolute deviation from the median) derived from a 9 point Likert scale. Results Of the 325 intensive care units approached, 187 (58%) responded, providing about 1000 suggestions for research. Of the 106 most frequently suggested topics considered by the nominal group, 37 attracted strong support, 48 moderate support and 21 weak support. There was more agreement after the group had met—overall mean of the mean absolute deviations from the median fell from 1.41 to 1.26. The group's views represented the views of the wider community of critical care staff (r=0.73, P<0.01). There was no significant difference in the views of staff from teaching or from non-teaching hospitals. Of the 37 topics that attracted the strongest support, 24 were concerned with organisational aspects of critical care and only 13 with technology assessment or clinical research. Conclusions A nominal group technique is feasible and reliable for determining research priorities among clinicians. This approach is more democratic and transparent than the traditional methods used by research funding bodies. The results suggest that clinicians perceive research into the best ways of delivering and organising services as a high priority. PMID:10753149

Low back pain and postural control, effects of task difficulty on centre of pressure and spinal kinematics.

PubMed

Schelldorfer, Sarah; Ernst, Markus Josef; Rast, Fabian Marcel; Bauer, Christoph Michael; Meichtry, André; Kool, Jan

2015-01-01

Association of low back pain and standing postural control (PC) deficits are reported inconsistently. Demands on PC adaptation strategies are increased by restraining the input of visual or somatosensory senses. The objectives of the current study are, to investigate whether PC adaptations of the spine, hip and the centre of pressure (COP) differ between patients reporting non-specific low back pain (NSLBP) and asymptomatic controls. The PC adaption strategies of the thoracic and lumbar spine, the hip and the COP were measured in fifty-seven NSLBP patients and 22 asymptomatic controls. We tested three "feet together" conditions with increasing demands on PC strategies, using inertial measurement units (IMUs) on the spine and a Wii balance board for centre of pressure (COP) parameters. The differences between NSLBP patients and controls were most apparent when the participants were blindfolded, but remaining on a firm surface. While NSLBP patients had larger thoracic and lumbar spine mean absolute deviations of position (MADpos) in the frontal plane, the same parameters decreased in control subjects (relative change (RC): 0.23, 95% confidence interval: 0.03 to 0.45 and 0.03 to 0.48). The Mean absolute deviation of velocity (MADvel) of the thoracic spine in the frontal plane showed a similar and significant effect (RC: 0.12 95% CI: 0.01 to 0.25). Gender, age and pain during the measurements affected some parameters significantly. PC adaptions differ between NSLBP patients and asymptomatic controls. The differences are most apparent for the thoracic and lumbar parameters of MADpos, in the frontal plane and while the visual condition was removed. Copyright © 2014 Elsevier B.V. All rights reserved.

G4CEP: A G4 theory modification by including pseudopotential for molecules containing first-, second- and third-row representative elements.

PubMed

Silva, Cleuton de Souza; Pereira, Douglas Henrique; Custodio, Rogério

2016-05-28

The G4CEP composite method was developed from the respective G4 all-electron version by considering the implementation of compact effective pseudopotential (CEP). The G3/05 test set was used as reference to benchmark the adaptation by treating in this work atoms and compounds from the first and second periods of the periodic table, as well as representative elements of the third period, comprising 440 thermochemical data. G4CEP has not reached a so high level of accuracy as the G4 all-electron theory. G4CEP presented a mean absolute error around 1.09 kcal mol(-1), while the original method presents a deviation corresponding to 0.83 kcal mol(-1). The similarity of the optimized molecular geometries between G4 and G4CEP indicates that the core-electron effects and basis set adjustments may be pointed out as a significant factor responsible for the large discrepancies between the pseudopotential results and the experimental data, or even that the all-electron calculations are more efficient either in its formulation or in the cancellation of errors. When the G4CEP mean absolute error (1.09 kcal mol(-1)) is compared to 1.29 kcal mol(-1) from G3CEP, it does not seem so efficient. However, while the G3CEP uncertainty is ±4.06 kcal mol(-1), the G4CEP deviation is ±2.72 kcal mol(-1). Therefore, the G4CEP theory is considerably more reliable than any previous combination of composite theory and pseudopotential, particularly for enthalpies of formation and electron affinities.

Pinocchio testing in the forensic analysis of waiting lists: using public waiting list data from Finland and Spain for testing Newcomb-Benford’s Law

PubMed Central

López-Valcárcel, Beatriz G; González-Martel, Christian; Peiro, Salvador

2018-01-01

Objective Newcomb-Benford’s Law (NBL) proposes a regular distribution for first digits, second digits and digit combinations applicable to many different naturally occurring sources of data. Testing deviations from NBL is used in many datasets as a screening tool for identifying data trustworthiness problems. This study aims to compare public available waiting lists (WL) data from Finland and Spain for testing NBL as an instrument to flag up potential manipulation in WLs. Design Analysis of the frequency of Finnish and Spanish WLs first digits to determine if their distribution is similar to the pattern documented by NBL. Deviations from the expected first digit frequency were analysed using Pearson’s χ2, mean absolute deviation and Kuiper tests. Setting/participants Publicly available WL data from Finland and Spain, two countries with universal health insurance and National Health Systems but characterised by different levels of transparency and good governance standards. Main outcome measures Adjustment of the observed distribution of the numbers reported in Finnish and Spanish WL data to the expected distribution according to NBL. Results WL data reported by the Finnish health system fits first digit NBL according to all statistical tests used (p=0.6519 in χ2 test). For Spanish data, this hypothesis was rejected in all tests (p<0.0001 in χ2 test). Conclusions Testing deviations from NBL distribution can be a useful tool to identify problems with WL data trustworthiness and signalling the need for further testing. PMID:29743333

A simple method to relate microwave radiances to upper tropospheric humidity

NASA Astrophysics Data System (ADS)

Buehler, S. A.; John, V. O.

2005-01-01

A brightness temperature (BT) transformation method can be applied to microwave data to retrieve Jacobian weighted upper tropospheric relative humidity (UTH) in a broad layer centered roughly between 6 and 8 km altitude. The UTH bias is below 4% RH, and the relative UTH bias below 20%. The UTH standard deviation is between 2 and 6.5% RH in absolute numbers, or between 10 and 27% in relative numbers. The standard deviation is dominated by the regression noise, resulting from vertical structure not accounted for by the simple transformation relation. The UTH standard deviation due to radiometric noise alone has a relative standard deviation of approximately 7% for a radiometric noise level of 1 K. The retrieval performance was shown to be of almost constant quality for all viewing angles and latitudes, except for problems at high latitudes due to surface effects. A validation of AMSU UTH against radiosonde UTH shows reasonable agreement if known systematic differences between AMSU and radiosonde are taken into account. When the method is applied to supersaturation studies, regression noise and radiometric noise could lead to an apparent supersaturation even if there were no supersaturation. For a radiometer noise level of 1 K the drop-off slope of the apparent supersaturation is 0.17% RH-1, for a noise level of 2 K the slope is 0.12% RH-1. The main conclusion from this study is that the BT transformation method is very well suited for microwave data. Its particular strength is in climatological applications where the simplicity and the a priori independence are key advantages.

A digital, constant-frequency pulsed phase-locked-loop instrument for real-time, absolute ultrasonic phase measurements

NASA Astrophysics Data System (ADS)

Haldren, H. A.; Perey, D. F.; Yost, W. T.; Cramer, K. E.; Gupta, M. C.

2018-05-01

A digitally controlled instrument for conducting single-frequency and swept-frequency ultrasonic phase measurements has been developed based on a constant-frequency pulsed phase-locked-loop (CFPPLL) design. This instrument uses a pair of direct digital synthesizers to generate an ultrasonically transceived tone-burst and an internal reference wave for phase comparison. Real-time, constant-frequency phase tracking in an interrogated specimen is possible with a resolution of 0.000 38 rad (0.022°), and swept-frequency phase measurements can be obtained. Using phase measurements, an absolute thickness in borosilicate glass is presented to show the instrument's efficacy, and these results are compared to conventional ultrasonic pulse-echo time-of-flight (ToF) measurements. The newly developed instrument predicted the thickness with a mean error of -0.04 μm and a standard deviation of error of 1.35 μm. Additionally, the CFPPLL instrument shows a lower measured phase error in the absence of changing temperature and couplant thickness than high-resolution cross-correlation ToF measurements at a similar signal-to-noise ratio. By showing higher accuracy and precision than conventional pulse-echo ToF measurements and lower phase errors than cross-correlation ToF measurements, the new digitally controlled CFPPLL instrument provides high-resolution absolute ultrasonic velocity or path-length measurements in solids or liquids, as well as tracking of material property changes with high sensitivity. The ability to obtain absolute phase measurements allows for many new applications than possible with previous ultrasonic pulsed phase-locked loop instruments. In addition to improved resolution, swept-frequency phase measurements add useful capability in measuring properties of layered structures, such as bonded joints, or materials which exhibit non-linear frequency-dependent behavior, such as dispersive media.

Absolute Quantification of Rifampicin by MALDI Imaging Mass Spectrometry Using Multiple TOF/TOF Events in a Single Laser Shot

NASA Astrophysics Data System (ADS)

Prentice, Boone M.; Chumbley, Chad W.; Caprioli, Richard M.

2017-01-01

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) allows for the visualization of molecular distributions within tissue sections. While providing excellent molecular specificity and spatial information, absolute quantification by MALDI IMS remains challenging. Especially in the low molecular weight region of the spectrum, analysis is complicated by matrix interferences and ionization suppression. Though tandem mass spectrometry (MS/MS) can be used to ensure chemical specificity and improve sensitivity by eliminating chemical noise, typical MALDI MS/MS modalities only scan for a single MS/MS event per laser shot. Herein, we describe TOF/TOF instrumentation that enables multiple fragmentation events to be performed in a single laser shot, allowing the intensity of the analyte to be referenced to the intensity of the internal standard in each laser shot while maintaining the benefits of MS/MS. This approach is illustrated by the quantitative analyses of rifampicin (RIF), an antibiotic used to treat tuberculosis, in pooled human plasma using rifapentine (RPT) as an internal standard. The results show greater than 4-fold improvements in relative standard deviation as well as improved coefficients of determination (R2) and accuracy (>93% quality controls, <9% relative errors). This technology is used as an imaging modality to measure absolute RIF concentrations in liver tissue from an animal dosed in vivo. Each microspot in the quantitative image measures the local RIF concentration in the tissue section, providing absolute pixel-to-pixel quantification from different tissue microenvironments. The average concentration determined by IMS is in agreement with the concentration determined by HPLC-MS/MS, showing a percent difference of 10.6%.

Dosimetric verification of lung cancer treatment using the CBCTs estimated from limited-angle on-board projections

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

Zhang, You; Yin, Fang-Fang; Ren, Lei, E-mail: lei.ren@duke.edu

2015-08-15

Purpose: Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Methods: Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated onmore » the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the “gold-standard” on-board images (gold-standard doses). The absolute deviations of minimum dose (ΔD{sub min}), maximum dose (ΔD{sub max}), and mean dose (ΔD{sub mean}), and the absolute deviations of prescription dose coverage (ΔV{sub 100%}) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Results: Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ΔD{sub min}, ΔD{sub max}, ΔD{sub mean}, and ΔV{sub 100%} (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ΔD{sub min}, ΔD{sub max}, ΔD{sub mean}, and ΔV{sub 100%} of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). Conclusions: MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.« less

Microfabricated Collector-Generator Electrode Sensor for Measuring Absolute pH and Oxygen Concentrations.

PubMed

Dengler, Adam K; Wightman, R Mark; McCarty, Gregory S

2015-10-20

Fast-scan cyclic voltammetry (FSCV) has attracted attention for studying in vivo neurotransmission due to its subsecond temporal resolution, selectivity, and sensitivity. Traditional FSCV measurements use background subtraction to isolate changes in the local electrochemical environment, providing detailed information on fluctuations in the concentration of electroactive species. This background subtraction removes information about constant or slowly changing concentrations. However, determination of background concentrations is still important for understanding functioning brain tissue. For example, neural activity is known to consume oxygen and produce carbon dioxide which affects local levels of oxygen and pH. Here, we present a microfabricated microelectrode array which uses FSCV to detect the absolute levels of oxygen and pH in vitro. The sensor is a collector-generator electrode array with carbon microelectrodes spaced 5 μm apart. In this work, a periodic potential step is applied at the generator producing transient local changes in the electrochemical environment. The collector electrode continuously performs FSCV enabling these induced changes in concentration to be recorded with the sensitivity and selectivity of FSCV. A negative potential step applied at the generator produces a transient local pH shift at the collector. The generator-induced pH signal is detected using FSCV at the collector and correlated to absolute solution pH by postcalibration of the anodic peak position. In addition, in oxygenated solutions a negative potential step at the generator produces hydrogen peroxide by reducing oxygen. Hydrogen peroxide is detected with FSCV at the collector electrode, and the magnitude of the oxidative peak is proportional to absolute oxygen concentrations. Oxygen interference on the pH signal is minimal and can be accounted for with a postcalibration.

Galaxy And Mass Assembly (GAMA): colour- and luminosity-dependent clustering from calibrated photometric redshifts

NASA Astrophysics Data System (ADS)

Christodoulou, L.; Eminian, C.; Loveday, J.; Norberg, P.; Baldry, I. K.; Hurley, P. D.; Driver, S. P.; Bamford, S. P.; Hopkins, A. M.; Liske, J.; Peacock, J. A.; Bland-Hawthorn, J.; Brough, S.; Cameron, E.; Conselice, C. J.; Croom, S. M.; Frenk, C. S.; Gunawardhana, M.; Jones, D. H.; Kelvin, L. S.; Kuijken, K.; Nichol, R. C.; Parkinson, H.; Pimbblet, K. A.; Popescu, C. C.; Prescott, M.; Robotham, A. S. G.; Sharp, R. G.; Sutherland, W. J.; Taylor, E. N.; Thomas, D.; Tuffs, R. J.; van Kampen, E.; Wijesinghe, D.

2012-09-01

We measure the two-point angular correlation function of a sample of 4289 223 galaxies with r < 19.4 mag from the Sloan Digital Sky Survey (SDSS) as a function of photometric redshift, absolute magnitude and colour down to Mr - 5 log h = -14 mag. Photometric redshifts are estimated from ugriz model magnitudes and two Petrosian radii using the artificial neural network package ANNz, taking advantage of the Galaxy And Mass Assembly (GAMA) spectroscopic sample as our training set. These photometric redshifts are then used to determine absolute magnitudes and colours. For all our samples, we estimate the underlying redshift and absolute magnitude distributions using Monte Carlo resampling. These redshift distributions are used in Limber's equation to obtain spatial correlation function parameters from power-law fits to the angular correlation function. We confirm an increase in clustering strength for sub-L* red galaxies compared with ˜L* red galaxies at small scales in all redshift bins, whereas for the blue population the correlation length is almost independent of luminosity for ˜L* galaxies and fainter. A linear relation between relative bias and log luminosity is found to hold down to luminosities L ˜ 0.03L*. We find that the redshift dependence of the bias of the L* population can be described by the passive evolution model of Tegmark & Peebles. A visual inspection of a random sample from our r < 19.4 sample of SDSS galaxies reveals that about 10 per cent are spurious, with a higher contamination rate towards very faint absolute magnitudes due to over-deblended nearby galaxies. We correct for this contamination in our clustering analysis.

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China

PubMed Central

Liang, Hao; Gao, Lian; Liang, Bingyu; Huang, Jiegang; Zang, Ning; Liao, Yanyan; Yu, Jun; Lai, Jingzhen; Qin, Fengxiang; Su, Jinming; Ye, Li; Chen, Hui

2016-01-01

Background Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. PMID:27258555

Left-hemisphere activation is associated with enhanced vocal pitch error detection in musicians with absolute pitch.

PubMed

Behroozmand, Roozbeh; Ibrahim, Nadine; Korzyukov, Oleg; Robin, Donald A; Larson, Charles R

2014-02-01

The ability to process auditory feedback for vocal pitch control is crucial during speaking and singing. Previous studies have suggested that musicians with absolute pitch (AP) develop specialized left-hemisphere mechanisms for pitch processing. The present study adopted an auditory feedback pitch perturbation paradigm combined with ERP recordings to test the hypothesis whether the neural mechanisms of the left-hemisphere enhance vocal pitch error detection and control in AP musicians compared with relative pitch (RP) musicians and non-musicians (NM). Results showed a stronger N1 response to pitch-shifted voice feedback in the right-hemisphere for both AP and RP musicians compared with the NM group. However, the left-hemisphere P2 component activation was greater in AP and RP musicians compared with NMs and also for the AP compared with RP musicians. The NM group was slower in generating compensatory vocal reactions to feedback pitch perturbation compared with musicians, and they failed to re-adjust their vocal pitch after the feedback perturbation was removed. These findings suggest that in the earlier stages of cortical neural processing, the right hemisphere is more active in musicians for detecting pitch changes in voice feedback. In the later stages, the left-hemisphere is more active during the processing of auditory feedback for vocal motor control and seems to involve specialized mechanisms that facilitate pitch processing in the AP compared with RP musicians. These findings indicate that the left hemisphere mechanisms of AP ability are associated with improved auditory feedback pitch processing during vocal pitch control in tasks such as speaking or singing. Copyright © 2013 Elsevier Inc. All rights reserved.

Relative location prediction in CT scan images using convolutional neural networks.

PubMed

Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

2018-07-01

Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

Towards a unifying basis of auditory thresholds: binaural summation.

PubMed

Heil, Peter

2014-04-01

Absolute auditory threshold decreases with increasing sound duration, a phenomenon explainable by the assumptions that the sound evokes neural events whose probabilities of occurrence are proportional to the sound's amplitude raised to an exponent of about 3 and that a constant number of events are required for threshold (Heil and Neubauer, Proc Natl Acad Sci USA 100:6151-6156, 2003). Based on this probabilistic model and on the assumption of perfect binaural summation, an equation is derived here that provides an explicit expression of the binaural threshold as a function of the two monaural thresholds, irrespective of whether they are equal or unequal, and of the exponent in the model. For exponents >0, the predicted binaural advantage is largest when the two monaural thresholds are equal and decreases towards zero as the monaural threshold difference increases. This equation is tested and the exponent derived by comparing binaural thresholds with those predicted on the basis of the two monaural thresholds for different values of the exponent. The thresholds, measured in a large sample of human subjects with equal and unequal monaural thresholds and for stimuli with different temporal envelopes, are compatible only with an exponent close to 3. An exponent of 3 predicts a binaural advantage of 2 dB when the two ears are equally sensitive. Thus, listening with two (equally sensitive) ears rather than one has the same effect on absolute threshold as doubling duration. The data suggest that perfect binaural summation occurs at threshold and that peripheral neural signals are governed by an exponent close to 3. They might also shed new light on mechanisms underlying binaural summation of loudness.

Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany

PubMed Central

Wang, Zhu; Shuangge, Ma; Wang, Ching-Yun

2017-01-01

In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package mpath. PMID:26059498

Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672

Human sensitivity to vertical self-motion.

PubMed

Nesti, Alessandro; Barnett-Cowan, Michael; Macneilage, Paul R; Bülthoff, Heinrich H

2014-01-01

Perceiving vertical self-motion is crucial for maintaining balance as well as for controlling an aircraft. Whereas heave absolute thresholds have been exhaustively studied, little work has been done in investigating how vertical sensitivity depends on motion intensity (i.e., differential thresholds). Here we measure human sensitivity for 1-Hz sinusoidal accelerations for 10 participants in darkness. Absolute and differential thresholds are measured for upward and downward translations independently at 5 different peak amplitudes ranging from 0 to 2 m/s(2). Overall vertical differential thresholds are higher than horizontal differential thresholds found in the literature. Psychometric functions are fit in linear and logarithmic space, with goodness of fit being similar in both cases. Differential thresholds are higher for upward as compared to downward motion and increase with stimulus intensity following a trend best described by two power laws. The power laws' exponents of 0.60 and 0.42 for upward and downward motion, respectively, deviate from Weber's Law in that thresholds increase less than expected at high stimulus intensity. We speculate that increased sensitivity at high accelerations and greater sensitivity to downward than upward self-motion may reflect adaptations to avoid falling.

Control of the interaction strength of photonic molecules by nanometer precise 3D fabrication.

PubMed

Rawlings, Colin D; Zientek, Michal; Spieser, Martin; Urbonas, Darius; Stöferle, Thilo; Mahrt, Rainer F; Lisunova, Yuliya; Brugger, Juergen; Duerig, Urs; Knoll, Armin W

2017-11-28

Applications for high resolution 3D profiles, so-called grayscale lithography, exist in diverse fields such as optics, nanofluidics and tribology. All of them require the fabrication of patterns with reliable absolute patterning depth independent of the substrate location and target materials. Here we present a complete patterning and pattern-transfer solution based on thermal scanning probe lithography (t-SPL) and dry etching. We demonstrate the fabrication of 3D profiles in silicon and silicon oxide with nanometer scale accuracy of absolute depth levels. An accuracy of less than 1nm standard deviation in t-SPL is achieved by providing an accurate physical model of the writing process to a model-based implementation of a closed-loop lithography process. For transfering the pattern to a target substrate we optimized the etch process and demonstrate linear amplification of grayscale patterns into silicon and silicon oxide with amplification ratios of ∼6 and ∼1, respectively. The performance of the entire process is demonstrated by manufacturing photonic molecules of desired interaction strength. Excellent agreement of fabricated and simulated structures has been achieved.

Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

NASA Astrophysics Data System (ADS)

Griffin, James M.; Diaz, Fernanda; Geerling, Edgar; Clasing, Matias; Ponce, Vicente; Taylor, Chris; Turner, Sam; Michael, Ernest A.; Patricio Mena, F.; Bronfman, Leonardo

2017-02-01

By using acoustic emission (AE) it is possible to control deviations and surface quality during micro milling operations. The method of micro milling is used to manufacture a submillimetre waveguide where micro machining is employed to achieve the required superior finish and geometrical tolerances. Submillimetre waveguide technology is used in deep space signal retrieval where highest detection efficiencies are needed and therefore every possible signal loss in the receiver has to be avoided and stringent tolerances achieved. With a sub-standard surface finish the signals travelling along the waveguides dissipate away faster than with perfect surfaces where the residual roughness becomes comparable with the electromagnetic skin depth. Therefore, the higher the radio frequency the more critical this becomes. The method of time-frequency analysis (STFT) is used to transfer raw AE into more meaningful salient signal features (SF). This information was then correlated against the measured geometrical deviations and, the onset of catastrophic tool wear. Such deviations can be offset from different AE signals (different deviations from subsequent tests) and feedback for a final spring cut ensuring the geometrical accuracies are met. Geometrical differences can impact on the required transfer of AE signals (change in cut off frequencies and diminished SNR at the interface) and therefore errors have to be minimised to within 1 μm. Rules based on both Classification and Regression Trees (CART) and Neural Networks (NN) were used to implement a simulation displaying how such a control regime could be used as a real time controller, be it corrective measures (via spring cuts) over several initial machining passes or, with a micron cut introducing a level plain measure for allowing setup corrective measures (similar to a spirit level).

Relative populations of excited levels within the ground configuration of Si-like Cu, Zn, Ge and Se ions

NASA Technical Reports Server (NTRS)

Datla, R. U.; Roberts, J. R.; Bhatia, A. K.

1991-01-01

Populations of 3p2 1D2, 3P1, 3P2 levels in Si-like Cu, Zn, Ge, and Se ions have been deduced from the measurements of absolute intensities of magnetic dipole transitions within the 3s2 3p2 ground configuration. The measured population ratios are compared with theoretical calculations based on the distorted-wave approximation for the electron collisions and a semiclassical approximation for the proton collisions. The observed deviation from the statistical distribution for the excited-level populations within the ground configuration along the silicon isoelectronic sequence is in agreement with theoretical prediction.

Corresponding states correlation for temperature dependent surface tension of normal saturated liquids

NASA Astrophysics Data System (ADS)

Yi, Huili; Tian, Jianxiang

2014-07-01

A new simple correlation based on the principle of corresponding state is proposed to estimate the temperature-dependent surface tension of normal saturated liquids. The correlation is a linear one and strongly stands for 41 saturated normal liquids. The new correlation requires only the triple point temperature, triple point surface tension and critical point temperature as input and is able to represent the experimental surface tension data for these 41 saturated normal liquids with a mean absolute average percent deviation of 1.26% in the temperature regions considered. For most substances, the temperature covers the range from the triple temperature to the one beyond the boiling temperature.

Multiplicative noise removal via a learned dictionary.

PubMed

Huang, Yu-Mei; Moisan, Lionel; Ng, Michael K; Zeng, Tieyong

2012-11-01

Multiplicative noise removal is a challenging image processing problem, and most existing methods are based on the maximum a posteriori formulation and the logarithmic transformation of multiplicative denoising problems into additive denoising problems. Sparse representations of images have shown to be efficient approaches for image recovery. Following this idea, in this paper, we propose to learn a dictionary from the logarithmic transformed image, and then to use it in a variational model built for noise removal. Extensive experimental results suggest that in terms of visual quality, peak signal-to-noise ratio, and mean absolute deviation error, the proposed algorithm outperforms state-of-the-art methods.

Experimental Evidence for Quantum Tunneling Time.

PubMed

Camus, Nicolas; Yakaboylu, Enderalp; Fechner, Lutz; Klaiber, Michael; Laux, Martin; Mi, Yonghao; Hatsagortsyan, Karen Z; Pfeifer, Thomas; Keitel, Christoph H; Moshammer, Robert

2017-07-14

The first hundred attoseconds of the electron dynamics during strong field tunneling ionization are investigated. We quantify theoretically how the electron's classical trajectories in the continuum emerge from the tunneling process and test the results with those achieved in parallel from attoclock measurements. An especially high sensitivity on the tunneling barrier is accomplished here by comparing the momentum distributions of two atomic species of slightly deviating atomic potentials (argon and krypton) being ionized under absolutely identical conditions with near-infrared laser pulses (1300 nm). The agreement between experiment and theory provides clear evidence for a nonzero tunneling time delay and a nonvanishing longitudinal momentum of the electron at the "tunnel exit."

Experimental Evidence for Quantum Tunneling Time

NASA Astrophysics Data System (ADS)

Camus, Nicolas; Yakaboylu, Enderalp; Fechner, Lutz; Klaiber, Michael; Laux, Martin; Mi, Yonghao; Hatsagortsyan, Karen Z.; Pfeifer, Thomas; Keitel, Christoph H.; Moshammer, Robert

2017-07-01

The first hundred attoseconds of the electron dynamics during strong field tunneling ionization are investigated. We quantify theoretically how the electron's classical trajectories in the continuum emerge from the tunneling process and test the results with those achieved in parallel from attoclock measurements. An especially high sensitivity on the tunneling barrier is accomplished here by comparing the momentum distributions of two atomic species of slightly deviating atomic potentials (argon and krypton) being ionized under absolutely identical conditions with near-infrared laser pulses (1300 nm). The agreement between experiment and theory provides clear evidence for a nonzero tunneling time delay and a nonvanishing longitudinal momentum of the electron at the "tunnel exit."

Analyses of S-Box in Image Encryption Applications Based on Fuzzy Decision Making Criterion

NASA Astrophysics Data System (ADS)

Rehman, Inayatur; Shah, Tariq; Hussain, Iqtadar

2014-06-01

In this manuscript, we put forward a standard based on fuzzy decision making criterion to examine the current substitution boxes and study their strengths and weaknesses in order to decide their appropriateness in image encryption applications. The proposed standard utilizes the results of correlation analysis, entropy analysis, contrast analysis, homogeneity analysis, energy analysis, and mean of absolute deviation analysis. These analyses are applied to well-known substitution boxes. The outcome of these analyses are additional observed and a fuzzy soft set decision making criterion is used to decide the suitability of an S-box to image encryption applications.

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

Manaa, M. Riad; Fried, Laurence E.; Kuo, I-Feng W.

We report gas-phase enthalpies of formation for the set of energetic molecules NTO, DADE, LLM-105, TNT, RDX, TATB, HMX, and PETN using the G2, G3, G4, and ccCA-PS3 quantum composite methods. Calculations for HMX and PETN hitherto represent the largest molecules attempted with these methods. G3 and G4 calculations are typically close to one another, with a larger difference found between these methods and ccCA-PS3. Furthermore there is significant uncertainty in experimental values, the mean absolute deviation between the average experimental value and calculations are 12, 6, 7, and 3 kcal/mol for G2, G3, G4, and ccCA-PS3, respectively.

π0 mass reconstruction in NOvA Far Detector.

NASA Astrophysics Data System (ADS)

Edayath, Sijith

2017-01-01

NOvA is a long-baseline neutrino oscillation experiment with functionally identical, segmented, tracking calorimeter Near and Far detectors. The detectors lie 14.6 mrad off-axis from the Fermilab NuMI beam, with a well-defined peak in neutrino energy at 2 GeV. The absolute calibration of the energy scale of the detectors is a major systematic uncertainty in long-baseline oscillation search in NOvA. Neutrino detectors make use of some standard candles for absolute energy calibration. Stopping muon energy distributions, Michel electron energy distributions, and invariant π0 mass are among them. In this talk, we cover NOvA's use of a new method to identify π0 with cosmic origins in the NOvA Far Detector. We employ a computer vision based particle identifier using convolutional neural networks (CVN) to identify π0s, complementing an existing strategy to identify π0 from the neutrino beam using more traditional methods in the Near Detector. Registered for PhD at Cochin University of Science and Technology, India and doing research in NOvA experiment at Fermilab.

Dissociable contributions of motor-execution and action-observation to intramanual transfer.

PubMed

Hayes, Spencer J; Elliott, Digby; Andrew, Matthew; Roberts, James W; Bennett, Simon J

2012-09-01

We examined the hypothesis that different processes and representations are associated with the learning of a movement sequence through motor-execution and action-observation. Following a pre-test in which participants attempted to achieve an absolute, and relative, time goal in a sequential goal-directed aiming movement, participants received either physical or observational practice with feedback. Post-test performance indicated that motor-execution and action-observation participants learned equally well. Participants then transferred to conditions where the gain between the limb movements and their visual consequences were manipulated. Under both bigger and smaller transfer conditions, motor-execution and action-observation participants exhibited similar intramanual transfer of absolute timing. However, participants in the action-observation group exhibited superior transfer of relative timing than the motor-execution group. These findings suggest that learning via action-observation is underpinned by a visual-spatial representation, while learning via motor-execution depends more on specific force-time planning (feed forward) and afferent processing associated with sensorimotor feedback. These behavioural effects are discussed with reference to neural processes associated with striatum, cerebellum and motor cortical regions (pre-motor cortex; SMA; pre-SMA).

A multi-channel low-power system-on-chip for single-unit recording and narrowband wireless transmission of neural signal.

PubMed

Bonfanti, A; Ceravolo, M; Zambra, G; Gusmeroli, R; Spinelli, A S; Lacaita, A L; Angotzi, G N; Baranauskas, G; Fadiga, L

2010-01-01

This paper reports a multi-channel neural recording system-on-chip (SoC) with digital data compression and wireless telemetry. The circuit consists of a 16 amplifiers, an analog time division multiplexer, an 8-bit SAR AD converter, a digital signal processor (DSP) and a wireless narrowband 400-MHz binary FSK transmitter. Even though only 16 amplifiers are present in our current die version, the whole system is designed to work with 64 channels demonstrating the feasibility of a digital processing and narrowband wireless transmission of 64 neural recording channels. A digital data compression, based on the detection of action potentials and storage of correspondent waveforms, allows the use of a 1.25-Mbit/s binary FSK wireless transmission. This moderate bit-rate and a low frequency deviation, Manchester-coded modulation are crucial for exploiting a narrowband wireless link and an efficient embeddable antenna. The chip is realized in a 0.35- εm CMOS process with a power consumption of 105 εW per channel (269 εW per channel with an extended transmission range of 4 m) and an area of 3.1 × 2.7 mm(2). The transmitted signal is captured by a digital TV tuner and demodulated by a wideband phase-locked loop (PLL), and then sent to a PC via an FPGA module. The system has been tested for electrical specifications and its functionality verified in in-vivo neural recording experiments.

Peak alpha frequency is a neural marker of cognitive function across the autism spectrum.

PubMed

Dickinson, Abigail; DiStefano, Charlotte; Senturk, Damla; Jeste, Shafali Spurling

2018-03-01

Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Heavy Ozone Enrichments from ATMOS Infrared Solar Spectra

NASA Technical Reports Server (NTRS)

Irion, F. W.; Gunson, M. R.; Rinsland, C. P.; Yung, Y. L.; Abrams, M. C.; Chang, A. Y.; Goldman, A.

1996-01-01

Vertical enrichment profiles of stratospheric O-16O-16O-18 and O-16O-18O-16 (hereafter referred to as (668)O3 and (686)O3 respectively) have been derived from space-based solar occultation spectra recorded at 0.01 cm(exp-1) resolution by the ATMOS (Atmospheric Trace MOlecule Spectroscopy) Fourier transform infrared (FTIR) spectrometer. The observations, made during the Spacelab 3 and ATLAS-1, -2, and -3 shuttle missions, cover polar, mid-latitude and tropical regions between 26 to 2.6 mb inclusive (approximately 25 to 41 km). Average enrichments, weighted by molecular (48)O3 density, of (15 +/- 6)% were found for (668)O3 and (10 +/- 7)% for (686)O3. Defining the mixing ratio of (50)O3 as the sum of those for (668)O3 and (686)O3, an enrichment of (13 plus or minus 5)% was found for (50)O3 (1 sigma standard deviation). No latitudinal or vertical gradients were found outside this standard deviation. From a series of ground-based measurements by the ATMOS instrument at Table Mountain, California (34.4 deg N), an average total column (668)O3 enrichment of (17 +/- 4)% (1 sigma standard deviation) was determined, with no significant seasonal variation discernable. Possible biases in the spectral intensities that affect the determination of absolute enrichments are discussed.

Dynamic Modeling and Interactive Performance of PARM: A Parallel Upper-Limb Rehabilitation Robot Using Impedance Control for Patients after Stroke.

PubMed

Guang, Hui; Ji, Linhong; Shi, Yingying; Misgeld, Berno J E

2018-01-01

The robot-assisted therapy has been demonstrated to be effective in the improvements of limb function and even activities of daily living for patients after stroke. This paper presents an interactive upper-limb rehabilitation robot with a parallel mechanism and an isometric screen embedded in the platform to display trajectories. In the dynamic modeling for impedance control, the effects of friction and inertia are reduced by introducing the principle of virtual work and derivative of Jacobian matrix. To achieve the assist-as-needed impedance control for arbitrary trajectories, the strategy based on orthogonal deviations is proposed. Simulations and experiments were performed to validate the dynamic modeling and impedance control. Besides, to investigate the influence of the impedance in practice, a subject participated in experiments and performed two types of movements with the robot, that is, rectilinear and circular movements, under four conditions, that is, with/without resistance or impedance, respectively. The results showed that the impedance and resistance affected both mean absolute error and standard deviation of movements and also demonstrated the significant differences between movements with/without impedance and resistance ( p < 0.001). Furthermore, the error patterns were discussed, which suggested that the impedance environment was capable of alleviating movement deviations by compensating the synergetic inadequacy between the shoulder and elbow joints.

Dynamic Modeling and Interactive Performance of PARM: A Parallel Upper-Limb Rehabilitation Robot Using Impedance Control for Patients after Stroke

PubMed Central

Shi, Yingying; Misgeld, Berno J. E.

2018-01-01

The robot-assisted therapy has been demonstrated to be effective in the improvements of limb function and even activities of daily living for patients after stroke. This paper presents an interactive upper-limb rehabilitation robot with a parallel mechanism and an isometric screen embedded in the platform to display trajectories. In the dynamic modeling for impedance control, the effects of friction and inertia are reduced by introducing the principle of virtual work and derivative of Jacobian matrix. To achieve the assist-as-needed impedance control for arbitrary trajectories, the strategy based on orthogonal deviations is proposed. Simulations and experiments were performed to validate the dynamic modeling and impedance control. Besides, to investigate the influence of the impedance in practice, a subject participated in experiments and performed two types of movements with the robot, that is, rectilinear and circular movements, under four conditions, that is, with/without resistance or impedance, respectively. The results showed that the impedance and resistance affected both mean absolute error and standard deviation of movements and also demonstrated the significant differences between movements with/without impedance and resistance (p < 0.001). Furthermore, the error patterns were discussed, which suggested that the impedance environment was capable of alleviating movement deviations by compensating the synergetic inadequacy between the shoulder and elbow joints. PMID:29850004

Are Study and Journal Characteristics Reliable Indicators of "Truth" in Imaging Research?

PubMed

Frank, Robert A; McInnes, Matthew D F; Levine, Deborah; Kressel, Herbert Y; Jesurum, Julia S; Petrcich, William; McGrath, Trevor A; Bossuyt, Patrick M

2018-04-01

Purpose To evaluate whether journal-level variables (impact factor, cited half-life, and Standards for Reporting of Diagnostic Accuracy Studies [STARD] endorsement) and study-level variables (citation rate, timing of publication, and order of publication) are associated with the distance between primary study results and summary estimates from meta-analyses. Materials and Methods MEDLINE was searched for meta-analyses of imaging diagnostic accuracy studies, published from January 2005 to April 2016. Data on journal-level and primary-study variables were extracted for each meta-analysis. Primary studies were dichotomized by variable as first versus subsequent publication, publication before versus after STARD introduction, STARD endorsement, or by median split. The mean absolute deviation of primary study estimates from the corresponding summary estimates for sensitivity and specificity was compared between groups. Means and confidence intervals were obtained by using bootstrap resampling; P values were calculated by using a t test. Results Ninety-eight meta-analyses summarizing 1458 primary studies met the inclusion criteria. There was substantial variability, but no significant differences, in deviations from the summary estimate between paired groups (P > .0041 in all comparisons). The largest difference found was in mean deviation for sensitivity, which was observed for publication timing, where studies published first on a topic demonstrated a mean deviation that was 2.5 percentage points smaller than subsequently published studies (P = .005). For journal-level factors, the greatest difference found (1.8 percentage points; P = .088) was in mean deviation for sensitivity in journals with impact factors above the median compared with those below the median. Conclusion Journal- and study-level variables considered important when evaluating diagnostic accuracy information to guide clinical decisions are not systematically associated with distance from the truth; critical appraisal of individual articles is recommended. © RSNA, 2017 Online supplemental material is available for this article.

Deployment and evaluation of a dual-sensor autofocusing method for on-machine measurement of patterns of small holes on freeform surfaces.

PubMed

Chen, Xiaomei; Longstaff, Andrew; Fletcher, Simon; Myers, Alan

2014-04-01

This paper presents and evaluates an active dual-sensor autofocusing system that combines an optical vision sensor and a tactile probe for autofocusing on arrays of small holes on freeform surfaces. The system has been tested on a two-axis test rig and then integrated onto a three-axis computer numerical control (CNC) milling machine, where the aim is to rapidly and controllably measure the hole position errors while the part is still on the machine. The principle of operation is for the tactile probe to locate the nominal positions of holes, and the optical vision sensor follows to focus and capture the images of the holes. The images are then processed to provide hole position measurement. In this paper, the autofocusing deviations are analyzed. First, the deviations caused by the geometric errors of the axes on which the dual-sensor unit is deployed are estimated to be 11 μm when deployed on a test rig and 7 μm on the CNC machine tool. Subsequently, the autofocusing deviations caused by the interaction of the tactile probe, surface, and small hole are mathematically analyzed and evaluated. The deviations are a result of the tactile probe radius, the curvatures at the positions where small holes are drilled on the freeform surface, and the effect of the position error of the hole on focusing. An example case study is provided for the measurement of a pattern of small holes on an elliptical cylinder on the two machines. The absolute sum of the autofocusing deviations is 118 μm on the test rig and 144 μm on the machine tool. This is much less than the 500 μm depth of field of the optical microscope. Therefore, the method is capable of capturing a group of clear images of the small holes on this workpiece for either implementation.

Central X-ray beam correction of radiographic acetabular cup measurement after THA: an experimental study.

PubMed

Schwarz, T; Weber, M; Wörner, M; Renkawitz, T; Grifka, J; Craiovan, B

2017-05-01

Accurate assessment of cup orientation on postoperative radiographs is essential for evaluating outcome after THA. However, accuracy is impeded by the deviation of the central X-ray beam in relation to the cup and the impossibility of measuring retroversion on standard pelvic radiographs. In an experimental trial, we built an artificial cup holder enabling the setting of different angles of anatomical anteversion and inclination. Twelve different cup orientations were investigated by three examiners. After comparing the two methods for radiographic measurement of the cup position developed by Lewinnek and Widmer, we showed how to differentiate between anteversion and retroversion in each cup position by using a second plane. To show the effect of the central beam offset on the cup, we X-rayed a defined cup position using a multidirectional central beam offset. According to Murray's definition of anteversion and inclination, we created a novel corrective procedure to balance measurement errors caused by deviation of the central beam. Measurement of the 12 different cup positions with the Lewinnek's method yielded a mean deviation of [Formula: see text] (95 % CI 1.3-2.3) from the original cup anteversion. The respective deviation with the Widmer/Liaw's method was [Formula: see text] (95 % CI 2.4-4.0). In each case, retroversion could be differentiated from anteversion with a second radiograph. Because of the multidirectional central beam offset ([Formula: see text] cm) from the acetabular cup in the cup holder ([Formula: see text] anteversion and [Formula: see text] inclination), the mean absolute difference for anteversion was [Formula: see text] (range [Formula: see text] to [Formula: see text] and [Formula: see text] (range [Formula: see text] to [Formula: see text] for inclination. The application of our novel mathematical correction of the central beam offset reduced deviation to a mean difference of [Formula: see text] for anteversion and [Formula: see text] for inclination. This novel calculation for central beam offset correction enables highly accurate measurement of the cup position.

Effect of Examiner Experience and Technique on the Alternate Cover Test

PubMed Central

Anderson, Heather A.; Manny, Ruth E.; Cotter, Susan A.; Mitchell, G. Lynn; Irani, Jasmine A.

2013-01-01

Purpose To compare the repeatability of the alternate cover test between experienced and inexperienced examiners and the effects of dissociation time and examiner bias. Methods Two sites each had an experienced examiner train 10 subjects (inexperienced examiners) to perform short and long dissociation time alternate cover test protocols at near. Each site conducted testing sessions with an examiner triad (experienced examiner and two inexperienced examiners) who were masked to each other’s results. Each triad performed the alternate cover test on 24 patients using both dissociation protocols. In an attempt to introduce bias, each of the paired inexperienced examiners was given a different graph of phoria distribution for the general population. Analysis techniques that adjust for correlations introduced when multiple measurements are obtained on the same patient were used to investigate the effect of examiner and dissociation time on each outcome. Results The range of measured deviations spanned 27.5 prism diopters (Δ) base-in to 17.5Δ base-out. The absolute mean difference between experienced and inexperienced examiners was 2.28 ± 2.4Δ and at least 60% of differences were ≤2Δ. Larger deviations were measured with the long dissociation protocol for both experienced and inexperienced examiners (mean difference range = 1.17 to 2.14Δ, p < 0.0001). The percentage of measured small deviations (2Δ base-out to 2Δ base-in) did not differ between inexperienced examiners biased with the narrow vs. wide theoretical distributions (p = 0.41). The magnitude and direction of the deviation had no effect on the size of the differences obtained with different examiners or dissociation times. Conclusions Although inexperienced examiners differed significantly from experienced examiners, most differences were <2Δ suggesting good reliability of inexperienced examiners’ measurements. Examiner bias did not have a substantial effect on inexperienced examiner measurements; however, increased dissociation resulted in larger measured deviations for all examiners. PMID:20125058

Big data driven cycle time parallel prediction for production planning in wafer manufacturing

NASA Astrophysics Data System (ADS)

Wang, Junliang; Yang, Jungang; Zhang, Jie; Wang, Xiaoxi; Zhang, Wenjun Chris

2018-07-01

Cycle time forecasting (CTF) is one of the most crucial issues for production planning to keep high delivery reliability in semiconductor wafer fabrication systems (SWFS). This paper proposes a novel data-intensive cycle time (CT) prediction system with parallel computing to rapidly forecast the CT of wafer lots with large datasets. First, a density peak based radial basis function network (DP-RBFN) is designed to forecast the CT with the diverse and agglomerative CT data. Second, the network learning method based on a clustering technique is proposed to determine the density peak. Third, a parallel computing approach for network training is proposed in order to speed up the training process with large scaled CT data. Finally, an experiment with respect to SWFS is presented, which demonstrates that the proposed CTF system can not only speed up the training process of the model but also outperform the radial basis function network, the back-propagation-network and multivariate regression methodology based CTF methods in terms of the mean absolute deviation and standard deviation.

Size- and shape-dependent surface thermodynamic properties of nanocrystals

NASA Astrophysics Data System (ADS)

Fu, Qingshan; Xue, Yongqiang; Cui, Zixiang

2018-05-01

As the fundamental properties, the surface thermodynamic properties of nanocrystals play a key role in the physical and chemical changes. However, it remains ambiguous about the quantitative influence regularities of size and shape on the surface thermodynamic properties of nanocrystals. Thus by introducing interface variables into the Gibbs energy and combining Young-Laplace equation, relations between the surface thermodynamic properties (surface Gibbs energy, surface enthalpy, surface entropy, surface energy and surface heat capacity), respectively, and size of nanocrystals with different shapes were derived. Theoretical estimations of the orders of the surface thermodynamic properties of nanocrystals agree with available experimental values. Calculated results of the surface thermodynamic properties of Au, Bi and Al nanocrystals suggest that when r > 10 nm, the surface thermodynamic properties linearly vary with the reciprocal of particle size, and when r < 10 nm, the effect of particle size on the surface thermodynamic properties becomes greater and deviates from linear variation. For nanocrystals with identical equivalent diameter, the more the shape deviates from sphere, the larger the surface thermodynamic properties (absolute value) are.

Detection of Epileptic Seizure Event and Onset Using EEG

PubMed Central

Ahammad, Nabeel; Fathima, Thasneem; Joseph, Paul

2014-01-01

This study proposes a method of automatic detection of epileptic seizure event and onset using wavelet based features and certain statistical features without wavelet decomposition. Normal and epileptic EEG signals were classified using linear classifier. For seizure event detection, Bonn University EEG database has been used. Three types of EEG signals (EEG signal recorded from healthy volunteer with eye open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. Important features such as energy, entropy, standard deviation, maximum, minimum, and mean at different subbands were computed and classification was done using linear classifier. The performance of classifier was determined in terms of specificity, sensitivity, and accuracy. The overall accuracy was 84.2%. In the case of seizure onset detection, the database used is CHB-MIT scalp EEG database. Along with wavelet based features, interquartile range (IQR) and mean absolute deviation (MAD) without wavelet decomposition were extracted. Latency was used to study the performance of seizure onset detection. Classifier gave a sensitivity of 98.5% with an average latency of 1.76 seconds. PMID:24616892

A Fully Sensorized Cooperative Robotic System for Surgical Interventions

PubMed Central

Tovar-Arriaga, Saúl; Vargas, José Emilio; Ramos, Juan M.; Aceves, Marco A.; Gorrostieta, Efren; Kalender, Willi A.

2012-01-01

In this research a fully sensorized cooperative robot system for manipulation of needles is presented. The setup consists of a DLR/KUKA Light Weight Robot III especially designed for safe human/robot interaction, a FD-CT robot-driven angiographic C-arm system, and a navigation camera. Also, new control strategies for robot manipulation in the clinical environment are introduced. A method for fast calibration of the involved components and the preliminary accuracy tests of the whole possible errors chain are presented. Calibration of the robot with the navigation system has a residual error of 0.81 mm (rms) with a standard deviation of ±0.41 mm. The accuracy of the robotic system while targeting fixed points at different positions within the workspace is of 1.2 mm (rms) with a standard deviation of ±0.4 mm. After calibration, and due to close loop control, the absolute positioning accuracy was reduced to the navigation camera accuracy which is of 0.35 mm (rms). The implemented control allows the robot to compensate for small patient movements. PMID:23012551

Unveiling the Dependence of Glass Transitions on Mixing Thermodynamics in Miscible Systems

NASA Astrophysics Data System (ADS)

Tu, Wenkang; Wang, Yunxi; Li, Xin; Zhang, Peng; Tian, Yongjun; Jin, Shaohua; Wang, Li-Min

2015-02-01

The dependence of the glass transition in mixtures on mixing thermodynamics is examined by focusing on enthalpy of mixing, ΔHmix with the change in sign (positive vs. negative) and magnitude (small vs. large). The effects of positive and negative ΔHmix are demonstrated based on two isomeric systems of o- vs. m- methoxymethylbenzene (MMB) and o- vs. m- dibromobenzene (DBB) with comparably small absolute ΔHmix. Two opposite composition dependences of the glass transition temperature, Tg, are observed with the MMB mixtures showing a distinct negative deviation from the ideal mixing rule and the DBB mixtures having a marginally positive deviation. The system of 1, 2- propanediamine (12PDA) vs. propylene glycol (PG) with large and negative ΔHmix is compared with the systems of small ΔHmix, and a considerably positive Tg shift is seen. Models involving the properties of pure components such as Tg, glass transition heat capacity increment, ΔCp, and density, ρ, do not interpret the observed Tg shifts in the systems. In contrast, a linear correlation is revealed between ΔHmix and maximum Tg shifts.

Point-based and model-based geolocation analysis of airborne laser scanning data

NASA Astrophysics Data System (ADS)

Sefercik, Umut Gunes; Buyuksalih, Gurcan; Jacobsen, Karsten; Alkan, Mehmet

2017-01-01

Airborne laser scanning (ALS) is one of the most effective remote sensing technologies providing precise three-dimensional (3-D) dense point clouds. A large-size ALS digital surface model (DSM) covering the whole Istanbul province was analyzed by point-based and model-based comprehensive statistical approaches. Point-based analysis was performed using checkpoints on flat areas. Model-based approaches were implemented in two steps as strip to strip comparing overlapping ALS DSMs individually in three subareas and comparing the merged ALS DSMs with terrestrial laser scanning (TLS) DSMs in four other subareas. In the model-based approach, the standard deviation of height and normalized median absolute deviation were used as the accuracy indicators combined with the dependency of terrain inclination. The results demonstrate that terrain roughness has a strong impact on the vertical accuracy of ALS DSMs. From the relative horizontal shifts determined and partially improved by merging the overlapping strips and comparison of the ALS, and the TLS, data were found not to be negligible. The analysis of ALS DSM in relation to TLS DSM allowed us to determine the characteristics of the DSM in detail.

Recurrent connectivity can account for the dynamics of disparity processing in V1

PubMed Central

Samonds, Jason M.; Potetz, Brian R.; Tyler, Christopher W.; Lee, Tai Sing

2013-01-01

Disparity tuning measured in the primary visual cortex (V1) is described well by the disparity energy model, but not all aspects of disparity tuning are fully explained by the model. Such deviations from the disparity energy model provide us with insight into how network interactions may play a role in disparity processing and help to solve the stereo correspondence problem. Here, we propose a neuronal circuit model with recurrent connections that provides a simple account of the observed deviations. The model is based on recurrent connections inferred from neurophysiological observations on spike timing correlations, and is in good accord with existing data on disparity tuning dynamics. We further performed two additional experiments to test predictions of the model. First, we increased the size of stimuli to drive more neurons and provide a stronger recurrent input. Our model predicted sharper disparity tuning for larger stimuli. Second, we displayed anti-correlated stereograms, where dots of opposite luminance polarity are matched between the left- and right-eye images and result in inverted disparity tuning in the disparity energy model. In this case, our model predicted reduced sharpening and strength of inverted disparity tuning. For both experiments, the dynamics of disparity tuning observed from the neurophysiological recordings in macaque V1 matched model simulation predictions. Overall, the results of this study support the notion that, while the disparity energy model provides a primary account of disparity tuning in V1 neurons, neural disparity processing in V1 neurons is refined by recurrent interactions among elements in the neural circuit. PMID:23407952

Relationship among visual field, blood flow, and neural structure measurements in glaucoma.

PubMed

Hwang, John C; Konduru, Ranjith; Zhang, Xinbo; Tan, Ou; Francis, Brian A; Varma, Rohit; Sehi, Mitra; Greenfield, David S; Sadda, Srinivas R; Huang, David

2012-05-17

To determine the relationship among visual field, neural structural, and blood flow measurements in glaucoma. Case-control study. Forty-seven eyes of 42 patients with perimetric glaucoma were age-matched with 27 normal eyes of 27 patients. All patients underwent Doppler Fourier-domain optical coherence tomography to measure retinal blood flow and standard glaucoma evaluation with visual field testing and quantitative structural imaging. Linear regression analysis was performed to analyze the relationship among visual field, blood flow, and structure, after all variables were converted to logarithmic decibel scale. Retinal blood flow was reduced in glaucoma eyes compared to normal eyes (P < 0.001). Visual field loss was correlated with both reduced retinal blood flow and structural loss of rim area and retinal nerve fiber layer (RNFL). There was no correlation or paradoxical correlation between blood flow and structure. Multivariate regression analysis revealed that reduced blood flow and structural loss are independent predictors of visual field loss. Each dB decrease in blood flow was associated with at least 1.62 dB loss in mean deviation (P ≤ 0.001), whereas each dB decrease in rim area and RNFL was associated with 1.15 dB and 2.56 dB loss in mean deviation, respectively (P ≤ 0.03). There is a close link between reduced retinal blood flow and visual field loss in glaucoma that is largely independent of structural loss. Further studies are needed to elucidate the causes of the vascular dysfunction and potential avenues for therapeutic intervention. Blood flow measurement may be useful as an independent assessment of glaucoma severity.

Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study

NASA Astrophysics Data System (ADS)

Stier, P.; Schutgens, N. A. J.; Bellouin, N.; Bian, H.; Boucher, O.; Chin, M.; Ghan, S.; Huneeus, N.; Kinne, S.; Lin, G.; Ma, X.; Myhre, G.; Penner, J. E.; Randles, C. A.; Samset, B.; Schulz, M.; Takemura, T.; Yu, F.; Yu, H.; Zhou, C.

2013-03-01

Simulated multi-model "diversity" in aerosol direct radiative forcing estimates is often perceived as a measure of aerosol uncertainty. However, current models used for aerosol radiative forcing calculations vary considerably in model components relevant for forcing calculations and the associated "host-model uncertainties" are generally convoluted with the actual aerosol uncertainty. In this AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in twelve participating models. Even with prescribed aerosol radiative properties, simulated clear-sky and all-sky aerosol radiative forcings show significant diversity. For a purely scattering case with globally constant optical depth of 0.2, the global-mean all-sky top-of-atmosphere radiative forcing is -4.47 Wm-2 and the inter-model standard deviation is 0.55 Wm-2, corresponding to a relative standard deviation of 12%. For a case with partially absorbing aerosol with an aerosol optical depth of 0.2 and single scattering albedo of 0.8, the forcing changes to 1.04 Wm-2, and the standard deviation increases to 1.01 W-2, corresponding to a significant relative standard deviation of 97%. However, the top-of-atmosphere forcing variability owing to absorption (subtracting the scattering case from the case with scattering and absorption) is low, with absolute (relative) standard deviations of 0.45 Wm-2 (8%) clear-sky and 0.62 Wm-2 (11%) all-sky. Scaling the forcing standard deviation for a purely scattering case to match the sulfate radiative forcing in the AeroCom Direct Effect experiment demonstrates that host model uncertainties could explain about 36% of the overall sulfate forcing diversity of 0.11 Wm-2 in the AeroCom Direct Radiative Effect experiment. Host model errors in aerosol radiative forcing are largest in regions of uncertain host model components, such as stratocumulus cloud decks or areas with poorly constrained surface albedos, such as sea ice. Our results demonstrate that host model uncertainties are an important component of aerosol forcing uncertainty that require further attention.

Patient-specific IMRT verification using independent fluence-based dose calculation software: experimental benchmarking and initial clinical experience.

PubMed

Georg, Dietmar; Stock, Markus; Kroupa, Bernhard; Olofsson, Jörgen; Nyholm, Tufve; Ahnesjö, Anders; Karlsson, Mikael

2007-08-21

Experimental methods are commonly used for patient-specific intensity-modulated radiotherapy (IMRT) verification. The purpose of this study was to investigate the accuracy and performance of independent dose calculation software (denoted as 'MUV' (monitor unit verification)) for patient-specific quality assurance (QA). 52 patients receiving step-and-shoot IMRT were considered. IMRT plans were recalculated by the treatment planning systems (TPS) in a dedicated QA phantom, in which an experimental 1D and 2D verification (0.3 cm(3) ionization chamber; films) was performed. Additionally, an independent dose calculation was performed. The fluence-based algorithm of MUV accounts for collimator transmission, rounded leaf ends, tongue-and-groove effect, backscatter to the monitor chamber and scatter from the flattening filter. The dose calculation utilizes a pencil beam model based on a beam quality index. DICOM RT files from patient plans, exported from the TPS, were directly used as patient-specific input data in MUV. For composite IMRT plans, average deviations in the high dose region between ionization chamber measurements and point dose calculations performed with the TPS and MUV were 1.6 +/- 1.2% and 0.5 +/- 1.1% (1 S.D.). The dose deviations between MUV and TPS slightly depended on the distance from the isocentre position. For individual intensity-modulated beams (total 367), an average deviation of 1.1 +/- 2.9% was determined between calculations performed with the TPS and with MUV, with maximum deviations up to 14%. However, absolute dose deviations were mostly less than 3 cGy. Based on the current results, we aim to apply a confidence limit of 3% (with respect to the prescribed dose) or 6 cGy for routine IMRT verification. For off-axis points at distances larger than 5 cm and for low dose regions, we consider 5% dose deviation or 10 cGy acceptable. The time needed for an independent calculation compares very favourably with the net time for an experimental approach. The physical effects modelled in the dose calculation software MUV allow accurate dose calculations in individual verification points. Independent calculations may be used to replace experimental dose verification once the IMRT programme is mature.

Modulation of electric brain responses evoked by pitch deviants through transcranial direct current stimulation.

PubMed

Royal, Isabelle; Zendel, Benjamin Rich; Desjardins, Marie-Ève; Robitaille, Nicolas; Peretz, Isabelle

2018-01-31

Congenital amusia is a neurodevelopmental disorder, characterized by a difficulty detecting pitch deviation that is related to abnormal electrical brain responses. Abnormalities found along the right fronto-temporal pathway between the inferior frontal gyrus (IFG) and the auditory cortex (AC) are the likely neural mechanism responsible for amusia. To investigate the causal role of these regions during the detection of pitch deviants, we applied cathodal (inhibitory) transcranial direct current stimulation (tDCS) over right frontal and right temporal regions during separate testing sessions. We recorded participants' electrical brain activity (EEG) before and after tDCS stimulation while they performed a pitch change detection task. Relative to a sham condition, there was a decrease in P3 amplitude after cathodal stimulation over both frontal and temporal regions compared to pre-stimulation baseline. This decrease was associated with small pitch deviations (6.25 cents), but not large pitch deviations (200 cents). Overall, this demonstrates that using tDCS to disrupt regions around the IFG and AC can induce temporary changes in evoked brain activity when processing pitch deviants. These electrophysiological changes are similar to those observed in amusia and provide causal support for the connection between P3 and fronto-temporal brain regions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Use of nonlinear models for describing scrotal circumference growth in Guzerat bulls raised under grazing conditions.

PubMed

Loaiza-Echeverri, A M; Bergmann, J A G; Toral, F L B; Osorio, J P; Carmo, A S; Mendonça, L F; Moustacas, V S; Henry, M

2013-03-15

The objective was to use various nonlinear models to describe scrotal circumference (SC) growth in Guzerat bulls on three farms in the state of Minas Gerais, Brazil. The nonlinear models were: Brody, Logistic, Gompertz, Richards, Von Bertalanffy, and Tanaka, where parameter A is the estimated testis size at maturity, B is the integration constant, k is a maturating index and, for the Richards and Tanaka models, m determines the inflection point. In Tanaka, A is an indefinite size of the testis, and B and k adjust the shape and inclination of the curve. A total of 7410 SC records were obtained every 3 months from 1034 bulls with ages varying between 2 and 69 months (<240 days of age = 159; 241-365 days = 451; 366-550 days = 1443; 551-730 days = 1705; and >731 days = 3652 SC measurements). Goodness of fit was evaluated by coefficients of determination (R(2)), error sum of squares, average prediction error (APE), and mean absolute deviation. The Richards model did not reach the convergence criterion. The R(2) were similar for all models (0.68-0.69). The error sum of squares was lowest for the Tanaka model. All models fit the SC data poorly in the early and late periods. Logistic was the model which best estimated SC in the early phase (based on APE and mean absolute deviation). The Tanaka and Logistic models had the lowest APE between 300 and 1600 days of age. The Logistic model was chosen for analysis of the environmental influence on parameters A and k. Based on absolute growth rate, SC increased from 0.019 cm/d, peaking at 0.025 cm/d between 318 and 435 days of age. Farm, year, and season of birth significantly affected size of adult SC and SC growth rate. An increase in SC adult size (parameter A) was accompanied by decreased SC growth rate (parameter k). In conclusion, SC growth in Guzerat bulls was characterized by an accelerated growth phase, followed by decreased growth; this was best represented by the Logistic model. The inflection point occurred at approximately 376 days of age (mean SC of 17.9 cm). We inferred that early selection of testicular size might result in smaller testes at maturity. Copyright © 2013 Elsevier Inc. All rights reserved.

Laboratory validation of an in-home method for assessing circadian phase using dim light melatonin onset (DLMO).

PubMed

Pullman, Rebecca E; Roepke, Stephanie E; Duffy, Jeanne F

2012-06-01

To determine whether an accurate circadian phase assessment could be obtained from saliva samples collected by patients in their home. Twenty-four individuals with a complaint of sleep initiation or sleep maintenance difficulty were studied for two evenings. Each participant received instructions for collecting eight hourly saliva samples in dim light at home. On the following evening they spent 9h in a laboratory room with controlled dim (<20 lux) light where hourly saliva samples were collected. Circadian phase of dim light melatonin onset (DLMO) was determined using both an absolute threshold (3 pg ml(-1)) and a relative threshold (two standard deviations above the mean of three baseline values). Neither threshold method worked well for one participant who was a "low-secretor". In four cases the participants' in-lab melatonin levels rose much earlier or were much higher than their at-home levels, and one participant appeared to take the at home samples out of order. Overall, the at-home and in-lab DLMO values were significantly correlated using both methods, and differed on average by 37 (± 19)min using the absolute threshold and by 54 (± 36)min using the relative threshold. The at-home assessment procedure was able to determine an accurate DLMO using an absolute threshold in 62.5% of the participants. Thus, an at-home procedure for assessing circadian phase could be practical for evaluating patients for circadian rhythm sleep disorders. Copyright © 2012 Elsevier B.V. All rights reserved.