Manifold absolute pressure estimation using neural network with hybrid training algorithm.

Science.gov (United States)

Muslim, Mohd Taufiq; 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.

Adaptive control of two-wheeled mobile balance robot capable to adapt different surfaces using a novel artificial neural network–based real-time switching dynamic controller

Directory of Open Access Journals (Sweden)

Ali Unluturk

2017-03-01

Full Text Available In this article, a novel real-time artificial neural network–based adaptable switching dynamic controller is developed and practically implemented. It will be used for real-time control of two-wheeled balance robot which can balance itself upright position on different surfaces. In order to examine the efficiency of the proposed controller, a two-wheeled mobile balance robot is designed and a test platform for experimental setup is made for balance problem on different surfaces. In a developed adaptive controller algorithm which is capable to adapt different surfaces, mean absolute target angle deviation error, mean absolute target displacement deviation error and mean absolute controller output data are employed for surface estimation by using artificial neural network. In a designed two-wheeled mobile balance robot system, robot tilt angle is estimated via Kalman filter from accelerometer and gyroscope sensor signals. Furthermore, a visual robot control interface is developed in C++ software development environment so that robot controller parameters can be changed as desired. In addition, robot balance angle, linear displacement and controller output can be observed online on personal computer. According to the real-time experimental results, the proposed novel type controller gives more effective results than the classic ones.

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

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Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

Assessing the Liquidity of Firms: Robust Neural Network Regression as an Alternative to the Current Ratio

Science.gov (United States)

de Andrés, Javier; Landajo, Manuel; Lorca, Pedro; Labra, Jose; Ordóñez, Patricia

Artificial neural networks have proven to be useful tools for solving financial analysis problems such as financial distress prediction and audit risk assessment. In this paper we focus on the performance of robust (least absolute deviation-based) neural networks on measuring liquidity of firms. The problem of learning the bivariate relationship between the components (namely, current liabilities and current assets) of the so-called current ratio is analyzed, and the predictive performance of several modelling paradigms (namely, linear and log-linear regressions, classical ratios and neural networks) is compared. An empirical analysis is conducted on a representative data base from the Spanish economy. Results indicate that classical ratio models are largely inadequate as a realistic description of the studied relationship, especially when used for predictive purposes. In a number of cases, especially when the analyzed firms are microenterprises, the linear specification is improved by considering the flexible non-linear structures provided by neural networks.

Human Age Recognition by Electrocardiogram Signal Based on Artificial Neural Network

Science.gov (United States)

Dasgupta, Hirak

2016-12-01

The objective of this work is to make a neural network function approximation model to detect human age from the electrocardiogram (ECG) signal. The input vectors of the neural network are the Katz fractal dimension of the ECG signal, frequencies in the QRS complex, male or female (represented by numeric constant) and the average of successive R-R peak distance of a particular ECG signal. The QRS complex has been detected by short time Fourier transform algorithm. The successive R peak has been detected by, first cutting the signal into periods by auto-correlation method and then finding the absolute of the highest point in each period. The neural network used in this problem consists of two layers, with Sigmoid neuron in the input and linear neuron in the output layer. The result shows the mean of errors as -0.49, 1.03, 0.79 years and the standard deviation of errors as 1.81, 1.77, 2.70 years during training, cross validation and testing with unknown data sets, respectively.

Prediction of the binary density of the ILs+ water using back-propagated feed forward artificial neural network

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Shojaee Safar Ali

2014-01-01

Full Text Available In this study, feasibility of a back-propagated artificial neural network to correlate the binary density of ionic liquids (ILs mixtures containing water as the common solvent has been investigated. To verify the optimized parameters of the neural network, total of 1668 data were collected and divided into two different subsets. The first subsets consisted of more than two-third (1251 data points of data bank was used to find the optimum parameters including weights and biases, number of neurons (7 neurons, transfer functions in hidden and output layer which were tansig and purelin, respectively. In addition, the correlative capability of network was examined using testing subset (417 data points not considered during the training stage. The overall obtained results revealed that the proposed network is accurate enough to correlate the binary density of the ionic liquids mixtures with average absolute relative deviation (AARD % and average relative deviation (ARD % of 1.56% and -0.04 %, respectively. Finally, the correlative capability of the proposed ANN model was compared with one of the available correlations proposed by Rodríguez and Brennecke.

Binomial Distribution Sample Confidence Intervals Estimation 7. Absolute Risk Reduction and ARR-like Expressions

Directory of Open Access Journals (Sweden)

Andrei ACHIMAŞ CADARIU

2004-08-01

Full Text Available Assessments of a controlled clinical trial suppose to interpret some key parameters as the controlled event rate, experimental event date, relative risk, absolute risk reduction, relative risk reduction, number needed to treat when the effect of the treatment are dichotomous variables. Defined as the difference in the event rate between treatment and control groups, the absolute risk reduction is the parameter that allowed computing the number needed to treat. The absolute risk reduction is compute when the experimental treatment reduces the risk for an undesirable outcome/event. In medical literature when the absolute risk reduction is report with its confidence intervals, the method used is the asymptotic one, even if it is well know that may be inadequate. The aim of this paper is to introduce and assess nine methods of computing confidence intervals for absolute risk reduction and absolute risk reduction – like function.Computer implementations of the methods use the PHP language. Methods comparison uses the experimental errors, the standard deviations, and the deviation relative to the imposed significance level for specified sample sizes. Six methods of computing confidence intervals for absolute risk reduction and absolute risk reduction-like functions were assessed using random binomial variables and random sample sizes.The experiments shows that the ADAC, and ADAC1 methods obtains the best overall performance of computing confidence intervals for absolute risk reduction.

Sea Surface Height, Absolute, Aviso, 0.25 degrees, Global, Science Quality

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aviso Absolute Sea Surface Height is the Sea Surface Height Deviation plus the long term mean dynamic height. This is Science Quality data.

The standard deviation method: data analysis by classical means and by neural networks

International Nuclear Information System (INIS)

Bugmann, G.; Stockar, U. von; Lister, J.B.

1989-08-01

The Standard Deviation Method is a method for determining particle size which can be used, for instance, to determine air-bubble sizes in a fermentation bio-reactor. The transmission coefficient of an ultrasound beam through a gassy liquid is measured repetitively. Due to the displacements and random positions of the bubbles, the measurements show a scatter whose standard deviation is dependent on the bubble-size. The precise relationship between the measured standard deviation, the transmission and the particle size has been obtained from a set of computer-simulated data. (author) 9 figs., 5 refs

On the robustness of EC-PC spike detection method for online neural recording.

Science.gov (United States)

Zhou, Yin; Wu, Tong; Rastegarnia, Amir; Guan, Cuntai; Keefer, Edward; Yang, Zhi

2014-09-30

Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13 μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

Optimization of Burr size, Surface Roughness and Circularity Deviation during Drilling of Al 6061 using Taguchi Design Method and Artificial Neural Network

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Reddy Sreenivasulu

2015-03-01

Full Text Available This paper presents the influence of cutting parameters like cutting speed, feed rate, drill diameter, point angle and clearance angle on the burr size, surface roughness and circularity deviation of Al 6061 during drilling on CNC vertical machining center. A plan of experiments based on Taguchi technique has been used to acquire the data. An orthogonal array, signal to noise (S/N ratio and analysis of variance (ANOVA are employed to investigate machining characteristics of Al 6061 using HSS twist drill bits of variable tool geometry and maintain constant helix angle of 45 degrees. Confirmation tests have been carried out to predict the optimal setting of process parameters to validate the used approach, obtained the values of 0.2618mm, 0.1821mm, 3.7451µm, 0.0676mm for burr height, burr thickness, surface roughness and circularity deviation respectively. Finally, artificial neural network has been applied to compare the predicted values with the experimental values, good agreement was shown between the predictive model results and the experimental measurements. Normal 0 false false false EN-US X-NONE X-NONE

New large-deviation local theorems for sums of independent and identically distributed random vectors when the limit distribution is α-stable

OpenAIRE

Nagaev, Alexander; Zaigraev, Alexander

2005-01-01

A class of absolutely continuous distributions in Rd is considered. Each distribution belongs to the domain of normal attraction of an α-stable law. The limit law is characterized by a spectral measure which is absolutely continuous with respect to the spherical Lebesgue measure. The large-deviation problem for sums of independent and identically distributed random vectors when the underlying distribution belongs to that class is studied. At the focus of attention are the deviations in the di...

Systematics of Absolute Gamma Ray Transition Probabilities in Deformed Odd-A Nuclei

Energy Technology Data Exchange (ETDEWEB)

Malmskog, S G

1965-11-15

All known experimentally determined absolute gamma ray transition probabilities between different intrinsic states of deformed odd-A nuclei in the rare earth, region (153 < A < 181) and in the actinide region (A {>=} 227) are compared with transition probabilities (Weisskopf and Nilsson estimate). Systematic deviations from the theoretical values are found. Possible explanations for these deviations are given. This discussion includes Coriolis coupling, {delta}K ={+-}2 band-mixing effects and pairing interaction.

The neural basis of financial risk taking.

Science.gov (United States)

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.

Prediction of enthalpy of fusion of pure compounds using an Artificial Neural Network-Group Contribution method

International Nuclear Information System (INIS)

Gharagheizi, Farhad; Salehi, Gholam Reza

2011-01-01

Highlights: → An Artificial Neural Network-Group Contribution method is presented for prediction of enthalpy of fusion of pure compounds at their normal melting point. → Validity of the model is confirmed using a large evaluated data set containing 4157 pure compounds. → The average percent error of the model is equal to 2.65% in comparison with the experimental data. - Abstract: In this work, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient (R 2 ) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.

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

Science.gov (United States)

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.

A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

Science.gov (United States)

Yeh, Wei-Chang

Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

Neural network based method for conversion of solar radiation data

International Nuclear Information System (INIS)

Celik, Ali N.; Muneer, Tariq

2013-01-01

Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m 2 . - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m 2 . The other statistical values of coefficient of determination and relative mean absolute error also indicate the

Neural network cloud top pressure and height for MODIS

Science.gov (United States)

Håkansson, Nina; Adok, Claudia; Thoss, Anke; Scheirer, Ronald; Hörnquist, Sara

2018-06-01

Cloud top height retrieval from imager instruments is important for nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) dataset. Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 Level 2 height product and the cloud top temperature and height algorithm in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height. Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance. Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighboring pixels are very important. The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found

Allan deviation analysis of financial return series

Science.gov (United States)

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.

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

Science.gov (United States)

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

2017-10-01

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 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.

Estimation of the neural drive to the muscle from surface electromyograms

Science.gov (United States)

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.

Large deviations

CERN Document Server

Varadhan, S R S

2016-01-01

The theory of large deviations deals with rates at which probabilities of certain events decay as a natural parameter in the problem varies. This book, which is based on a graduate course on large deviations at the Courant Institute, focuses on three concrete sets of examples: (i) diffusions with small noise and the exit problem, (ii) large time behavior of Markov processes and their connection to the Feynman-Kac formula and the related large deviation behavior of the number of distinct sites visited by a random walk, and (iii) interacting particle systems, their scaling limits, and large deviations from their expected limits. For the most part the examples are worked out in detail, and in the process the subject of large deviations is developed. The book will give the reader a flavor of how large deviation theory can help in problems that are not posed directly in terms of large deviations. The reader is assumed to have some familiarity with probability, Markov processes, and interacting particle systems.

The Relativistic Effect of the Deviation between the CMB Temperatures Obtained by the COBE Satellite

Directory of Open Access Journals (Sweden)

Rabounski D.

2007-01-01

Full Text Available The Far-Infrared Absolute Spectrophotometer (FIRAS on the COBE satellite, gives different temperatures of the Cosmic Microwave Background. This deviation has a theoretical explanation in the Doppler effect on the dipole (weak component of the radiation, the true microwave background of the Universe that moves at 365 km/sec, if the monopole (strong component of the radiation is due to the Earth. Owing to the Doppler effect, the dipole radiation temperature (determined by the 1st derivative of the monopole is lower than the monopole radiation temperature, with a value equal to the observed deviation. By this theory, the WMAP and PLANCK satellites, targeting the L2 point in the Sun-Earth-Moon system, should be insensitive to the monopole radiation. In contrast to the launched WMAP satellite, the PLANCK satellite will have on board absolute instruments which will not be able to detect the measured temperature of the Cosmic Microwave Background. That the monopole (strong component of the observed Cosmic Microwave Background is generated by the Earth is given a complete theoretical proof herein.

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

Science.gov (United States)

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.

Deviations of the lepton mapping matrix form the harrison-perkins-scott form

International Nuclear Information System (INIS)

Friedberg, R.; Lee, T.D.

2010-01-01

We propose a simple set of hypotheses governing the deviations of the leptonic mapping matrix from the Harrison-Perkins-Scott (HPS) form. These deviations are supposed to arise entirely from a perturbation of the mass matrix in the charged lepton sector. The perturbing matrix is assumed to be purely imaginary (thus maximally T-violating) and to have a strength in energy scale no greater (but perhaps smaller) than the muon mass. As we shall show,it then follows that the absolute value of the mapping matrix elements pertaining to the tau lepton deviate by no more than O((m μ /m τ ) 2 ) ≅ 3.5 x 10 -3 from their HPS values. Assuming that(m μ /m τ ) 2 can be neglected, we derive two simple constraints on the four parameters θ12, θ23, θ31, and δ of the mapping matrix. These constraints are independent of the details of the imaginary T-violating perturbation of the charged lepton mass matrix. We also show that the e and μ parts of the mapping matrix have a definite form governed by two parameters α and β; any deviation of order m μ /m τ can be accommodated by adjusting these two parameters. (authors)

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

Science.gov (United States)

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.

Artificial Neural Network Model for Predicting Compressive

Directory of Open Access Journals (Sweden)

Salim T. Yousif

2013-05-01

Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

The relative and absolute speed of radiographic screen - film systems

International Nuclear Information System (INIS)

Lee, In Ja; Huh, Joon

1993-01-01

Recently, a large number of new screen-film systems have become available for use in diagnostic radiology. These new screens are made of materials generally known as rare - earth phosphors which have high x-ray absorption and high x-ray to light conversion efficiency compared to calcium tungstate phosphors. The major advantage of these new systems is reduction of patient exposure due to their high speed or high sensitivity. However, a system with excessively high speed can result in a significant degradation of radiographic image quality. Therefore, the speed is important parameters for users of these system. Our aim of in this was to determine accurately and precisely the absolute speed and relative speeds of both new and conventional screen - film system. We determined the absolute speed in condition of BRH phantom beam quality and the relative speed were measured by a split - screen technique in condition of BRH and ANSI phantom beam quality. The absolute and the relative speed were determined for 8 kinds of screen - 4 kinds of film in regular system and 7 kinds pf screen - 7 kinds of film in ortho system. In this study we could know the New Rx, T - MAT G has the highest film speed, also know Green system's standard deviation of relative speed larger than blue system. It was realized that there were no relationship between the absolute speed and the blue system. It was realized that there were no relationship between the absolute speed and the relative speed in ortho or regular system

Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

International Nuclear Information System (INIS)

Zhou Gang; Yang Li

2009-01-01

A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

A comparison of artificial neural networks with other statistical approaches for the prediction of true metabolizable energy of meat and bone meal.

Science.gov (United States)

Perai, A H; Nassiri Moghaddam, H; Asadpour, S; Bahrampour, J; Mansoori, Gh

2010-07-01

There has been a considerable and continuous interest to develop equations for rapid and accurate prediction of the ME of meat and bone meal. In this study, an artificial neural network (ANN), a partial least squares (PLS), and a multiple linear regression (MLR) statistical method were used to predict the TME(n) of meat and bone meal based on its CP, ether extract, and ash content. The accuracy of the models was calculated by R(2) value, MS error, mean absolute percentage error, mean absolute deviation, bias, and Theil's U. The predictive ability of an ANN was compared with a PLS and a MLR model using the same training data sets. The squared regression coefficients of prediction for the MLR, PLS, and ANN models were 0.38, 0.36, and 0.94, respectively. The results revealed that ANN produced more accurate predictions of TME(n) as compared with PLS and MLR methods. Based on the results of this study, ANN could be used as a promising approach for rapid prediction of nutritive value of meat and bone meal.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Absolute risk, absolute risk reduction and relative risk

Directory of Open Access Journals (Sweden)

Jose Andres Calvache

2012-12-01

Full Text Available This article illustrates the epidemiological concepts of absolute risk, absolute risk reduction and relative risk through a clinical example. In addition, it emphasizes the usefulness of these concepts in clinical practice, clinical research and health decision-making process.

The Prediction of Surface Tension of Ternary Mixtures at Different Temperatures Using Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Ali Khazaei

2014-07-01

Full Text Available In this work, artificial neural network (ANN has been employed to propose a practical model for predicting the surface tension of multi-component mixtures. In order to develop a reliable model based on the ANN, a comprehensive experimental data set including 15 ternary liquid mixtures at different temperatures was employed. These systems consist of 777 data points generally containing hydrocarbon components. The ANN model has been developed as a function of temperature, critical properties, and acentric factor of the mixture according to conventional corresponding-state models. 80% of the data points were employed for training ANN and the remaining data were utilized for testing the generated model. The average absolute relative deviations (AARD% of the model for the training set, the testing set, and the total data points were obtained 1.69, 1.86, and 1.72 respectively. Comparing the results with Flory theory, Brok-Bird equation, and group contribution theory has proved the high prediction capability of the attained model.

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

Science.gov (United States)

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.

The Distance Standard Deviation

OpenAIRE

Edelmann, Dominic; Richards, Donald; Vogel, Daniel

2017-01-01

The distance standard deviation, which arises in distance correlation analysis of multivariate data, is studied as a measure of spread. New representations for the distance standard deviation are obtained in terms of Gini's mean difference and in terms of the moments of spacings of order statistics. Inequalities for the distance variance are derived, proving that the distance standard deviation is bounded above by the classical standard deviation and by Gini's mean difference. Further, it is ...

Encasing the Absolutes

Directory of Open Access Journals (Sweden)

Uroš Martinčič

2014-05-01

Full Text Available The paper explores the issue of structure and case in English absolute constructions, whose subjects are deduced by several descriptive grammars as being in the nominative case due to its supposed neutrality in terms of register. This deduction is countered by systematic accounts presented within the framework of the Minimalist Program which relate the case of absolute constructions to specific grammatical factors. Each proposal is shown as an attempt of analysing absolute constructions as basic predication structures, either full clauses or small clauses. I argue in favour of the small clause approach due to its minimal reliance on transformations and unique stipulations. Furthermore, I propose that small clauses project a singular category, and show that the use of two cases in English absolute constructions can be accounted for if they are analysed as depictive phrases, possibly selected by prepositions. The case of the subject in absolutes is shown to be a result of syntactic and non-syntactic factors. I thus argue in accordance with Minimalist goals that syntactic case does not exist, attributing its role in absolutes to other mechanisms.

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

Directory of Open Access Journals (Sweden)

Guochao Wang

2018-02-01

Full Text Available 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.

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Directory of Open Access Journals (Sweden)

Ghanbari Shahram

2017-10-01

Full Text Available Zeolites are microporous aluminosilicate/silicate crystalline materials with three-dimensional tetrahedral configuration. In this study, the degree of crystallinity of the synthesized Linde Type A (LTA zeolite, which is the main indicator of its quality/purity is tried to be modeled. Effect of crystallization time, temperature, molar ratio of the synthesis gel on the relative crystallinity of the LTA zeolites is investigated using artificial neural networks. Our experimental observations and some data collected from literature have been used for adjusting the parameters of the proposed model and evaluating its performance. It has been observed that two-layer perceptron network with eight hidden neurons is the most accurate approach for the considered task. The designed model predicts the experimental datasets with a mean square error of 3.99 × 10-6, absolute average relative deviation of 8.69 %, and regression coefficient of 0.9596. The proposed model can decrease the required time and number of experiments to evaluate the extent of crystallinity of the LTA zeolites.

Absolute advantage

NARCIS (Netherlands)

J.G.M. van Marrewijk (Charles)

2008-01-01

textabstractA country is said to have an absolute advantage over another country in the production of a good or service if it can produce that good or service using fewer real resources. Equivalently, using the same inputs, the country can produce more output. The concept of absolute advantage can

Introducing a new formula based on an artificial neural network for prediction of droplet size in venturi scrubbers

Directory of Open Access Journals (Sweden)

A. Sharifi

2012-09-01

Full Text Available Droplet size is a fundamental parameter for Venturi scrubber performance. For many years, the correlations proposed by Nukiyama and Tanasawa (1938 and Boll et al. (1974 were used for calculating mean droplet size in Venturi scrubbers with limited operating parameters. This study proposes an alternative approach on the basis of artificial neural networks (ANNs to determine the mean droplet size in Venturi scrubbers, in a wide range of operating parameters. Experimental data were used to design the ANNs. A neural network was trained based on the liquid to gas ratio (L/G and throat gas velocity (Vgth, as input parameters, and the Sauter mean diameter (D32 as the desired parameter. The back-propagation learning algorithms were used in the network and the best approach was found. A new formula for the prediction of D32 using the weights of the network was then generated. This formula predicts mean droplet size in Venturi scrubbers more accurately than the correlations of Boll et al. (1974 and Nukiyama and Tanasawa (1938. The Average Absolute Percent Deviation (AAPD of our formula and the Boll et al. and Nukiyama and Tanasawa correlations for the full ranges of experimental data are 26.04%, 40.19% and 32.99%, respectively.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Claudia eLappe

2013-06-01

Full Text Available 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 mismatch negativity 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 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 bilatral activation within the vicinity of auditory cortices and in the inferior parietal lobule, 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 the dual-pathway model of auditory processing.

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

Science.gov (United States)

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.

Large deviations and idempotent probability

CERN Document Server

Puhalskii, Anatolii

2001-01-01

In the view of many probabilists, author Anatolii Puhalskii''s research results stand among the most significant achievements in the modern theory of large deviations. In fact, his work marked a turning point in the depth of our understanding of the connections between the large deviation principle (LDP) and well-known methods for establishing weak convergence results.Large Deviations and Idempotent Probability expounds upon the recent methodology of building large deviation theory along the lines of weak convergence theory. The author develops an idempotent (or maxitive) probability theory, introduces idempotent analogues of martingales (maxingales), Wiener and Poisson processes, and Ito differential equations, and studies their properties. The large deviation principle for stochastic processes is formulated as a certain type of convergence of stochastic processes to idempotent processes. The author calls this large deviation convergence.The approach to establishing large deviation convergence uses novel com...

SAMPLE STANDARD DEVIATION(s) CHART UNDER THE ASSUMPTION OF MODERATENESS AND ITS PERFORMANCE ANALYSIS

OpenAIRE

Kalpesh S. Tailor

2017-01-01

Moderate distribution proposed by Naik V.D and Desai J.M., is a sound alternative of normal distribution, which has mean and mean deviation as pivotal parameters and which has properties similar to normal distribution. Mean deviation (δ) is a very good alternative of standard deviation (σ) as mean deviation is considered to be the most intuitively and rationally defined measure of dispersion. This fact can be very useful in the field of quality control to construct the control limits of the c...

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

Energy Technology Data Exchange (ETDEWEB)

Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu [Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 and Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States); Radiation Oncology Department, Stanford University School of Medicine, Stanford, California 94305 (United States); Radiation Oncology Department, University of Texas, Health Science Center San Antonio, Texas 78229 (United States)

2011-10-15

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE{sup 3} v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the {gamma}-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average {gamma}-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average {gamma}-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations

Toward IMRT 2D dose modeling using artificial neural networks: A feasibility study

International Nuclear Information System (INIS)

Kalantzis, Georgios; Vasquez-Quino, Luis A.; Zalman, Travis; Pratx, Guillem; Lei, Yu

2011-01-01

Purpose: To investigate the feasibility of artificial neural networks (ANN) to reconstruct dose maps for intensity modulated radiation treatment (IMRT) fields compared with those of the treatment planning system (TPS). Methods: An artificial feed forward neural network and the back-propagation learning algorithm have been used to replicate dose calculations of IMRT fields obtained from PINNACLE 3 v9.0. The ANN was trained with fluence and dose maps of IMRT fields for 6 MV x-rays, which were obtained from the amorphous silicon (a-Si) electronic portal imaging device of Novalis TX. Those fluence distributions were imported to the TPS and the dose maps were calculated on the horizontal midpoint plane of a water equivalent homogeneous cylindrical virtual phantom. Each exported 2D dose distribution from the TPS was classified into two clusters of high and low dose regions, respectively, based on the K-means algorithm and the Euclidian metric in the fluence-dose domain. The data of each cluster were divided into two sets for the training and validation phase of the ANN, respectively. After the completion of the ANN training phase, 2D dose maps were reconstructed by the ANN and isodose distributions were created. The dose maps reconstructed by ANN were evaluated and compared with the TPS, where the mean absolute deviation of the dose and the γ-index were used. Results: A good agreement between the doses calculated from the TPS and the trained ANN was achieved. In particular, an average relative dosimetric difference of 4.6% and an average γ-index passing rate of 93% were obtained for low dose regions, and a dosimetric difference of 2.3% and an average γ-index passing rate of 97% for high dose region. Conclusions: An artificial neural network has been developed to convert fluence maps to corresponding dose maps. The feasibility and potential of an artificial neural network to replicate complex convolution kernels in the TPS for IMRT dose calculations have been

48 CFR 2001.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 2001... Individual deviations. In individual cases, deviations from either the FAR or the NRCAR will be authorized... deviations clearly in the best interest of the Government. Individual deviations must be authorized in...

48 CFR 801.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Individual deviations. 801... Individual deviations. (a) Authority to authorize individual deviations from the FAR and VAAR is delegated to... nature of the deviation. (d) The DSPE may authorize individual deviations from the FAR and VAAR when an...

Detecting deviating behaviors without models

NARCIS (Netherlands)

Lu, X.; Fahland, D.; van den Biggelaar, F.J.H.M.; van der Aalst, W.M.P.; Reichert, M.; Reijers, H.A.

2016-01-01

Deviation detection is a set of techniques that identify deviations from normative processes in real process executions. These diagnostics are used to derive recommendations for improving business processes. Existing detection techniques identify deviations either only on the process instance level

Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

Energy Technology Data Exchange (ETDEWEB)

Wijayasekara, Dumidu, E-mail: wija2589@vandals.uidaho.edu [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Manic, Milos [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Sabharwall, Piyush [Idaho National Laboratory, Idaho Falls, ID (United States); Utgikar, Vivek [Department of Chemical Engineering, University of Idaho, Idaho Falls, ID 83402 (United States)

2011-07-15

Highlights: > Performance prediction of PCHE using artificial neural networks. > Evaluating artificial neural network performance for PCHE modeling. > Selection of over-training resilient artificial neural networks. > Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing

Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

International Nuclear Information System (INIS)

Wijayasekara, Dumidu; Manic, Milos; Sabharwall, Piyush; Utgikar, Vivek

2011-01-01

Highlights: → Performance prediction of PCHE using artificial neural networks. → Evaluating artificial neural network performance for PCHE modeling. → Selection of over-training resilient artificial neural networks. → Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

DEFF Research Database (Denmark)

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

2018-01-01

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...... 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....

48 CFR 1501.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 1501.403 Section 1501.403 Federal Acquisition Regulations System ENVIRONMENTAL PROTECTION AGENCY GENERAL GENERAL Deviations 1501.403 Individual deviations. Requests for individual deviations from the FAR and the...

48 CFR 2401.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 2401... DEVELOPMENT GENERAL FEDERAL ACQUISITION REGULATION SYSTEM Deviations 2401.403 Individual deviations. In individual cases, proposed deviations from the FAR or HUDAR shall be submitted to the Senior Procurement...

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

Science.gov (United States)

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 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 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.

INDICATIVE MODEL OF DEVIATIONS IN PROJECT

Directory of Open Access Journals (Sweden)

Олена Борисівна ДАНЧЕНКО

2016-02-01

Full Text Available The article shows the process of constructing the project deviations indicator model. It based on a conceptual model of project deviations integrated management (PDIM. During the project different causes (such as risks, changes, problems, crises, conflicts, stress lead to deviations of integrated project indicators - time, cost, quality, and content. For a more detailed definition of where in the project deviations occur and how they are dangerous for the whole project, it needs to develop an indicative model of project deviations. It allows identifying the most dangerous deviations that require PDIM. As a basis for evaluation of project's success has been taken famous model IPMA Delta. During the evaluation, IPMA Delta estimated project management competence of organization in three modules: I-Module ("Individuals" - a self-assessment personnel, P-module ("Projects" - self-assessment of projects and/or programs, and O-module ("Organization" - used to conduct interviews with selected people during auditing company. In the process of building an indicative model of deviations in the project, the first step is the assessment of project management in the organization by IPMA Delta. In the future, built cognitive map and matrix of system interconnections of the project, which conducted simulations and built a scale of deviations for the selected project. They determined a size and place of deviations. To identify the detailed causes of deviations in the project management has been proposed to use the extended system of indicators, which is based on indicators of project management model Project Excellence. The proposed indicative model of deviations in projects allows to estimate the size of variation and more accurately identify the place of negative deviations in the project and provides the project manager information for operational decision making for the management of deviations in the implementation of the project

48 CFR 1301.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Individual deviations... DEPARTMENT OF COMMERCE ACQUISITION REGULATIONS SYSTEM Deviations From the FAR 1301.403 Individual deviations. The designee authorized to approve individual deviations from the FAR is set forth in CAM 1301.70. ...

48 CFR 301.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Individual deviations. 301... ACQUISITION REGULATION SYSTEM Deviations From the FAR 301.403 Individual deviations. Contracting activities shall prepare requests for individual deviations to either the FAR or HHSAR in accordance with 301.470. ...

48 CFR 1201.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Individual deviations... FEDERAL ACQUISITION REGULATIONS SYSTEM 70-Deviations From the FAR and TAR 1201.403 Individual deviations... Executive Service (SES) official or that of a Flag Officer, may authorize individual deviations (unless (FAR...

48 CFR 501.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Individual deviations. 501... Individual deviations. (a) An individual deviation affects only one contract action. (1) The Head of the Contracting Activity (HCA) must approve an individual deviation to the FAR. The authority to grant an...

48 CFR 401.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Individual deviations. 401... AGRICULTURE ACQUISITION REGULATION SYSTEM Deviations From the FAR and AGAR 401.403 Individual deviations. In individual cases, deviations from either the FAR or the AGAR will be authorized only when essential to effect...

48 CFR 2801.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 2801... OF JUSTICE ACQUISITION REGULATIONS SYSTEM Deviations From the FAR and JAR 2801.403 Individual deviations. Individual deviations from the FAR or the JAR shall be approved by the head of the contracting...

Neural networks for the monitoring of rotating machinery

International Nuclear Information System (INIS)

Alguindigue, I.E.; Loskiewicz-Buczak

1991-01-01

Vibration monitoring of components in engineering systems and plants involves the collection of vibration data and detailed analysis to detect features which reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper describes a methodology for the automation of some of the activities related to motion and vibration monitoring in these systems. The technique involves training a neural network to model the inter- relationship between signals from two related sensors mounted on an engineering system or component at a time when it is known to be operating properly. Then one signal (or its characteristics) is put into the neural network model to predict the second signal (or its characteristics). This predicted signal is continuously compared with the actual signal A deviation between the predicted and actual signal indicates a changing relationship, usually failure of the component or system. This deviation may be quantified and provides meaningful information about the degree of degradation and deterioration of the component

Visualizing the Sample Standard Deviation

Science.gov (United States)

Sarkar, Jyotirmoy; Rashid, Mamunur

2017-01-01

The standard deviation (SD) of a random sample is defined as the square-root of the sample variance, which is the "mean" squared deviation of the sample observations from the sample mean. Here, we interpret the sample SD as the square-root of twice the mean square of all pairwise half deviations between any two sample observations. This…

ABSOLUTE NEUTRINO MASSES

DEFF Research Database (Denmark)

Schechter, J.; Shahid, M. N.

2012-01-01

We discuss the possibility of using experiments timing the propagation of neutrino beams over large distances to help determine the absolute masses of the three neutrinos.......We discuss the possibility of using experiments timing the propagation of neutrino beams over large distances to help determine the absolute masses of the three neutrinos....

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

Science.gov (United States)

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.

Integrating neural network technology and noise analysis

International Nuclear Information System (INIS)

Uhrig, R.E.; Oak Ridge National Lab., TN

1995-01-01

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

The new Absolute Quantum Gravimeter (AQG): first results and perspectives

Science.gov (United States)

Bonvalot, Sylvain; Le Moigne, Nicolas; Merlet, Sebastien; Desruelle, Bruno; Lautier-Gaud, Jean; Menoret, Vincent; Vermeulen, Pierre

2016-04-01

the AQG01. This paper summarizes the latest results obtained from these experiments. The evaluation of the AQG01 is still in progress but this study confirmed that the AQG01 enables absolute gravity measurements with a sensitivity of 2 μGal standard deviation after 1000 s of data integration. Perspectives of expected instrumental developments for monitoring both spatial and temporal gravity variations at the microGal level in both laboratory and field conditions will be also discussed.

Direct infusion-SIM as fast and robust method for absolute protein quantification in complex samples

Directory of Open Access Journals (Sweden)

Christina Looße

2015-06-01

Full Text Available Relative and absolute quantification of proteins in biological and clinical samples are common approaches in proteomics. Until now, targeted protein quantification is mainly performed using a combination of HPLC-based peptide separation and selected reaction monitoring on triple quadrupole mass spectrometers. Here, we show for the first time the potential of absolute quantification using a direct infusion strategy combined with single ion monitoring (SIM on a Q Exactive mass spectrometer. By using complex membrane fractions of Escherichia coli, we absolutely quantified the recombinant expressed heterologous human cytochrome P450 monooxygenase 3A4 (CYP3A4 comparing direct infusion-SIM with conventional HPLC-SIM. Direct-infusion SIM revealed only 14.7% (±4.1 (s.e.m. deviation on average, compared to HPLC-SIM and a decreased processing and analysis time of 4.5 min (that could be further decreased to 30 s for a single sample in contrast to 65 min by the LC–MS method. Summarized, our simplified workflow using direct infusion-SIM provides a fast and robust method for quantification of proteins in complex protein mixtures.

Absolute nuclear material assay

Science.gov (United States)

Prasad, Manoj K [Pleasanton, CA; Snyderman, Neal J [Berkeley, CA; Rowland, Mark S [Alamo, CA

2010-07-13

A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.

Thermodynamics of negative absolute pressures

International Nuclear Information System (INIS)

Lukacs, B.; Martinas, K.

1984-03-01

The authors show that the possibility of negative absolute pressure can be incorporated into the axiomatic thermodynamics, analogously to the negative absolute temperature. There are examples for such systems (GUT, QCD) processing negative absolute pressure in such domains where it can be expected from thermodynamical considerations. (author)

The reinterpretation of standard deviation concept

OpenAIRE

Ye, Xiaoming

2017-01-01

Existing mathematical theory interprets the concept of standard deviation as the dispersion degree. Therefore, in measurement theory, both uncertainty concept and precision concept, which are expressed with standard deviation or times standard deviation, are also defined as the dispersion of measurement result, so that the concept logic is tangled. Through comparative analysis of the standard deviation concept and re-interpreting the measurement error evaluation principle, this paper points o...

Perceiving pitch absolutely: Comparing absolute and relative pitch possessors in a pitch memory task

Directory of Open Access Journals (Sweden)

Schlaug Gottfried

2009-08-01

Full Text Available Abstract Background The perceptual-cognitive mechanisms and neural correlates of Absolute Pitch (AP are not fully understood. The aim of this fMRI study was to examine the neural network underlying AP using a pitch memory experiment and contrasting two groups of musicians with each other, those that have AP and those that do not. Results We found a common activation pattern for both groups that included the superior temporal gyrus (STG extending into the adjacent superior temporal sulcus (STS, the inferior parietal lobule (IPL extending into the adjacent intraparietal sulcus (IPS, the posterior part of the inferior frontal gyrus (IFG, the pre-supplementary motor area (pre-SMA, and superior lateral cerebellar regions. Significant between-group differences were seen in the left STS during the early encoding phase of the pitch memory task (more activation in AP musicians and in the right superior parietal lobule (SPL/intraparietal sulcus (IPS during the early perceptual phase (ITP 0–3 and later working memory/multimodal encoding phase of the pitch memory task (more activation in non-AP musicians. Non-significant between-group trends were seen in the posterior IFG (more in AP musicians and the IPL (more anterior activations in the non-AP group and more posterior activations in the AP group. Conclusion Since the increased activation of the left STS in AP musicians was observed during the early perceptual encoding phase and since the STS has been shown to be involved in categorization tasks, its activation might suggest that AP musicians involve categorization regions in tonal tasks. The increased activation of the right SPL/IPS in non-AP musicians indicates either an increased use of regions that are part of a tonal working memory (WM network, or the use of a multimodal encoding strategy such as the utilization of a visual-spatial mapping scheme (i.e., imagining notes on a staff or using a spatial coding for their relative pitch height for pitch

48 CFR 3401.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 7 2010-10-01 2010-10-01 false Individual deviations. 3401.403 Section 3401.403 Federal Acquisition Regulations System DEPARTMENT OF EDUCATION ACQUISITION REGULATION GENERAL ED ACQUISITION REGULATION SYSTEM Deviations 3401.403 Individual deviations. An individual...

48 CFR 1.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Individual deviations. 1.403 Section 1.403 Federal Acquisition Regulations System FEDERAL ACQUISITION REGULATION GENERAL FEDERAL ACQUISITION REGULATIONS SYSTEM Deviations from the FAR 1.403 Individual deviations. Individual...

48 CFR 2501.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 2501.403 Section 2501.403 Federal Acquisition Regulations System NATIONAL SCIENCE FOUNDATION GENERAL FEDERAL ACQUISITION REGULATIONS SYSTEM Deviations From the FAR 2501.403 Individual deviations. Individual...

Fidelity deviation in quantum teleportation

OpenAIRE

Bang, Jeongho; Ryu, Junghee; Kaszlikowski, Dagomir

2018-01-01

We analyze the performance of quantum teleportation in terms of average fidelity and fidelity deviation. The average fidelity is defined as the average value of the fidelities over all possible input states and the fidelity deviation is their standard deviation, which is referred to as a concept of fluctuation or universality. In the analysis, we find the condition to optimize both measures under a noisy quantum channel---we here consider the so-called Werner channel. To characterize our resu...

48 CFR 601.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 4 2010-10-01 2010-10-01 false Individual deviations. 601.403 Section 601.403 Federal Acquisition Regulations System DEPARTMENT OF STATE GENERAL DEPARTMENT OF STATE ACQUISITION REGULATIONS SYSTEM Deviations from the FAR 601.403 Individual deviations. The...

48 CFR 201.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 3 2010-10-01 2010-10-01 false Individual deviations. 201.403 Section 201.403 Federal Acquisition Regulations System DEFENSE ACQUISITION REGULATIONS SYSTEM... Individual deviations. (1) Individual deviations, except those described in 201.402(1) and paragraph (2) of...

Refrigerant flow through electronic expansion valve: Experiment and neural network modeling

International Nuclear Information System (INIS)

Cao, Xiang; Li, Ze-Yu; Shao, Liang-Liang; Zhang, Chun-Lu

2016-01-01

Highlights: • Experimental data from different sources were used in comparison of EEV models. • Artificial neural network in EEV modeling is superior to literature correlations. • Artificial neural network with 4-4-1 structure and S function is recommended. • Artificial neural network is flexible for EEV mass flow rate and opening prediction. - Abstract: Electronic expansion valve (EEV) plays a crucial role in controlling refrigerant mass flow rate of refrigeration or heat pump systems for energy savings. However, complexities in two-phase throttling process and geometry make accurate modeling of EEV flow characteristics more difficult. This paper developed an artificial neural network (ANN) model using refrigerant inlet and outlet pressures, inlet subcooling, EEV opening as ANN inputs, refrigerant mass flow rate as ANN output. Both linear and nonlinear transfer functions in hidden layer were used and compared to each other. Experimental data from multiple sources including in-house experiments of one EEV with R410A were used for ANN training and test. In addition, literature correlations were compared with ANN as well. Results showed that the ANN model with nonlinear transfer function worked well in all cases and it is much accurate than the literature correlations. In all cases, nonlinear ANN predicted refrigerant mass flow rates within ±0.4% average relative deviation (A.D.) and 2.7% standard deviation (S.D.), meanwhile it predicted the EEV opening at 0.1% A.D. and 2.1% S.D.

48 CFR 3001.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 7 2010-10-01 2010-10-01 false Individual deviations... from the FAR and HSAR 3001.403 Individual deviations. Unless precluded by law, executive order, or other regulation, the HCA is authorized to approve individual deviation (except with respect to (FAR) 48...

48 CFR 1901.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 6 2010-10-01 2010-10-01 true Individual deviations. 1901.403 Section 1901.403 Federal Acquisition Regulations System BROADCASTING BOARD OF GOVERNORS GENERAL... Individual deviations. Deviations from the IAAR or the FAR in individual cases shall be authorized by the...

Deviating From the Benchmarks

DEFF Research Database (Denmark)

Rocha, Vera; Van Praag, Mirjam; Carneiro, Anabela

This paper studies three related questions: To what extent otherwise similar startups employ different quantities and qualities of human capital at the moment of entry? How persistent are initial human capital choices over time? And how does deviating from human capital benchmarks influence firm......, founders human capital, and the ownership structure of startups (solo entrepreneurs versus entrepreneurial teams). We then study the survival implications of exogenous deviations from these benchmarks, based on spline models for survival data. Our results indicate that (especially negative) deviations from...... the benchmark can be substantial, are persistent over time, and hinder the survival of firms. The implications may, however, vary according to the sector and the ownership structure at entry. Given the stickiness of initial choices, wrong human capital decisions at entry turn out to be a close to irreversible...

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

Science.gov (United States)

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.

Computer generation of random deviates

International Nuclear Information System (INIS)

Cormack, John

1991-01-01

The need for random deviates arises in many scientific applications. In medical physics, Monte Carlo simulations have been used in radiology, radiation therapy and nuclear medicine. Specific instances include the modelling of x-ray scattering processes and the addition of random noise to images or curves in order to assess the effects of various processing procedures. Reliable sources of random deviates with statistical properties indistinguishable from true random deviates are a fundamental necessity for such tasks. This paper provides a review of computer algorithms which can be used to generate uniform random deviates and other distributions of interest to medical physicists, along with a few caveats relating to various problems and pitfalls which can occur. Source code listings for the generators discussed (in FORTRAN, Turbo-PASCAL and Data General ASSEMBLER) are available on request from the authors. 27 refs., 3 tabs., 5 figs

Danish Towns during Absolutism

DEFF Research Database (Denmark)

This anthology, No. 4 in the Danish Urban Studies Series, presents in English recent significant research on Denmark's urban development during the Age of Absolutism, 1660-1848, and features 13 articles written by leading Danish urban historians. The years of Absolutism were marked by a general...

Statistical optimization of the phytoremediation of arsenic by Ludwigia octovalvis- in a pilot reed bed using response surface methodology (RSM) versus an artificial neural network (ANN).

Science.gov (United States)

Titah, Harmin Sulistiyaning; Halmi, Mohd Izuan Effendi Bin; Abdullah, Siti Rozaimah Sheikh; Hasan, Hassimi Abu; Idris, Mushrifah; Anuar, Nurina

2018-06-07

In this study, the removal of arsenic (As) by plant, Ludwigia octovalvis, in a pilot reed bed was optimized. A Box-Behnken design was employed including a comparative analysis of both Response Surface Methodology (RSM) and an Artificial Neural Network (ANN) for the prediction of maximum arsenic removal. The predicted optimum condition using the desirability function of both models was 39 mg kg -1 for the arsenic concentration in soil, an elapsed time of 42 days (the sampling day) and an aeration rate of 0.22 L/min, with the predicted values of arsenic removal by RSM and ANN being 72.6% and 71.4%, respectively. The validation of the predicted optimum point showed an actual arsenic removal of 70.6%. This was achieved with the deviation between the validation value and the predicted values being within 3.49% (RSM) and 1.87% (ANN). The performance evaluation of the RSM and ANN models showed that ANN performs better than RSM with a higher R 2 (0.97) close to 1.0 and very small Average Absolute Deviation (AAD) (0.02) and Root Mean Square Error (RMSE) (0.004) values close to zero. Both models were appropriate for the optimization of arsenic removal with ANN demonstrating significantly higher predictive and fitting ability than RSM.

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

Science.gov (United States)

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.

Surgical Success Rates for Horizontal Concomitant Deviations According to the Type and Degree of Deviation

Directory of Open Access Journals (Sweden)

İhsan Çaça

2004-01-01

Full Text Available We evaluated the correlation with success rates and deviation type and degree inhorizontal concomitant deviations. 104 horizontal concomitan strabismus cases whowere operated in our clinic between January 1994 – December 2000 were included in thestudy. 56 cases undergone recession-resection procedure in the same eye 19 cases twomuscle recession and one muscle resection, 20 cases two muscle recession, 9 cases onlyone muscle recession. 10 ± prism diopter deviation in postoperative sixth monthexamination was accepted as surgical success.Surgical success rate was 90% and 89.3% in the cases with deviation angle of 15-30and 31-50 prism diopter respectively. Success rate was 78.9% if the angle was more than50 prism diopter. According to strabismus type when surgical success rate examined; inalternan esotropia 88.33%, in alternan exotropia 84.6%, in monocular esotropia 88%and in monocular exotropia 83.3% success was fixed. Statistically significant differencewas not found between strabismus type and surgical success rate. The binocular visiongaining rate was found as 51.8% after the treatment of cases.In strabismus surgery, preoperative deviation angle was found to be an effectivefactor on the success rate.

14 CFR 21.609 - Approval for deviation.

Science.gov (United States)

2010-01-01

... deviation. (a) Each manufacturer who requests approval to deviate from any performance standard of a TSO shall show that the standards from which a deviation is requested are compensated for by factors or... 14 Aeronautics and Space 1 2010-01-01 2010-01-01 false Approval for deviation. 21.609 Section 21...

Early results from the Far Infrared Absolute Spectrophotometer (FIRAS)

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Entanglement transitions induced by large deviations

Science.gov (United States)

Bhosale, Udaysinh T.

2017-12-01

The probability of large deviations of the smallest Schmidt eigenvalue for random pure states of bipartite systems, denoted as A and B , is computed analytically using a Coulomb gas method. It is shown that this probability, for large N , goes as exp[-β N2Φ (ζ ) ] , where the parameter β is the Dyson index of the ensemble, ζ is the large deviation parameter, while the rate function Φ (ζ ) is calculated exactly. Corresponding equilibrium Coulomb charge density is derived for its large deviations. Effects of the large deviations of the extreme (largest and smallest) Schmidt eigenvalues on the bipartite entanglement are studied using the von Neumann entropy. Effect of these deviations is also studied on the entanglement between subsystems 1 and 2, obtained by further partitioning the subsystem A , using the properties of the density matrix's partial transpose ρ12Γ. The density of states of ρ12Γ is found to be close to the Wigner's semicircle law with these large deviations. The entanglement properties are captured very well by a simple random matrix model for the partial transpose. The model predicts the entanglement transition across a critical large deviation parameter ζ . Log negativity is used to quantify the entanglement between subsystems 1 and 2. Analytical formulas for it are derived using the simple model. Numerical simulations are in excellent agreement with the analytical results.

Maximum solid concentrations of coal water slurries predicted by neural network models

Energy Technology Data Exchange (ETDEWEB)

Cheng, Jun; Li, Yanchang; Zhou, Junhu; Liu, Jianzhong; Cen, Kefa

2010-12-15

The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation. (author)

22 CFR 226.4 - Deviations.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Deviations. 226.4 Section 226.4 Foreign Relations AGENCY FOR INTERNATIONAL DEVELOPMENT ADMINISTRATION OF ASSISTANCE AWARDS TO U.S. NON-GOVERNMENTAL ORGANIZATIONS General § 226.4 Deviations. The Office of Management and Budget (OMB) may grant exceptions for...

The Standard Deviation of Launch Vehicle Environments

Science.gov (United States)

Yunis, Isam

2005-01-01

Statistical analysis is used in the development of the launch vehicle environments of acoustics, vibrations, and shock. The standard deviation of these environments is critical to accurate statistical extrema. However, often very little data exists to define the standard deviation and it is better to use a typical standard deviation than one derived from a few measurements. This paper uses Space Shuttle and expendable launch vehicle flight data to define a typical standard deviation for acoustics and vibrations. The results suggest that 3dB is a conservative and reasonable standard deviation for the source environment and the payload environment.

Deviations from thermal equilibrium in plasmas

International Nuclear Information System (INIS)

Burm, K.T.A.L.

2004-01-01

A plasma system in local thermal equilibrium can usually be described with only two parameters. To describe deviations from equilibrium two extra parameters are needed. However, it will be shown that deviations from temperature equilibrium and deviations from Saha equilibrium depend on one another. As a result, non-equilibrium plasmas can be described with three parameters. This reduction in parameter space will ease the plasma describing effort enormously

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Optimal neural networks for protein-structure prediction

International Nuclear Information System (INIS)

Head-Gordon, T.; Stillinger, F.H.

1993-01-01

The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

Deviating measurements in radiation protection. Legal assessment of deviations in radiation protection measurements

International Nuclear Information System (INIS)

Hoegl, A.

1996-01-01

This study investigates how, from a legal point of view, deviations in radiation protection measurements should be treated in comparisons between measured results and limits stipulated by nuclear legislation or goods transport regulations. A case-by-case distinction is proposed which is based on the legal concequences of the respective measurement. Commentaries on nuclear law contain no references to the legal assessment of deviating measurements in radiation protection. The examples quoted in legal commentaries on civil and criminal proceedings of the way in which errors made in measurements for speed control and determinations of the alcohol content in the blood are to be taken into account, and a commentary on ozone legislation, are examined for analogies with radiation protection measurements. Leading cases in the nuclear field are evaluated in the light of the requirements applying in case of deviations in measurements. The final section summarizes the most important findings and conclusions. (orig.) [de

Cosmic backgrounds of relic gravitons and their absolute normalization

CERN Document Server

Giovannini, Massimo

2014-01-01

Provided the consistency relations are not violated, the recent Bicep2 observations pin down the absolute normalization, the spectral slope and the maximal frequency of the cosmic graviton background produced during inflation. The properly normalized spectra are hereby computed from the lowest frequencies (of the order of the present Hubble rate) up to the highest frequency range in the GHz region. Deviations from the conventional paradigm cannot be excluded and are examined by allowing for different physical possibilities including, in particular, a running of the tensor spectral index, an explicit breaking of the consistency relations and a spike in the high-frequency tail of the spectrum coming either from a post-inflationary phase dominated by a stiff fluid of from the contribution of waterfall fields in a hybrid inflationary context. The direct determinations of the tensor to scalar ratio at low frequencies, if confirmed by the forthcoming observations, will also affect and constrain the high-frequencies...

Wet gas metering with the v-cone and neural nets

Energy Technology Data Exchange (ETDEWEB)

Toral, Haluk; Cai, Shiqian; Peters, Robert

2005-07-01

The paper presents analysis of extensive measurements taken at NEL, K-Lab and CEESI wet gas test loops. Differential and absolute pressure signals were sampled at high frequency across V-Cone meters. Turbulence characteristics of the flow captured in the sampled signals were characterized by pattern recognition techniques and related to the fractions and flow rates of individual phases. The sensitivity of over-reading to first and higher order features of the high frequency signals were investigated qualitatively. The sensitivities were quantified by means of the saliency test based on back propagating neural nets. A self contained wet gas meter based on neural net characterization of first and higher order features of the pressure, differential pressure and capacitance signals was proposed. Alternatively, a wet gas meter based on a neural net model of just pressure sensor inputs (based on currently available data) and liquid Froude number was shown to offer an accuracy of under 5% if the Froude number could be estimated with 25% accuracy. (author) (tk)

Neural redundancy applied to the parity space for signal validation

International Nuclear Information System (INIS)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu; Martinez, Aquilino Senra

2005-01-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

Neural redundancy applied to the parity space for signal validation

Energy Technology Data Exchange (ETDEWEB)

Mol, Antonio Carlos de Abreu; Pereira, Claudio Marcio Nascimento Abreu [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil)]. E-mail: cmnap@ien.gov.br; Martinez, Aquilino Senra [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia]. E-mail: aquilino@lmp.br

2005-07-01

The objective of signal validation is to provide more reliable information from the plant sensor data The method presented in this work introduces the concept of neural redundancy and applies it to the space parity method [1] to overcome an inherent deficiency of this method - the determination of the best estimative of the redundant measures when they are inconsistent. The concept of neural redundancy consists on the calculation of a redundancy through neural networks based on the time series of the own state variable. Therefore, neural networks, dynamically trained with the time series, will estimate the current value of the own measure, which will be used as referee of the redundant measures in the parity space. For this purpose the neural network should have the capacity to supply the neural redundancy in real time and with maximum error corresponding to the group deviation. The historical series should be enough to allow the estimate of the next value, during transients and at the same time, it should be optimized to facilitate the retraining of the neural network to each acquisition. In order to have the capacity to reproduce the tendency of the time series even under accident condition, the dynamic training of the neural network privileges the recent points of the time series. The tests accomplished with simulated data of a nuclear plant, demonstrated that this method applied on the parity space method improves the signal validation process. (author)

Nuclear power plant monitoring method by neural network and its application to actual nuclear reactor

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni; Tuerkcan, E.

1995-11-01

In this paper, the anomaly detection method for nuclear power plant monitoring and its program are described by using a neural network approach, which is based on the deviation between measured signals and output signals of neural network model. The neural network used in this study has three layered auto-associative network with 12 input/output, and backpropagation algorithm is adopted for learning. Furthermore, to obtain better dynamical model of the reactor plant, a new learning technique was developed in which the learning process of the present neural network is divided into initial and adaptive learning modes. The test results at the actual nuclear reactor shows that the neural network plant monitoring system is successfull in detecting in real-time the symptom of small anomaly over a wide power range including reactor start-up, shut-down and stationary operation. (author)

Full-Field Calibration of Color Camera Chromatic Aberration using Absolute Phase Maps.

Science.gov (United States)

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.

Hierarchical modeling of molecular energies using a deep neural network

Science.gov (United States)

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.

Near threshold absolute TDCS: First results

International Nuclear Information System (INIS)

Roesel, T.; Schlemmer, P.; Roeder, J.; Frost, L.; Jung, K.; Ehrhardt, H.

1992-01-01

A new method, and first results for an impact energy 2 eV above the threshold of ionisation of helium, are presented for the measurement of absolute triple differential cross sections (TDCS) in a crossed beam experiment. The method is based upon measurement of beam/target overlap densities using known absolute total ionisation cross sections and of detection efficiencies using known absolute double differential cross sections (DDCS). For the present work the necessary absolute DDCS for 1 eV electrons had also to be measured. Results are presented for several different coplanar kinematics and are compared with recent DWBA calculations. (orig.)

Absolute entropy of ions in methanol

International Nuclear Information System (INIS)

Abakshin, V.A.; Kobenin, V.A.; Krestov, G.A.

1978-01-01

By measuring the initial thermoelectromotive forces of chains with bromo-silver electrodes in tetraalkylammonium bromide solutions the absolute entropy of bromide-ion in methanol is determined in the 298.15-318.15 K range. The anti Ssub(Brsup(-))sup(0) = 9.8 entropy units value is used for calculation of the absolute partial molar entropy of alkali metal ions and halogenide ions. It has been found that, absolute entropy of Cs + =12.0 entropy units, I - =14.0 entropy units. The obtained ion absolute entropies in methanol at 298.15 K within 1-2 entropy units is in an agreement with published data

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

Science.gov (United States)

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.

9 CFR 318.308 - Deviations in processing.

Science.gov (United States)

2010-01-01

...) Deviations in processing (or process deviations) must be handled according to: (1)(i) A HACCP plan for canned...) of this section. (c) [Reserved] (d) Procedures for handling process deviations where the HACCP plan... accordance with the following procedures: (a) Emergency stops. (1) When retort jams or breakdowns occur...

Adaptive PID control based on orthogonal endocrine neural networks.

Science.gov (United States)

Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D

2016-12-01

A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.

Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks

International Nuclear Information System (INIS)

Notton, Gilles; Paoli, Christophe; Vasileva, Siyana; Nivet, Marie Laure; Canaletti, Jean-Louis; Cristofari, Christian

2012-01-01

Calculating global solar irradiation from global horizontal irradiation only is a difficult task, especially when the time step is small and the data are not averaged. We used an Artificial Neural Network (ANN) to realize this conversion. The ANN is optimized and tested on the basis of five years of solar data; the accuracy of the optimal configuration is around 6% for the RRMSE (relative root mean square error) and around 3.5% for the RMAE (relative mean absolute value) i.e. a better performance than the empirical correlations available in the literature. -- Highlights: ► ANN (Artificial Neural Network) methodology applied to hourly global solar irradiation in order to estimate tilted irradiations. ► Model validation with more than 23,000 data. ► Comparison with “conventional” models. ► The precision in the results is better than with empirical correlations. ► 6% for the RMSE (root means square error) and around 3.5% for the RMAE (Relative Mean Absolute Value).

Control of 12-Cylinder Camless Engine with Neural Networks

Directory of Open Access Journals (Sweden)

Ashhab Moh’d Sami

2017-01-01

Full Text Available The 12-cyliner camless engine breathing process is modeled with artificial neural networks (ANN’s. The inputs to the net are the intake valve lift (IVL and intake valve closing timing (IVC whereas the output of the net is the cylinder air charge (CAC. The ANN is trained with data collected from an engine simulation model which is based on thermodynamics principles and calibrated against real engine data. A method for adapting single-output feed-forward neural networks is proposed and applied to the camless engine ANN model. As a consequence the overall 12-cyliner camless engine feedback controller is upgraded and the necessary changes are implemented in order to contain the adaptive neural network with the objective of tracking the cylinder air charge (driver’s torque demand while minimizing the pumping losses (increasing engine efficiency. All the needed measurements are extracted only from the two conventional and inexpensive sensors, namely, the mass air flow through the throttle body (MAF and the intake manifold absolute pressure (MAP sensors. The feedback controller’s capability is demonstrated through computer simulation.

Investigate the capability of INAA absolute method to determine the concentrations of 238U and 232Th in rock samples

International Nuclear Information System (INIS)

Alnour, I.A.

2014-01-01

This work aimed to study the capability of INAA absolute method in determining the elemental concentration of 238 U and 232 Th in the rock samples. The INAA absolute method was implemented in PUSPATI TRIGA Mark II research reactor, Malaysian Nuclear Agency (NM). The accuracy of INAA absolute method was performed by analyzing the IAEA certified reference material (CRM) Soil-7. The analytical results showed the deviations between experimental and certified values were mostly less than 10 % with Z-score in most cases less than 1. In general, the results of analysed CRM Soil-7 show a good agreement between certified and experimental results which mean that the INAA absolute method can be used accurately for elemental analysis of uranium and thorium in various types of samples. The concentration of 238 U and 232 Th ranged from 1.77 to 24.25 and 0.88 to 95.50 ppm respectively. The highest value of 238 U and 232 Th was recorded for granite rock sample G17 of 238 U and sample G9 of 232 Th, whereas the lower value was 1.77 ppm of 238 U recorded in sandstone rock and 0.88 ppm of 232 Th for gabbro. Moreover, a comparison of the 238 U and 232 Th results obtained by the INAA absolute method shows an acceptable level of consistency with those obtained by the INAA relative method. (author)

An Artificial Neural Networks Approach to Estimate Occupational Accident: A National Perspective for Turkey

Directory of Open Access Journals (Sweden)

Hüseyin Ceylan

2014-01-01

Full Text Available Occupational accident estimation models were developed by using artificial neural networks (ANNs for Turkey. Using these models the number of occupational accidents and death and permanent incapacity numbers resulting from occupational accidents were estimated for Turkey until the year of 2025 by the three different scenarios. In the development of the models, insured workers, workplace, occupational accident, death, and permanent incapacity values were used as model parameters with data between 1970 and 2012. 2-5-1 neural network architecture was selected as the best network architecture. Sigmoid was used in hidden layers and linear function was used at output layer. The feed forward back propagation algorithm was used to train the network. In order to obtain a useful model, the network was trained between 1970 and 1999 to estimate the values of 2000 to 2012. The result was compared with the real values and it was seen that it is applicable for this aim. The performances of all developed models were evaluated using mean absolute percent errors (MAPE, mean absolute errors (MAE, and root mean square errors (RMSE.

Artificial Neural Network Model to Estimate the Viscosity of Polymer Solutions for Enhanced Oil Recovery

Directory of Open Access Journals (Sweden)

Pan-Sang Kang

2016-06-01

Full Text Available Polymer flooding is now considered a technically- and commercially-proven method for enhanced oil recovery (EOR. The viscosity of the injected polymer solution is the key property for successful polymer flooding. Given that the viscosity of a polymer solution has a non-linear relationship with various influential parameters (molecular weight, degree of hydrolysis, polymer concentration, cation concentration of polymer solution, shear rate, temperature and that measurement of viscosity based on these parameters is a time-consuming process, the range of solution samples and the measurement conditions need to be limited and precise. Viscosity estimation of the polymer solution is effective for these purposes. An artificial neural network (ANN was applied to the viscosity estimation of FlopaamTM 3330S, FlopaamTM 3630S and AN-125 solutions, three commonly-used EOR polymers. The viscosities measured and estimated by ANN and the Carreau model using Lee’s correlation, the only method for estimating the viscosity of an EOR polymer solution in unmeasured conditions, were compared. Estimation accuracy was evaluated by the average absolute relative deviation, which has been widely used for accuracy evaluation of the results of ANN models. In all conditions, the accuracy of the ANN model is higher than that of the Carreau model using Lee’s correlation.

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

Directory of Open Access Journals (Sweden)

Yongcheng Li

Full Text Available 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.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Modeling and prediction of Turkey's electricity consumption using Artificial Neural Networks

International Nuclear Information System (INIS)

Kavaklioglu, Kadir; Ozturk, Harun Kemal; Canyurt, Olcay Ersel; Ceylan, Halim

2009-01-01

Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption. (author)

A study of reactor monitoring method with neural network

Energy Technology Data Exchange (ETDEWEB)

Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

2001-03-01

The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

Epigenetic profiles in children with a neural tube defect; a case-control study in two populations.

Directory of Open Access Journals (Sweden)

Lisette Stolk

Full Text Available Folate deficiency is implicated in the causation of neural tube defects (NTDs. The preventive effect of periconceptional folic acid supplement use is partially explained by the treatment of a deranged folate-dependent one carbon metabolism, which provides methyl groups for DNA-methylation as an epigenetic mechanism. Here, we hypothesize that variations in DNA-methylation of genes implicated in the development of NTDs and embryonic growth are part of the underlying mechanism. In 48 children with a neural tube defect and 62 controls from a Dutch case-control study and 34 children with a neural tube defect and 78 controls from a Texan case-control study, we measured the DNA-methylation levels of imprinted candidate genes (IGF2-DMR, H19, KCNQ1OT1 and non-imprinted genes (the LEKR/CCNL gene region associated with birth weight, and MTHFR and VANGL1 associated with NTD. We used the MassARRAY EpiTYPER assay from Sequenom for the assessment of DNA-methylation. Linear mixed model analysis was used to estimate associations between DNA-methylation levels of the genes and a neural tube defect. In the Dutch study group, but not in the Texan study group we found a significant association between the risk of having an NTD and DNA methylation levels of MTHFR (absolute decrease in methylation of -0.33% in cases, P-value = 0.001, and LEKR/CCNL (absolute increase in methylation: 1.36% in cases, P-value = 0.048, and a borderline significant association for VANGL (absolute increase in methylation: 0.17% in cases, P-value = 0.063. Only the association between MTHFR and NTD-risk remained significant after multiple testing correction. The associations in the Dutch study were not replicated in the Texan study. We conclude that the associations between NTDs and the methylation of the MTHFR gene, and maybe VANGL and LEKKR/CNNL, are in line with previous studies showing polymorphisms in the same genes in association with NTDs and embryonic development

Response variance in functional maps: neural darwinism revisited.

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Hirokazu Takahashi

Full Text Available 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.

Science.gov (United States)

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.

The role of stochasticity in an information-optimal neural population code

International Nuclear Information System (INIS)

Stocks, N G; Nikitin, A P; McDonnell, M D; Morse, R P

2009-01-01

In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

Projective absoluteness for Sacks forcing

NARCIS (Netherlands)

Ikegami, D.

2009-01-01

We show that Sigma(1)(3)-absoluteness for Sacks forcing is equivalent to the nonexistence of a Delta(1)(2) Bernstein set. We also show that Sacks forcing is the weakest forcing notion among all of the preorders that add a new real with respect to Sigma(1)(3) forcing absoluteness.

Fidelity deviation in quantum teleportation

Science.gov (United States)

Bang, Jeongho; Ryu, Junghee; Kaszlikowski, Dagomir

2018-04-01

We analyze the performance of quantum teleportation in terms of average fidelity and fidelity deviation. The average fidelity is defined as the average value of the fidelities over all possible input states and the fidelity deviation is their standard deviation, which is referred to as a concept of fluctuation or universality. In the analysis, we find the condition to optimize both measures under a noisy quantum channel—we here consider the so-called Werner channel. To characterize our results, we introduce a 2D space defined by the aforementioned measures, in which the performance of the teleportation is represented as a point with the channel noise parameter. Through further analysis, we specify some regions drawn for different channel conditions, establishing the connection to the dissimilar contributions of the entanglement to the teleportation and the Bell inequality violation.

Large deviations for noninteracting infinite-particle systems

International Nuclear Information System (INIS)

Donsker, M.D.; Varadhan, S.R.S.

1987-01-01

A large deviation property is established for noninteracting infinite particle systems. Previous large deviation results obtained by the authors involved a single I-function because the cases treated always involved a unique invariant measure for the process. In the context of this paper there is an infinite family of invariant measures and a corresponding infinite family of I-functions governing the large deviations

48 CFR 1401.403 - Individual deviations.

Science.gov (United States)

2010-10-01

... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Individual deviations. 1401.403 Section 1401.403 Federal Acquisition Regulations System DEPARTMENT OF THE INTERIOR GENERAL DEPARTMENT OF THE INTERIOR ACQUISITION REGULATION SYSTEM Deviations from the FAR and DIAR 1401.403 Individual...

TERMINOLOGY MANAGEMENT FRAMEWORK DEVIATIONS IN PROJECTS

Directory of Open Access Journals (Sweden)

Олена Борисівна ДАНЧЕНКО

2015-05-01

Full Text Available The article reviews new approaches to managing projects deviations (risks, changes, problems. By offering integrated control these parameters of the project and by analogy with medical terminological systems building a new system for managing terminological variations in the projects. With an improved method of triads system definitions are analyzed medical terms that make up terminological basis. Using the method of analogy proposed new definitions for managing deviations in projects. By using triad integrity built a new system triad in project management, which will subsequently also analogous to develop a new methodology of deviations in projects.

Learning in Artificial Neural Systems

Science.gov (United States)

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.

41 CFR 115-1.110 - Deviations.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 3 2010-07-01 2010-07-01 false Deviations. 115-1.110 Section 115-1.110 Public Contracts and Property Management Federal Property Management Regulations System (Continued) ENVIRONMENTAL PROTECTION AGENCY 1-INTRODUCTION 1.1-Regulation System § 115-1.110 Deviations...

41 CFR 105-1.110 - Deviation.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 3 2010-07-01 2010-07-01 false Deviation. 105-1.110 Section 105-1.110 Public Contracts and Property Management Federal Property Management Regulations System (Continued) GENERAL SERVICES ADMINISTRATION 1-INTRODUCTION 1.1-Regulations System § 105-1.110 Deviation. (a...

41 CFR 101-1.110 - Deviation.

Science.gov (United States)

2010-07-01

... 41 Public Contracts and Property Management 2 2010-07-01 2010-07-01 true Deviation. 101-1.110 Section 101-1.110 Public Contracts and Property Management Federal Property Management Regulations System FEDERAL PROPERTY MANAGEMENT REGULATIONS GENERAL 1-INTRODUCTION 1.1-Regulation System § 101-1.110 Deviation...

Modeling of surface dust concentrations using neural networks and kriging

Science.gov (United States)

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.

Microfabricated Collector-Generator Electrode Sensor for Measuring Absolute pH and Oxygen Concentrations.

Science.gov (United States)

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.

Variance computations for functional of absolute risk estimates.

Science.gov (United States)

Pfeiffer, R M; Petracci, E

2011-07-01

We present a simple influence function based approach to compute the variances of estimates of absolute risk and functions of absolute risk. We apply this approach to criteria that assess the impact of changes in the risk factor distribution on absolute risk for an individual and at the population level. As an illustration we use an absolute risk prediction model for breast cancer that includes modifiable risk factors in addition to standard breast cancer risk factors. Influence function based variance estimates for absolute risk and the criteria are compared to bootstrap variance estimates.

Absolute determination of the deuterium content of heavy water, measurement of absolute density

International Nuclear Information System (INIS)

Ceccaldi, M.; Riedinger, M.; Menache, M.

1975-01-01

The absolute density of two heavy water samples rich in deuterium (with a grade higher than 99.9%) was determined with the hydrostatic method. The exact isotopic composition of this water (hydrogen and oxygen isotopes) was very carefully studied. A theoretical estimate enabled us to get the absolute density value of isotopically pure D 2 16 O. This value was found to be 1104.750 kg.m -3 at t 68 =22.3 0 C and under the pressure of one atmosphere. (orig.) [de

Comparing Standard Deviation Effects across Contexts

Science.gov (United States)

Ost, Ben; Gangopadhyaya, Anuj; Schiman, Jeffrey C.

2017-01-01

Studies using tests scores as the dependent variable often report point estimates in student standard deviation units. We note that a standard deviation is not a standard unit of measurement since the distribution of test scores can vary across contexts. As such, researchers should be cautious when interpreting differences in the numerical size of…

Prediction of biodiesel ignition delay in a diesel engine using artificial neural networks

International Nuclear Information System (INIS)

Piloto-Rodríguez, Ramón; Sánchez-Borroto, Yisel

2017-01-01

Ignition delay is one of the most important parameters of the combustion process and have a strong influence in exhaust emissions and engines performance. In the present work, the results of the mathematical modeling of ignition delay through artificial neural networks are shown. The modeling starts from input values that cover thermodynamic variables, engines parameters and biodiesel properties. The model obtained is only useful for biodiesel samples and several neural network algorithms were applied in order to predict the ignition delay. From its correlation coefficient, prediction capability and lowest absolute error, the best model was selected. Among other network’s input parameters, the cetane number was taken into account, also previously predicted by the use of ANN. (author)

Transport Coefficients from Large Deviation Functions

Directory of Open Access Journals (Sweden)

Chloe Ya Gao

2017-10-01

Full Text Available We describe a method for computing transport coefficients from the direct evaluation of large deviation functions. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which are scaled cumulant generating functions analogous to the free energies. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green–Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.

Transport Coefficients from Large Deviation Functions

Science.gov (United States)

Gao, Chloe; Limmer, David

2017-10-01

We describe a method for computing transport coefficients from the direct evaluation of large deviation function. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which is a scaled cumulant generating function analogous to the free energy. A diffusion Monte Carlo algorithm is used to evaluate the large deviation functions, from which arbitrary transport coefficients are derivable. We find significant statistical improvement over traditional Green-Kubo based calculations. The systematic and statistical errors of this method are analyzed in the context of specific transport coefficient calculations, including the shear viscosity, interfacial friction coefficient, and thermal conductivity.

The absolute environmental performance of buildings

DEFF Research Database (Denmark)

Brejnrod, Kathrine Nykjær; Kalbar, Pradip; Petersen, Steffen

2017-01-01

Our paper presents a novel approach for absolute sustainability assessment of a building's environmental performance. It is demonstrated how the absolute sustainable share of the earth carrying capacity of a specific building type can be estimated using carrying capacity based normalization factors....... A building is considered absolute sustainable if its annual environmental burden is less than its share of the earth environmental carrying capacity. Two case buildings – a standard house and an upcycled single-family house located in Denmark – were assessed according to this approach and both were found...... to exceed the target values of three (almost four) of the eleven impact categories included in the study. The worst-case excess was for the case building, representing prevalent Danish building practices, which utilized 1563% of the Climate Change carrying capacity. Four paths to reach absolute...

Absolute Summ

Science.gov (United States)

Phillips, Alfred, Jr.

Summ means the entirety of the multiverse. It seems clear, from the inflation theories of A. Guth and others, that the creation of many universes is plausible. We argue that Absolute cosmological ideas, not unlike those of I. Newton, may be consistent with dynamic multiverse creations. As suggested in W. Heisenberg's uncertainty principle, and with the Anthropic Principle defended by S. Hawking, et al., human consciousness, buttressed by findings of neuroscience, may have to be considered in our models. Predictability, as A. Einstein realized with Invariants and General Relativity, may be required for new ideas to be part of physics. We present here a two postulate model geared to an Absolute Summ. The seedbed of this work is part of Akhnaton's philosophy (see S. Freud, Moses and Monotheism). Most important, however, is that the structure of human consciousness, manifest in Kenya's Rift Valley 200,000 years ago as Homo sapiens, who were the culmination of the six million year co-creation process of Hominins and Nature in Africa, allows us to do the physics that we do. .

41 CFR 109-1.110-50 - Deviation procedures.

Science.gov (United States)

2010-07-01

... best interest of the Government; (3) If applicable, the name of the contractor and identification of... background information which will contribute to a full understanding of the desired deviation. (b)(1... authorized to grant deviations to the DOE-PMR. (d) Requests for deviations from the FPMR will be coordinated...

The role of stochasticity in an information-optimal neural population code

Energy Technology Data Exchange (ETDEWEB)

Stocks, N G; Nikitin, A P [School of Engineering, University of Warwick, Coventry CV4 7AL (United Kingdom); McDonnell, M D [Institute for Telecommunications Research, University of South Australia, SA 5095 (Australia); Morse, R P, E-mail: n.g.stocks@warwick.ac.u [School of Life and Health Sciences, Aston University, Birmingham B4 7ET (United Kingdom)

2009-12-01

In this paper we consider the optimisation of Shannon mutual information (MI) in the context of two model neural systems. The first is a stochastic pooling network (population) of McCulloch-Pitts (MP) type neurons (logical threshold units) subject to stochastic forcing; the second is (in a rate coding paradigm) a population of neurons that each displays Poisson statistics (the so called 'Poisson neuron'). The mutual information is optimised as a function of a parameter that characterises the 'noise level'-in the MP array this parameter is the standard deviation of the noise; in the population of Poisson neurons it is the window length used to determine the spike count. In both systems we find that the emergent neural architecture and, hence, code that maximises the MI is strongly influenced by the noise level. Low noise levels leads to a heterogeneous distribution of neural parameters (diversity), whereas, medium to high noise levels result in the clustering of neural parameters into distinct groups that can be interpreted as subpopulations. In both cases the number of subpopulations increases with a decrease in noise level. Our results suggest that subpopulations are a generic feature of an information optimal neural population.

Absolute flux scale for radioastronomy

International Nuclear Information System (INIS)

Ivanov, V.P.; Stankevich, K.S.

1986-01-01

The authors propose and provide support for a new absolute flux scale for radio astronomy, which is not encumbered with the inadequacies of the previous scales. In constructing it the method of relative spectra was used (a powerful tool for choosing reference spectra). A review is given of previous flux scales. The authors compare the AIS scale with the scale they propose. Both scales are based on absolute measurements by the ''artificial moon'' method, and they are practically coincident in the range from 0.96 to 6 GHz. At frequencies above 6 GHz, 0.96 GHz, the AIS scale is overestimated because of incorrect extrapolation of the spectra of the primary and secondary standards. The major results which have emerged from this review of absolute scales in radio astronomy are summarized

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

Science.gov (United States)

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 global algorithm for estimating Absolute Salinity

Science.gov (United States)

McDougall, T. J.; Jackett, D. R.; Millero, F. J.; Pawlowicz, R.; Barker, P. M.

2012-12-01

The International Thermodynamic Equation of Seawater - 2010 has defined the thermodynamic properties of seawater in terms of a new salinity variable, Absolute Salinity, which takes into account the spatial variation of the composition of seawater. Absolute Salinity more accurately reflects the effects of the dissolved material in seawater on the thermodynamic properties (particularly density) than does Practical Salinity. When a seawater sample has standard composition (i.e. the ratios of the constituents of sea salt are the same as those of surface water of the North Atlantic), Practical Salinity can be used to accurately evaluate the thermodynamic properties of seawater. When seawater is not of standard composition, Practical Salinity alone is not sufficient and the Absolute Salinity Anomaly needs to be estimated; this anomaly is as large as 0.025 g kg-1 in the northernmost North Pacific. Here we provide an algorithm for estimating Absolute Salinity Anomaly for any location (x, y, p) in the world ocean. To develop this algorithm, we used the Absolute Salinity Anomaly that is found by comparing the density calculated from Practical Salinity to the density measured in the laboratory. These estimates of Absolute Salinity Anomaly however are limited to the number of available observations (namely 811). In order to provide a practical method that can be used at any location in the world ocean, we take advantage of approximate relationships between Absolute Salinity Anomaly and silicate concentrations (which are available globally).

The large deviation approach to statistical mechanics

International Nuclear Information System (INIS)

Touchette, Hugo

2009-01-01

The theory of large deviations is concerned with the exponential decay of probabilities of large fluctuations in random systems. These probabilities are important in many fields of study, including statistics, finance, and engineering, as they often yield valuable information about the large fluctuations of a random system around its most probable state or trajectory. In the context of equilibrium statistical mechanics, the theory of large deviations provides exponential-order estimates of probabilities that refine and generalize Einstein's theory of fluctuations. This review explores this and other connections between large deviation theory and statistical mechanics, in an effort to show that the mathematical language of statistical mechanics is the language of large deviation theory. The first part of the review presents the basics of large deviation theory, and works out many of its classical applications related to sums of random variables and Markov processes. The second part goes through many problems and results of statistical mechanics, and shows how these can be formulated and derived within the context of large deviation theory. The problems and results treated cover a wide range of physical systems, including equilibrium many-particle systems, noise-perturbed dynamics, nonequilibrium systems, as well as multifractals, disordered systems, and chaotic systems. This review also covers many fundamental aspects of statistical mechanics, such as the derivation of variational principles characterizing equilibrium and nonequilibrium states, the breaking of the Legendre transform for nonconcave entropies, and the characterization of nonequilibrium fluctuations through fluctuation relations.

The large deviation approach to statistical mechanics

Science.gov (United States)

Touchette, Hugo

2009-07-01

The theory of large deviations is concerned with the exponential decay of probabilities of large fluctuations in random systems. These probabilities are important in many fields of study, including statistics, finance, and engineering, as they often yield valuable information about the large fluctuations of a random system around its most probable state or trajectory. In the context of equilibrium statistical mechanics, the theory of large deviations provides exponential-order estimates of probabilities that refine and generalize Einstein’s theory of fluctuations. This review explores this and other connections between large deviation theory and statistical mechanics, in an effort to show that the mathematical language of statistical mechanics is the language of large deviation theory. The first part of the review presents the basics of large deviation theory, and works out many of its classical applications related to sums of random variables and Markov processes. The second part goes through many problems and results of statistical mechanics, and shows how these can be formulated and derived within the context of large deviation theory. The problems and results treated cover a wide range of physical systems, including equilibrium many-particle systems, noise-perturbed dynamics, nonequilibrium systems, as well as multifractals, disordered systems, and chaotic systems. This review also covers many fundamental aspects of statistical mechanics, such as the derivation of variational principles characterizing equilibrium and nonequilibrium states, the breaking of the Legendre transform for nonconcave entropies, and the characterization of nonequilibrium fluctuations through fluctuation relations.

Transport Coefficients from Large Deviation Functions

OpenAIRE

Gao, Chloe Ya; Limmer, David T.

2017-01-01

We describe a method for computing transport coefficients from the direct evaluation of large deviation functions. This method is general, relying on only equilibrium fluctuations, and is statistically efficient, employing trajectory based importance sampling. Equilibrium fluctuations of molecular currents are characterized by their large deviation functions, which are scaled cumulant generating functions analogous to the free energies. A diffusion Monte Carlo algorithm is used to evaluate th...

SU-F-T-492: The Impact of Water Temperature On Absolute Dose Calibration

Energy Technology Data Exchange (ETDEWEB)

Islam, N [State University of New York at Buffalo, Buffalo, NY (United States); Podgorsak, M [State University of New York at Buffalo, Buffalo, NY (United States); Roswell Park Cancer Institute, Buffalo, NY (United States)

2016-06-15

Purpose: The Task Group 51 (TG 51) protocol prescribes that dose calibration of photon beams be done by irradiating an ionization chamber in a water tank at pre-defined depths. Methodologies are provided to account for variations in measurement conditions by applying correction factors. However, the protocol does not completely account for the impact of water temperature. It is well established that water temperature will influence the density of air in the ion chamber collecting volume. Water temperature, however, will also influence the size of the collecting volume via thermal expansion of the cavity wall and the density of the water in the tank. In this work the overall effect of water temperature on absolute dosimetry has been investigated. Methods: Dose measurements were made using a Farmer-type ion chamber for 6 and 23 MV photon beams with water temperatures ranging from 10 to 40°C. A reference ion chamber was used to account for fluctuations in beam output between successive measurements. Results: For the same beam output, the dose determined using TG 51 was dependent on the temperature of the water in the tank. A linear regression of the data suggests that the dependence is statistically significant with p-values of the slope equal to 0.003 and 0.01 for 6 and 23 MV beams, respectively. For a 10 degree increase in water phantom temperature, the absolute dose determined with TG 51 increased by 0.27% and 0.31% for 6 and 23 MV beams, respectively. Conclusion: There is a measurable effect of water temperature on absolute dose calibration. To account for this effect, a reference temperature can be defined and a correction factor applied to account for deviations from this reference temperature during beam calibration. Such a factor is expected to be of similar magnitude to most of the existing TG 51 correction factors.

SU-F-T-492: The Impact of Water Temperature On Absolute Dose Calibration

International Nuclear Information System (INIS)

Islam, N; Podgorsak, M

2016-01-01

Purpose: The Task Group 51 (TG 51) protocol prescribes that dose calibration of photon beams be done by irradiating an ionization chamber in a water tank at pre-defined depths. Methodologies are provided to account for variations in measurement conditions by applying correction factors. However, the protocol does not completely account for the impact of water temperature. It is well established that water temperature will influence the density of air in the ion chamber collecting volume. Water temperature, however, will also influence the size of the collecting volume via thermal expansion of the cavity wall and the density of the water in the tank. In this work the overall effect of water temperature on absolute dosimetry has been investigated. Methods: Dose measurements were made using a Farmer-type ion chamber for 6 and 23 MV photon beams with water temperatures ranging from 10 to 40°C. A reference ion chamber was used to account for fluctuations in beam output between successive measurements. Results: For the same beam output, the dose determined using TG 51 was dependent on the temperature of the water in the tank. A linear regression of the data suggests that the dependence is statistically significant with p-values of the slope equal to 0.003 and 0.01 for 6 and 23 MV beams, respectively. For a 10 degree increase in water phantom temperature, the absolute dose determined with TG 51 increased by 0.27% and 0.31% for 6 and 23 MV beams, respectively. Conclusion: There is a measurable effect of water temperature on absolute dose calibration. To account for this effect, a reference temperature can be defined and a correction factor applied to account for deviations from this reference temperature during beam calibration. Such a factor is expected to be of similar magnitude to most of the existing TG 51 correction factors.

Excessive Neural Responses and Visual Discomfort

Directory of Open Access Journals (Sweden)

L O'Hare

2014-08-01

Full Text Available Spatially and temporally periodic patterns can look aversive to some individuals (Wilkins et al, 1984, Brain, 107, 989-1017, especially clinical populations such as migraine (Marcus and Soso, 1989, Arch Neurol., 46(10, 1129-32 epilepsy (Wilkins, Darby and Binnie, 1979, Brain, 102, 1-25. It has been suggested that this might be due to excessive neural responses (Juricevic, Land, Wilkins and Webster, 2010, Perception, 39(7, 884-899. Spatial frequency content has been shown to affect both relative and absolute discomfort judgements for spatially periodic riloid stimuli (Clark, O'Hare and Hibbard, 2013, Perception, ECVP Supp.; O'Hare, Clark and Hibbard, 2013, Perception ECVP Supplement. The current study investigated the possibility of whether neural correlates of visual discomfort from periodic stimuli could be measured using EEG. Stimuli were first matched for perceived contrast using a self adjustment task. EEG measurements were then obtained, alongside subjective discomfort judgements. Subjective discomfort judgements support those found previously, under various circumstances, indicating that spatial frequency plays a role in the perceived discomfort of periodic images. However, trends in EEG responses do not appear to have a straightforward relationship to subjective discomfort judgements.

Towards a large deviation theory for strongly correlated systems

International Nuclear Information System (INIS)

Ruiz, Guiomar; Tsallis, Constantino

2012-01-01

A large-deviation connection of statistical mechanics is provided by N independent binary variables, the (N→∞) limit yielding Gaussian distributions. The probability of n≠N/2 out of N throws is governed by e −Nr , r related to the entropy. Large deviations for a strong correlated model characterized by indices (Q,γ) are studied, the (N→∞) limit yielding Q-Gaussians (Q→1 recovers a Gaussian). Its large deviations are governed by e q −Nr q (∝1/N 1/(q−1) , q>1), q=(Q−1)/(γ[3−Q])+1. This illustration opens the door towards a large-deviation foundation of nonextensive statistical mechanics. -- Highlights: ► We introduce the formalism of relative entropy for a single random binary variable and its q-generalization. ► We study a model of N strongly correlated binary random variables and their large-deviation probabilities. ► Large-deviation probability of strongly correlated model exhibits a q-exponential decay whose argument is proportional to N, as extensivity requires. ► Our results point to a q-generalized large deviation theory and suggest a large-deviation foundation of nonextensive statistical mechanics.

FINDING STANDARD DEVIATION OF A FUZZY NUMBER

OpenAIRE

Fokrul Alom Mazarbhuiya

2017-01-01

Two probability laws can be root of a possibility law. Considering two probability densities over two disjoint ranges, we can define the fuzzy standard deviation of a fuzzy variable with the help of the standard deviation two random variables in two disjoint spaces.

Artificial neural networks in variable process control: application in particleboard manufacture

Energy Technology Data Exchange (ETDEWEB)

Esteban, L. G.; Garcia Fernandez, F.; Palacios, P. de; Conde, M.

2009-07-01

Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control. (Author) 47 refs.

A global algorithm for estimating Absolute Salinity

Directory of Open Access Journals (Sweden)

T. J. McDougall

2012-12-01

Full Text Available The International Thermodynamic Equation of Seawater – 2010 has defined the thermodynamic properties of seawater in terms of a new salinity variable, Absolute Salinity, which takes into account the spatial variation of the composition of seawater. Absolute Salinity more accurately reflects the effects of the dissolved material in seawater on the thermodynamic properties (particularly density than does Practical Salinity.

When a seawater sample has standard composition (i.e. the ratios of the constituents of sea salt are the same as those of surface water of the North Atlantic, Practical Salinity can be used to accurately evaluate the thermodynamic properties of seawater. When seawater is not of standard composition, Practical Salinity alone is not sufficient and the Absolute Salinity Anomaly needs to be estimated; this anomaly is as large as 0.025 g kg⁻¹ in the northernmost North Pacific. Here we provide an algorithm for estimating Absolute Salinity Anomaly for any location (x, y, p in the world ocean.

To develop this algorithm, we used the Absolute Salinity Anomaly that is found by comparing the density calculated from Practical Salinity to the density measured in the laboratory. These estimates of Absolute Salinity Anomaly however are limited to the number of available observations (namely 811. In order to provide a practical method that can be used at any location in the world ocean, we take advantage of approximate relationships between Absolute Salinity Anomaly and silicate concentrations (which are available globally.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Dental anomalies in different cleft groups related to neural crest developmental fields contributes to the understanding of cleft aetiology

DEFF Research Database (Denmark)

Riis, Louise Claudius; Kjær, Inger; Mølsted, Kirsten

2014-01-01

OBJECTIVE: To analyze dental deviations in three cleft groups and relate findings to embryological neural crest fields (frontonasal, maxillary, and palatal). The overall purpose was to evaluate how fields are involved in different cleft types. DESIGN: Retrospective audit of clinical photographs...

Invariant and Absolute Invariant Means of Double Sequences

Directory of Open Access Journals (Sweden)

Abdullah Alotaibi

2012-01-01

Full Text Available We examine some properties of the invariant mean, define the concepts of strong σ-convergence and absolute σ-convergence for double sequences, and determine the associated sublinear functionals. We also define the absolute invariant mean through which the space of absolutely σ-convergent double sequences is characterized.

The large deviations theorem and ergodicity

International Nuclear Information System (INIS)

Gu Rongbao

2007-01-01

In this paper, some relationships between stochastic and topological properties of dynamical systems are studied. For a continuous map f from a compact metric space X into itself, we show that if f satisfies the large deviations theorem then it is topologically ergodic. Moreover, we introduce the topologically strong ergodicity, and prove that if f is a topologically strongly ergodic map satisfying the large deviations theorem then it is sensitively dependent on initial conditions

Large deviations

CERN Document Server

Deuschel, Jean-Dominique; Deuschel, Jean-Dominique

2001-01-01

This is the second printing of the book first published in 1988. The first four chapters of the volume are based on lectures given by Stroock at MIT in 1987. They form an introduction to the basic ideas of the theory of large deviations and make a suitable package on which to base a semester-length course for advanced graduate students with a strong background in analysis and some probability theory. A large selection of exercises presents important material and many applications. The last two chapters present various non-uniform results (Chapter 5) and outline the analytic approach that allow

PoDMan: Policy Deviation Management

Directory of Open Access Journals (Sweden)

Aishwarya Bakshi

2017-07-01

Full Text Available Whenever an unexpected or exceptional situation occurs, complying with the existing policies may not be possible. The main objective of this work is to assist individuals and organizations to decide in the process of deviating from policies and performing a non-complying action. The paper proposes utilizing software agents as supportive tools to provide the best non-complying action while deviating from policies. The article also introduces a process in which the decision on the choice of non-complying action can be made. The work is motivated by a real scenario observed in a hospital in Norway and demonstrated through the same settings.

Absolute measurement of a tritium standard

International Nuclear Information System (INIS)

Hadzisehovic, M.; Mocilnik, I.; Buraei, K.; Pongrac, S.; Milojevic, A.

1978-01-01

For the determination of a tritium absolute activity standard, a method of internal gas counting has been used. The procedure involves water reduction by uranium and zinc further the measurement of the absolute disintegration rate of tritium per unit of the effective volume of the counter by a compensation method. Criteria for the choice of methods and procedures concerning the determination and measurement of gaseous 3 H yield, parameters of gaseous hydrogen, sample mass of HTO and the absolute disintegration rate of tritium are discussed. In order to obtain gaseous sources of 3 H (and 2 H), the same reversible chemical reaction was used, namely, the water - uranium hydride - hydrogen system. This reaction was proved to be quantitative above 500 deg C by measuring the yield of the gas obtained and the absolute activity of an HTO standard. A brief description of the measuring apparatus is given, as well as a critical discussion of the brass counter quality and the possibility of obtaining equal working conditions at the counter ends. (T.G.)

Quantum uncertainty relation based on the mean deviation

OpenAIRE

Sharma, Gautam; Mukhopadhyay, Chiranjib; Sazim, Sk; Pati, Arun Kumar

2018-01-01

Traditional forms of quantum uncertainty relations are invariably based on the standard deviation. This can be understood in the historical context of simultaneous development of quantum theory and mathematical statistics. Here, we present alternative forms of uncertainty relations, in both state dependent and state independent forms, based on the mean deviation. We illustrate the robustness of this formulation in situations where the standard deviation based uncertainty relation is inapplica...

Exploring Students' Conceptions of the Standard Deviation

Science.gov (United States)

delMas, Robert; Liu, Yan

2005-01-01

This study investigated introductory statistics students' conceptual understanding of the standard deviation. A computer environment was designed to promote students' ability to coordinate characteristics of variation of values about the mean with the size of the standard deviation as a measure of that variation. Twelve students participated in an…

Cryogenic, Absolute, High Pressure Sensor

Science.gov (United States)

Chapman, John J. (Inventor); Shams. Qamar A. (Inventor); Powers, William T. (Inventor)

2001-01-01

A pressure sensor is provided for cryogenic, high pressure applications. A highly doped silicon piezoresistive pressure sensor is bonded to a silicon substrate in an absolute pressure sensing configuration. The absolute pressure sensor is bonded to an aluminum nitride substrate. Aluminum nitride has appropriate coefficient of thermal expansion for use with highly doped silicon at cryogenic temperatures. A group of sensors, either two sensors on two substrates or four sensors on a single substrate are packaged in a pressure vessel.

A developmental study of latent absolute pitch memory.

Science.gov (United States)

Jakubowski, Kelly; Müllensiefen, Daniel; Stewart, Lauren

2017-03-01

The ability to recall the absolute pitch level of familiar music (latent absolute pitch memory) is widespread in adults, in contrast to the rare ability to label single pitches without a reference tone (overt absolute pitch memory). The present research investigated the developmental profile of latent absolute pitch (AP) memory and explored individual differences related to this ability. In two experiments, 288 children from 4 to12 years of age performed significantly above chance at recognizing the absolute pitch level of familiar melodies. No age-related improvement or decline, nor effects of musical training, gender, or familiarity with the stimuli were found in regard to latent AP task performance. These findings suggest that latent AP memory is a stable ability that is developed from as early as age 4 and persists into adulthood.

Calculation of radiative corrections to virtual compton scattering - absolute measurement of the energy of Jefferson Lab. electron beam (hall A) by a magnetic method: arc project

International Nuclear Information System (INIS)

Marchand, D.

1998-11-01

This thesis presents the radiative corrections to the virtual compton scattering and the magnetic method adopted in the Hall A at Jefferson Laboratory, to measure the electrons beam energy with an accuracy of 10 4 . The virtual compton scattering experiments allow the access to the generalised polarizabilities of the protons. The extraction of these polarizabilities is obtained by the experimental and theoretical cross sections comparison. That's why the systematic errors and the radiative effects of the experiments have to be controlled very seriously. In this scope, a whole calculation of the internal radiative corrections has been realised in the framework of the quantum electrodynamic. The method of the dimensional regularisation has been used to the treatment of the ultraviolet and infra-red divergences. The absolute measure method of the energy, takes into account the magnetic deviation, made up of eight identical dipoles. The energy is determined from the deviation angle calculation of the beam and the measure of the magnetic field integral along the deviation

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

Science.gov (United States)

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 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.

Two examples of non strictly convex large deviations

OpenAIRE

De Marco, Stefano; Jacquier, Antoine; Roome, Patrick

2016-01-01

We present two examples of a large deviations principle where the rate function is not strictly convex. This is motivated by a model used in mathematical finance (the Heston model), and adds a new item to the zoology of non strictly convex large deviations. For one of these examples, we show that the rate function of the Cramer-type of large deviations coincides with that of the Freidlin-Wentzell when contraction principles are applied.

A Visual Model for the Variance and Standard Deviation

Science.gov (United States)

Orris, J. B.

2011-01-01

This paper shows how the variance and standard deviation can be represented graphically by looking at each squared deviation as a graphical object--in particular, as a square. A series of displays show how the standard deviation is the size of the average square.

Flow Regime Identification of Co-Current Downward Two-Phase Flow With Neural Network Approach

International Nuclear Information System (INIS)

Hiroshi Goda; Seungjin Kim; Ye Mi; Finch, Joshua P.; Mamoru Ishii; Jennifer Uhle

2002-01-01

Flow regime identification for an adiabatic vertical co-current downward air-water two-phase flow in the 25.4 mm ID and the 50.8 mm ID round tubes was performed by employing an impedance void meter coupled with the neural network classification approach. This approach minimizes the subjective judgment in determining the flow regimes. The signals obtained by an impedance void meter were applied to train the self-organizing neural network to categorize these impedance signals into a certain number of groups. The characteristic parameters set into the neural network classification included the mean, standard deviation and skewness of impedance signals in the present experiment. The classification categories adopted in the present investigation were four widely accepted flow regimes, viz. bubbly, slug, churn-turbulent, and annular flows. These four flow regimes were recognized based upon the conventional flow visualization approach by a high-speed motion analyzer. The resulting flow regime maps classified by the neural network were compared with the results obtained through the flow visualization method, and consequently the efficiency of the neural network classification for flow regime identification was demonstrated. (authors)

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

Science.gov (United States)

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.

Advancing Absolute Calibration for JWST and Other Applications

Science.gov (United States)

Rieke, George; Bohlin, Ralph; Boyajian, Tabetha; Carey, Sean; Casagrande, Luca; Deustua, Susana; Gordon, Karl; Kraemer, Kathleen; Marengo, Massimo; Schlawin, Everett; Su, Kate; Sloan, Greg; Volk, Kevin

2017-10-01

We propose to exploit the unique optical stability of the Spitzer telescope, along with that of IRAC, to (1) transfer the accurate absolute calibration obtained with MSX on very bright stars directly to two reference stars within the dynamic range of the JWST imagers (and of other modern instrumentation); (2) establish a second accurate absolute calibration based on the absolutely calibrated spectrum of the sun, transferred onto the astronomical system via alpha Cen A; and (3) provide accurate infrared measurements for the 11 (of 15) highest priority stars with no such data but with accurate interferometrically measured diameters, allowing us to optimize determinations of effective temperatures using the infrared flux method and thus to extend the accurate absolute calibration spectrally. This program is integral to plans for an accurate absolute calibration of JWST and will also provide a valuable Spitzer legacy.

7 CFR 400.204 - Notification of deviation from standards.

Science.gov (United States)

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Notification of deviation from standards. 400.204... Contract-Standards for Approval § 400.204 Notification of deviation from standards. A Contractor shall advise the Corporation immediately if the Contractor deviates from the requirements of these standards...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

21 CFR 330.11 - NDA deviations from applicable monograph.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false NDA deviations from applicable monograph. 330.11... EFFECTIVE AND NOT MISBRANDED Administrative Procedures § 330.11 NDA deviations from applicable monograph. A new drug application requesting approval of an OTC drug deviating in any respect from a monograph that...

Sensitivity Analysis of Deviation Source for Fast Assembly Precision Optimization

Directory of Open Access Journals (Sweden)

Jianjun Tang

2014-01-01

Full Text Available Assembly precision optimization of complex product has a huge benefit in improving the quality of our products. Due to the impact of a variety of deviation source coupling phenomena, the goal of assembly precision optimization is difficult to be confirmed accurately. In order to achieve optimization of assembly precision accurately and rapidly, sensitivity analysis of deviation source is proposed. First, deviation source sensitivity is defined as the ratio of assembly dimension variation and deviation source dimension variation. Second, according to assembly constraint relations, assembly sequences and locating, deviation transmission paths are established by locating the joints between the adjacent parts, and establishing each part’s datum reference frame. Third, assembly multidimensional vector loops are created using deviation transmission paths, and the corresponding scalar equations of each dimension are established. Then, assembly deviation source sensitivity is calculated by using a first-order Taylor expansion and matrix transformation method. Finally, taking assembly precision optimization of wing flap rocker as an example, the effectiveness and efficiency of the deviation source sensitivity analysis method are verified.

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

Directory of Open Access Journals (Sweden)

Jinjun Tang

Full Text Available 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.

Science.gov (United States)

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).

Control of deviations and prediction of surface roughness from micro machining of THz waveguides using acoustic emission signals

Science.gov (United States)

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).

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks

DEFF Research Database (Denmark)

Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl

2018-01-01

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 conditi...... 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....

SU-F-T-566: Absolute Film Dosimetry for Stereotactic Radiosurgery and Stereotactic Body Radiotherapy Quality Assurance Using Gafchromic EBT3 Films

Energy Technology Data Exchange (ETDEWEB)

Wen, N; Lu, S; Qin, Y; Huang, Y; Zhao, B; Liu, C; Chetty, I [Henry Ford Health System, Detroit, MI (United States)

2016-06-15

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 different 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.

Comparison of setup deviations for two thermoplastic immobilization masks in glottis cancer

Energy Technology Data Exchange (ETDEWEB)

Jung, Jae Hong [Dept. of Biomedical Engineering, College of Medicine, The Catholic University, Seoul (Korea, Republic of)

2017-03-15

The purpose of this study was compare to the patient setup deviation of two different type thermoplastic immobilization masks for glottis cancer in the intensity-modulated radiation therapy (IMRT). A total of 16 glottis cancer cases were divided into two groups based on applied mask type: standard or alternative group. The mean error (M), three-dimensional setup displacement error (3D-error), systematic error (Σ), random error (σ) were calculated for each group, and also analyzed setup margin (mm). The 3D-errors were 5.2 ± 1.3 mm and 5.9 ± 0.7 mm for the standard and alternative groups, respectively; the alternative group was 13.6% higher than the standard group. The systematic errors in the roll angle and the x, y, z directions were 0.8°, 1.7 mm, 1.0 mm, and 1.5 mm in the alternative group and 0.8°, 1.1 mm, 1.8 mm, and 2.0 mm in the alternative group. The random errors in the x, y, z directions were 10.9%, 1.7%, and 23.1% lower in the alternative group than in the standard group. However, absolute rotational angle (i.e., roll) in the alternative group was 12.4% higher than in the standard group. For calculated setup margin, the alternative group in x direction was 31.8% lower than in standard group. In contrast, the y and z direction were 52.6% and 21.6% higher than in the standard group. Although using a modified thermoplastic immobilization mask could be affect patient setup deviation in terms of numerical results, various point of view for an immobilization masks has need to research in terms of clinic issue.

NGS Absolute Gravity Data

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The NGS Absolute Gravity data (78 stations) was received in July 1993. Principal gravity parameters include Gravity Value, Uncertainty, and Vertical Gradient. The...

Absolute isotopic abundances of Ti in meteorites

International Nuclear Information System (INIS)

Niederer, F.R.; Papanastassiou, D.A.; Wasserburg, G.J.

1985-01-01

The absolute isotope abundance of Ti has been determined in Ca-Al-rich inclusions from the Allende and Leoville meteorites and in samples of whole meteorites. The absolute Ti isotope abundances differ by a significant mass dependent isotope fractionation transformation from the previously reported abundances, which were normalized for fractionation using 46 Ti/ 48 Ti. Therefore, the absolute compositions define distinct nucleosynthetic components from those previously identified or reflect the existence of significant mass dependent isotope fractionation in nature. We provide a general formalism for determining the possible isotope compositions of the exotic Ti from the measured composition, for different values of isotope fractionation in nature and for different mixing ratios of the exotic and normal components. The absolute Ti and Ca isotopic compositions still support the correlation of 50 Ti and 48 Ca effects in the FUN inclusions and imply contributions from neutron-rich equilibrium or quasi-equilibrium nucleosynthesis. The present identification of endemic effects at 46 Ti, for the absolute composition, implies a shortfall of an explosive-oxygen component or reflects significant isotope fractionation. Additional nucleosynthetic components are required by 47 Ti and 49 Ti effects. Components are also defined in which 48 Ti is enhanced. Results are given and discussed. (author)

Perception of midline deviations in smile esthetics by laypersons.

Science.gov (United States)

Ferreira, Jamille Barros; Silva, Licínio Esmeraldo da; Caetano, Márcia Tereza de Oliveira; Motta, Andrea Fonseca Jardim da; Cury-Saramago, Adriana de Alcantara; Mucha, José Nelson

2016-01-01

To evaluate the esthetic perception of upper dental midline deviation by laypersons and if adjacent structures influence their judgment. An album with 12 randomly distributed frontal view photographs of the smile of a woman with the midline digitally deviated was evaluated by 95 laypersons. The frontal view smiling photograph was modified to create from 1 mm to 5 mm deviations in the upper midline to the left side. The photographs were cropped in two different manners and divided into two groups of six photographs each: group LCN included the lips, chin, and two-thirds of the nose, and group L included the lips only. The laypersons performed the rate of each smile using a visual analog scale (VAS). Wilcoxon test, Student's t-test and Mann-Whitney test were applied, adopting a 5% level of significance. Laypersons were able to perceive midline deviations starting at 1 mm. Statistically significant results (p< 0.05) were found for all multiple comparisons of the values in photographs of group LCN and for almost all comparisons in photographs of group L. Comparisons between the photographs of groups LCN and L showed statistically significant values (p< 0.05) when the deviation was 1 mm. Laypersons were able to perceive the upper dental midline deviations of 1 mm, and above when the adjacent structures of the smiles were included. Deviations of 2 mm and above when the lips only were included. The visualization of structures adjacent to the smile demonstrated influence on the perception of midline deviation.

Effect of nasal deviation on quality of life.

Science.gov (United States)

de Lima Ramos, Sueli; Hochman, Bernardo; Gomes, Heitor Carvalho; Abla, Luiz Eduardo Felipe; Veiga, Daniela Francescato; Juliano, Yara; Dini, Gal Moreira; Ferreira, Lydia Masako

2011-07-01

Nasal deviation is a common complaint in otorhinolaryngology and plastic surgery. This condition not only causes impairment of nasal function but also affects quality of life, leading to psychological distress. The subjective assessment of quality of life, as an important aspect of outcomes research, has received increasing attention in recent decades. Quality of life is measured using standardized questionnaires that have been tested for reliability, validity, and sensitivity. The aim of this study was to evaluate health-related quality of life, self-esteem, and depression in patients with nasal deviation. Sixty patients were selected for the study. Patients with nasal deviation (n = 32) were assigned to the study group, and patients without nasal deviation (n = 28) were assigned to the control group. The diagnosis of nasal deviation was made by digital photogrammetry. Quality of life was assessed using the Medical Outcomes Study 36-Item Short Form Health Survey questionnaire; the Rosenberg Self-Esteem/Federal University of São Paulo, Escola Paulista de Medicina Scale; and the 20-item Self-Report Questionnaire. There were significant differences between groups in the physical functioning and general health subscales of the Medical Outcomes Study 36-Item Short Form Health Survey (p < 0.05). Depression was detected in 11 patients (34.4 percent) in the study group and in two patients in the control group, with a significant difference between groups (p < 0.05). Nasal deviation is an aspect of rhinoplasty of which the surgeon should be aware so that proper psychological diagnosis can be made and suitable treatment can be planned because psychologically the patients with nasal deviation have significantly worse quality of life and are more prone to depression. Risk, II.(Figure is included in full-text article.).

Association between septal deviation and sinonasal papilloma.

Science.gov (United States)

Nomura, Kazuhiro; Ogawa, Takenori; Sugawara, Mitsuru; Honkura, Yohei; Oshima, Hidetoshi; Arakawa, Kazuya; Oshima, Takeshi; Katori, Yukio

2013-12-01

Sinonasal papilloma is a common benign epithelial tumor of the sinonasal tract and accounts for 0.5% to 4% of all nasal tumors. The etiology of sinonasal papilloma remains unclear, although human papilloma virus has been proposed as a major risk factor. Other etiological factors, such as anatomical variations of the nasal cavity, may be related to the pathogenesis of sinonasal papilloma, because deviated nasal septum is seen in patients with chronic rhinosinusitis. We, therefore, investigated the involvement of deviated nasal septum in the development of sinonasal papilloma. Preoperative computed tomography or magnetic resonance imaging findings of 83 patients with sinonasal papilloma were evaluated retrospectively. The side of papilloma and the direction of septal deviation showed a significant correlation. Septum deviated to the intact side in 51 of 83 patients (61.4%) and to the affected side in 18 of 83 patients (21.7%). Straight or S-shaped septum was observed in 14 of 83 patients (16.9%). Even after excluding 27 patients who underwent revision surgery and 15 patients in whom the papilloma touched the concave portion of the nasal septum, the concave side of septal deviation was associated with the development of sinonasal papilloma (p = 0.040). The high incidence of sinonasal papilloma in the concave side may reflect the consequences of the traumatic effects caused by wall shear stress of the high-velocity airflow and the increased chance of inhaling viruses and pollutants. The present study supports the causative role of human papilloma virus and toxic chemicals in the occurrence of sinonasal papilloma.

Hearing protector performance and standard deviation.

Science.gov (United States)

Williams, W; Dillon, H

2005-01-01

The attenuation performance of a hearing protector is used to estimate the protected exposure level of the user. The aim is to reduce the exposed level to an acceptable value. Users should expect the attenuation to fall within a reasonable range of values around a norm. However, an analysis of extensive test data indicates that there is a negative relationship between attenuation performance and the standard deviation. This result is deduced using a variation in the method of calculating a single number rating of attenuation that is more amenable to drawing statistical inferences. As performance is typically specified as a function of the mean attenuation minus one or two standard deviations from the mean to ensure that greater than 50% of the wearer population are well protected, the implication of increasing standard deviation with decreasing attenuation found in this study means that a significant number of users are, in fact, experiencing over-protection. These users may be disinclined to use their hearing protectors because of an increased feeling of acoustic isolation. This problem is exacerbated in areas with lower noise levels.

Top Yukawa deviation in extra dimension

International Nuclear Information System (INIS)

Haba, Naoyuki; Oda, Kin-ya; Takahashi, Ryo

2009-01-01

We suggest a simple one-Higgs-doublet model living in the bulk of five-dimensional spacetime compactified on S 1 /Z 2 , in which the top Yukawa coupling can be smaller than the naive standard-model expectation, i.e. the top quark mass divided by the Higgs vacuum expectation value. If we find only single Higgs particle at the LHC and also observe the top Yukawa deviation, our scenario becomes a realistic candidate beyond the standard model. The Yukawa deviation comes from the fact that the wave function profile of the free physical Higgs field can become different from that of the vacuum expectation value, due to the presence of the brane-localized Higgs potentials. In the Brane-Localized Fermion scenario, we find sizable top Yukawa deviation, which could be checked at the LHC experiment, with a dominant Higgs production channel being the WW fusion. We also study the Bulk Fermion scenario with brane-localized Higgs potential, which resembles the Universal Extra Dimension model with a stable dark matter candidate. We show that both scenarios are consistent with the current electroweak precision measurements.

Investigating Absolute Value: A Real World Application

Science.gov (United States)

Kidd, Margaret; Pagni, David

2009-01-01

Making connections between various representations is important in mathematics. In this article, the authors discuss the numeric, algebraic, and graphical representations of sums of absolute values of linear functions. The initial explanations are accessible to all students who have experience graphing and who understand that absolute value simply…

Determining the confidence levels of sensor outputs using neural networks

Energy Technology Data Exchange (ETDEWEB)

Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

1996-12-31

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Determining the confidence levels of sensor outputs using neural networks

International Nuclear Information System (INIS)

Broten, G.S.; Wood, H.C.

1995-01-01

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

Directory of Open Access Journals (Sweden)

Gang Yang

2017-09-01

Full Text Available The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin (SM in both pure supercritical carbon dioxide (SCCO2 and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models and a back-propagation artificial neural networks (BPANN model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation (AARD% in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.

Approach To Absolute Zero

Indian Academy of Sciences (India)

more and more difficult to remove heat as one approaches absolute zero. This is the ... A new and active branch of engineering ... This temperature is called the critical temperature, Te' For sulfur dioxide the critical ..... adsorbent charcoal.

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

The Pragmatics of "Unruly" Dative Absolutes in Early Slavic

Directory of Open Access Journals (Sweden)

Daniel E. Collins

2011-08-01

Full Text Available This chapter examines some uses of the dative absolute in Old Church Slavonic and in early recensional Slavonic texts that depart from notions of how Indo-European absolute constructions should behave, either because they have subjects coreferential with the (putative main-clause subjects or because they function as if they were main clauses in their own right. Such "noncanonical" absolutes have generally been written off as mechanistic translations or as mistakes by scribes who did not understand the proper uses of the construction. In reality, the problem is not with literalistic translators or incompetent scribes but with the definition of the construction itself; it is quite possible to redefine the Early Slavic dative absolute in a way that accounts for the supposedly deviant cases. While the absolute is generally dependent semantically on an adjacent unit of discourse, it should not always be regarded as subordinated syntactically. There are good grounds for viewing some absolutes not as dependent clauses but as independent sentences whose collateral character is an issue not of syntax but of the pragmatics of discourse.

Prosthodontic management of mandibular deviation using palatal ramp appliance

Directory of Open Access Journals (Sweden)

Prince Kumar

2012-08-01

Full Text Available Segmental resection of the mandible generally results in deviation of the mandible to the defective side. This loss of continuity of the mandible destroys the balance of the lower face and leads to decreased mandibular function by deviation of the residual segment toward the surgical site. Prosthetic methods advocated to reduce or eliminate mandibular deviation include intermaxillary fixation, removable mandibular guide flange, palatal ramp, implant-supported prosthesis and palatal guidance restorations which may be useful in reducing mandibular deviation and improving masticatory performance and efficiency. These methods and restorations would be combined with a well organized mandibular exercise regimen. This clinical report describes the rehabilitation following segmental mandibulectomy using palatal ramp prosthesis.

Scaphoid and lunate movement in different ranges of carpal radioulnar deviation.

Science.gov (United States)

Tang, Jin Bo; Xu, Jing; Xie, Ren Guo

2011-01-01

We aimed to investigate scaphoid and lunate movement in radial deviation and in slight and moderate ulnar deviation ranges in vivo. We obtained computed tomography scans of the right wrists from 20° radial deviation to 40° ulnar deviation in 20° increments in 6 volunteers. The 3-dimensional bony structures of the wrist, including the distal radius and ulna, were reconstructed with customized software. The changes in position of the scaphoid and lunate along flexion-extension motion (FEM), radioulnar deviation (RUD), and supination-pronation axes in 3 parts--radial deviation and slight and moderate ulnar deviation--of the carpal RUD were calculated and analyzed. During carpal RUD, scaphoid and lunate motion along 3 axes--FEM, RUD, and supination-pronation--were the greatest in the middle third of the measured RUD (from neutral position to 20° ulnar deviation) and the smallest in radial deviation. Scaphoid motion along the FEM, RUD, and supination-pronation axes in the middle third was about half that in the entire motion range. In the middle motion range, lunate movement along the FEM and RUD axes was also the greatest. During carpal RUD, the greatest scaphoid and lunate movement occurs in the middle of the arc--slight ulnar deviation--which the wrist frequently adopts to accomplish major hand actions. At radial deviation, scaphoid and lunate motion is the smallest. Copyright © 2011 American Society for Surgery of the Hand. Published by Elsevier Inc. All rights reserved.

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

Science.gov (United States)

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.

38 CFR 36.4304 - Deviations; changes of identity.

Science.gov (United States)

2010-07-01

... identity. 36.4304 Section 36.4304 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS... Deviations; changes of identity. A deviation of more than 5 percent between the estimates upon which a... change in the identity of the property upon which the original appraisal was based, will invalidate the...

Moderate deviations principles for the kernel estimator of ...

African Journals Online (AJOL)

Abstract. The aim of this paper is to provide pointwise and uniform moderate deviations principles for the kernel estimator of a nonrandom regression function. Moreover, we give an application of these moderate deviations principles to the construction of condence regions for the regression function. Resume. L'objectif de ...

48 CFR 1352.219-71 - Notification to delay performance (Deviation).

Science.gov (United States)

2010-10-01

... performance (Deviation). 1352.219-71 Section 1352.219-71 Federal Acquisition Regulations System DEPARTMENT OF....219-71 Notification to delay performance (Deviation). As prescribed in 48 CFR 1319.811-3(b), insert the following clause: Notification To Delay Performance (Deviation) (APR 2010) The contractor shall...

Comparing various artificial neural network types for water temperature prediction in rivers

Science.gov (United States)

Piotrowski, Adam P.; Napiorkowski, Maciej J.; Napiorkowski, Jaroslaw J.; Osuch, Marzena

2015-10-01

A number of methods have been proposed for the prediction of streamwater temperature based on various meteorological and hydrological variables. The present study shows a comparison of few types of data-driven neural networks (multi-layer perceptron, product-units, adaptive-network-based fuzzy inference systems and wavelet neural networks) and nearest neighbour approach for short time streamwater temperature predictions in two natural catchments (mountainous and lowland) located in temperate climate zone, with snowy winters and hot summers. To allow wide applicability of such models, autoregressive inputs are not used and only easily available measurements are considered. Each neural network type is calibrated independently 100 times and the mean, median and standard deviation of the results are used for the comparison. Finally, the ensemble aggregation approach is tested. The results show that simple and popular multi-layer perceptron neural networks are in most cases not outperformed by more complex and advanced models. The choice of neural network is dependent on the way the models are compared. This may be a warning for anyone who wish to promote own models, that their superiority should be verified in different ways. The best results are obtained when mean, maximum and minimum daily air temperatures from the previous days are used as inputs, together with the current runoff and declination of the Sun from two recent days. The ensemble aggregation approach allows reducing the mean square error up to several percent, depending on the case, and noticeably diminishes differences in modelling performance obtained by various neural network types.

Absolutyzm i pluralizm (ABSOLUTISM AND PLURALISM

Directory of Open Access Journals (Sweden)

Renata Ziemińska

2005-06-01

Full Text Available Alethic absolutism is a thesis that propositions can not be more or less true, that they are true or false for ever (if true at all and that their truth is independent on any circumstances of their assertion. In negative version, easier to defend, alethic absolutism claims the very same proposition can not be both true and false relative to circumstances of its assertion. Simple alethic pluralism is a thesis that we have many concepts of truth. It is a very good way to dissolve the controversy between alethic relativism and absolutism. Many philosophical concepts of truth are the best reason for such pluralism. If concept is meaning of a name, we have many concepts of truth because the name 'truth' was understood in many ways. The variety of meanings however can be superficial. Under it we can find one idea of truth expressed in correspondence truism or schema (T. The content of the truism is too poor to be content of anyone concept of truth, so it usually is connected with some picture of the world (ontology and we have so many concepts of truth as many pictures of the world. The authoress proposes the hierarchical pluralism with privileged classic (or correspondence in weak sense concept of truth as absolute property.Other author's publications:

Mammographic Image Analysis of Breast Using Neural Network

Directory of Open Access Journals (Sweden)

Lesa MAMBWE

2015-07-01

Full Text Available This paper discusses the various stages of detecting tumours of the breast mammogram images. A Neural Network algorithm is applied for obtaining the complete classification of the tumour into normal or abnormal. The most important procedure or technique for obtaining the classification is the feature extraction, by extracting a few of discriminative features, first-order statistical intensities and gradients. The Image Pre-processing technique is essential prior to Image Segmentation in order to obtain accurate segmentation. Thus mass detection can be carried out. The processes involved in achieving the three techniques mentioned above include global equalization transformation, denoising, binarization, breast orientation determination and the pectoral muscle suppression. The presented feature difference matrices could be created by five features extracted from a suspicious region of interest (ROI. Grey Level Co-occurrence Matrix (GLCM aids the obtaining of statistical features such as correlation, energy, entropy and homogeneity. The other statistical to features to obtain are area, moment, variance, entropy, standard deviation and moment. The Neural network technique yields results of abnormal mammograms.

7 CFR 400.174 - Notification of deviation from financial standards.

Science.gov (United States)

2010-01-01

... 7 Agriculture 6 2010-01-01 2010-01-01 false Notification of deviation from financial standards... Agreement-Standards for Approval; Regulations for the 1997 and Subsequent Reinsurance Years § 400.174 Notification of deviation from financial standards. An insurer must immediately advise FCIC if it deviates from...

Absolute calibration in vivo measurement systems

International Nuclear Information System (INIS)

Kruchten, D.A.; Hickman, D.P.

1991-02-01

Lawrence Livermore National Laboratory (LLNL) is currently investigating a new method for obtaining absolute calibration factors for radiation measurement systems used to measure internally deposited radionuclides in vivo. Absolute calibration of in vivo measurement systems will eliminate the need to generate a series of human surrogate structures (i.e., phantoms) for calibrating in vivo measurement systems. The absolute calibration of in vivo measurement systems utilizes magnetic resonance imaging (MRI) to define physiological structure, size, and composition. The MRI image provides a digitized representation of the physiological structure, which allows for any mathematical distribution of radionuclides within the body. Using Monte Carlo transport codes, the emission spectrum from the body is predicted. The in vivo measurement equipment is calibrated using the Monte Carlo code and adjusting for the intrinsic properties of the detection system. The calibration factors are verified using measurements of existing phantoms and previously obtained measurements of human volunteers. 8 refs

Heterodyne Angle Deviation Interferometry in Vibration and Bubble Measurements

OpenAIRE

Ming-Hung Chiu; Jia-Ze Shen; Jian-Ming Huang

2016-01-01

We proposed heterodyne angle deviation interferometry (HADI) for angle deviation measurements. The phase shift of an angular sensor (which can be a metal film or a surface plasmon resonance (SPR) prism) is proportional to the deviation angle of the test beam. The method has been demonstrated in bubble and speaker’s vibration measurements in this paper. In the speaker’s vibration measurement, the voltage from the phase channel of a lock-in amplifier includes the vibration level and frequency. ...

Incorrect Weighting of Absolute Performance in Self-Assessment

Science.gov (United States)

Jeffrey, Scott A.; Cozzarin, Brian

Students spend much of their life in an attempt to assess their aptitude for numerous tasks. For example, they expend a great deal of effort to determine their academic standing given a distribution of grades. This research finds that students use their absolute performance, or percentage correct as a yardstick for their self-assessment, even when relative standing is much more informative. An experiment shows that this reliance on absolute performance for self-evaluation causes a misallocation of time and financial resources. Reasons for this inappropriate responsiveness to absolute performance are explored.

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

Science.gov (United States)

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.

An absolute dose determination of helical tomotherapy accelerator, TomoTherapy High-Art II

International Nuclear Information System (INIS)

Bailat, Claude J.; Buchillier, Thierry; Pachoud, Marc; Moeckli, Raphaeel; Bochud, Francois O.

2009-01-01

therefore measured the dose using a Farmer-type instrument (model NE 2571) as well. Results: We found the tomotherapy TPR 20,10 value to be around 0.629, which is close to a 4 MV conventional linear accelerator value. During static irradiation, the secondary standard and the alanine dosimeters were compatible within 0.5%. The A1SL relative deviation to the secondary standard was 1.2% and the NE2571 relative deviation to the secondary standard was -1.7%. The measurement in dynamic helical mode found the different dosimeters compatible within 1.4% and the alanine dosimeters and the secondary standard were even found under 0.2%. Conclusions: We found that the different methods are all within uncertainties as well as globally coherent, and the specific limitations of the various dosimeters are discussed in order to help the medical physicist design an independent reference system. We demonstrated that, taking into account the particular reference conditions, one can use an ionization chamber calibrated for conventional linear accelerators to assert the absolute dose delivered by a tomotherapy accelerator.

[The crooked nose: correction of dorsal and caudal septal deviations].

Science.gov (United States)

Foda, H M T

2010-09-01

The deviated nose represents a complex cosmetic and functional problem. Septal surgery plays a central role in the successful management of the externally deviated nose. This study included 800 patients seeking rhinoplasty to correct external nasal deviations; 71% of these suffered from variable degrees of nasal obstruction. Septal surgery was necessary in 736 (92%) patients, not only to improve breathing, but also to achieve a straight, symmetric external nose. A graduated surgical approach was adopted to allow correction of the dorsal and caudal deviations of the nasal septum without weakening its structural support to the nasal dorsum or nasal tip. The approach depended on full mobilization of deviated cartilage, followed by straightening of the cartilage and its fixation in the corrected position by using bony splinting grafts through an external rhinoplasty approach.

Dosimetric verification and evaluation of segmental multileaf collimator (SMLC)-IMRT for quality assurance. The second report. Absolute dose

International Nuclear Information System (INIS)

Tateoka, Kunihiko; Hareyama, Masato; Oouchi, Atsushi; Nakata, Kensei; Nagase, Daiki; Saikawa, Tsunehiko; Shimizume, Kazunari; Sugimoto, Harumi; Waka, Masaaki

2003-01-01

Intensity-modulated radiation therapy (IMRT) was developed to irradiate the target are more conformally, sparing organs at risk (OARs). Since the beams are sequentially delivered by many, small, irregular, and off-center fields in IMRT, dosimetric quality assurance (QA) is an extremely important issue. QA is performed by verifying both the dose distribution and doses at arbitrary points. In this work, we describe the verification of doses at arbitrary points in our hospital for Segmental multileaf collimator (SMLC)-IMRT. In general, verification of the absolute doses for IMRT is performed by comparison between the calculated doses using Radiation Treatment Planning Systems (RTP) and the measured doses using an ionization chamber with a small volume at arbitrary points in relatively flat regions of the dose gradients. However, no clear definitions of the dose gradients and the flat regions have yet been reported. We carried out verification by comparison of the measured doses with the average dose and the central point dose in a virtual Farmer type ionization chamber (V-F) and a virtual PinPoint ionization chamber (V-P) equal to the Farmer-type ionization chamber volume and PinPoint ionization chamber volumes using the RTP. Furthermore, we defined the dose gradients as the deviation of the maximum dose from the minimum dose in the virtual ionization chamber volume. In IMRT, the dose gradients may be as high as 80% or more in the virtual ionization chamber volume. Therefore, it is thought that the effective center of the ionization chamber varies by segment for IMRT fields (i.e., the variation of the ionization chamber replacement effect). Additionally, in regions with a higher dose gradient, uncertainty in the measured doses is influenced by the variations in the ionization chamber replacement effect and the ionization chamber positioning error. We more objectively examined the verification method for the absolute dose in IMRT using the virtual ionization chamber

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

Science.gov (United States)

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

Absolute instrumental neutron activation analysis at Lawrence Livermore Laboratory

International Nuclear Information System (INIS)

Heft, R.E.

1977-01-01

The Environmental Science Division at Lawrence Livermore Laboratory has in use a system of absolute Instrumental Neutron Activation Analysis (INAA). Basically, absolute INAA is dependent upon the absolute measurement of the disintegration rates of the nuclides produced by neutron capture. From such disintegration rate data, the amount of the target element present in the irradiated sample is calculated by dividing the observed disintegration rate for each nuclide by the expected value for the disintegration rate per microgram of the target element that produced the nuclide. In absolute INAA, the expected value for disintegration rate per microgram is calculated from nuclear parameters and from measured values of both thermal and epithermal neutron fluxes which were present during irradiation. Absolute INAA does not depend on the concurrent irradiation of elemental standards but does depend on the values for thermal and epithermal neutron capture cross-sections for the target nuclides. A description of the analytical method is presented

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

Science.gov (United States)

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.

Absolute Navigation Information Estimation for Micro Planetary Rovers

Directory of Open Access Journals (Sweden)

Muhammad Ilyas

2016-03-01

Full Text Available This paper provides algorithms to estimate absolute navigation information, e.g., absolute attitude and position, by using low power, weight and volume Microelectromechanical Systems-type (MEMS sensors that are suitable for micro planetary rovers. Planetary rovers appear to be easily navigable robots due to their extreme slow speed and rotation but, unfortunately, the sensor suites available for terrestrial robots are not always available for planetary rover navigation. This makes them difficult to navigate in a completely unexplored, harsh and complex environment. Whereas the relative attitude and position can be tracked in a similar way as for ground robots, absolute navigation information, unlike in terrestrial applications, is difficult to obtain for a remote celestial body, such as Mars or the Moon. In this paper, an algorithm called the EASI algorithm (Estimation of Attitude using Sun sensor and Inclinometer is presented to estimate the absolute attitude using a MEMS-type sun sensor and inclinometer, only. Moreover, the output of the EASI algorithm is fused with MEMS gyros to produce more accurate and reliable attitude estimates. An absolute position estimation algorithm has also been presented based on these on-board sensors. Experimental results demonstrate the viability of the proposed algorithms and the sensor suite for low-cost and low-weight micro planetary rovers.

A digital, constant-frequency pulsed phase-locked-loop instrument for real-time, absolute ultrasonic phase measurements

Science.gov (United States)

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.

Complexity analysis based on generalized deviation for financial markets

Science.gov (United States)

Li, Chao; Shang, Pengjian

2018-03-01

In this paper, a new modified method is proposed as a measure to investigate the correlation between past price and future volatility for financial time series, known as the complexity analysis based on generalized deviation. In comparison with the former retarded volatility model, the new approach is both simple and computationally efficient. The method based on the generalized deviation function presents us an exhaustive way showing the quantization of the financial market rules. Robustness of this method is verified by numerical experiments with both artificial and financial time series. Results show that the generalized deviation complexity analysis method not only identifies the volatility of financial time series, but provides a comprehensive way distinguishing the different characteristics between stock indices and individual stocks. Exponential functions can be used to successfully fit the volatility curves and quantify the changes of complexity for stock market data. Then we study the influence for negative domain of deviation coefficient and differences during the volatile periods and calm periods. after the data analysis of the experimental model, we found that the generalized deviation model has definite advantages in exploring the relationship between the historical returns and future volatility.

Absolute-magnitude distributions of supernovae

Energy Technology Data Exchange (ETDEWEB)

Richardson, Dean; Wright, John [Department of Physics, Xavier University of Louisiana, New Orleans, LA 70125 (United States); Jenkins III, Robert L. [Applied Physics Department, Richard Stockton College, Galloway, NJ 08205 (United States); Maddox, Larry, E-mail: drichar7@xula.edu [Department of Chemistry and Physics, Southeastern Louisiana University, Hammond, LA 70402 (United States)

2014-05-01

The absolute-magnitude distributions of seven supernova (SN) types are presented. The data used here were primarily taken from the Asiago Supernova Catalogue, but were supplemented with additional data. We accounted for both foreground and host-galaxy extinction. A bootstrap method is used to correct the samples for Malmquist bias. Separately, we generate volume-limited samples, restricted to events within 100 Mpc. We find that the superluminous events (M{sub B} < –21) make up only about 0.1% of all SNe in the bias-corrected sample. The subluminous events (M{sub B} > –15) make up about 3%. The normal Ia distribution was the brightest with a mean absolute blue magnitude of –19.25. The IIP distribution was the dimmest at –16.75.

Calibration with Absolute Shrinkage

DEFF Research Database (Denmark)

Øjelund, Henrik; Madsen, Henrik; Thyregod, Poul

2001-01-01

In this paper, penalized regression using the L-1 norm on the estimated parameters is proposed for chemometric je calibration. The algorithm is of the lasso type, introduced by Tibshirani in 1996 as a linear regression method with bound on the absolute length of the parameters, but a modification...

Extended neural network-based scheme for real-time force tracking with magnetorheological dampers

DEFF Research Database (Denmark)

Weber, Felix; Bhowmik, Subrata; Høgsberg, Jan Becker

2014-01-01

This paper validates numerically and experimentally a new neural network-based real-time force tracking scheme for magnetorheological (MR) dampers on a five-storey shear frame with MR damper. The inverse model is trained with absolute values of measured velocity and force because the targeted...... the pre-yield to the post-yield region. A control-oriented approach is presented to compensate for these drawbacks. The resulting control force tracking scheme is validated for the emulation of viscous damping, clipped viscous damping with negative stiffness, and friction damping with negative stiffness...

Deviation equation in spaces with affine connection. Pts. 3 and 4

International Nuclear Information System (INIS)

Iliev, B.Z.

1987-01-01

The concept of a parallel transport is used to define a class of displacement vectors in spaces with affine connection. The nonlocal deviation equation in such spaces is introduced using a definition of the deviation vector based on the displacement vector. It turns out to be a special of the generalized deviation equation, but having an appropriate physical interpretation. The equation of geodesic deviation is presented as an example

Influence of asymmetrical drawing radius deviation in micro deep drawing

Science.gov (United States)

Heinrich, L.; Kobayashi, H.; Shimizu, T.; Yang, M.; Vollertsen, F.

2017-09-01

Nowadays, an increasing demand for small metal parts in electronic and automotive industries can be observed. Deep drawing is a well-suited technology for the production of such parts due to its excellent qualities for mass production. However, the downscaling of the forming process leads to new challenges in tooling and process design, such as high relative deviation of tool geometry or blank displacement compared to the macro scale. FEM simulation has been a widely-used tool to investigate the influence of symmetrical process deviations as for instance a global variance of the drawing radius. This study shows a different approach that allows to determine the impact of asymmetrical process deviations on micro deep drawing. In this particular case the impact of an asymmetrical drawing radius deviation and blank displacement on cup geometry deviation was investigated for different drawing ratios by experiments and FEM simulation. It was found that both variations result in an increasing cup height deviation. Nevertheless, with increasing drawing ratio a constant drawing radius deviation has an increasing impact, while blank displacement results in a decreasing offset of the cups geometry. This is explained by different mechanisms that result in an uneven cup geometry. While blank displacement leads to material surplus on one side of the cup, an unsymmetrical radius deviation on the other hand generates uneven stretching of the cups wall. This is intensified for higher drawing ratios. It can be concluded that the effect of uneven radius geometry proves to be of major importance for the production of accurately shaped micro cups and cannot be compensated by intentional blank displacement.

Efficacy of intrahepatic absolute alcohol in unrespectable hepatocellular carcinoma

International Nuclear Information System (INIS)

Farooqi, J.I.; Hameed, K.; Khan, I.U.; Shah, S.

2001-01-01

To determine efficacy of intrahepatic absolute alcohol injection in researchable hepatocellular carcinoma. A randomized, controlled, experimental and interventional clinical trial. Gastroenterology Department, PGMI, Hayatabad Medical Complex, Peshawar during the period from June, 1998 to June, 2000. Thirty patients were treated by percutaneous, intrahepatic absolute alcohol injection sin repeated sessions, 33 patients were not given or treated with alcohol to serve as control. Both the groups were comparable for age, sex and other baseline characteristics. Absolute alcohol therapy significantly improved quality of life of patients, reduced the tumor size and mortality as well as showed significantly better results regarding survival (P< 0.05) than the patients of control group. We conclude that absolute alcohol is a beneficial and safe palliative treatment measure in advanced hepatocellular carcinoma (HCC). (author)

Frequency comb calibrated frequency-sweeping interferometry for absolute group refractive index measurement of air.

Science.gov (United States)

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.

Planck absolute entropy of a rotating BTZ black hole

Science.gov (United States)

Riaz, S. M. Jawwad

2018-04-01

In this paper, the Planck absolute entropy and the Bekenstein-Smarr formula of the rotating Banados-Teitelboim-Zanelli (BTZ) black hole are presented via a complex thermodynamical system contributed by its inner and outer horizons. The redefined entropy approaches zero as the temperature of the rotating BTZ black hole tends to absolute zero, satisfying the Nernst formulation of a black hole. Hence, it can be regarded as the Planck absolute entropy of the rotating BTZ black hole.

Absolute nuclear material assay using count distribution (LAMBDA) space

Science.gov (United States)

Prasad, Manoj K [Pleasanton, CA; Snyderman, Neal J [Berkeley, CA; Rowland, Mark S [Alamo, CA

2012-06-05

A method of absolute nuclear material assay of an unknown source comprising counting neutrons from the unknown source and providing an absolute nuclear material assay utilizing a model to optimally compare to the measured count distributions. In one embodiment, the step of providing an absolute nuclear material assay comprises utilizing a random sampling of analytically computed fission chain distributions to generate a continuous time-evolving sequence of event-counts by spreading the fission chain distribution in time.

Limiting values of large deviation probabilities of quadratic statistics

NARCIS (Netherlands)

Jeurnink, Gerardus A.M.; Kallenberg, W.C.M.

1990-01-01

Application of exact Bahadur efficiencies in testing theory or exact inaccuracy rates in estimation theory needs evaluation of large deviation probabilities. Because of the complexity of the expressions, frequently a local limit of the nonlocal measure is considered. Local limits of large deviation

Refraction in Terms of the Deviation of the Light.

Science.gov (United States)

Goldberg, Fred M.

1985-01-01

Discusses refraction in terms of the deviation of light. Points out that in physics courses where very little mathematics is used, it might be more suitable to describe refraction entirely in terms of the deviation, rather than by introducing Snell's law. (DH)

The neural basis of suppression and amblyopia in strabismus.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Mean-deviation analysis in the theory of choice.

Science.gov (United States)

Grechuk, Bogdan; Molyboha, Anton; Zabarankin, Michael

2012-08-01

Mean-deviation analysis, along with the existing theories of coherent risk measures and dual utility, is examined in the context of the theory of choice under uncertainty, which studies rational preference relations for random outcomes based on different sets of axioms such as transitivity, monotonicity, continuity, etc. An axiomatic foundation of the theory of coherent risk measures is obtained as a relaxation of the axioms of the dual utility theory, and a further relaxation of the axioms are shown to lead to the mean-deviation analysis. Paradoxes arising from the sets of axioms corresponding to these theories and their possible resolutions are discussed, and application of the mean-deviation analysis to optimal risk sharing and portfolio selection in the context of rational choice is considered. © 2012 Society for Risk Analysis.

Illusory shadow person causing paradoxical gaze deviations during temporal lobe seizures

NARCIS (Netherlands)

Zijlmans, M.; van Eijsden, P.; Ferrier, C. H.; Kho, K. H.; van Rijen, P. C.; Leijten, F. S. S.

Generally, activation of the frontal eye field during seizures can cause versive (forced) gaze deviation, while non-versive head deviation is hypothesised to result from ictal neglect after inactivation of the ipsilateral temporoparietal area. Almost all non-versive head deviations occurring during

Predicting absolute risk of type 2 diabetes using age and waist circumference values in an aboriginal Australian community.

Directory of Open Access Journals (Sweden)

Odewumi Adegbija

Full Text Available To predict in an Australian Aboriginal community, the 10-year absolute risk of type 2 diabetes associated with waist circumference and age on baseline examination.A sample of 803 diabetes-free adults (82.3% of the age-eligible population from baseline data of participants collected from 1992 to 1998 were followed-up for up to 20 years till 2012. The Cox-proportional hazard model was used to estimate the effects of waist circumference and other risk factors, including age, smoking and alcohol consumption status, of males and females on prediction of type 2 diabetes, identified through subsequent hospitalisation data during the follow-up period. The Weibull regression model was used to calculate the absolute risk estimates of type 2 diabetes with waist circumference and age as predictors.Of 803 participants, 110 were recorded as having developed type 2 diabetes, in subsequent hospitalizations over a follow-up of 12633.4 person-years. Waist circumference was strongly associated with subsequent diagnosis of type 2 diabetes with P<0.0001 for both genders and remained statistically significant after adjusting for confounding factors. Hazard ratios of type 2 diabetes associated with 1 standard deviation increase in waist circumference were 1.7 (95%CI 1.3 to 2.2 for males and 2.1 (95%CI 1.7 to 2.6 for females. At 45 years of age with baseline waist circumference of 100 cm, a male had an absolute diabetic risk of 10.9%, while a female had a 14.3% risk of the disease.The constructed model predicts the 10-year absolute diabetes risk in an Aboriginal Australian community. It is simple and easily understood and will help identify individuals at risk of diabetes in relation to waist circumference values. Our findings on the relationship between waist circumference and diabetes on gender will be useful for clinical consultation, public health education and establishing WC cut-off points for Aboriginal Australians.

Deviations from Newton's law in supersymmetric large extra dimensions

International Nuclear Information System (INIS)

Callin, P.; Burgess, C.P.

2006-01-01

Deviations from Newton's inverse-squared law at the micron length scale are smoking-gun signals for models containing supersymmetric large extra dimensions (SLEDs), which have been proposed as approaches for resolving the cosmological constant problem. Just like their non-supersymmetric counterparts, SLED models predict gravity to deviate from the inverse-square law because of the advent of new dimensions at sub-millimeter scales. However SLED models differ from their non-supersymmetric counterparts in three important ways: (i) the size of the extra dimensions is fixed by the observed value of the dark energy density, making it impossible to shorten the range over which new deviations from Newton's law must be seen; (ii) supersymmetry predicts there to be more fields in the extra dimensions than just gravity, implying different types of couplings to matter and the possibility of repulsive as well as attractive interactions; and (iii) the same mechanism which is purported to keep the cosmological constant naturally small also keeps the extra-dimensional moduli effectively massless, leading to deviations from general relativity in the far infrared of the scalar-tensor form. We here explore the deviations from Newton's law which are predicted over micron distances, and show the ways in which they differ and resemble those in the non-supersymmetric case

Performance of Phonatory Deviation Diagrams in Synthesized Voice Analysis.

Science.gov (United States)

Lopes, Leonardo Wanderley; da Silva, Karoline Evangelista; da Silva Evangelista, Deyverson; Almeida, Anna Alice; Silva, Priscila Oliveira Costa; Lucero, Jorge; Behlau, Mara

2018-05-02

To analyze the performance of a phonatory deviation diagram (PDD) in discriminating the presence and severity of voice deviation and the predominant voice quality of synthesized voices. A speech-language pathologist performed the auditory-perceptual analysis of the synthesized voice (n = 871). The PDD distribution of voice signals was analyzed according to area, quadrant, shape, and density. Differences in signal distribution regarding the PDD area and quadrant were detected when differentiating the signals with and without voice deviation and with different predominant voice quality. Differences in signal distribution were found in all PDD parameters as a function of the severity of voice disorder. The PDD area and quadrant can differentiate normal voices from deviant synthesized voices. There are differences in signal distribution in PDD area and quadrant as a function of the severity of voice disorder and the predominant voice quality. However, the PDD area and quadrant do not differentiate the signals as a function of severity of voice disorder and differentiated only the breathy and rough voices from the normal and strained voices. PDD density is able to differentiate only signals with moderate and severe deviation. PDD shape shows differences between signals with different severities of voice deviation. © 2018 S. Karger AG, Basel.

CT Assessment of the axial deviation of the femoral and tibial prosthetic components in total knee arthroplasty

International Nuclear Information System (INIS)

Rimondi, E.; Molinari, M.; Moio, A.; Busacca, M.; Trentani, F.; Trentani, P.; Tigani, D.; Nigrosoli, M.

2000-01-01

CT assessment of the axial deviation of the femoral and tibial prosthetic components in total knee arthroplasty. From January to July 1999, 17 patients, 10 males and 7 females, mean age 66 years (standard deviation plus or minus 4) were examined after total knee arthroplasty. Exclusion criteriawere prosthesis loosening and severe (equal or superior to 7'' varus o valgus deviation. All patients were examined with knee radiography in the standing position completed by axial projection of patella and by CT scanning. It was used a modification of Berger technique and carried out comparative CT scans extended lower limbs and acquisitions perpendicular to the mechanical axis of the knee, from the femoral supracondylar region down to the plane crossing the distal end of the tibial prosthetic component. Reference lines were then drawn electronically on given scanning planes to reckon the axial deviation of the femoral and tibial prosthetic components. Six patients, one female and 5 males with normal rotational values of femoral and tibial prosthetic components presented no clinical symptoms. Eight patients, 4 females and 4 males, with abnormal values presented the following clinical symptoms: medial impingement, (incomplete) dislocation patella, and lateral instability. One female patient with a normal rotational value of femoral prosthetic component and an altered value of tibial prosthetic component presented medial impingement. Finally two patients, one female and one male, were absolutely asymptomatic although the rotational values of the two prosthetic components were beyond the normal range. Total knee arthroplasty is presently a standard treatment for many conditions involving this joint. There are several possible postoperative complications, namely fractures, dislocations (a)septic losening and femoropatellar instability. The latter condition is the most frequent complication among implant failures and is caused by bad orientation of the femoral and tibial

Generation of deviation parameters for amino acid singlets, doublets ...

Indian Academy of Sciences (India)

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on ...

Importance sampling large deviations in nonequilibrium steady states. I

Science.gov (United States)

Ray, Ushnish; Chan, Garnet Kin-Lic; Limmer, David T.

2018-03-01

Large deviation functions contain information on the stability and response of systems driven into nonequilibrium steady states and in such a way are similar to free energies for systems at equilibrium. As with equilibrium free energies, evaluating large deviation functions numerically for all but the simplest systems is difficult because by construction they depend on exponentially rare events. In this first paper of a series, we evaluate different trajectory-based sampling methods capable of computing large deviation functions of time integrated observables within nonequilibrium steady states. We illustrate some convergence criteria and best practices using a number of different models, including a biased Brownian walker, a driven lattice gas, and a model of self-assembly. We show how two popular methods for sampling trajectory ensembles, transition path sampling and diffusion Monte Carlo, suffer from exponentially diverging correlations in trajectory space as a function of the bias parameter when estimating large deviation functions. Improving the efficiencies of these algorithms requires introducing guiding functions for the trajectories.

Importance sampling large deviations in nonequilibrium steady states. I.

Science.gov (United States)

Ray, Ushnish; Chan, Garnet Kin-Lic; Limmer, David T

2018-03-28

Large deviation functions contain information on the stability and response of systems driven into nonequilibrium steady states and in such a way are similar to free energies for systems at equilibrium. As with equilibrium free energies, evaluating large deviation functions numerically for all but the simplest systems is difficult because by construction they depend on exponentially rare events. In this first paper of a series, we evaluate different trajectory-based sampling methods capable of computing large deviation functions of time integrated observables within nonequilibrium steady states. We illustrate some convergence criteria and best practices using a number of different models, including a biased Brownian walker, a driven lattice gas, and a model of self-assembly. We show how two popular methods for sampling trajectory ensembles, transition path sampling and diffusion Monte Carlo, suffer from exponentially diverging correlations in trajectory space as a function of the bias parameter when estimating large deviation functions. Improving the efficiencies of these algorithms requires introducing guiding functions for the trajectories.

Approach to Absolute Zero

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 10. Approach to Absolute Zero Below 10 milli-Kelvin. R Srinivasan. Series Article Volume 2 Issue 10 October 1997 pp 8-16. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/002/10/0008-0016 ...

Population-based absolute risk estimation with survey data

Science.gov (United States)

Kovalchik, Stephanie A.; Pfeiffer, Ruth M.

2013-01-01

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level. PMID:23686614

Artificial neural nets application in the cotton yarn industry

Directory of Open Access Journals (Sweden)

Gilberto Clóvis Antoneli

2016-06-01

Full Text Available The competitiveness in the yarn production sector has led companies to search for solutions to attain quality yarn at a low cost. Today, the difference between them, and thus the sector, is in the raw material, meaning processed cotton and its characteristics. There are many types of cotton with different characteristics due to its production region, harvest, storage and transportation. Yarn industries work with cotton mixtures, which makes it difficult to determine the quality of the yarn produced from the characteristics of the processed fibers. This study uses data from a conventional spinning, from a raw material made of 100% cotton, and presents a solution with artificial neural nets that determine the thread quality information, using the fibers’ characteristics values and settings of some process adjustments. In this solution a neural net of the type MultiLayer Perceptron with 11 entry neurons (8 characteristics of the fiber and 3 process adjustments, 7 output neurons (yarn quality and two types of training, Back propagation and Conjugate gradient descent. The selection and organization of the production data of the yarn industry of the cocamar® indústria de fios company are described, to apply the artificial neural nets developed. In the application of neural nets to determine yarn quality, one concludes that, although the ideal precision of absolute values is lacking, the presented solution represents an excellent tool to define yarn quality variations when modifying the raw material composition. The developed system enables a simulation to define the raw material percentage mixture to be processed in the plant using the information from the stocked cotton packs, thus obtaining a mixture that maintains the stability of the entire productive process.

Absolute marine gravimetry with matter-wave interferometry.

Science.gov (United States)

Bidel, Y; Zahzam, N; Blanchard, C; Bonnin, A; Cadoret, M; Bresson, A; Rouxel, D; Lequentrec-Lalancette, M F

2018-02-12

Measuring gravity from an aircraft or a ship is essential in geodesy, geophysics, mineral and hydrocarbon exploration, and navigation. Today, only relative sensors are available for onboard gravimetry. This is a major drawback because of the calibration and drift estimation procedures which lead to important operational constraints. Atom interferometry is a promising technology to obtain onboard absolute gravimeter. But, despite high performances obtained in static condition, no precise measurements were reported in dynamic. Here, we present absolute gravity measurements from a ship with a sensor based on atom interferometry. Despite rough sea conditions, we obtained precision below 10 -5  m s -2 . The atom gravimeter was also compared with a commercial spring gravimeter and showed better performances. This demonstration opens the way to the next generation of inertial sensors (accelerometer, gyroscope) based on atom interferometry which should provide high-precision absolute measurements from a moving platform.

Standard Deviation for Small Samples

Science.gov (United States)

Joarder, Anwar H.; Latif, Raja M.

2006-01-01

Neater representations for variance are given for small sample sizes, especially for 3 and 4. With these representations, variance can be calculated without a calculator if sample sizes are small and observations are integers, and an upper bound for the standard deviation is immediate. Accessible proofs of lower and upper bounds are presented for…

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

Directory of Open Access Journals (Sweden)

Ying Yu

2017-01-01

Full Text Available 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.

Prediction of absolute entropy of ideal gas at 298 K of pure chemicals through GAMLR and FFNN

International Nuclear Information System (INIS)

Fazeli, Ali; Bagheri, Mehdi; Ghaniyari-Benis, Saeid; Aslebagh, Roshanak; Kamaloo, Elaheh

2011-01-01

Thermodynamical optimization for energy conversion system can be performed by decreasing entropy generation. For calculation of entropy, we need to know entropy of ideal gases at 298 K as a reference point. Entropy is a thermodynamic quantity which is not easily measured and prediction of entropy by molecular structures for new designed molecules may be a challenge. An easy and accurate equation for prediction of absolute entropy of pure ideal gas at 298 K was introduced for the first time based on the quantitative structure property relationship (QSPR) approach. Thousand seven hundred pure chemical compounds and 3224 molecular descriptors were used for finding this easy equation by genetic algorithm multi-linear regression (GAMLR) subset variable selection. Our work are based on 1700 chemicals in 81 chemical families that is the most comprehensive available data sets for absolute entropy of ideal gases. The final model is linear and has three molecular descriptors with the squared correlation coefficient of 0.9885 (R 2 = 0.9885). Also, feed forward neural network (FFNN) was used for considering non linearity effect of the model. It has the squared correlation coefficient of 0.9909 (R 2 = 0.9909). The model passes all validity check methods. The novel proposed model has the predictability for new designed molecules by having the molecular structures of them.

Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on the Improved Backpropagation Neural Network with Momentum

Directory of Open Access Journals (Sweden)

Yi-jun Liu

2015-12-01

Full Text Available Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

Relative and absolute risk in epidemiology and health physics

International Nuclear Information System (INIS)

Goldsmith, R.; Peterson, H.T. Jr.

1983-01-01

The health risk from ionizing radiation commonly is expressed in two forms: (1) the relative risk, which is the percentage increase in natural disease rate and (2) the absolute or attributable risk which represents the difference between the natural rate and the rate associated with the agent in question. Relative risk estimates for ionizing radiation generally are higher than those expressed as the absolute risk. This raises the question of which risk estimator is the most appropriate under different conditions. The absolute risk has generally been used for radiation risk assessment, although mathematical combinations such as the arithmetic or geometric mean of both the absolute and relative risks, have also been used. Combinations of the two risk estimators are not valid because the absolute and relative risk are not independent variables. Both human epidemiologic studies and animal experimental data can be found to illustrate the functional relationship between the natural cancer risk and the risk associated with radiation. This implies that the radiation risk estimate derived from one population may not be appropriate for predictions in another population, unless it is adjusted for the difference in the natural disease incidence between the two populations

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

Directory of Open Access Journals (Sweden)

Andrew H. Micallef

2017-05-01

Full Text Available 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.

Redetermination and absolute configuration of atalaphylline

Directory of Open Access Journals (Sweden)

Hoong-Kun Fun

2010-02-01

Full Text Available The title acridone alkaloid [systematic name: 1,3,5-trihydroxy-2,4-bis(3-methylbut-2-enylacridin-9(10H-one], C23H25NO4, has previously been reported as crystallizing in the chiral orthorhombic space group P212121 [Chantrapromma et al. (2010. Acta Cryst. E66, o81–o82] but the absolute configuration could not be determined from data collected with Mo radiation. The absolute configuration has now been determined by refinement of the Flack parameter with data collected using Cu radiation. All features of the molecule and its crystal packing are similar to those previously described.

The deviation matrix of a continuous-time Markov chain

NARCIS (Netherlands)

Coolen-Schrijner, P.; van Doorn, E.A.

2001-01-01

The deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix $P(.)$ and ergodic matrix $\\Pi$ is the matrix $D \\equiv \\int_0^{\\infty} (P(t)-\\Pi)dt$. We give conditions for $D$ to exist and discuss properties and a representation of $D$. The deviation matrix of a

The deviation matrix of a continuous-time Markov chain

NARCIS (Netherlands)

Coolen-Schrijner, Pauline; van Doorn, Erik A.

2002-01-01

he deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix $P(.)$ and ergodic matrix $\\Pi$ is the matrix $D \\equiv \\int_0^{\\infty} (P(t)-\\Pi)dt$. We give conditions for $D$ to exist and discuss properties and a representation of $D$. The deviation matrix of a

Absolute calibration of sniffer probes on Wendelstein 7-X

International Nuclear Information System (INIS)

Moseev, D.; Laqua, H. P.; Marsen, S.; Stange, T.; Braune, H.; Erckmann, V.; Gellert, F.; Oosterbeek, J. W.

2016-01-01

Here we report the first measurements of the power levels of stray radiation in the vacuum vessel of Wendelstein 7-X using absolutely calibrated sniffer probes. The absolute calibration is achieved by using calibrated sources of stray radiation and the implicit measurement of the quality factor of the Wendelstein 7-X empty vacuum vessel. Normalized absolute calibration coefficients agree with the cross-calibration coefficients that are obtained by the direct measurements, indicating that the measured absolute calibration coefficients and stray radiation levels in the vessel are valid. Close to the launcher, the stray radiation in the empty vessel reaches power levels up to 340 kW/m 2 per MW injected beam power. Furthest away from the launcher, i.e., half a toroidal turn, still 90 kW/m 2 per MW injected beam power is measured.

Absolute calibration of sniffer probes on Wendelstein 7-X

Science.gov (United States)

Moseev, D.; Laqua, H. P.; Marsen, S.; Stange, T.; Braune, H.; Erckmann, V.; Gellert, F.; Oosterbeek, J. W.

2016-08-01

Here we report the first measurements of the power levels of stray radiation in the vacuum vessel of Wendelstein 7-X using absolutely calibrated sniffer probes. The absolute calibration is achieved by using calibrated sources of stray radiation and the implicit measurement of the quality factor of the Wendelstein 7-X empty vacuum vessel. Normalized absolute calibration coefficients agree with the cross-calibration coefficients that are obtained by the direct measurements, indicating that the measured absolute calibration coefficients and stray radiation levels in the vessel are valid. Close to the launcher, the stray radiation in the empty vessel reaches power levels up to 340 kW/m2 per MW injected beam power. Furthest away from the launcher, i.e., half a toroidal turn, still 90 kW/m2 per MW injected beam power is measured.

Absolute calibration of sniffer probes on Wendelstein 7-X

Energy Technology Data Exchange (ETDEWEB)

Moseev, D., E-mail: dmitry.moseev@ipp.mpg.de; Laqua, H. P.; Marsen, S.; Stange, T.; Braune, H.; Erckmann, V. [Max-Planck-Institut für Plasmaphysik, Greifswald (Germany); Gellert, F. [Max-Planck-Institut für Plasmaphysik, Greifswald (Germany); Ernst-Moritz-Arndt-Universität Greifswald, Greifswald (Germany); Oosterbeek, J. W. [Eindhoven University of Technology, Eindhoven (Netherlands)

2016-08-15

Here we report the first measurements of the power levels of stray radiation in the vacuum vessel of Wendelstein 7-X using absolutely calibrated sniffer probes. The absolute calibration is achieved by using calibrated sources of stray radiation and the implicit measurement of the quality factor of the Wendelstein 7-X empty vacuum vessel. Normalized absolute calibration coefficients agree with the cross-calibration coefficients that are obtained by the direct measurements, indicating that the measured absolute calibration coefficients and stray radiation levels in the vessel are valid. Close to the launcher, the stray radiation in the empty vessel reaches power levels up to 340 kW/m{sup 2} per MW injected beam power. Furthest away from the launcher, i.e., half a toroidal turn, still 90 kW/m{sup 2} per MW injected beam power is measured.

Absolute magnitudes by statistical parallaxes

International Nuclear Information System (INIS)

Heck, A.

1978-01-01

The author describes an algorithm for stellar luminosity calibrations (based on the principle of maximum likelihood) which allows the calibration of relations of the type: Msub(i)=sup(N)sub(j=1)Σqsub(j)Csub(ij), i=1,...,n, where n is the size of the sample at hand, Msub(i) are the individual absolute magnitudes, Csub(ij) are observational quantities (j=1,...,N), and qsub(j) are the coefficients to be determined. If one puts N=1 and Csub(iN)=1, one has q 1 =M(mean), the mean absolute magnitude of the sample. As additional output, the algorithm provides one also with the dispersion in magnitude of the sample sigmasub(M), the mean solar motion (U,V,W) and the corresponding velocity ellipsoid (sigmasub(u), sigmasub(v), sigmasub(w). The use of this algorithm is illustrated. (Auth.)

45 CFR 63.19 - Budget revisions and minor deviations.

Science.gov (United States)

2010-10-01

... 45 Public Welfare 1 2010-10-01 2010-10-01 false Budget revisions and minor deviations. 63.19 Section 63.19 Public Welfare DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL ADMINISTRATION GRANT PROGRAMS... Budget revisions and minor deviations. Pursuant to § 74.102(d) of this title, paragraphs (b)(3) and (b)(4...

The role of septal surgery in management of the deviated nose.

Science.gov (United States)

Foda, Hossam M T

2005-02-01

The deviated nose represents a complex cosmetic and functional problem. Septal surgery plays a central role in the successful management of the externally deviated nose. This study included 260 patients seeking rhinoplasty to correct external nasal deviations; 75 percent of them had various degrees of nasal obstruction. Septal surgery was necessary in 232 patients (89 percent), not only to improve breathing but also to achieve a straight, symmetrical, external nose as well. A graduated surgical approach was adopted to allow correction of the dorsal and caudal deviations of the nasal septum without weakening its structural support to the dorsum or nasal tip. The approach depended on full mobilization of deviated cartilage, followed by straightening of the cartilage and its fixation in the corrected position by using bony splinting grafts through an external rhinoplasty approach.

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

Science.gov (United States)

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 onset deviations as musical piece signatures.

Science.gov (United States)

Serrà, Joan; Özaslan, Tan Hakan; Arcos, Josep Lluis

2013-01-01

A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.

Note onset deviations as musical piece signatures.

Directory of Open Access Journals (Sweden)

Joan Serrà

Full Text Available A competent interpretation of a musical composition presents several non-explicit departures from the written score. Timing variations are perhaps the most important ones: they are fundamental for expressive performance and a key ingredient for conferring a human-like quality to machine-based music renditions. However, the nature of such variations is still an open research question, with diverse theories that indicate a multi-dimensional phenomenon. In the present study, we consider event-shift timing variations and show that sequences of note onset deviations are robust and reliable predictors of the musical piece being played, irrespective of the performer. In fact, our results suggest that only a few consecutive onset deviations are already enough to identify a musical composition with statistically significant accuracy. We consider a mid-size collection of commercial recordings of classical guitar pieces and follow a quantitative approach based on the combination of standard statistical tools and machine learning techniques with the semi-automatic estimation of onset deviations. Besides the reported results, we believe that the considered materials and the methodology followed widen the testing ground for studying musical timing and could open new perspectives in related research fields.

Strongly nonlinear theory of rapid solidification near absolute stability

Science.gov (United States)

Kowal, Katarzyna N.; Altieri, Anthony L.; Davis, Stephen H.

2017-10-01

We investigate the nonlinear evolution of the morphological deformation of a solid-liquid interface of a binary melt under rapid solidification conditions near two absolute stability limits. The first of these involves the complete stabilization of the system to cellular instabilities as a result of large enough surface energy. We derive nonlinear evolution equations in several limits in this scenario and investigate the effect of interfacial disequilibrium on the nonlinear deformations that arise. In contrast to the morphological stability problem in equilibrium, in which only cellular instabilities appear and only one absolute stability boundary exists, in disequilibrium the system is prone to oscillatory instabilities and a second absolute stability boundary involving attachment kinetics arises. Large enough attachment kinetics stabilize the oscillatory instabilities. We derive a nonlinear evolution equation to describe the nonlinear development of the solid-liquid interface near this oscillatory absolute stability limit. We find that strong asymmetries develop with time. For uniform oscillations, the evolution equation for the interface reduces to the simple form f''+(βf')2+f =0 , where β is the disequilibrium parameter. Lastly, we investigate a distinguished limit near both absolute stability limits in which the system is prone to both cellular and oscillatory instabilities and derive a nonlinear evolution equation that captures the nonlinear deformations in this limit. Common to all these scenarios is the emergence of larger asymmetries in the resulting shapes of the solid-liquid interface with greater departures from equilibrium and larger morphological numbers. The disturbances additionally sharpen near the oscillatory absolute stability boundary, where the interface becomes deep-rooted. The oscillations are time-periodic only for small-enough initial amplitudes and their frequency depends on a single combination of physical parameters, including the

An Analysis of the Linguistic Deviation in Chapter X of Oliver Twist

Institute of Scientific and Technical Information of China (English)

刘聪

2013-01-01

Charles Dickens is one of the greatest critical realist writers of the Victorian Age. In language, he is often compared with William Shakespeare for his adeptness with the vernacular and large vocabulary. Charles Dickens achieved a recognizable place among English writers through the use of the stylistic features in his fictional language. Oliver Twist is the best representative of Charles Dickens’style, which makes it the most appropriate choice for the present stylistic study on Charles Dickens. No one who has ever read the dehumanizing workhouse scenes of Oliver Twist and the dark, criminal underworld life can forget them. This thesis attempts to investigate Oliver Twist through the approach of modern stylistics, particularly the theory of linguistic devia-tion. This thesis consists of an introduction, the main body and a conclusion. The introduction offers a brief summary of the com-ments on Charles Dickens and Chapter X of Oliver Twist, introduces the newly rising linguistic deviation theories, and brings about the theories on which this thesis settles. The main body explores the deviation effects produced from four aspects: lexical deviation, grammatical deviation, graphological deviation, and semantic deviation. It endeavors to show Dickens ’manipulating language and the effects achieved through this manipulation. The conclusion mainly sums up the previous analysis, and reveals the theme of the novel, positive effect of linguistic deviation and significance of deviation application.

Decreasing the amplitude deviation of Guassian filter in surface roughness measurements

Science.gov (United States)

Liu, Bo; Wang, Yu

2008-12-01

A new approach for decreasing the amplitude characteristic deviation of Guassian filter in surface roughness measurements is presented in this paper. According to Central Limit Theorem, many different Guassian approximation filters could be constructed. By using first-order Butterworth filter and moving average filter to approximate Guassian filter, their directions of amplitude deviation are opposite, and their locations of extreme value are close. So the linear combination of them could reduce the amplitude deviation greatly. The maximum amplitude deviation is only about 0.11% through paralleling them. The algorithm of this new method is simple and its efficiency is high.

Forcing absoluteness and regularity properties

NARCIS (Netherlands)

Ikegami, D.

2010-01-01

For a large natural class of forcing notions, we prove general equivalence theorems between forcing absoluteness statements, regularity properties, and transcendence properties over L and the core model K. We use our results to answer open questions from set theory of the reals.

Character Recognition Using Genetically Trained Neural Networks

Energy Technology Data Exchange (ETDEWEB)

Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

1998-10-01

Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

Minimizing Hexapod Robot Foot Deviations Using Multilayer Perceptron

Directory of Open Access Journals (Sweden)

Vytautas Valaitis

2015-12-01

Full Text Available Rough-terrain traversability is one of the most valuable characteristics of walking robots. Even despite their slower speeds and more complex control algorithms, walking robots have far wider usability than wheeled or tracked robots. However, efficient movement over irregular surfaces can only be achieved by eliminating all possible difficulties, which in many cases are caused by a high number of degrees of freedom, feet slippage, frictions and inertias between different robot parts or even badly developed inverse kinematics (IK. In this paper we address the hexapod robot-foot deviation problem. We compare the foot-positioning accuracy of unconfigured inverse kinematics and Multilayer Perceptron-based (MLP methods via theory, computer modelling and experiments on a physical robot. Using MLP-based methods, we were able to significantly decrease deviations while reaching desired positions with the hexapod's foot. Furthermore, this method is able to compensate for deviations of the robot arising from any possible reason.

Deep Neural Network Based Demand Side Short Term Load Forecasting

Directory of Open Access Journals (Sweden)

Seunghyoung Ryu

2016-12-01

Full Text Available In the smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analyses to recent machine learning approaches and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management, including individual load forecasting, is becoming critical. In this paper, we propose deep neural network (DNN-based load forecasting models and apply them to a demand side empirical load database. DNNs are trained in two different ways: a pre-training restricted Boltzmann machine and using the rectified linear unit without pre-training. DNN forecasting models are trained by individual customer’s electricity consumption data and regional meteorological elements. To verify the performance of DNNs, forecasting results are compared with a shallow neural network (SNN, a double seasonal Holt–Winters (DSHW model and the autoregressive integrated moving average (ARIMA. The mean absolute percentage error (MAPE and relative root mean square error (RRMSE are used for verification. Our results show that DNNs exhibit accurate and robust predictions compared to other forecasting models, e.g., MAPE and RRMSE are reduced by up to 17% and 22% compared to SNN and 9% and 29% compared to DSHW.

The Impact of Advanced Technologies on Treatment Deviations in Radiation Treatment Delivery

International Nuclear Information System (INIS)

Marks, Lawrence B.; Light, Kim L.; Hubbs, Jessica L.; Georgas, Debra L.; Jones, Ellen L.; Wright, Melanie C.; Willett, Christopher G.; Yin Fangfang

2007-01-01

Purpose: To assess the impact of new technologies on deviation rates in radiation therapy (RT). Methods and Materials: Treatment delivery deviations in RT were prospectively monitored during a time of technology upgrade. In January 2003, our department had three accelerators, none with 'modern' technologies (e.g., without multileaf collimators [MLC]). In 2003 to 2004, we upgraded to five new accelerators, four with MLC, and associated advanced capabilities. The deviation rates among patients treated on 'high-technology' versus 'low-technology' machines (defined as those with vs. without MLC) were compared over time using the two-tailed Fisher's exact test. Results: In 2003, there was no significant difference between the deviation rate in the 'high-technology' versus 'low-technology' groups (0.16% vs. 0.11%, p = 0.45). In 2005 to 2006, the deviation rate for the 'high-technology' groups was lower than the 'low-technology' (0.083% vs. 0.21%, p = 0.009). This difference was caused by a decline in deviations on the 'high-technology' machines over time (p = 0.053), as well as an unexpected trend toward an increase in deviations over time on the 'low-technology' machines (p = 0.15). Conclusions: Advances in RT delivery systems appear to reduce the rate of treatment deviations. Deviation rates on 'high-technology' machines with MLC decline over time, suggesting a learning curve after the introduction of new technologies. Associated with the adoption of 'high-technology' was an unexpected increase in the deviation rate with 'low-technology' approaches, which may reflect an over-reliance on tools inherent to 'high-technology' machines. With the introduction of new technologies, continued diligence is needed to ensure that staff remain proficient with 'low-technology' approaches

Absolute calibration of sniffer probes on Wendelstein 7-X

NARCIS (Netherlands)

Moseev, D.; Laqua, H.P.; Marsen, S.; Stange, T.; Braune, H.; Erckmann, V.; Gellert, F.J.; Oosterbeek, J.W.

Here we report the first measurements of the power levels of stray radiation in the vacuum vessel of Wendelstein 7-X using absolutely calibrated sniffer probes. The absolute calibration is achieved by using calibrated sources of stray radiation and the implicit measurement of the quality factor of

Absolute tense forms in Tswana | Pretorius | Journal for Language ...

African Journals Online (AJOL)

These views were compared in an attempt to put forth an applicable framework for the classification of the tenses in Tswana and to identify the absolute tenses of Tswana. Keywords: tense; simple tenses; compound tenses; absolute tenses; relative tenses; aspect; auxiliary verbs; auxiliary verbal groups; Tswana Opsomming

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

Science.gov (United States)

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

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

Science.gov (United States)

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.

Probative value of absolute and relative judgments in eyewitness identification.

Science.gov (United States)

Clark, Steven E; Erickson, Michael A; Breneman, Jesse

2011-10-01

It is well-accepted that eyewitness identification decisions based on relative judgments are less accurate than identification decisions based on absolute judgments. However, the theoretical foundation for this view has not been established. In this study relative and absolute judgments were compared through simulations of the WITNESS model (Clark, Appl Cogn Psychol 17:629-654, 2003) to address the question: Do suspect identifications based on absolute judgments have higher probative value than suspect identifications based on relative judgments? Simulations of the WITNESS model showed a consistent advantage for absolute judgments over relative judgments for suspect-matched lineups. However, simulations of same-foils lineups showed a complex interaction based on the accuracy of memory and the similarity relationships among lineup members.

Large-Deviation Results for Discriminant Statistics of Gaussian Locally Stationary Processes

Directory of Open Access Journals (Sweden)

Junichi Hirukawa

2012-01-01

Full Text Available This paper discusses the large-deviation principle of discriminant statistics for Gaussian locally stationary processes. First, large-deviation theorems for quadratic forms and the log-likelihood ratio for a Gaussian locally stationary process with a mean function are proved. Their asymptotics are described by the large deviation rate functions. Second, we consider the situations where processes are misspecified to be stationary. In these misspecified cases, we formally make the log-likelihood ratio discriminant statistics and derive the large deviation theorems of them. Since they are complicated, they are evaluated and illustrated by numerical examples. We realize the misspecification of the process to be stationary seriously affecting our discrimination.

Positioning, alignment and absolute pointing of the ANTARES neutrino telescope

International Nuclear Information System (INIS)

Fehr, F; Distefano, C

2010-01-01

A precise detector alignment and absolute pointing is crucial for point-source searches. The ANTARES neutrino telescope utilises an array of hydrophones, tiltmeters and compasses for the relative positioning of the optical sensors. The absolute calibration is accomplished by long-baseline low-frequency triangulation of the acoustic reference devices in the deep-sea with a differential GPS system at the sea surface. The absolute pointing can be independently verified by detecting the shadow of the Moon in cosmic rays.

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

Science.gov (United States)

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.

Does Absolute Synonymy exist in Owere-Igbo? | Omego | AFRREV ...

African Journals Online (AJOL)

Among Igbo linguistic researchers, determining whether absolute synonymy exists in Owere–Igbo, a dialect of the Igbo language predominantly spoken by the people of Owerri, Imo State, Nigeria, has become a thorny issue. While some linguistic scholars strive to establish that absolute synonymy exists in the lexical ...

A robust standard deviation control chart

NARCIS (Netherlands)

Schoonhoven, M.; Does, R.J.M.M.

2012-01-01

This article studies the robustness of Phase I estimators for the standard deviation control chart. A Phase I estimator should be efficient in the absence of contaminations and resistant to disturbances. Most of the robust estimators proposed in the literature are robust against either diffuse

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

Science.gov (United States)

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).

A sparse neural code for some speech sounds but not for others.

Directory of Open Access Journals (Sweden)

Mathias Scharinger

Full Text Available The precise neural mechanisms underlying speech sound representations are still a matter of debate. Proponents of 'sparse representations' assume that on the level of speech sounds, only contrastive or otherwise not predictable information is stored in long-term memory. Here, in a passive oddball paradigm, we challenge the neural foundations of such a 'sparse' representation; we use words that differ only in their penultimate consonant ("coronal" [t] vs. "dorsal" [k] place of articulation and for example distinguish between the German nouns Latz ([lats]; bib and Lachs ([laks]; salmon. Changes from standard [t] to deviant [k] and vice versa elicited a discernible Mismatch Negativity (MMN response. Crucially, however, the MMN for the deviant [lats] was stronger than the MMN for the deviant [laks]. Source localization showed this difference to be due to enhanced brain activity in right superior temporal cortex. These findings reflect a difference in phonological 'sparsity': Coronal [t] segments, but not dorsal [k] segments, are based on more sparse representations and elicit less specific neural predictions; sensory deviations from this prediction are more readily 'tolerated' and accordingly trigger weaker MMNs. The results foster the neurocomputational reality of 'representationally sparse' models of speech perception that are compatible with more general predictive mechanisms in auditory perception.

Moral absolutism and ectopic pregnancy.

Science.gov (United States)

Kaczor, C

2001-02-01

If one accepts a version of absolutism that excludes the intentional killing of any innocent human person from conception to natural death, ectopic pregnancy poses vexing difficulties. Given that the embryonic life almost certainly will die anyway, how can one retain one's moral principle and yet adequately respond to a situation that gravely threatens the life of the mother and her future fertility? The four options of treatment most often discussed in the literature are non-intervention, salpingectomy (removal of tube with embryo), salpingostomy (removal of embryo alone), and use of methotrexate (MXT). In this essay, I review these four options and introduce a fifth (the milking technique). In order to assess these options in terms of the absolutism mentioned, it will also be necessary to discuss various accounts of the intention/foresight distinction. I conclude that salpingectomy, salpingostomy, and the milking technique are compatible with absolutist presuppositions, but not the use of methotrexate.

Weed Growth Stage Estimator Using Deep Convolutional Neural Networks.

Science.gov (United States)

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.

Deviation from Covered Interest Rate Parity in Korea

Directory of Open Access Journals (Sweden)

Seungho Lee

2003-06-01

Full Text Available This paper tested the factors which cause deviation from covered interest rate parity (CIRP in Korea, using regression and VAR models. The empirical evidence indicates that the difference between the swap rate and interest rate differential exists and is greatly affected by variables which represent the currency liquidity situation of foreign exchange banks. In other words, the deviation from CIRP can easily occur due to the lack of foreign exchange liquidity of banks in a thin market, despite few capital constraints, small transaction costs, and trivial default risk in Korea.

Evolvable synthetic neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

On-line validation of feedwater flow rate in nuclear power plants using neural networks

International Nuclear Information System (INIS)

Khadem, M.; Ipakchi, A.; Alexandro, F.J.; Colley, R.W.

1994-01-01

On-line calibration of feedwater flow rate measurement in nuclear power plants provides a continuous realistic value of feedwater flow rate. It also reduces the manpower required for periodic calibration needed due to the fouling and defouling of the venturi meter surface condition. This paper presents a method for on-line validation of feedwater flow rate in nuclear power plants. The method is an improvement of the previously developed method which is based on the use of a set of process variables dynamically related to the feedwater flow rate. The online measurements of this set of variables are used as inputs to a neural network to obtain an estimate of the feedwater flow rate reading. The difference between the on-line feedwater flow rate reading, and the neural network estimate establishes whether there is a need to apply a correction factor to the feedwater flow rate measurement for calculation of the actual reactor power. The method was applied to the feedwater flow meters in the two feedwater flow loops of the TMI-1 nuclear power plant. The venturi meters used for flow measurements are susceptible to frequent fouling that degrades their measurement accuracy. The fouling effects can cause an inaccuracy of up to 3% relative error in feedwater flow rate reading. A neural network, whose inputs were the readings of a set of reference instruments, was designed to predict both feedwater flow rates simultaneously. A multi-layer feedforward neural network employing the backpropagation algorithm was used. A number of neural network training tests were performed to obtain an optimum filtering technique of the input/output data of the neural networks. The result of the selection of the filtering technique was confirmed by numerous Fast Fourier Transform (FFT) tests. Training and testing were done on data from TMI-1 nuclear power plant. The results show that the neural network can predict the correct flow rates with an absolute relative error of less than 2%

Statistics as Unbiased Estimators: Exploring the Teaching of Standard Deviation

Science.gov (United States)

Wasserman, Nicholas H.; Casey, Stephanie; Champion, Joe; Huey, Maryann

2017-01-01

This manuscript presents findings from a study about the knowledge for and planned teaching of standard deviation. We investigate how understanding variance as an unbiased (inferential) estimator--not just a descriptive statistic for the variation (spread) in data--is related to teachers' instruction regarding standard deviation, particularly…

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

Energy Technology Data Exchange (ETDEWEB)

Jang, Jin-wook [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: Jinwook@kaeri.re.kr; Seong, Seung-Hwan [Korea Atomic Energy Research Institute, P.O. Box 105, Yusong, Daejon 305-600 (Korea, Republic of)], E-mail: shseong@kaeri.re.kr; Lee, Un-Chul [Department of Nuclear Engineering, Seoul National University, Shinlim-Dong, Gwanak-Gu, Seoul 151-742 (Korea, Republic of)

2007-09-15

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band.

A fast-running core prediction model based on neural networks for load-following operations in a soluble boron-free reactor

International Nuclear Information System (INIS)

Jang, Jin-wook; Seong, Seung-Hwan; Lee, Un-Chul

2007-01-01

A fast prediction model for load-following operations in a soluble boron-free reactor has been proposed, which can predict the core status when three or more control rod groups are moved at a time. This prediction model consists of two multilayer feedforward neural network models to retrieve the axial offset and the reactivity, and compensation models to compensate for the reactivity and axial offset arising from the xenon transient. The neural network training data were generated by taking various overlaps among the control rod groups into consideration for training the neural network models, and the accuracy of the constructed neural network models was verified. Validation results of predicting load following operations for a soluble boron-free reactor show that this model has a good capability to predict the positions of the control rods for sustaining the criticality of a core during load-following operations to ensure that the tolerable axial offset band is not exceeded and it can provide enough corresponding time for the operators to take the necessary actions to prevent a deviation from the tolerable operating band

A better norm-referenced grading using the standard deviation criterion.

Science.gov (United States)

Chan, Wing-shing

2014-01-01

The commonly used norm-referenced grading assigns grades to rank-ordered students in fixed percentiles. It has the disadvantage of ignoring the actual distance of scores among students. A simple norm-referenced grading via standard deviation is suggested for routine educational grading. The number of standard deviation of a student's score from the class mean was used as the common yardstick to measure achievement level. Cumulative probability of a normal distribution was referenced to help decide the amount of students included within a grade. RESULTS of the foremost 12 students from a medical examination were used for illustrating this grading method. Grading by standard deviation seemed to produce better cutoffs in allocating an appropriate grade to students more according to their differential achievements and had less chance in creating arbitrary cutoffs in between two similarly scored students than grading by fixed percentile. Grading by standard deviation has more advantages and is more flexible than grading by fixed percentile for norm-referenced grading.

Reassessment of carotid intima-media thickness by standard deviation score in children and adolescents after Kawasaki disease.

Science.gov (United States)

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.

Absolute and Relative Socioeconomic Health Inequalities across Age Groups.

Science.gov (United States)

van Zon, Sander K R; Bültmann, Ute; Mendes de Leon, Carlos F; Reijneveld, Sijmen A

2015-01-01

The magnitude of socioeconomic health inequalities differs across age groups. It is less clear whether socioeconomic health inequalities differ across age groups by other factors that are known to affect the relation between socioeconomic position and health, like the indicator of socioeconomic position, the health outcome, gender, and as to whether socioeconomic health inequalities are measured in absolute or in relative terms. The aim is to investigate whether absolute and relative socioeconomic health inequalities differ across age groups by indicator of socioeconomic position, health outcome and gender. The study sample was derived from the baseline measurement of the LifeLines Cohort Study and consisted of 95,432 participants. Socioeconomic position was measured as educational level and household income. Physical and mental health were measured with the RAND-36. Age concerned eleven 5-years age groups. Absolute inequalities were examined by comparing means. Relative inequalities were examined by comparing Gini-coefficients. Analyses were performed for both health outcomes by both educational level and household income. Analyses were performed for all age groups, and stratified by gender. Absolute and relative socioeconomic health inequalities differed across age groups by indicator of socioeconomic position, health outcome, and gender. Absolute inequalities were most pronounced for mental health by household income. They were larger in younger than older age groups. Relative inequalities were most pronounced for physical health by educational level. Gini-coefficients were largest in young age groups and smallest in older age groups. Absolute and relative socioeconomic health inequalities differed cross-sectionally across age groups by indicator of socioeconomic position, health outcome and gender. Researchers should critically consider the implications of choosing a specific age group, in addition to the indicator of socioeconomic position and health outcome

Chemical analysis of multicomponent aqueous solutions using a system of nonselective sensor and artificial neural networks

International Nuclear Information System (INIS)

Vlasov, Yu.G.; Legin, A.V.; Rudnitskaya, A.M.; Amiko, A.D.; Natale, K.D.

1997-01-01

With the aim of creating a multisensor system for determining heavy-metal cations (Cu 2+ , Pb 2+ , Cd 2+ , and Zn 2+ ) and inorganic anions (Cl - , F - , and SO 4 2- ), measurements in mixed solutions were carried out with the use of an array of sensors based on chalcogenide glass electrodes, and the possibility of using various methods of mathematical processing of the resulting intricate signals was studied. Three methods of data processing were used: multilinear regression, partial least squares, and artificial neural networks. It was found that the multisensor system proposed were suitable for determining all of the analytes with an accuracy of 1-10%. Because the responses of sensors in solutions of complex composition deviated from linearity, the lowest determination errors were obtained with the use of an artificial neural network. As to the method of data securing (nonselective response of a sensor array) and processing (artificial neural network), the multisensor system developed may be considered a prototype of a device of the electronic tongue type

Some things ought never be done: moral absolutes in clinical ethics.

Science.gov (United States)

Pellegrino, Edmund D

2005-01-01

Moral absolutes have little or no moral standing in our morally diverse modern society. Moral relativism is far more palatable for most ethicists and to the public at large. Yet, when pressed, every moral relativist will finally admit that there are some things which ought never be done. It is the rarest of moral relativists that will take rape, murder, theft, child sacrifice as morally neutral choices. In general ethics, the list of those things that must never be done will vary from person to person. In clinical ethics, however, the nature of the physician-patient relationship is such that certain moral absolutes are essential to the attainment of the good of the patient - the end of the relationship itself. These are all derivatives of the first moral absolute of all morality: Do good and avoid evil. In the clinical encounter, this absolute entails several subsidiary absolutes - act for the good of the patient, do not kill, keep promises, protect the dignity of the patient, do not lie, avoid complicity with evil. Each absolute is intrinsic to the healing and helping ends of the clinical encounter.

Relativistic Absolutism in Moral Education.

Science.gov (United States)

Vogt, W. Paul

1982-01-01

Discusses Emile Durkheim's "Moral Education: A Study in the Theory and Application of the Sociology of Education," which holds that morally healthy societies may vary in culture and organization but must possess absolute rules of moral behavior. Compares this moral theory with current theory and practice of American educators. (MJL)

Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients.

Science.gov (United States)

Edjabou, Maklawe Essonanawe; Martín-Fernández, Josep Antoni; Scheutz, Charlotte; Astrup, Thomas Fruergaard

2017-11-01

Data for fractional solid waste composition provide relative magnitudes of individual waste fractions, the percentages of which always sum to 100, thereby connecting them intrinsically. Due to this sum constraint, waste composition data represent closed data, and their interpretation and analysis require statistical methods, other than classical statistics that are suitable only for non-constrained data such as absolute values. However, the closed characteristics of waste composition data are often ignored when analysed. The results of this study showed, for example, that unavoidable animal-derived food waste amounted to 2.21±3.12% with a confidence interval of (-4.03; 8.45), which highlights the problem of the biased negative proportions. A Pearson's correlation test, applied to waste fraction generation (kg mass), indicated a positive correlation between avoidable vegetable food waste and plastic packaging. However, correlation tests applied to waste fraction compositions (percentage values) showed a negative association in this regard, thus demonstrating that statistical analyses applied to compositional waste fraction data, without addressing the closed characteristics of these data, have the potential to generate spurious or misleading results. Therefore, ¨compositional data should be transformed adequately prior to any statistical analysis, such as computing mean, standard deviation and correlation coefficients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measurement and analysis of the thoracic patient setup deviations in routine radiotherapy

International Nuclear Information System (INIS)

Jia Mingxuan; Zou Huawei; Wu Rong; Sun Jian; Dong Xiaoqi

2003-01-01

Objective: To determine the magnitude of the setup deviations of the thoracic patients in routine radiotherapy. Methods: Altogether 408 films for 21 thoracic patients were recorded using the electronic portal imaging device (EPID), and comparison with reference CT simulator digitally-reconstructed radiograph (DRR) for anterior-posterior fields was performed. The deviation of setup for 21 patients in the left-right (RL), superior-inferior (SI) directions and rotation about the anterior-posterior (AP) axis were measured and analyzed. Results: Without immobilization device, the mean translational and rotational setup deviations were (0.7±3.1) mm and (1.5±4.1) mm in the RL and SI directions, respectively, and (0.3±2.4) degree about AP axis. With immobilization device, the mean translational and rotational setup deviations were (0.5±2.4) mm and (0.8±2.7) mm in the RL and SI directions respectively, and (0.2±1.6) degree about AP axis. Conclusion: The setup deviations in thoracic patients irradiation may be reduced with the use of the immobilization device. The setup deviation in the SI direction is greater than that in the RL direction. The setup deviations are mainly random errors

Sea Surface Height Deviation, Aviso, 0.25 degrees, Global, Science Quality

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Aviso Sea Surface Height Deviation is the deviation from the mean geoid as measured from 1993 - 1995. This is Science Quality data.

48 CFR 201.404 - Class deviations.

Science.gov (United States)

2010-10-01

..., and the Defense Logistics Agency, may approve any class deviation, other than those described in 201...) Diminish any preference given small business concerns by the FAR or DFARS; or (D) Extend to requirements imposed by statute or by regulations of other agencies such as the Small Business Administration and the...

Large deviations in the presence of cooperativity and slow dynamics

Science.gov (United States)

Whitelam, Stephen

2018-06-01

We study simple models of intermittency, involving switching between two states, within the dynamical large-deviation formalism. Singularities appear in the formalism when switching is cooperative or when its basic time scale diverges. In the first case the unbiased trajectory distribution undergoes a symmetry breaking, leading to a change in shape of the large-deviation rate function for a particular dynamical observable. In the second case the symmetry of the unbiased trajectory distribution remains unbroken. Comparison of these models suggests that singularities of the dynamical large-deviation formalism can signal the dynamical equivalent of an equilibrium phase transition but do not necessarily do so.

Effekten af absolut kumulation

DEFF Research Database (Denmark)

Kyvsgaard, Britta; Klement, Christian

2012-01-01

Som led i finansloven for 2011 blev regeringen og forligspartierne enige om at undersøge reglerne om strafudmåling ved samtidig pådømmelse af flere kriminelle forhold og i forbindelse hermed vurdere konsekvenserne af at ændre de gældende regler i forhold til kapacitetsbehovet i Kriminalforsorgens...... samlet bødesum ved en absolut kumulation i forhold til en modereret kumulation, som nu er gældende....

The phonatory deviation diagram: a novel objective measurement of vocal function.

Science.gov (United States)

Madazio, Glaucya; Leão, Sylvia; Behlau, Mara

2011-01-01

To identify the discriminative characteristics of the phonatory deviation diagram (PDD) in rough, breathy and tense voices. One hundred and ninety-six samples of normal and dysphonic voices from adults were submitted to perceptual auditory evaluation, focusing on the predominant vocal quality and the degree of deviation. Acoustic analysis was performed with the VoxMetria (CTS Informatica). Significant differences were observed between the dysphonic and normal groups (p < 0.001), and also between the breathy and rough samples (p = 0.044) and the breathy and tense samples (p < 0.001). All normal voices were positioned in the inferior left quadrant, 45% of the rough voices in the inferior right quadrant, 52.6% of the breathy voices in the superior right quadrant and 54.3% of the tense voices in the inferior left quadrant of the PDD. In the inferior left quadrant, 93.8% of voices with no deviation were located and 72.7% of voices with mild deviation; voices with moderate deviation were distributed in the inferior and superior right quadrants, the latter ones containing the most deviant voices and 80% of voices with severe deviation. The PDD was able to discriminate normal from dysphonic voices, and the distribution was related to the type and degree of voice alteration. Copyright © 2011 S. Karger AG, Basel.

Numerical simulation of shower cooling tower based on artificial neural network

International Nuclear Information System (INIS)

Qi Xiaoni; Liu Zhenyan; Li Dandan

2008-01-01

This study was prompted by the need to design towers for applications in which, due to salt deposition on the packing and subsequent blockage, the use of tower packing is not practical. The cooling tower analyzed in this study is void of fill, named shower cooling tower (SCT). However, the present study focuses mostly on experimental investigation of the SCT, and no systematic numerical method is available. In this paper, we first developed a one dimensional model and analyzed the heat and mass transfer processes of the SCT; then we used the concept of artificial neural network (ANN) to propose a computer design tool that can help the designer evaluate the outlet water temperature from a given set of experimentally obtained data. For comparison purposes and accurate evaluation of the predictions, part of the experimental data was used to train the neural network and the remainder to test the model. The results predicted by the ANN model were compared with those of the standard model and the experimental data. The ANN model predicted the outlet water temperature with a MAE (mean absolute error) of 1.31%, whereas the standard one dimensional model showed a MAE of 9.42%

Some absolutely effective product methods

Directory of Open Access Journals (Sweden)

H. P. Dikshit

1992-01-01

Full Text Available It is proved that the product method A(C,1, where (C,1 is the Cesàro arithmetic mean matrix, is totally effective under certain conditions concerning the matrix A. This general result is applied to study absolute Nörlund summability of Fourier series and other related series.

Statistical properties of the deviations of f 0 F 2 from monthly medians

Directory of Open Access Journals (Sweden)

Y. Tulunay

2002-06-01

Full Text Available The deviations of hourly f 0 F 2 from monthly medians for 20 stations in Europe during the period 1958-1998 are studied. Spectral analysis is used to show that, both for original data (for each hour and for the deviations from monthly medians, the deterministic components are the harmonics of 11 years (solar cycle, 1 year and its harmonics, 27 days and 12 h 50.49 m (2nd harmonic of lunar rotation period L 2 periodicities. Using histograms for one year samples, it is shown that the deviations from monthly medians are nearly zero mean (mean < 0.5 and approximately Gaussian (relative difference range between %10 to %20 and their standard deviations are larger for daylight hours (in the range 5-7. It is shown that the amplitude distribution of the positive and negative deviations is nearly symmetrical at night hours, but asymmetrical for day hours. The positive and negative deviations are then studied separately and it is observed that the positive deviations are nearly independent of R12 except for high latitudes, but negative deviations are modulated by R12 . The 90% confidence interval for negative deviations for each station and each hour is computed as a linear model in terms of R12. After correction for local time, it is shown that for all hours the confidence intervals increase with latitude but decrease above 60N. Long-term trend analysis showed that there is an increase in the amplitude of positive deviations from monthly means irrespective of the solar conditions. Using spectral analysis it is also shown that the seasonal dependency of negative deviations is more accentuated than the seasonal dependency of positive deviations especially at low latitudes. In certain stations, it is also observed that the 4th harmonic of 1 year corresponding to a periodicity of 3 months, which is missing in f 0 F 2 data, appears in the spectra of negative variations.

Absolute measurement method of environment radon content

International Nuclear Information System (INIS)

Ji Changsong

1989-11-01

A portable environment radon content device with a 40 liter decay chamber based on the method of Thomas double filter radon content absolute measurement has been developed. The correctness of the method of Thomas double filter absolute measurement has been verified by the experiments to measure the sampling gas density of radon that the theoretical density has been known. In addition, the intrinsic uncertainty of this method is also determined in the experiments. The confidence of this device is about 95%, the sensitivity is better than 0.37 Bqm -3 and the intrinsic uncertainty is less than 10%. The results show that the selected measuring and structure parameters are reasonable and the experimental methods are acceptable. In this method, the influence on the measured values from the radioactive equilibrium of radon and its daughters, the ratio of combination daughters to the total daughters and the fraction of charged particles has been excluded in the theory and experimental methods. The formula of Thomas double filter absolute measuring radon is applicable to the cylinder decay chamber, and the applicability is also verified when the diameter of exit filter is much smaller than the diameter of inlet filter

Study on absolute humidity influence of NRL-1 measuring apparatus for radon

International Nuclear Information System (INIS)

Shan Jian; Xiao Detao; Zhao Guizhi; Zhou Qingzhi; Liu Yan; Qiu Shoukang; Meng Yecheng; Xiong Xinming; Liu Xiaosong; Ma Wenrong

2014-01-01

The absolute humidity and temperature's effects on the NRL-1 measuring apparatus for radon were studied in this paper. By controlling the radon activity concentration of the radon laboratory in University of South China and improving the temperature and humidity adjust strategy, different correction factor values under different absolute humidities were obtained. Moreover, a correction curve between 1.90 and 14.91 g/m"3 was also attained. The results show that in the case of absolute humidity, when it is less than 2.4 g/m"3, collection efficiency of the NRL-1 measuring apparatus for radon tends to be constant, and the correction factor of the absolute humidity closes to 1. However, the correction factor increases nonlinearly along with the absolute humidity. (authors)

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

Science.gov (United States)

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.

Large deviation function for a driven underdamped particle in a periodic potential

Science.gov (United States)

Fischer, Lukas P.; Pietzonka, Patrick; Seifert, Udo

2018-02-01

Employing large deviation theory, we explore current fluctuations of underdamped Brownian motion for the paradigmatic example of a single particle in a one-dimensional periodic potential. Two different approaches to the large deviation function of the particle current are presented. First, we derive an explicit expression for the large deviation functional of the empirical phase space density, which replaces the level 2.5 functional used for overdamped dynamics. Using this approach, we obtain several bounds on the large deviation function of the particle current. We compare these to bounds for overdamped dynamics that have recently been derived, motivated by the thermodynamic uncertainty relation. Second, we provide a method to calculate the large deviation function via the cumulant generating function. We use this method to assess the tightness of the bounds in a numerical case study for a cosine potential.

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Genomic DNA-based absolute quantification of gene expression in Vitis.

Science.gov (United States)

Gambetta, Gregory A; McElrone, Andrew J; Matthews, Mark A

2013-07-01

Many studies in which gene expression is quantified by polymerase chain reaction represent the expression of a gene of interest (GOI) relative to that of a reference gene (RG). Relative expression is founded on the assumptions that RG expression is stable across samples, treatments, organs, etc., and that reaction efficiencies of the GOI and RG are equal; assumptions which are often faulty. The true variability in RG expression and actual reaction efficiencies are seldom determined experimentally. Here we present a rapid and robust method for absolute quantification of expression in Vitis where varying concentrations of genomic DNA were used to construct GOI standard curves. This methodology was utilized to absolutely quantify and determine the variability of the previously validated RG ubiquitin (VvUbi) across three test studies in three different tissues (roots, leaves and berries). In addition, in each study a GOI was absolutely quantified. Data sets resulting from relative and absolute methods of quantification were compared and the differences were striking. VvUbi expression was significantly different in magnitude between test studies and variable among individual samples. Absolute quantification consistently reduced the coefficients of variation of the GOIs by more than half, often resulting in differences in statistical significance and in some cases even changing the fundamental nature of the result. Utilizing genomic DNA-based absolute quantification is fast and efficient. Through eliminating error introduced by assuming RG stability and equal reaction efficiencies between the RG and GOI this methodology produces less variation, increased accuracy and greater statistical power. © 2012 Scandinavian Plant Physiology Society.

Absolutely minimal extensions of functions on metric spaces

International Nuclear Information System (INIS)

Milman, V A

1999-01-01

Extensions of a real-valued function from the boundary ∂X 0 of an open subset X 0 of a metric space (X,d) to X 0 are discussed. For the broad class of initial data coming under discussion (linearly bounded functions) locally Lipschitz extensions to X 0 that preserve localized moduli of continuity are constructed. In the set of these extensions an absolutely minimal extension is selected, which was considered before by Aronsson for Lipschitz initial functions in the case X 0 subset of R n . An absolutely minimal extension can be regarded as an ∞-harmonic function, that is, a limit of p-harmonic functions as p→+∞. The proof of the existence of absolutely minimal extensions in a metric space with intrinsic metric is carried out by the Perron method. To this end, ∞-subharmonic, ∞-superharmonic, and ∞-harmonic functions on a metric space are defined and their properties are established

Validating neural-network refinements of nuclear mass models

Science.gov (United States)

Utama, R.; Piekarewicz, J.

2018-01-01

Background: Nuclear astrophysics centers on the role of nuclear physics in the cosmos. In particular, nuclear masses at the limits of stability are critical in the development of stellar structure and the origin of the elements. Purpose: We aim to test and validate the predictions of recently refined nuclear mass models against the newly published AME2016 compilation. Methods: The basic paradigm underlining the recently refined nuclear mass models is based on existing state-of-the-art models that are subsequently refined through the training of an artificial neural network. Bayesian inference is used to determine the parameters of the neural network so that statistical uncertainties are provided for all model predictions. Results: We observe a significant improvement in the Bayesian neural network (BNN) predictions relative to the corresponding "bare" models when compared to the nearly 50 new masses reported in the AME2016 compilation. Further, AME2016 estimates for the handful of impactful isotopes in the determination of r -process abundances are found to be in fairly good agreement with our theoretical predictions. Indeed, the BNN-improved Duflo-Zuker model predicts a root-mean-square deviation relative to experiment of σrms≃400 keV. Conclusions: Given the excellent performance of the BNN refinement in confronting the recently published AME2016 compilation, we are confident of its critical role in our quest for mass models of the highest quality. Moreover, as uncertainty quantification is at the core of the BNN approach, the improved mass models are in a unique position to identify those nuclei that will have the strongest impact in resolving some of the outstanding questions in nuclear astrophysics.

Absolute measurement of 152Eu

International Nuclear Information System (INIS)

Baba, Hiroshi; Baba, Sumiko; Ichikawa, Shinichi; Sekine, Toshiaki; Ishikawa, Isamu

1981-08-01

A new method of the absolute measurement for 152 Eu was established based on the 4πβ-γ spectroscopic anti-coincidence method. It is a coincidence counting method consisting of a 4πβ-counter and a Ge(Li) γ-ray detector, in which the effective counting efficiencies of the 4πβ-counter for β-rays, conversion electrons, and Auger electrons were obtained by taking the intensity ratios for certain γ-rays between the single spectrum and the spectrum coincident with the pulses from the 4πβ-counter. First, in order to verify the method, three different methods of the absolute measurement were performed with a prepared 60 Co source to find excellent agreement among the results deduced by them. Next, the 4πβ-γ spectroscopic coincidence measurement was applied to 152 Eu sources prepared by irradiating an enriched 151 Eu target in a reactor. The result was compared with that obtained by the γ-ray spectrometry using a 152 Eu standard source supplied by LMRI. They agreed with each other within the error of 2%. (author)

A Preliminary Analysis on Empirical Attenuation of Absolute Velocity Response Spectra (1 to 10s) in Japan

Science.gov (United States)

Dhakal, Y. P.; Kunugi, T.; Suzuki, W.; Aoi, S.

2013-12-01

(T) = c+ aMw - log10R - bR +∑gS +hD where Y (T) is the 5% damped peak vector response in cm/s derived from two horizontal component records for a natural period T in second; in (2) S is a dummy variable which is one if a site is located inside a sedimentary basin, otherwise zero. In (3), D is depth to the top of layer having a particular S-wave velocity. We used the deep underground S-wave velocity model available from Japan Seismic Hazard Information Station (J-SHIS). In (5), sites are classified to various sedimentary basins. Analyses show that the standard deviations decrease in the order of the models listed and the all coefficients are significant. Interestingly, coefficients g are found to be different from basin to basin at most periods, and the depth to the top of layer having S-wave velocity of 1.7km/s gives the smallest standard deviation of 0.31 at T=4.4s in (5). This study shows the possibility of describing the observed peak absolute velocity response values by using simple model parameters like site location and sedimentary depth soon after the location and magnitude of an earthquake are known.

Absolute carrier phase effects in the two-color excitation of dipolar molecules

International Nuclear Information System (INIS)

Brown, Alex; Meath, W.J.; Kondo, A.E.

2002-01-01

The pump-probe excitation of a two-level dipolar (d≠0) molecule, where the pump frequency is tuned to the energy level separation while the probe frequency is extremely small, is examined theoretically as an example of absolute phase control of excitation processes. The state populations depend on the probe field's absolute carrier phase but are independent of the pump field's absolute carrier phase. Interestingly, the absolute phase effects occur for pulse durations much longer and field intensities much weaker than those required to see such effects in single pulse excitation

Determination of absolute detection efficiencies for detectors of interest in homeland security

International Nuclear Information System (INIS)

Ayaz-Maierhafer, Birsen; DeVol, Timothy A.

2007-01-01

The absolute total and absolute peak detection efficiencies of gamma ray detector materials NaI:Tl, CdZnTe, HPGe, HPXe, LaBr 3 :Ce and LaCl 3 :Ce were simulated and compared to that of polyvinyltoluene (PVT). The dimensions of the PVT detector were 188.82 cmx60.96 cmx5.08 cm, which is a typical size for a single-panel portal monitor. The absolute total and peak detection efficiencies for these detector materials for the point, line and spherical source geometries of 60 Co (1332 keV), 137 Cs (662 keV) and 241 Am (59.5 keV) were simulated at various source-to-detector distances using the Monte Carlo N-Particle software (MCNP5-V1.30). The comparison of the absolute total detection efficiencies for a point, line and spherical source geometry of 60 Co and 137 Cs at different source-to-detector distance showed that the absolute detection efficiency for PVT is higher relative to the other detectors of typical dimensions for that material. However, the absolute peak detection efficiency of some of these detectors are higher relative to PVT, for example the absolute peak detection efficiency of NaI:Tl (7.62 cm diameterx7.62 cm long), HPGe (7.62 cm diameterx7.62 cm long), HPXe (11.43 cm diameterx60.96 cm long), and LaCl 3 :Ce (5.08 cm diameterx5.08 cm long) are all greater than that of a 188.82 cmx60.96 cmx5.08 cm PVT detector for 60 Co and 137 Cs for all geometries studied. The absolute total and absolute peak detection efficiencies of a right circular cylinder of NaI:Tl with various diameters and thicknesses were determined for a point source. The effect of changing the solid angle on the NaI:Tl detectors showed that with increasing solid angle and detector thickness, the absolute efficiency increases. This work establishes a common basis for differentiating detector materials for passive portal monitoring of gamma ray radiation

Regional and site-specific absolute humidity data for use in tritium dose calculations

International Nuclear Information System (INIS)

Etnier, E.L.

1980-01-01

Due to the potential variability in average absolute humidity over the continental U.S., and the dependence of atmospheric 3 H specific activity on absolute humidity, availability of regional absolute humidity data is of value in estimating the radiological significance of 3 H releases. Most climatological data are in the form of relative humidity, which must be converted to absolute humidity for dose calculations. Absolute humidity was calculated for 218 points across the U.S., using the 1977 annual summary of U.S. Climatological Data, and is given in a table. Mean regional values are shown on a map. (author)

Absolute decay parametric instability of high-temperature plasma

International Nuclear Information System (INIS)

Zozulya, A.A.; Silin, V.P.; Tikhonchuk, V.T.

1986-01-01

A new absolute decay parametric instability having wide spatial localization region is shown to be possible near critical plasma density. Its excitation is conditioned by distributed feedback of counter-running Langmuir waves occurring during parametric decay of incident and reflected pumping wave components. In a hot plasma with the temperature of the order of kiloelectronvolt its threshold is lower than that of a known convective decay parametric instability. Minimum absolute instability threshold is shown to be realized under conditions of spatial parametric resonance of higher orders

Locating cloud-to-ground lightning return strokes by a neural network algorithm

International Nuclear Information System (INIS)

2001-01-01

A neuro-based approach is proposed for locating cloud-to-ground lightning strokes. Due to insufficient experimental data, we have use the results of an electromagnetic simulator for training the developed artificial neural network. The simulator utilizes the well-known transmission line and is capable of predicting the electromagnetic field due to a return stroke channel for various parameters associated with the shape of the channel base-current. The training process has been successfully done using the Levenberg-Marquard technique. The simulation results demonstrate that the return stroke channel locations can be predicted with an absolute error not greater than 1 km for return stroke channels located within 80 km of a lightning detection station

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Confidence-Accuracy Calibration in Absolute and Relative Face Recognition Judgments

Science.gov (United States)

Weber, Nathan; Brewer, Neil

2004-01-01

Confidence-accuracy (CA) calibration was examined for absolute and relative face recognition judgments as well as for recognition judgments from groups of stimuli presented simultaneously or sequentially (i.e., simultaneous or sequential mini-lineups). When the effect of difficulty was controlled, absolute and relative judgments produced…

Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Zenghai Chen

2018-01-01

Full Text Available Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

SU-F-T-472: Validation of Absolute Dose Measurements for MR-IGRT With and Without Magnetic Field

International Nuclear Information System (INIS)

Green, O; Li, H; Goddu, S; Mutic, S; Kawrakow, I

2016-01-01

Purpose: To validate absolute dose measurements for a MR-IGRT system without presence of the magnetic field. Methods: The standard method (AAPM’s TG-51) of absolute dose measurement with ionization chambers was tested with and without the presence of the magnetic field for a clinical 0.32-T Co-60 MR-IGRT system. Two ionization chambers were used - the Standard Imaging (Madison, WI) A18 (0.123 cc) and the PTW (Freiburg, Germany). A previously reported Monte Carlo simulation suggested a difference on the order of 0.5% for dose measured with and without the presence of the magnetic field, but testing this was not possible until an engineering solution to allow the radiation system to be used without the nominal magnetic field was found. A previously identified effect of orientation in the magnetic field was also tested by placing the chamber either parallel or perpendicular to the field and irradiating from two opposing angles (90 and 270). Finally, the Imaging and Radiation Oncology Core provided OSLD detectors for five irradiations each with and without the field - with two heads at both 0 and 90 degrees, and one head at 90 degrees only as it doesn’t reach 0 (IEC convention). Results: For the TG-51 comparison, expected dose was obtained by decaying values measured at the time of source installation. The average measured difference was 0.4%±0.12% for A18 and 0.06%±0.15% for Farmer chamber. There was minimal (0.3%) orientation dependence without the magnetic field for the A18 chamber, while previous measurements with the magnetic field had a deviation of 3.2% with chamber perpendicular to magnetic field. Results reported by IROC for the OSLDs with and without the field had a maximum difference of 2%. Conclusion: Accurate absolute dosimetry was verified by measurement under the same conditions with and without the magnetic field for both ionization chambers and independently-verifiable OSLDs.

Evolutionary implications of genetic code deviations

International Nuclear Information System (INIS)

Chela Flores, J.

1986-07-01

By extending the standard genetic code into a temperature dependent regime, we propose a train of molecular events leading to alternative coding. The first few examples of these deviations have already been reported in some ciliated protozoans and Gram positive bacteria. A possible range of further alternative coding, still within the context of universality, is pointed out. (author)

Pantomime-grasping: Advance knowledge of haptic feedback availability supports an absolute visuo-haptic calibration

Directory of Open Access Journals (Sweden)

Shirin eDavarpanah Jazi

2016-05-01

Full Text Available An emerging issue in movement neurosciences is whether haptic feedback influences the nature of the information supporting a simulated grasping response (i.e., pantomime-grasping. In particular, recent work by our group contrasted pantomime-grasping responses performed with (i.e., PH+ trials and without (i.e., PH- trials terminal haptic feedback in separate blocks of trials. Results showed that PH- trials were mediated via relative visual information. In contrast, PH+ trials showed evidence of an absolute visuo-haptic calibration – a finding attributed to an error signal derived from a comparison between expected and actual haptic feedback (i.e., an internal forward model. The present study examined whether advanced knowledge of haptic feedback availability influences the aforementioned calibration process. To that end, PH- and PH+ trials were completed in separate blocks (i.e., the feedback schedule used in our group’s previous study and a block wherein PH- and PH+ trials were randomly interleaved on a trial-by-trial basis (i.e., random feedback schedule. In other words, the random feedback schedule precluded participants from predicting whether haptic feedback would be available at the movement goal location. We computed just-noticeable-difference (JND values to determine whether responses adhered to, or violated, the relative psychophysical principles of Weber’s law. Results for the blocked feedback schedule replicated our group’s previous work, whereas in the random feedback schedule PH- and PH+ trials were supported via relative visual information. Accordingly, we propose that a priori knowledge of haptic feedback is necessary to support an absolute visuo-haptic calibration. Moreover, our results demonstrate that the presence and expectancy of haptic feedback is an important consideration in contrasting the behavioral and neural properties of natural and stimulated (i.e., pantomime-grasping grasping.

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

International Nuclear Information System (INIS)

Shen Yang; Bax, Ad

2010-01-01

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13 C β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and 13 C β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ 15 N, δ 13 C', δ 13 C α , δ 13 C β , δ 1 H α and δ 1 H N , respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

Auditory working memory predicts individual differences in absolute pitch learning.

Science.gov (United States)

Van Hedger, Stephen C; Heald, Shannon L M; Koch, Rachelle; Nusbaum, Howard C

2015-07-01

Absolute pitch (AP) is typically defined as the ability to label an isolated tone as a musical note in the absence of a reference tone. At first glance the acquisition of AP note categories seems like a perceptual learning task, since individuals must assign a category label to a stimulus based on a single perceptual dimension (pitch) while ignoring other perceptual dimensions (e.g., loudness, octave, instrument). AP, however, is rarely discussed in terms of domain-general perceptual learning mechanisms. This is because AP is typically assumed to depend on a critical period of development, in which early exposure to pitches and musical labels is thought to be necessary for the development of AP precluding the possibility of adult acquisition of AP. Despite this view of AP, several previous studies have found evidence that absolute pitch category learning is, to an extent, trainable in a post-critical period adult population, even if the performance typically achieved by this population is below the performance of a "true" AP possessor. The current studies attempt to understand the individual differences in learning to categorize notes using absolute pitch cues by testing a specific prediction regarding cognitive capacity related to categorization - to what extent does an individual's general auditory working memory capacity (WMC) predict the success of absolute pitch category acquisition. Since WMC has been shown to predict performance on a wide variety of other perceptual and category learning tasks, we predict that individuals with higher WMC should be better at learning absolute pitch note categories than individuals with lower WMC. Across two studies, we demonstrate that auditory WMC predicts the efficacy of learning absolute pitch note categories. These results suggest that a higher general auditory WMC might underlie the formation of absolute pitch categories for post-critical period adults. Implications for understanding the mechanisms that underlie the

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Direct process estimation from tomographic data using artificial neural systems

Science.gov (United States)

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.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection

Directory of Open Access Journals (Sweden)

Erik Marchi

2017-01-01

Full Text Available 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.

Deep Recurrent Neural Network-Based Autoencoders for Acoustic Novelty Detection.

Science.gov (United States)

Marchi, Erik; Vesperini, Fabio; Squartini, Stefano; 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.

Calibrating the absolute amplitude scale for air showers measured at LOFAR

International Nuclear Information System (INIS)

Nelles, A.; Hörandel, J. R.; Karskens, T.; Krause, M.; Corstanje, A.; Enriquez, J. E.; Falcke, H.; Rachen, J. P.; Rossetto, L.; Schellart, P.; Buitink, S.; Erdmann, M.; Krause, R.; Haungs, A.; Hiller, R.; Huege, T.; Link, K.; Schröder, F. G.; Norden, M. J.; Scholten, O.

2015-01-01

Air showers induced by cosmic rays create nanosecond pulses detectable at radio frequencies. These pulses have been measured successfully in the past few years at the LOw-Frequency ARray (LOFAR) and are used to study the properties of cosmic rays. For a complete understanding of this phenomenon and the underlying physical processes, an absolute calibration of the detecting antenna system is needed. We present three approaches that were used to check and improve the antenna model of LOFAR and to provide an absolute calibration of the whole system for air shower measurements. Two methods are based on calibrated reference sources and one on a calibration approach using the diffuse radio emission of the Galaxy, optimized for short data-sets. An accuracy of 19% in amplitude is reached. The absolute calibration is also compared to predictions from air shower simulations. These results are used to set an absolute energy scale for air shower measurements and can be used as a basis for an absolute scale for the measurement of astronomical transients with LOFAR

New design and facilities for the International Database for Absolute Gravity Measurements (AGrav): A support for the Establishment of a new Global Absolute Gravity Reference System

Science.gov (United States)

Wziontek, Hartmut; Falk, Reinhard; Bonvalot, Sylvain; Rülke, Axel

2017-04-01

After about 10 years of successful joint operation by BGI and BKG, the International Database for Absolute Gravity Measurements "AGrav" (see references hereafter) was under a major revision. The outdated web interface was replaced by a responsive, high level web application framework based on Python and built on top of Pyramid. Functionality was added, like interactive time series plots or a report generator and the interactive map-based station overview was updated completely, comprising now clustering and the classification of stations. Furthermore, the database backend was migrated to PostgreSQL for better support of the application framework and long-term availability. As comparisons of absolute gravimeters (AG) become essential to realize a precise and uniform gravity standard, the database was extended to document the results on international and regional level, including those performed at monitoring stations equipped with SGs. By this it will be possible to link different AGs and to trace their equivalence back to the key comparisons under the auspices of International Committee for Weights and Measures (CIPM) as the best metrological realization of the absolute gravity standard. In this way the new AGrav database accommodates the demands of the new Global Absolute Gravity Reference System as recommended by the IAG Resolution No. 2 adopted in Prague 2015. The new database will be presented with focus on the new user interface and new functionality, calling all institutions involved in absolute gravimetry to participate and contribute with their information to built up a most complete picture of high precision absolute gravimetry and improve its visibility. A Digital Object Identifier (DOI) will be provided by BGI to contributors to give a better traceability and facilitate the referencing of their gravity surveys. Links and references: BGI mirror site : http://bgi.obs-mip.fr/data-products/Gravity-Databases/Absolute-Gravity-data/ BKG mirror site: http

Absolute cross sections from the ''boomerang model'' for resonant electron-molecule scattering

International Nuclear Information System (INIS)

Dube, L.; Herzenberg, A.

1979-01-01

The boomerang model is used to calculate absolute cross sections near the 2 Pi/sub g/ shape resonance in e-N 2 scattering. The calculated cross sections are shown to satisfy detailed balancing. The exchange of electrons is taken into account. A parametrized complex-potential curve for the intermediate N 2 /sup ts-/ ion is determined from a small part of the experimental data, and then used to calculate other properties. The calculations are in good agreement with the absolute cross sections for vibrational excitation from the ground state, the absolute cross section v = 1 → 2, and the absolute total cross section

Left-hemisphere activation is associated with enhanced vocal pitch error detection in musicians with absolute pitch

Science.gov (United States)

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

PERAMALAN BEBAN JANGKA PENDEK PADA HARI LIBUR DI BALI MENGGUNAKAN METODE GENERALIZED REGRESSION NEURAL NETWORK (GRNN

Directory of Open Access Journals (Sweden)

Juniar Doan Wihardono

2016-12-01

Full Text Available Peramalan beban merupakan suatu kegiatan untuk memperkirakan kondisi beban pada hari yang akan datang. Kondisi beban pada saat hari libur merupakan suatu fenomena yang sangat menarik untuk diketahui. Fenomena ini terjadi di Bali yaitu pada saat hari Raya Nyepi. Karena, kondisi beban pada hari Raya Nyepi akan mengalami penurunan yang sangat drastis. Kondisi tersebut perlu diketahui agar operasi sistem tenaga listrik dapat berjalan secara optimal. Metode peramalan beban pada penelitian ini menggunakan metode Generalized Regression Neural Nework (GRNN yang dibandingkan dengan metode Radial Basis Function Neural Network (RBFNN. Data pada proses peramalan menggunakan data beban puncak harian pada hari libur di Bali antara tahun 2010 sampai 2014. Pemilihan data difokuskan pada data beban puncak pada 5 hari sebelum hari libur (h-4 sampai hari libur (h. Metode GRNN menghasilkan Mean Square Error (MSE sebesar 0.020089 dan Mean Absolute Percentage Error (MAPE sebesar 2.01%. sedangkan metode RBFNN menghasilkan MSE sebesar 0.022757 dan MAPE sebesar 2,28%.

A highly accurate absolute gravimetric network for Albania, Kosovo and Montenegro

Science.gov (United States)

Ullrich, Christian; Ruess, Diethard; Butta, Hubert; Qirko, Kristaq; Pavicevic, Bozidar; Murat, Meha

2016-04-01

The objective of this project is to establish a basic gravity network in Albania, Kosovo and Montenegro to enable further investigations in geodetic and geophysical issues. Therefore the first time in history absolute gravity measurements were performed in these countries. The Norwegian mapping authority Kartverket is assisting the national mapping authorities in Kosovo (KCA) (Kosovo Cadastral Agency - Agjencia Kadastrale e Kosovës), Albania (ASIG) (Autoriteti Shtetëror i Informacionit Gjeohapësinor) and in Montenegro (REA) (Real Estate Administration of Montenegro - Uprava za nekretnine Crne Gore) in improving the geodetic frameworks. The gravity measurements are funded by Kartverket. The absolute gravimetric measurements were performed from BEV (Federal Office of Metrology and Surveying) with the absolute gravimeter FG5-242. As a national metrology institute (NMI) the Metrology Service of the BEV maintains the national standards for the realisation of the legal units of measurement and ensures their international equivalence and recognition. Laser and clock of the absolute gravimeter were calibrated before and after the measurements. The absolute gravimetric survey was carried out from September to October 2015. Finally all 8 scheduled stations were successfully measured: there are three stations located in Montenegro, two stations in Kosovo and three stations in Albania. The stations are distributed over the countries to establish a gravity network for each country. The vertical gradients were measured at all 8 stations with the relative gravimeter Scintrex CG5. The high class quality of some absolute gravity stations can be used for gravity monitoring activities in future. The measurement uncertainties of the absolute gravity measurements range around 2.5 micro Gal at all stations (1 microgal = 10-8 m/s2). In Montenegro the large gravity difference of 200 MilliGal between station Zabljak and Podgorica can be even used for calibration of relative gravimeters

Artificial neural networks applied to DNBR calculation in digital core protection systems

International Nuclear Information System (INIS)

Lee, H. C.; Chang, S. H.

2003-01-01

The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a method using artificial neural networks is studied in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about ±2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes appeared during accidents, the deviation is about ±10∼15%. The suggested method could be the alternative that can calculate DNBR very quickly while increasing the plant availability

Full-field transmission-type angle-deviation optical microscope with reflectivity-height transformation.

Science.gov (United States)

Chiu, Ming-Hung; Tan, Chen-Tai; Tsai, Ming-Hung; Yang, Ya-Hsin

2015-10-01

This full-field transmission-type three-dimensional (3D) optical microscope is constructed based on the angle deviation method (ADM) and the algorithm of reflectivity-height transformation (RHT). The surface height is proportional to the deviation angle of light passing through the object. The angle deviation and surface height can be measured based on the reflectivity closed to the critical angle using a parallelogram prism and two CCDs.

A course on large deviations with an introduction to Gibbs measures

CERN Document Server

Rassoul-Agha, Firas

2015-01-01

This is an introductory course on the methods of computing asymptotics of probabilities of rare events: the theory of large deviations. The book combines large deviation theory with basic statistical mechanics, namely Gibbs measures with their variational characterization and the phase transition of the Ising model, in a text intended for a one semester or quarter course. The book begins with a straightforward approach to the key ideas and results of large deviation theory in the context of independent identically distributed random variables. This includes Cramér's theorem, relative entropy, Sanov's theorem, process level large deviations, convex duality, and change of measure arguments. Dependence is introduced through the interactions potentials of equilibrium statistical mechanics. The phase transition of the Ising model is proved in two different ways: first in the classical way with the Peierls argument, Dobrushin's uniqueness condition, and correlation inequalities and then a second time through the ...

Absolute and relative dosimetry for ELIMED

Energy Technology Data Exchange (ETDEWEB)

Cirrone, G. A. P.; Schillaci, F.; Scuderi, V. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Institute of Physics Czech Academy of Science, ELI-Beamlines project, Na Slovance 2, Prague (Czech Republic); Cuttone, G.; Candiano, G.; Musumarra, A.; Pisciotta, P.; Romano, F. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania (Italy); Carpinelli, M. [INFN Sezione di Cagliari, c/o Dipartimento di Fisica, Università di Cagliari, Cagliari (Italy); Leonora, E.; Randazzo, N. [INFN-Sezione di Catania, Via Santa Sofia 64, Catania (Italy); Presti, D. Lo [INFN-Sezione di Catania, Via Santa Sofia 64, Catania, Italy and Università di Catania, Dipartimento di Fisica e Astronomia, Via S. Sofia 64, Catania (Italy); Raffaele, L. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and INFN-Sezione di Catania, Via Santa Sofia 64, Catania (Italy); Tramontana, A. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Università di Catania, Dipartimento di Fisica e Astronomia, Via S. Sofia 64, Catania (Italy); Cirio, R.; Sacchi, R.; Monaco, V. [INFN, Sezione di Torino, Via P.Giuria, 1 10125 Torino, Italy and Università di Torino, Dipartimento di Fisica, Via P.Giuria, 1 10125 Torino (Italy); Marchetto, F.; Giordanengo, S. [INFN, Sezione di Torino, Via P.Giuria, 1 10125 Torino (Italy)

2013-07-26

The definition of detectors, methods and procedures for the absolute and relative dosimetry of laser-driven proton beams is a crucial step toward the clinical use of this new kind of beams. Hence, one of the ELIMED task, will be the definition of procedures aiming to obtain an absolute dose measure at the end of the transport beamline with an accuracy as close as possible to the one required for clinical applications (i.e. of the order of 5% or less). Relative dosimetry procedures must be established, as well: they are necessary in order to determine and verify the beam dose distributions and to monitor the beam fluence and the energetic spectra during irradiations. Radiochromic films, CR39, Faraday Cup, Secondary Emission Monitor (SEM) and transmission ionization chamber will be considered, designed and studied in order to perform a fully dosimetric characterization of the ELIMED proton beam.

Quantum Entanglement in Neural Network States

Directory of Open Access Journals (Sweden)

Dong-Ling Deng

2017-05-01

Full Text Available Machine learning, one of today’s most rapidly growing interdisciplinary fields, promises an unprecedented perspective for solving intricate quantum many-body problems. Understanding the physical aspects of the representative artificial neural-network states has recently become highly desirable in the applications of machine-learning techniques to quantum many-body physics. In this paper, we explore the data structures that encode the physical features in the network states by studying the quantum entanglement properties, with a focus on the restricted-Boltzmann-machine (RBM architecture. We prove that the entanglement entropy of all short-range RBM states satisfies an area law for arbitrary dimensions and bipartition geometry. For long-range RBM states, we show by using an exact construction that such states could exhibit volume-law entanglement, implying a notable capability of RBM in representing quantum states with massive entanglement. Strikingly, the neural-network representation for these states is remarkably efficient, in the sense that the number of nonzero parameters scales only linearly with the system size. We further examine the entanglement properties of generic RBM states by randomly sampling the weight parameters of the RBM. We find that their averaged entanglement entropy obeys volume-law scaling, and the meantime strongly deviates from the Page entropy of the completely random pure states. We show that their entanglement spectrum has no universal part associated with random matrix theory and bears a Poisson-type level statistics. Using reinforcement learning, we demonstrate that RBM is capable of finding the ground state (with power-law entanglement of a model Hamiltonian with a long-range interaction. In addition, we show, through a concrete example of the one-dimensional symmetry-protected topological cluster states, that the RBM representation may also be used as a tool to analytically compute the entanglement spectrum. Our

Ion track based tunable device as humidity sensor: a neural network approach

Science.gov (United States)

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

Analysis of form deviation in non-isothermal glass molding

Science.gov (United States)

Kreilkamp, H.; Grunwald, T.; Dambon, O.; Klocke, F.

2018-02-01

Especially in the market of sensors, LED lighting and medical technologies, there is a growing demand for precise yet low-cost glass optics. This demand poses a major challenge for glass manufacturers who are confronted with the challenge arising from the trend towards ever-higher levels of precision combined with immense pressure on market prices. Since current manufacturing technologies especially grinding and polishing as well as Precision Glass Molding (PGM) are not able to achieve the desired production costs, glass manufacturers are looking for alternative technologies. Non-isothermal Glass Molding (NGM) has been shown to have a big potential for low-cost mass manufacturing of complex glass optics. However, the biggest drawback of this technology at the moment is the limited accuracy of the manufactured glass optics. This research is addressing the specific challenges of non-isothermal glass molding with respect to form deviation of molded glass optics. Based on empirical models, the influencing factors on form deviation in particular form accuracy, waviness and surface roughness will be discussed. A comparison with traditional isothermal glass molding processes (PGM) will point out the specific challenges of non-isothermal process conditions. Furthermore, the underlying physical principle leading to the formation of form deviations will be analyzed in detail with the help of numerical simulation. In this way, this research contributes to a better understanding of form deviations in non-isothermal glass molding and is an important step towards new applications demanding precise yet low-cost glass optics.

Philosophy as Inquiry Aimed at the Absolute Knowledge

Directory of Open Access Journals (Sweden)

Ekaterina Snarskaya

2017-09-01

Full Text Available Philosophy as the absolute knowledge has been studied from two different but closely related approaches: historical and logical. The first approach exposes four main stages in the history of European metaphysics that marked out types of “philosophical absolutism”: the evolution of philosophy brought to light metaphysics of being, method, morals and logic. All of them are associated with the names of Aristotle, Bacon/Descartes, Kant and Hegel. Then these forms are considered in the second approach that defined them as subject-matter of philosophy as such. Due to their overall, comprehensive character, the focus of philosophy on them justifies its claim on absoluteness as far as philosophy is aimed at comprehension of the world’s unity regardless of the philosopher’s background, values and other preferences. And that is its prerogative since no other form of consciousness lays down this kind of aim. Thus, philosophy is defined as an everlasting attempt to succeed in conceiving the world in all its multifold manifestations. This article is to try to clarify the claim of philosophy on the absolute knowledge.

Incorporating assumption deviation risk in quantitative risk assessments: A semi-quantitative approach

International Nuclear Information System (INIS)

Khorsandi, Jahon; Aven, Terje

2017-01-01

Quantitative risk assessments (QRAs) of complex engineering systems are based on numerous assumptions and expert judgments, as there is limited information available for supporting the analysis. In addition to sensitivity analyses, the concept of assumption deviation risk has been suggested as a means for explicitly considering the risk related to inaccuracies and deviations in the assumptions, which can significantly impact the results of the QRAs. However, challenges remain for its practical implementation, considering the number of assumptions and magnitude of deviations to be considered. This paper presents an approach for integrating an assumption deviation risk analysis as part of QRAs. The approach begins with identifying the safety objectives for which the QRA aims to support, and then identifies critical assumptions with respect to ensuring the objectives are met. Key issues addressed include the deviations required to violate the safety objectives, the uncertainties related to the occurrence of such events, and the strength of knowledge supporting the assessments. Three levels of assumptions are considered, which include assumptions related to the system's structural and operational characteristics, the effectiveness of the established barriers, as well as the consequence analysis process. The approach is illustrated for the case of an offshore installation. - Highlights: • An approach for assessing the risk of deviations in QRA assumptions is presented. • Critical deviations and uncertainties related to their occurrence are addressed. • The analysis promotes critical thinking about the foundation and results of QRAs. • The approach is illustrated for the case of an offshore installation.

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

Does standard deviation matter? Using "standard deviation" to quantify security of multistage testing.

Science.gov (United States)

Wang, Chun; Zheng, Yi; Chang, Hua-Hua

2014-01-01

With the advent of web-based technology, online testing is becoming a mainstream mode in large-scale educational assessments. Most online tests are administered continuously in a testing window, which may post test security problems because examinees who take the test earlier may share information with those who take the test later. Researchers have proposed various statistical indices to assess the test security, and one most often used index is the average test-overlap rate, which was further generalized to the item pooling index (Chang & Zhang, 2002, 2003). These indices, however, are all defined as the means (that is, the expected proportion of common items among examinees) and they were originally proposed for computerized adaptive testing (CAT). Recently, multistage testing (MST) has become a popular alternative to CAT. The unique features of MST make it important to report not only the mean, but also the standard deviation (SD) of test overlap rate, as we advocate in this paper. The standard deviation of test overlap rate adds important information to the test security profile, because for the same mean, a large SD reflects that certain groups of examinees share more common items than other groups. In this study, we analytically derived the lower bounds of the SD under MST, with the results under CAT as a benchmark. It is shown that when the mean overlap rate is the same between MST and CAT, the SD of test overlap tends to be larger in MST. A simulation study was conducted to provide empirical evidence. We also compared the security of MST under the single-pool versus the multiple-pool designs; both analytical and simulation studies show that the non-overlapping multiple-pool design will slightly increase the security risk.

Absolute pitch: a case study.

Science.gov (United States)

Vernon, P E

1977-11-01

The auditory skill known as 'absolute pitch' is discussed, and it is shown that this differs greatly in accuracy of identification or reproduction of musical tones from ordinary discrimination of 'tonal height' which is to some extent trainable. The present writer possessed absolute pitch for almost any tone or chord over the normal musical range, from about the age of 17 to 52. He then started to hear all music one semitone too high, and now at the age of 71 it is heard a full tone above the true pitch. Tests were carried out under controlled conditions, in which 68 to 95 per cent of notes were identified as one semitone or one tone higher than they should be. Changes with ageing seem more likely to occur in the elasticity of the basilar membrane mechanisms than in the long-term memory which is used for aural analysis of complex sounds. Thus this experience supports the view that some resolution of complex sounds takes place at the peripheral sense organ, and this provides information which can be incorrect, for interpretation by the cortical centres.

Absolute calibration technique for spontaneous fission sources

International Nuclear Information System (INIS)

Zucker, M.S.; Karpf, E.

1984-01-01

An absolute calibration technique for a spontaneously fissioning nuclide (which involves no arbitrary parameters) allows unique determination of the detector efficiency for that nuclide, hence of the fission source strength

Absolute luminosity measurements with the LHCb detector at the LHC

CERN Document Server

Aaij, R; Adinolfi, M; Adrover, C; Affolder, A; Ajaltouni, Z; Albrecht, J; Alessio, F; Alexander, M; Alkhazov, G; Alvarez Cartelle, P; Alves, A A; Amato, S; Amhis, Y; Anderson, J; Appleby, R B; Aquines Gutierrez, O; Archilli, F; Arrabito, L; Artamonov, A; Artuso, M; Aslanides, E; Auriemma, G; Bachmann, S; Back, J J; Bailey, D S; Balagura, V; Baldini, W; Barlow, R J; Barschel, C; Barsuk, S; Barter, W; Bates, A; Bauer, C; Bauer, Th; Bay, A; Bediaga, I; Belous, K; Belyaev, I; Ben-Haim, E; Benayoun, M; Bencivenni, G; Benson, S; Benton, J; Bernet, R; Bettler, M-O; van Beuzekom, M; Bien, A; Bifani, S; Bizzeti, A; Bjørnstad, P M; Blake, T; Blanc, F; Blanks, C; Blouw, J; Blusk, S; Bobrov, A; Bocci, V; Bondar, A; Bondar, N; Bonivento, W; Borghi, S; Borgia, A; Bowcock, T J V; Bozzi, C; Brambach, T; van den Brand, J; Bressieux, J; Brett, D; Brisbane, S; Britsch, M; Britton, T; Brook, N H; Brown, H; Büchler-Germann, A; Burducea, I; Bursche, A; Buytaert, J; Cadeddu, S; Caicedo Carvajal, J M; Callot, O; Calvi, M; Calvo Gomez, M; Camboni, A; Campana, P; Carbone, A; Carboni, G; Cardinale, R; Cardini, A; Carson, L; Carvalho Akiba, K; Casse, G; Cattaneo, M; Charles, M; Charpentier, Ph; Chiapolini, N; Ciba, K; Cid Vidal, X; Ciezarek, G; Clarke, P E L; Clemencic, M; Cliff, H V; Closier, J; Coca, C; Coco, V; Cogan, J; Collins, P; Constantin, F; Conti, G; Contu, A; Cook, A; Coombes, M; Corti, G; Cowan, G A; Currie, R; D'Almagne, B; D'Ambrosio, C; David, P; De Bonis, I; De Capua, S; De Cian, M; De Lorenzi, F; De Miranda, J M; De Paula, L; De Simone, P; Decamp, D; Deckenhoff, M; Degaudenzi, H; Deissenroth, M; Del Buono, L; Deplano, C; Deschamps, O; Dettori, F; Dickens, J; Dijkstra, H; Diniz Batista, P; Donleavy, S; Dordei, F; Dosil Suárez, A; Dossett, D; Dovbnya, A; Dupertuis, F; Dzhelyadin, R; Eames, C; Easo, S; Egede, U; Egorychev, V; Eidelman, S; van Eijk, D; Eisele, F; Eisenhardt, S; Ekelhof, R; Eklund, L; Elsasser, Ch; d'Enterria, D G; Esperante Pereira, D; Estève, L; Falabella, A; Fanchini, E; Färber, C; Fardell, G; Farinelli, C; Farry, S; Fave, V; Fernandez Albor, V; Ferro-Luzzi, M; Filippov, S; Fitzpatrick, C; Fontana, M; Fontanelli, F; Forty, R; Frank, M; Frei, C; Frosini, M; Furcas, S; Gallas Torreira, A; Galli, D; Gandelman, M; Gandini, P; Gao, Y; Garnier, J-C; Garofoli, J; Garra Tico, J; Garrido, L; Gaspar, C; Gauvin, N; Gersabeck, M; Gershon, T; Ghez, Ph; Gibson, V; Gligorov, V V; Göbel, C; Golubkov, D; Golutvin, A; Gomes, A; Gordon, H; Grabalosa Gándara, M; Graciani Diaz, R; Granado Cardoso, L A; Graugés, E; Graziani, G; Grecu, A; Gregson, S; Gui, B; Gushchin, E; Guz, Yu; Gys, T; Haefeli, G; Haen, C; Haines, S C; Hampson, T; Hansmann-Menzemer, S; Harji, R; Harnew, N; Harrison, J; Harrison, P F; He, J; Heijne, V; Hennessy, K; Henrard, P; Hernando Morata, J A; van Herwijnen, E; Hicks, E; Hofmann, W; Holubyev, K; Hopchev, P; Hulsbergen, W; Hunt, P; Huse, T; Huston, R S; Hutchcroft, D; Hynds, D; Iakovenko, V; Ilten, P; Imong, J; Jacobsson, R; Jaeger, A; Jahjah Hussein, M; Jans, E; Jansen, F; Jaton, P; Jean-Marie, B; Jing, F; John, M; Johnson, D; Jones, C R; Jost, B; Kandybei, S; Karacson, M; Karbach, T M; Keaveney, J; Kerzel, U; Ketel, T; Keune, A; Khanji, B; Kim, Y M; Knecht, M; Koblitz, S; Koppenburg, P; Kozlinskiy, A; Kravchuk, L; Kreplin, K; Kreps, M; Krocker, G; Krokovny, P; Kruse, F; Kruzelecki, K; Kucharczyk, M; Kukulak, S; Kumar, R; Kvaratskheliya, T; La Thi, V N; Lacarrere, D; Lafferty, G; Lai, A; Lambert, D; Lambert, R W; Lanciotti, E; Lanfranchi, G; Langenbruch, C; Latham, T; Le Gac, R; van Leerdam, J; Lees, J-P; Lefèvre, R; Leflat, A; Lefrançois, J; Leroy, O; Lesiak, T; Li, L; Li Gioi, L; Lieng, M; Liles, M; Lindner, R; Linn, C; Liu, B; Liu, G; Lopes, J H; Lopez Asamar, E; Lopez-March, N; Luisier, J; Machefert, F; Machikhiliyan, I V; Maciuc, F; Maev, O; Magnin, J; Malde, S; Mamunur, R M D; Manca, G; Mancinelli, G; Mangiafave, N; Marconi, U; Märki, R; Marks, J; Martellotti, G; Martens, A; Martin, L; Martín Sánchez, A; Martinez Santos, D; Massafferri, A; Matev, R; Mathe, Z; Matteuzzi, C; Matveev, M; Maurice, E; Maynard, B; Mazurov, A; McGregor, G; McNulty, R; Mclean, C; Meissner, M; Merk, M; Merkel, J; Messi, R; Miglioranzi, S; Milanes, D A; Minard, M-N; Monteil, S; Moran, D; Morawski, P; Mountain, R; Mous, I; Muheim, F; Müller, K; Muresan, R; Muryn, B; Musy, M; Mylroie-Smith, J; Naik, P; Nakada, T; Nandakumar, R; Nardulli, J; Nasteva, I; Nedos, M; Needham, M; Neufeld, N; Nguyen-Mau, C; Nicol, M; Nies, S; Niess, V; Nikitin, N; Oblakowska-Mucha, A; Obraztsov, V; Oggero, S; Ogilvy, S; Okhrimenko, O; Oldeman, R; Orlandea, M; Otalora Goicochea, J M; Owen, P; Pal, B; Palacios, J; Palutan, M; Panman, J; Papanestis, A; Pappagallo, M; Parkes, C; Parkinson, C J; Passaleva, G; Patel, G D; Patel, M; Paterson, S K; Patrick, G N; Patrignani, C; Pavel-Nicorescu, C; Pazos Alvarez, A; Pellegrino, A; Penso, G; Pepe Altarelli, M; Perazzini, S; Perego, D L; Perez Trigo, E; Pérez-Calero Yzquierdo, A; Perret, P; Perrin-Terrin, M; Pessina, G; Petrella, A; Petrolini, A; Pie Valls, B; Pietrzyk, B; Pilar, T; Pinci, D; Plackett, R; Playfer, S; Plo Casasus, M; Polok, G; Poluektov, A; Polycarpo, E; Popov, D; Popovici, B; Potterat, C; Powell, A; du Pree, T; Prisciandaro, J; Pugatch, V; Puig Navarro, A; Qian, W; Rademacker, J H; Rakotomiaramanana, B; Rangel, M S; Raniuk, I; Raven, G; Redford, S; Reid, M M; dos Reis, A C; Ricciardi, S; Rinnert, K; Roa Romero, D A; Robbe, P; Rodrigues, E; Rodrigues, F; Rodriguez Perez, P; Rogers, G J; Roiser, S; Romanovsky, V; Rouvinet, J; Ruf, T; Ruiz, H; Sabatino, G; Saborido Silva, J J; Sagidova, N; Sail, P; Saitta, B; Salzmann, C; Sannino, M; Santacesaria, R; Santamarina Rios, C; Santinelli, R; Santovetti, E; Sapunov, M; Sarti, A; Satriano, C; Satta, A; Savrie, M; Savrina, D; Schaack, P; Schiller, M; Schleich, S; Schmelling, M; Schmidt, B; Schneider, O; Schopper, A; Schune, M -H; Schwemmer, R; Sciubba, A; Seco, M; Semennikov, A; Senderowska, K; Sepp, I; Serra, N; Serrano, J; Seyfert, P; Shao, B; Shapkin, M; Shapoval, I; Shatalov, P; Shcheglov, Y; Shears, T; Shekhtman, L; Shevchenko, O; Shevchenko, V; Shires, A; Silva Coutinho, R; Skottowe, H P; Skwarnicki, T; Smith, A C; Smith, N A; Sobczak, K; Soler, F J P; Solomin, A; Soomro, F; Souza De Paula, B; Spaan, B; Sparkes, A; Spradlin, P; Stagni, F; Stahl, S; Steinkamp, O; Stoica, S; Stone, S; Storaci, B; Straticiuc, M; Straumann, U; Styles, N; Subbiah, V K; Swientek, S; Szczekowski, M; Szczypka, P; Szumlak, T; T'Jampens, S; Teodorescu, E; Teubert, F; Thomas, C; Thomas, E; van Tilburg, J; Tisserand, V; Tobin, M; Topp-Joergensen, S; Tran, M T; Tsaregorodtsev, A; Tuning, N; Ubeda Garcia, M; Ukleja, A; Urquijo, P; Uwer, U; Vagnoni, V; Valenti, G; Vazquez Gomez, R; Vazquez Regueiro, P; Vecchi, S; Velthuis, J J; Veltri, M; Vervink, K; Viaud, B; Videau, I; Vilasis-Cardona, X; Visniakov, J; Vollhardt, A; Voong, D; Vorobyev, A; Voss, H; Wacker, K; Wandernoth, S; Wang, J; Ward, D R; Webber, A D; Websdale, D; Whitehead, M; Wiedner, D; Wiggers, L; Wilkinson, G; Williams, M P; Williams, M; Wilson, F F; Wishahi, J; Witek, M; Witzeling, W; Wotton, S A; Wyllie, K; Xie, Y; Xing, F; Yang, Z; Young, R; Yushchenko, O; Zavertyaev, M; Zhang, F; Zhang, L; Zhang, W C; Zhang, Y; Zhelezov, A; Zhong, L; Zverev, E; Zvyagin, A

2012-01-01

Absolute luminosity measurements are of general interest for colliding-beam experiments at storage rings. These measurements are necessary to determine the absolute cross-sections of reaction processes and are valuable to quantify the performance of the accelerator. LHCb has applied two methods to determine the absolute scale of its luminosity measurements for proton-proton collisions at the LHC with a centre-of-mass energy of 7 TeV. In addition to the classic ``van der Meer scan'' method a novel technique has been developed which makes use of direct imaging of the individual beams using beam-gas and beam-beam interactions. This beam imaging method is made possible by the high resolution of the LHCb vertex detector and the close proximity of the detector to the beams, and allows beam parameters such as positions, angles and widths to be determined. The results of the two methods have comparable precision and are in good agreement. Combining the two methods, an overall precision of 3.5\\% in the absolute lumi...

Absolute calibration of TFTR helium proportional counters

International Nuclear Information System (INIS)

Strachan, J.D.; Diesso, M.; Jassby, D.; Johnson, L.; McCauley, S.; Munsat, T.; Roquemore, A.L.; Loughlin, M.

1995-06-01

The TFTR helium proportional counters are located in the central five (5) channels of the TFTR multichannel neutron collimator. These detectors were absolutely calibrated using a 14 MeV neutron generator positioned at the horizontal midplane of the TFTR vacuum vessel. The neutron generator position was scanned in centimeter steps to determine the collimator aperture width to 14 MeV neutrons and the absolute sensitivity of each channel. Neutron profiles were measured for TFTR plasmas with time resolution between 5 msec and 50 msec depending upon count rates. The He detectors were used to measure the burnup of 1 MeV tritons in deuterium plasmas, the transport of tritium in trace tritium experiments, and the residual tritium levels in plasmas following 50:50 DT experiments

Precision analysis for standard deviation measurements of immobile single fluorescent molecule images.

Science.gov (United States)

DeSantis, Michael C; DeCenzo, Shawn H; Li, Je-Luen; Wang, Y M

2010-03-29

Standard deviation measurements of intensity profiles of stationary single fluorescent molecules are useful for studying axial localization, molecular orientation, and a fluorescence imaging system's spatial resolution. Here we report on the analysis of the precision of standard deviation measurements of intensity profiles of single fluorescent molecules imaged using an EMCCD camera.We have developed an analytical expression for the standard deviation measurement error of a single image which is a function of the total number of detected photons, the background photon noise, and the camera pixel size. The theoretical results agree well with the experimental, simulation, and numerical integration results. Using this expression, we show that single-molecule standard deviation measurements offer nanometer precision for a large range of experimental parameters.

Wavelet neural network modeling in QSPR for prediction of solubility of 25 anthraquinone dyes at different temperatures and pressures in supercritical carbon dioxide.

Science.gov (United States)

Tabaraki, R; Khayamian, T; Ensafi, A A

2006-09-01

A wavelet neural network (WNN) model in quantitative structure property relationship (QSPR) was developed for predicting solubility of 25 anthraquinone dyes in supercritical carbon dioxide over a wide range of pressures (70-770 bar) and temperatures (291-423 K). A large number of descriptors were calculated with Dragon software and a subset of calculated descriptors was selected from 18 classes of Dragon descriptors with a stepwise multiple linear regression (MLR) as a feature selection technique. Six calculated and two experimental descriptors, pressure and temperature, were selected as the most feasible descriptors. The selected descriptors were used as input nodes in a wavelet neural network (WNN) model. The wavelet neural network architecture and its parameters were optimized simultaneously. The data was randomly divided to the training, prediction and validation sets. The predictive ability of the model was evaluated using validation data set. The root mean squares error (RMSE) and mean absolute errors were 0.339 and 0.221, respectively, for the validation data set. The performance of the WNN model was also compared with artificial neural network (ANN) model and the results showed the superiority of the WNN over ANN model.

A correlational study of scoliosis and trunk balance in adult patients with mandibular deviation.

Directory of Open Access Journals (Sweden)

Shuncheng Zhou

Full Text Available Previous studies have confirmed that patients with mandibular deviation often have abnormal morphology of their cervical vertebrae. However, the relationship between mandibular deviation, scoliosis, and trunk balance has not been studied. Currently, mandibular deviation is usually treated as a single pathology, which leads to poor clinical efficiency. We investigated the relationship of spine coronal morphology and trunk balance in adult patients with mandibular deviation, and compared the finding to those in healthy volunteers. 35 adult patients with skeletal mandibular deviation and 10 healthy volunteers underwent anterior X-ray films of the head and posteroanterior X-ray films of the spine. Landmarks and lines were drawn and measured on these films. The axis distance method was used to measure the degree of scoliosis and the balance angle method was used to measure trunk balance. The relationship of mandibular deviation, spine coronal morphology and trunk balance was evaluated with the Pearson correlation method. The spine coronal morphology of patients with mandibular deviation demonstrated an "S" type curve, while a straight line parallel with the gravity line was found in the control group (significant difference, p1°, while the control group had a normal trunk balance (imbalance angle <1°. There was a significant difference between the two groups (p<0.01. The degree of scoliosis and shoulder imbalance correlated with the degree of mandibular deviation, and presented a linear trend. The direction of mandibular deviation was the same as that of the lateral bending of thoracolumbar vertebrae, which was opposite to the direction of lateral bending of cervical vertebrae. Our study shows the degree of mandibular deviation has a high correlation with the degree of scoliosis and trunk imbalance, all the three deformities should be clinically evaluated in the management of mandibular deviation.

Coding of level of ambiguity within neural systems mediating choice.

Science.gov (United States)

Lopez-Paniagua, Dan; Seger, Carol A

2013-01-01

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

Optical track width measurements below 100 nm using artificial neural networks

Science.gov (United States)

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.

Stimulus Probability Effects in Absolute Identification

Science.gov (United States)

Kent, Christopher; Lamberts, Koen

2016-01-01

This study investigated the effect of stimulus presentation probability on accuracy and response times in an absolute identification task. Three schedules of presentation were used to investigate the interaction between presentation probability and stimulus position within the set. Data from individual participants indicated strong effects of…

Absolute gravity measurements in California

Science.gov (United States)

Zumberge, M. A.; Sasagawa, G.; Kappus, M.

1986-08-01

An absolute gravity meter that determines the local gravitational acceleration by timing a freely falling mass with a laser interferometer has been constructed. The instrument has made measurements at 11 sites in California, four in Nevada, and one in France. The uncertainty in the results is typically 10 microgal. Repeated measurements have been made at several of the sites; only one shows a substantial change in gravity.

Linear maps preserving maximal deviation and the Jordan structure of quantum systems

International Nuclear Information System (INIS)

Hamhalter, Jan

2012-01-01

In the algebraic approach to quantum theory, a quantum observable is given by an element of a Jordan algebra and a state of the system is modelled by a normalized positive functional on the underlying algebra. Maximal deviation of a quantum observable is the largest statistical deviation one can obtain in a particular state of the system. The main result of the paper shows that each linear bijective transformation between JBW algebras preserving maximal deviations is formed by a Jordan isomorphism or a minus Jordan isomorphism perturbed by a linear functional multiple of an identity. It shows that only one numerical statistical characteristic has the power to determine the Jordan algebraic structure completely. As a consequence, we obtain that only very special maps can preserve the diameter of the spectra of elements. Nonlinear maps preserving the pseudometric given by maximal deviation are also described. The results generalize hitherto known theorems on preservers of maximal deviation in the case of self-adjoint parts of von Neumann algebras proved by Molnár.

Deviations in human gut microbiota

DEFF Research Database (Denmark)

Casén, C; Vebø, H C; Sekelja, M

2015-01-01

microbiome profiling. AIM: To develop and validate a novel diagnostic test using faecal samples to profile the intestinal microbiota and identify and characterise dysbiosis. METHODS: Fifty-four DNA probes targeting ≥300 bacteria on different taxonomic levels were selected based on ability to distinguish......, and potential clinically relevant deviation in the microbiome from normobiosis. This model was tested in different samples from healthy volunteers and IBS and IBD patients (n = 330) to determine the ability to detect dysbiosis. RESULTS: Validation confirms dysbiosis was detected in 73% of IBS patients, 70...

Relational versus absolute representation in categorization.

Science.gov (United States)

Edwards, Darren J; Pothos, Emmanuel M; Perlman, Amotz

2012-01-01

This study explores relational-like and absolute-like representations in categorization. Although there is much evidence that categorization processes can involve information about both the particular physical properties of studied instances and abstract (relational) properties, there has been little work on the factors that lead to one kind of representation as opposed to the other. We tested 370 participants in 6 experiments, in which participants had to classify new items into predefined artificial categories. In 4 experiments, we observed a predominantly relational-like mode of classification, and in 2 experiments we observed a shift toward an absolute-like mode of classification. These results suggest 3 factors that promote a relational-like mode of classification: fewer items per group, more training groups, and the presence of a time delay. Overall, we propose that less information about the distributional properties of a category or weaker memory traces for the category exemplars (induced, e.g., by having smaller categories or a time delay) can encourage relational-like categorization.

9 CFR 381.308 - Deviations in processing.

Science.gov (United States)

2010-01-01

...) must be handled according to: (1)(i) A HACCP plan for canned product that addresses hazards associated... (d) of this section. (c) [Reserved] (d) Procedures for handling process deviations where the HACCP... accordance with the following procedures: (a) Emergency stops. (1) When retort jams or breakdowns occur...

Deviations from LTE in a stellar atmosphere

International Nuclear Information System (INIS)

Kalkofen, W.; Klein, R.I.; Stein, R.F.

1979-01-01

Deviations from LTE are investigated in an atmosphere of hydrogen atoms with one bound level, satisfying the equations of radiative, hydrostatic, and statistical equilibrium. The departure coefficient and the kinetic temperature as functions of the frequency dependence of the radiative cross section are studied analytically and numerically. Near the outer boundary of the atmosphere, the departure coefficient b is smaller than unity when the radiative cross section αsub(ν) grows with frequency ν faster than ν 2 ; b exceeds unity otherwise. Far from the boundary the departure coefficient tends to exceed unity for any frequency dependence of αsub(ν). Overpopulation (b > 1) always implies that the kinetic temperature in the statistical equilibrium atmosphere is higher than the temperature in the corresponding LTE atmosphere. Upper and lower bounds on the kinetic temperature are given for an atmosphere with deviations from LTE only in the optically shallow layers when the emergent intensity can be described by a radiation temperature. (author)

Deviations from LTE in a stellar atmosphere

Science.gov (United States)

Kalkofen, W.; Klein, R. I.; Stein, R. F.

1979-01-01

Deviations for LTE are investigated in an atmosphere of hydrogen atoms with one bound level, satisfying the equations of radiative, hydrostatic, and statistical equilibrium. The departure coefficient and the kinetic temperature as functions of the frequency dependence of the radiative cross section are studied analytically and numerically. Near the outer boundary of the atmosphere, the departure coefficient is smaller than unity when the radiative cross section grows with frequency faster than with the square of frequency; it exceeds unity otherwise. Far from the boundary the departure coefficient tends to exceed unity for any frequency dependence of the radiative cross section. Overpopulation always implies that the kinetic temperature in the statistical-equilibrium atmosphere is higher than the temperature in the corresponding LTE atmosphere. Upper and lower bounds on the kinetic temperature are given for an atmosphere with deviations from LTE only in the optically shallow layers when the emergent intensity can be described by a radiation temperature.

Process Measurement Deviation Analysis for Flow Rate due to Miscalibration

Energy Technology Data Exchange (ETDEWEB)

Oh, Eunsuk; Kim, Byung Rae; Jeong, Seog Hwan; Choi, Ji Hye; Shin, Yong Chul; Yun, Jae Hee [KEPCO Engineering and Construction Co., Deajeon (Korea, Republic of)

2016-10-15

An analysis was initiated to identify the root cause, and the exemption of high static line pressure correction to differential pressure (DP) transmitters was one of the major deviation factors. Also the miscalibrated DP transmitter range was identified as another major deviation factor. This paper presents considerations to be incorporated in the process flow measurement instrumentation calibration and the analysis results identified that the DP flow transmitter electrical output decreased by 3%. Thereafter, flow rate indication decreased by 1.9% resulting from the high static line pressure correction exemption and measurement range miscalibration. After re-calibration, the flow rate indication increased by 1.9%, which is consistent with the analysis result. This paper presents the brief calibration procedures for Rosemount DP flow transmitter, and analyzes possible three cases of measurement deviation including error and cause. Generally, the DP transmitter is required to be calibrated with precise process input range according to the calibration procedure provided for specific DP transmitter. Especially, in case of the DP transmitter installed in high static line pressure, it is important to correct the high static line pressure effect to avoid the inherent systematic error for Rosemount DP transmitter. Otherwise, failure to notice the correction may lead to indicating deviation from actual value.

Effect of Absolute From Hibiscus syriacus L. Flower on Wound Healing in Keratinocytes

Science.gov (United States)

Yoon, Seok Won; Lee, Kang Pa; Kim, Do-Yoon; Hwang, Dae Il; Won, Kyung-Jong; Lee, Dae Won; Lee, Hwan Myung

2017-01-01

Background: Proliferation and migration of keratinocytes are essential for the repair of cutaneous wounds. Hibiscus syriacus L. has been used in Asian medicine; however, research on keratinocytes is inadequate. Objective: To establish the dermatological properties of absolute from Hibiscus syriacus L. flower (HSF) and to provide fundamental research for alternative medicine. Materials and Methods: We identified the composition of HSF absolute using gas chromatography-mass spectrometry analysis. We also examined the effect of HSF absolute in HaCaT cells using the XTT assay, Boyden chamber assay, sprout-out growth assay, and western blotting. We conducted an in-vivo wound healing assay in rat tail-skin. Results: Ten major active compounds were identified from HSF absolute. As determined by the XTT assay, Boyden chamber assay, and sprout-out growth assay results, HSF absolute exhibited similar effects as that of epidermal growth factor on the proliferation and migration patterns of keratinocytes (HaCaT cells), which were significantly increased after HSF absolute treatment. The expression levels of the phosphorylated signaling proteins relevant to proliferation, including extracellular signal-regulated kinase 1/2 (Erk 1/2) and Akt, were also determined by western blot analysis. Conclusion: These results of our in-vitro and ex-vivo studies indicate that HSF absolute induced cell growth and migration of HaCaT cells by phosphorylating both Erk 1/2 and Akt. Moreover, we confirmed the wound-healing effect of HSF on injury of the rat tail-skin. Therefore, our results suggest that HSF absolute is promising for use in cosmetics and alternative medicine. SUMMARY Hisbiscus syriacus L. flower absolute increases HaCaT cell migration and proliferation.Hisbiscus syriacus L. flower absolute regulates phosphorylation of ERK 1/2 and Akt in HaCaT cell.Treatment with Hisbiscus syriacus L. flower induced sprout outgrowth.The wound in the tail-skin of rat was reduced by Hisbiscus syriacus

Absolute and convective instability of a liquid sheet with transverse temperature gradient

International Nuclear Information System (INIS)

Fu, Qing-Fei; Yang, Li-Jun; Tong, Ming-Xi; Wang, Chen

2013-01-01

Highlights: • The spatial–temporal instability of a liquid sheet with thermal effects was studied. • The flow can transit to absolutely unstable with certain flow parameters. • The effects of non-dimensional parameters on the transition were studied. -- Abstract: The spatial–temporal instability behavior of a viscous liquid sheet with temperature difference between the two surfaces was investigated theoretically. The practical situation motivating this investigation is liquid sheet heated by ambient gas, usually encountered in industrial heat transfer and liquid propellant rocket engines. The existing dispersion relation was used, to explore the spatial–temporal instability of viscous liquid sheets with a nonuniform temperature profile, by setting both the wave number and frequency complex. A parametric study was performed in both sinuous and varicose modes to test the influence of dimensionless numbers on the transition between absolute and convective instability of the flow. For a small value of liquid Weber number, or a great value of gas-to-liquid density ratio, the flow was found to be absolutely unstable. The absolute instability was enhanced by increasing the liquid viscosity. It was found that variation of the Marangoni number hardly influenced the absolute instability of the sinuous mode of oscillations; however it slightly affected the absolute instability in the varicose mode

The fading American dream: Trends in absolute income mobility since 1940.

Science.gov (United States)

Chetty, Raj; Grusky, David; Hell, Maximilian; Hendren, Nathaniel; Manduca, Robert; Narang, Jimmy

2017-04-28

We estimated rates of "absolute income mobility"-the fraction of children who earn more than their parents-by combining data from U.S. Census and Current Population Survey cross sections with panel data from de-identified tax records. We found that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Increasing Gross Domestic Product (GDP) growth rates alone cannot restore absolute mobility to the rates experienced by children born in the 1940s. However, distributing current GDP growth more equally across income groups as in the 1940 birth cohort would reverse more than 70% of the decline in mobility. These results imply that reviving the "American dream" of high rates of absolute mobility would require economic growth that is shared more broadly across the income distribution. Copyright © 2017, American Association for the Advancement of Science.

Advantages of heavy metal collars in directional drilling and deviation control

International Nuclear Information System (INIS)

Bradley, W.B.; Murphey, C.E.; McLamore, R.T.; Dickson, L.L.

1976-01-01

A heavy, stiff-bottom drill collar can substantially improve deviation performance, theoretically increasing penetration rates by 50 to 100 percent in deviation-prone areas. This paper presents the underlying theory, practical charts on performance characteristics, and Shell Development Co.'s experience in fabricating and field testing two depleted-uranium alloy, heavy metal collars

The Analysis of a Deviation of Investment and Corporate Governance

OpenAIRE

Shoichi Hisa

2008-01-01

Investment of firms is affected by not only fundamentals factors, but liquidity constraint, ownership or corporate structure. Information structure between manager and owner is a significant factor to decide the level of investment, and deviation of investment from optimal condition. The reputation model between manager and owner suggest that the separate of ownership and management may induce the deviation of investment, and indicate that governance structure is important to reduce it. In th...

An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

Directory of Open Access Journals (Sweden)

Ping-Huan Kuo

2018-04-01

Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

Sample-path large deviations in credit risk

NARCIS (Netherlands)

Leijdekker, V.J.G.; Mandjes, M.R.H.; Spreij, P.J.C.

2011-01-01

The event of large losses plays an important role in credit risk. As these large losses are typically rare, and portfolios usually consist of a large number of positions, large deviation theory is the natural tool to analyze the tail asymptotics of the probabilities involved. We first derive a

Absolute total cross sections for noble gas systems

International Nuclear Information System (INIS)

Kam, P. van der.

1981-01-01

This thesis deals with experiments on the elastic scattering of Ar, Kr and Xe, using the molecular beam technique. The aim of this work was the measurement of the absolute value of the total cross section and the behaviour of the total cross section, Q, as function of the relative velocity g of the scattering partners. The author gives an extensive analysis of the glory structure in the total cross section and parametrizes the experimental results using a semiclassical model function. This allows a detailed comparison of the phase and amplitude of the predicted and measured glory undulations. He indicates how the depth and position of the potential well should be changed in order to come to an optimum description of the glory structure. With this model function he has also been able to separate the glory and attractive contribution to Q, and using the results from the extrapolation measurements he has obtained absolute values for Qsub(a). From these absolute values he has calculated the parameter C 6 that determines the strength of the attractive region of the potential. In two of the four investigated gas combinations the obtained values lie outside the theoretical bounds. (Auth.)

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Shen Yang; Bax, Ad, E-mail: bax@nih.go [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States)

2010-09-15

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and {sup 13}C{sup {beta}} chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and {sup 13}C{sup {beta}} atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for {delta}{sup 15}N, {delta}{sup 13}C', {delta}{sup 13}C{sup {alpha}}, {delta}{sup 13}C{sup {beta}}, {delta}{sup 1}H{sup {alpha}} and {delta}{sup 1}H{sup N}, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

Bodily Deviations and Body Image in Adolescence

Science.gov (United States)

Vilhjalmsson, Runar; Kristjansdottir, Gudrun; Ward, Dianne S.

2012-01-01

Adolescents with unusually sized or shaped bodies may experience ridicule, rejection, or exclusion based on their negatively valued bodily characteristics. Such experiences can have negative consequences for a person's image and evaluation of self. This study focuses on the relationship between bodily deviations and body image and is based on a…

Analyzing Menton Deviation in Posteroanterior Cephalogram in Early Detection of Temporomandibular Disorder

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Trelia Boel

2017-01-01

Full Text Available Introduction. Some clinicians believed that mandibular deviation leads to facial asymmetry and it also had a correlation with temporomandibular disorders (TMDs. Posteroanterior (PA cephalogram was widely reported as a regular record in treating facial asymmetry and craniofacial anomalies. The objective of this study was to analyze the relationship of menton deviation in PA cephalogram with temporomandibular disorders (TMDs symptoms. Materials and Methods. TMJ function was initially screened based on TMD-DI questionnaire. PA cephalogram of volunteer subjects with TMDs (n=37 and without TMDs (n=33 with mean age of 21.61±2.08 years was taken. The menton deviation was measured by the distance (mm from menton point to midsagittal reference (MSR horizontally, using software digitized measurement, and categorized as asymmetric if the value is greater than 3 mm. The prevalence and difference of menton deviation in both groups were evaluated by unpaired t-test. Result. The prevalence of symmetry group showed that 65.9% had no TMDs with mean of 1,815 ± 0,71 mm; in contrast, the prevalence of asymmetry group showed that 95.5% reported TMDs with mean of 3,159 ± 1,053 mm. There was a significant difference of menton deviation to TMDs (p=0.000 in subjects with and without TMDs. Conclusion. There was a significant relationship of menton deviation in PA cephalogram with TMDs based on TMD-DI index.

Why do lesser toes deviate laterally in hallux valgus? A radiographic study.

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Roan, Li-Yi; Tanaka, Yasuhito; Taniguchi, Akira; Tomiwa, Kiyonori; Kumai, Tsukasa; Cheng, Yuh-Min

2015-06-01

Hallux valgus foot with laterally deviated lesser toes is a complex condition to treat. Ignoring the laterally deviated lesser toes in hallux valgus might result in unsatisfactory foot shape. Without lateral support of the lesser toes, it might increase the risk of recurrence of hallux valgus. We sought to identify associated radiographic findings in patients where lesser toes follow the great toe in hallux valgus and deviate laterally. The weight-bearing, anteroposterior foot radiographs of 24 female hallux valgus feet with laterally deviated lesser toes (group L), 34 female hallux valgus feet with normal lesser toes (group H), and 43 normal female feet (group N) were selected for the study. A 2-dimensional coordinated system was used to analyze the shapes and angles of these feet by converting each dot made on the radiographs onto X and Y coordinates. Diagrams of the feet in each group were drawn for comparison. The hallux valgus angle, lateral deviation angle of the second toe, intermetatarsal angles, toe length, metatarsal length, and metatarsus adductus were calculated according to the coordinates of the corresponding points. The mapping showed the bases of the second, third, and fourth toe in group L shifted laterally away from their corresponding metatarsal head (P hallux valgus angles (P hallux valgus angle, more adducted first metatarsal, and divergent lateral splaying of the lesser metatarsals were associated with lateral deviation of the lesser toes in hallux valgus. Level III, comparative study. © The Author(s) 2015.

Customer churn prediction using a hybrid method and censored data

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Reza Tavakkoli-Moghaddam

2013-05-01

Full Text Available Customers are believed to be the main part of any organization’s assets and customer retention as well as customer churn management are important responsibilities of organizations. In today’s competitive environment, organization must do their best to retain their existing customers since attracting new customers cost significantly more than taking care of existing ones. In this paper, we present a hybrid method based on neural network and Cox regression analysis where neural network is used for outlier data and Cox regression method is implemented for prediction of future events. The proposed model of this paper has been implemented on some data and the results are compared based on five criteria including prediction accuracy, errors’ type I and II, root mean square error and mean absolute deviation. The preliminary results indicate that the proposed model of this paper performs better than alternative methods.

Evolvable Neural Software System

Science.gov (United States)

Curtis, Steven A.

2009-01-01

The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

Large deviations and portfolio optimization

Science.gov (United States)

Sornette, Didier

Risk control and optimal diversification constitute a major focus in the finance and insurance industries as well as, more or less consciously, in our everyday life. We present a discussion of the characterization of risks and of the optimization of portfolios that starts from a simple illustrative model and ends by a general functional integral formulation. A major item is that risk, usually thought of as one-dimensional in the conventional mean-variance approach, has to be addressed by the full distribution of losses. Furthermore, the time-horizon of the investment is shown to play a major role. We show the importance of accounting for large fluctuations and use the theory of Cramér for large deviations in this context. We first treat a simple model with a single risky asset that exemplifies the distinction between the average return and the typical return and the role of large deviations in multiplicative processes, and the different optimal strategies for the investors depending on their size. We then analyze the case of assets whose price variations are distributed according to exponential laws, a situation that is found to describe daily price variations reasonably well. Several portfolio optimization strategies are presented that aim at controlling large risks. We end by extending the standard mean-variance portfolio optimization theory, first within the quasi-Gaussian approximation and then using a general formulation for non-Gaussian correlated assets in terms of the formalism of functional integrals developed in the field theory of critical phenomena.

Using Long-Short-Term-Memory Recurrent Neural Networks to Predict Aviation Engine Vibrations

Science.gov (United States)

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.

A robust neural network-based approach for microseismic event detection

KAUST Repository

Akram, Jubran

2017-08-17

We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.

Absolute instabilities of travelling wave solutions in a Keller-Segel model

Science.gov (United States)

Davis, P. N.; van Heijster, P.; Marangell, R.

2017-11-01

We investigate the spectral stability of travelling wave solutions in a Keller-Segel model of bacterial chemotaxis with a logarithmic chemosensitivity function and a constant, sublinear, and linear consumption rate. Linearising around the travelling wave solutions, we locate the essential and absolute spectrum of the associated linear operators and find that all travelling wave solutions have parts of the essential spectrum in the right half plane. However, we show that in the case of constant or sublinear consumption there exists a range of parameters such that the absolute spectrum is contained in the open left half plane and the essential spectrum can thus be weighted into the open left half plane. For the constant and sublinear consumption rate models we also determine critical parameter values for which the absolute spectrum crosses into the right half plane, indicating the onset of an absolute instability of the travelling wave solution. We observe that this crossing always occurs off of the real axis.

Absolute photonic band gap in 2D honeycomb annular photonic crystals

International Nuclear Information System (INIS)

Liu, Dan; Gao, Yihua; Tong, Aihong; Hu, Sen

2015-01-01

Highlights: • A two-dimensional honeycomb annular photonic crystal (PC) is proposed. • The absolute photonic band gap (PBG) is studied. • Annular PCs show larger PBGs than usual air-hole PCs for high refractive index. • Annular PCs with anisotropic rods show large PBGs for low refractive index. • There exist optimal parameters to open largest band gaps. - Abstract: Using the plane wave expansion method, we investigate the effects of structural parameters on absolute photonic band gap (PBG) in two-dimensional honeycomb annular photonic crystals (PCs). The results reveal that the annular PCs possess absolute PBGs that are larger than those of the conventional air-hole PCs only when the refractive index of the material from which the PC is made is equal to 4.5 or larger. If the refractive index is smaller than 4.5, utilization of anisotropic inner rods in honeycomb annular PCs can lead to the formation of larger PBGs. The optimal structural parameters that yield the largest absolute PBGs are obtained

A study on the deviation aspects of the poem “The Eightieth Stage”

Directory of Open Access Journals (Sweden)

Soghra Salmaninejad Mehrabadi

2016-02-01

Full Text Available Abstract  One of the methods of literature review is addressing the literary forms that deeply attracted formalists’ attention. Formalists emphasizes certain shapes and forms and methods of literary language. In their view, the task of literature and art is not making known but representing the elements in the realm of art. So, for creating an outstanding work, novelty is not just important, but modes of expression can show a new dimension of the world to audience.   Shklovsky raised for the first time the term “de familiarization”. In his opinion, art renews sensory perception and transforms the familiar rules and seemingly enduring structures of reality. He believes defamiliarization is seeing things out of their natural context. According to Leach, the two usual methods to distinguish the general language from the language of arts are rule-increasing and rule-decreasing.   In deviation occurs a resurrection of words and the author creates a double pleasure through language games in contact with the audience. Imagery, music and syntax can produce a kind of defamiliarization.   Akhavan in the poem “The Eightieth Stag e ” has used a variety of deviations. The first is in the name of poetry. In this paper, the norms of lexical, semantic and stylistic in the poem “Eightieth Stage” is checked.   Lexical deviation Sartre says the poet does not use the words, but sometimes words would use the new syntax. Akhavan in the words sometimes making terms such as “mardestan (masculinity, faramoshzar (plains of oblivion, gandomand (Robust, golazin (inflorescence, parhib (deception and ...” and sometimes new compounds alters magically the effect of poetry. His compounds like talaei makhmal avayan (golden voices and velvet, pak ayin (clean rituals, malmalin dastar (silver turban, marde mardestan (men of masculinity, rakhshe rakhshande (the brilliant horse and…" makes illustration that in some of synergistic base has helped to the poet

Absolute Distance Measurements with Tunable Semiconductor Laser

Czech Academy of Sciences Publication Activity Database

Mikel, Břetislav; Číp, Ondřej; Lazar, Josef

T118, - (2005), s. 41-44 ISSN 0031-8949 R&D Projects: GA AV ČR(CZ) IAB2065001 Keywords : tunable laser * absolute interferometer Subject RIV: BH - Optics, Masers, Lasers Impact factor: 0.661, year: 2004

MEAN OF MEDIAN ABSOLUTE DERIVATION TECHNIQUE MEAN ...

African Journals Online (AJOL)

eobe

development of mean of median absolute derivation technique based on the based on the based on .... of noise mean to estimate the speckle noise variance. Noise mean property ..... Foraging Optimization,” International Journal of. Advanced ...

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

Science.gov (United States)

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.

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

Science.gov (United States)

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

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches.

Directory of Open Access Journals (Sweden)

Eric Lowet

Full Text Available 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

OBSERVABLE DEVIATIONS FROM HOMOGENEITY IN AN INHOMOGENEOUS UNIVERSE

Energy Technology Data Exchange (ETDEWEB)

Giblin, John T. Jr. [Department of Physics, Kenyon College, 201 N College Road Gambier, OH 43022 (United States); Mertens, James B.; Starkman, Glenn D. [CERCA/ISO, Department of Physics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 (United States)

2016-12-20

How does inhomogeneity affect our interpretation of cosmological observations? It has long been wondered to what extent the observable properties of an inhomogeneous universe differ from those of a corresponding Friedmann–Lemaître–Robertson–Walker (FLRW) model, and how the inhomogeneities affect that correspondence. Here, we use numerical relativity to study the behavior of light beams traversing an inhomogeneous universe, and construct the resulting Hubble diagrams. The universe that emerges exhibits an average FLRW behavior, but inhomogeneous structures contribute to deviations in observables across the observer’s sky. We also investigate the relationship between angular diameter distance and the angular extent of a source, finding deviations that grow with source redshift. These departures from FLRW are important path-dependent effects, with implications for using real observables in an inhomogeneous universe such as our own.

OBSERVABLE DEVIATIONS FROM HOMOGENEITY IN AN INHOMOGENEOUS UNIVERSE

International Nuclear Information System (INIS)

Giblin, John T. Jr.; Mertens, James B.; Starkman, Glenn D.

2016-01-01

How does inhomogeneity affect our interpretation of cosmological observations? It has long been wondered to what extent the observable properties of an inhomogeneous universe differ from those of a corresponding Friedmann–Lemaître–Robertson–Walker (FLRW) model, and how the inhomogeneities affect that correspondence. Here, we use numerical relativity to study the behavior of light beams traversing an inhomogeneous universe, and construct the resulting Hubble diagrams. The universe that emerges exhibits an average FLRW behavior, but inhomogeneous structures contribute to deviations in observables across the observer’s sky. We also investigate the relationship between angular diameter distance and the angular extent of a source, finding deviations that grow with source redshift. These departures from FLRW are important path-dependent effects, with implications for using real observables in an inhomogeneous universe such as our own.

Absolute continuity of autophage measures on finite-dimensional vector spaces

Energy Technology Data Exchange (ETDEWEB)

Raja, C R.E. [Stat-Math Unit, Indian Statistical Institute, Bangalore (India); [Abdus Salam International Centre for Theoretical Physics, Trieste (Italy)]. E-mail: creraja@isibang.ac.in

2002-06-01

We consider a class of measures called autophage which was introduced and studied by Szekely for measures on the real line. We show that the autophage measures on finite-dimensional vector spaces over real or Q{sub p} are infinitely divisible without idempotent factors and are absolutely continuous with bounded continuous density. We also show that certain semistable measures on such vector spaces are absolutely continuous. (author)

Det demokratiske argument for absolut ytringsfrihed

DEFF Research Database (Denmark)

Lægaard, Sune

2014-01-01

Artiklen diskuterer den påstand, at absolut ytringsfrihed er en nødvendig forudsætning for demokratisk legitimitet med udgangspunkt i en rekonstruktion af et argument fremsat af Ronald Dworkin. Spørgsmålet er, hvorfor ytringsfrihed skulle være en forudsætning for demokratisk legitimitet, og hvorfor...

Thin-film magnetoresistive absolute position detector

NARCIS (Netherlands)

Groenland, J.P.J.

1990-01-01

The subject of this thesis is the investigation of a digital absolute posi- tion-detection system, which is based on a position-information carrier (i.e. a magnetic tape) with one single code track on the one hand, and an array of magnetoresistive sensors for the detection of the information on the

A comparative study on patient specific absolute dosimetry using slab phantom, acrylic body phantom and goat head phantom

Directory of Open Access Journals (Sweden)

Om Prakash Gurjar

2015-01-01

Full Text Available Purpose: To compare the results of patient specific absolute dosimetry using slab phantom, acrylic body phantom and goat head phantom. Methods: Fifteen intensity modulated radiotherapy (IMRT plans already planned on treatment planning system (TPS for head-and-neck cancer patients were exported on all three kinds of phantoms viz. slab phantom, acrylic body phantom and goat head phantom, and dose was calculated using anisotropic analytic algorithm (AAA. All the gantry angles were set to zero in case of slab phantom while set to as it is in actual plan in case of other two phantoms. All the plans were delivered by linear accelerator (LA and dose for each plan was measured by 0.13 cc ion chamber. The percentage (% variations between planned and measured doses were calculated and analyzed. Results: The mean % variations between planned and measured doses of all IMRT quality assurance (QA plans were as 0.65 (Standard deviation (SD: 0.38 with confidence limit (CL 1.39, 1.16 (SD: 0.61 with CL 2.36 and 2.40 (SD: 0.86 with CL 4.09 for slab phantom, acrylic head phantom and goat head phantom respectively. Conclusion: Higher dose variations found in case of real tissue phantom compare to results in case of slab and acrylic body phantoms. The algorithm AAA does not calculate doses in heterogeneous medium as accurate as it calculates in homogeneous medium. Therefore the patient specific absolute dosimetry should be done using heterogeneous phantom mimicking density wise as well as design wise to the actual human body.  

Chaotic diagonal recurrent neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Zhang Yi

2012-01-01

We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

DOES ABSOLUTE SYNONYMY EXIST IN OWERE-IGBO?

African Journals Online (AJOL)

USER

The researcher also interviewed native speakers of the dialect. The study ... The word 'synonymy' means sameness of meaning, i.e., a relationship in which more ... whether absolute synonymy exists in Owere–Igbo or not. ..... 'close this book'.

Computational modeling of neural plasticity for self-organization of neural networks.

Science.gov (United States)

Chrol-Cannon, Joseph; Jin, Yaochu

2014-11-01

Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Design of a heart rate controller for treadmill exercise using a recurrent fuzzy neural network.

Science.gov (United States)

Lu, Chun-Hao; Wang, Wei-Cheng; Tai, Cheng-Chi; Chen, Tien-Chi

2016-05-01

In this study, we developed a computer controlled treadmill system using a recurrent fuzzy neural network heart rate controller (RFNNHRC). Treadmill speeds and inclines were controlled by corresponding control servo motors. The RFNNHRC was used to generate the control signals to automatically control treadmill speed and incline to minimize the user heart rate deviations from a preset profile. The RFNNHRC combines a fuzzy reasoning capability to accommodate uncertain information and an artificial recurrent neural network learning process that corrects for treadmill system nonlinearities and uncertainties. Treadmill speeds and inclines are controlled by the RFNNHRC to achieve minimal heart rate deviation from a pre-set profile using adjustable parameters and an on-line learning algorithm that provides robust performance against parameter variations. The on-line learning algorithm of RFNNHRC was developed and implemented using a dsPIC 30F4011 DSP. Application of the proposed control scheme to heart rate responses of runners resulted in smaller fluctuations than those produced by using proportional integra control, and treadmill speeds and inclines were smoother. The present experiments demonstrate improved heart rate tracking performance with the proposed control scheme. The RFNNHRC scheme with adjustable parameters and an on-line learning algorithm was applied to a computer controlled treadmill system with heart rate control during treadmill exercise. Novel RFNNHRC structure and controller stability analyses were introduced. The RFNNHRC were tuned using a Lyapunov function to ensure system stability. The superior heart rate control with the proposed RFNNHRC scheme was demonstrated with various pre-set heart rates. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

1 CFR 21.14 - Deviations from standard organization of the Code of Federal Regulations.

Science.gov (United States)

2010-01-01

... 1 General Provisions 1 2010-01-01 2010-01-01 false Deviations from standard organization of the... CODIFICATION General Numbering § 21.14 Deviations from standard organization of the Code of Federal Regulations. (a) Any deviation from standard Code of Federal Regulations designations must be approved in advance...

Towards absolute neutrino masses

Energy Technology Data Exchange (ETDEWEB)

Vogel, Petr [Kellogg Radiation Laboratory 106-38, Caltech, Pasadena, CA 91125 (United States)

2007-06-15

Various ways of determining the absolute neutrino masses are briefly reviewed and their sensitivities compared. The apparent tension between the announced but unconfirmed observation of the 0{nu}{beta}{beta} decay and the neutrino mass upper limit based on observational cosmology is used as an example of what could happen eventually. The possibility of a 'nonstandard' mechanism of the 0{nu}{beta}{beta} decay is stressed and the ways of deciding which of the possible mechanisms is actually operational are described. The importance of the 0{nu}{beta}{beta} nuclear matrix elements is discussed and their uncertainty estimated.

Absolute migration of Pacific basin mid-ocean ridges since 85 Ma ...

African Journals Online (AJOL)

Mid-ocean ridges are major physiographic features that dominate the world seafloor. Their absolute motion and tectonics are recorded in magnetic lineations they created. The absolute migration of mid-ocean ridges in the Pacific basin since 85 Ma and their tectonic implications was investigated in this work and the results ...

A deviation display method for visualising data in mobile gamma-ray spectrometry

Energy Technology Data Exchange (ETDEWEB)

Kock, Peder, E-mail: Peder.Kock@med.lu.s [Department of Medical Radiation Physics, Clinical Sciences, Lund University, University Hospital, SE-221 85 Lund (Sweden); Finck, Robert R. [Swedish Radiation Protection Authority, SE-171 16 Stockholm (Sweden); Nilsson, Jonas M.C.; Ostlund, Karl; Samuelsson, Christer [Department of Medical Radiation Physics, Clinical Sciences, Lund University, University Hospital, SE-221 85 Lund (Sweden)

2010-09-15

A real time visualisation method, to be used in mobile gamma-spectrometric search operations using standard detector systems is presented. The new method, called deviation display, uses a modified waterfall display to present relative changes in spectral data over energy and time. Using unshielded {sup 137}Cs and {sup 241}Am point sources and different natural background environments, the behaviour of the deviation displays is demonstrated and analysed for two standard detector types (NaI(Tl) and HPGe). The deviation display enhances positive significant changes while suppressing the natural background fluctuations. After an initialisation time of about 10 min this technique leads to a homogeneous display dominated by the background colour, where even small changes in spectral data are easy to discover. As this paper shows, the deviation display method works well for all tested gamma energies and natural background radiation levels and with both tested detector systems.

A deviation display method for visualising data in mobile gamma-ray spectrometry

International Nuclear Information System (INIS)

Kock, Peder; Finck, Robert R.; Nilsson, Jonas M.C.; Ostlund, Karl; Samuelsson, Christer

2010-01-01

A real time visualisation method, to be used in mobile gamma-spectrometric search operations using standard detector systems is presented. The new method, called deviation display, uses a modified waterfall display to present relative changes in spectral data over energy and time. Using unshielded 137 Cs and 241 Am point sources and different natural background environments, the behaviour of the deviation displays is demonstrated and analysed for two standard detector types (NaI(Tl) and HPGe). The deviation display enhances positive significant changes while suppressing the natural background fluctuations. After an initialisation time of about 10 min this technique leads to a homogeneous display dominated by the background colour, where even small changes in spectral data are easy to discover. As this paper shows, the deviation display method works well for all tested gamma energies and natural background radiation levels and with both tested detector systems.

Absolute humidity and the seasonal onset of influenza in the continental United States.

Directory of Open Access Journals (Sweden)

Jeffrey Shaman

2010-02-01

Full Text Available Much of the observed wintertime increase of mortality in temperate regions is attributed to seasonal influenza. A recent reanalysis of laboratory experiments indicates that absolute humidity strongly modulates the airborne survival and transmission of the influenza virus. Here, we extend these findings to the human population level, showing that the onset of increased wintertime influenza-related mortality in the United States is associated with anomalously low absolute humidity levels during the prior weeks. We then use an epidemiological model, in which observed absolute humidity conditions temper influenza transmission rates, to successfully simulate the seasonal cycle of observed influenza-related mortality. The model results indicate that direct modulation of influenza transmissibility by absolute humidity alone is sufficient to produce this observed seasonality. These findings provide epidemiological support for the hypothesis that absolute humidity drives seasonal variations of influenza transmission in temperate regions.

Deviations from Vegard’s law in ternary III-V alloys

KAUST Repository

Murphy, S. T.

2010-08-03

Vegard’s law states that, at a constant temperature, the volume of an alloy can be determined from a linear interpolation of its constituent’s volumes. Deviations from this description occur such that volumes are both greater and smaller than the linear relationship would predict. Here we use special quasirandom structures and density functional theory to investigate such deviations for MxN1−xAs ternary alloys, where M and N are group III species (B, Al, Ga, and In). Our simulations predict a tendency, with the exception of AlxGa1−xAs, for the volume of the ternary alloys to be smaller than that determined from the linear interpolation of the volumes of the MAs and BAs binary alloys. Importantly, we establish a simple relationship linking the relative size of the group III atoms in the alloy and the predicted magnitude of the deviation from Vegard’s law.

Overspecification of colour, pattern, and size: Salience, absoluteness, and consistency

OpenAIRE

Sammie eTarenskeen; Mirjam eBroersma; Mirjam eBroersma; Bart eGeurts

2015-01-01

The rates of overspecification of colour, pattern, and size are compared, to investigate how salience and absoluteness contribute to the production of overspecification. Colour and pattern are absolute attributes, whereas size is relative and less salient. Additionally, a tendency towards consistent responses is assessed. Using a within-participants design, we find similar rates of colour and pattern overspecification, which are both higher than the rate of size overspecification. Using a bet...

Overspecification of color, pattern, and size: salience, absoluteness, and consistency

OpenAIRE

Tarenskeen, S.L.; Broersma, M.; Geurts, B.

2015-01-01

The rates of overspecification of color, pattern, and size are compared, to investigate how salience and absoluteness contribute to the production of overspecification. Color and pattern are absolute and salient attributes, whereas size is relative and less salient. Additionally, a tendency toward consistent responses is assessed. Using a within-participants design, we find similar rates of color and pattern overspecification, which are both higher than the rate of size overspecification. Usi...

Improvements in absolute seismometer sensitivity calibration using local earth gravity measurements

Science.gov (United States)

Anthony, Robert E.; Ringler, Adam; Wilson, David

2018-01-01

The ability to determine both absolute and relative seismic amplitudes is fundamentally limited by the accuracy and precision with which scientists are able to calibrate seismometer sensitivities and characterize their response. Currently, across the Global Seismic Network (GSN), errors in midband sensitivity exceed 3% at the 95% confidence interval and are the least‐constrained response parameter in seismic recording systems. We explore a new methodology utilizing precise absolute Earth gravity measurements to determine the midband sensitivity of seismic instruments. We first determine the absolute sensitivity of Kinemetrics EpiSensor accelerometers to 0.06% at the 99% confidence interval by inverting them in a known gravity field at the Albuquerque Seismological Laboratory (ASL). After the accelerometer is calibrated, we install it in its normal configuration next to broadband seismometers and subject the sensors to identical ground motions to perform relative calibrations of the broadband sensors. Using this technique, we are able to determine the absolute midband sensitivity of the vertical components of Nanometrics Trillium Compact seismometers to within 0.11% and Streckeisen STS‐2 seismometers to within 0.14% at the 99% confidence interval. The technique enables absolute calibrations from first principles that are traceable to National Institute of Standards and Technology (NIST) measurements while providing nearly an order of magnitude more precision than step‐table calibrations.

Absolute photoionization cross-section of the methyl radical.

Science.gov (United States)

Taatjes, Craig A; Osborn, David L; Selby, Talitha M; Meloni, Giovanni; Fan, Haiyan; Pratt, Stephen T

2008-10-02

The absolute photoionization cross-section of the methyl radical has been measured using two completely independent methods. The CH3 photoionization cross-section was determined relative to that of acetone and methyl vinyl ketone at photon energies of 10.2 and 11.0 eV by using a pulsed laser-photolysis/time-resolved synchrotron photoionization mass spectrometry method. The time-resolved depletion of the acetone or methyl vinyl ketone precursor and the production of methyl radicals following 193 nm photolysis are monitored simultaneously by using time-resolved synchrotron photoionization mass spectrometry. Comparison of the initial methyl signal with the decrease in precursor signal, in combination with previously measured absolute photoionization cross-sections of the precursors, yields the absolute photoionization cross-section of the methyl radical; sigma(CH3)(10.2 eV) = (5.7 +/- 0.9) x 10(-18) cm(2) and sigma(CH3)(11.0 eV) = (6.0 +/- 2.0) x 10(-18) cm(2). The photoionization cross-section for vinyl radical determined by photolysis of methyl vinyl ketone is in good agreement with previous measurements. The methyl radical photoionization cross-section was also independently measured relative to that of the iodine atom by comparison of ionization signals from CH3 and I fragments following 266 nm photolysis of methyl iodide in a molecular-beam ion-imaging apparatus. These measurements gave a cross-section of (5.4 +/- 2.0) x 10(-18) cm(2) at 10.460 eV, (5.5 +/- 2.0) x 10(-18) cm(2) at 10.466 eV, and (4.9 +/- 2.0) x 10(-18) cm(2) at 10.471 eV. The measurements allow relative photoionization efficiency spectra of methyl radical to be placed on an absolute scale and will facilitate quantitative measurements of methyl concentrations by photoionization mass spectrometry.

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

Science.gov (United States)

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.

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Solar radiation pressure and deviations from Keplerian orbits

Energy Technology Data Exchange (ETDEWEB)

Kezerashvili, Roman Ya. [Physics Department, New York City College of Technology, the City University of New York, Brooklyn, NY 11201 (United States); Vazquez-Poritz, Justin F. [Physics Department, New York City College of Technology, City University of New York, Brooklyn, NY 11201 (United States)], E-mail: jporitz@gmail.com

2009-05-04

Newtonian gravity and general relativity give exactly the same expression for the period of an object in circular orbit around a static central mass. However, when the effects of the curvature of spacetime and solar radiation pressure are considered simultaneously for a solar sail propelled satellite, there is a deviation from Kepler's third law. It is shown that solar radiation pressure affects the period of this satellite in two ways: by effectively decreasing the solar mass, thereby increasing the period, and by enhancing the effects of other phenomena, potentially rendering some of them detectable. In particular, we consider deviations from Keplerian orbits due to spacetime curvature, frame dragging from the rotation of the sun, the oblateness of the sun, a possible net electric charge of the sun, and a very small positive cosmological constant.

Absolute dissipative drift-wave instabilities in tokamaks

International Nuclear Information System (INIS)

Chen, L.; Chance, M.S.; Cheng, C.Z.

1979-07-01

Contrary to previous theoretical predictions, it is shown that the dissipative drift-wave instabilities are absolute in tokamak plasmas. The existence of unstable eigenmodes is shown to be associated with a new eigenmode branch induced by the finite toroidal couplings

Neural chips, neural computers and application in high and superhigh energy physics experiments

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

Internal descriptions of absolute Borel classes

Czech Academy of Sciences Publication Activity Database

Holický, P.; Pelant, Jan

2004-01-01

Roč. 141, č. 1 (2004), s. 87-104 ISSN 0166-8641 R&D Projects: GA ČR GA201/00/1466; GA ČR GA201/03/0933 Institutional research plan: CEZ:AV0Z1019905 Keywords : absolute Borel class * complete sequence of covers * open map Subject RIV: BA - General Mathematics Impact factor: 0.364, year: 2004

Effect of density deviations of concrete on its attenuation efficiency

International Nuclear Information System (INIS)

Szymendera, L.; Wincel, K.; Blociszewski, S.; Kordyasz, D.; Sobolewska, I.

In the work, the influence of concrete density deviation on shield thickness and total dose ratio outside the reactor shield, has--on the basis of numerical analysis--been considered. It has been noticed the possibility of introducing flexible corrections--without additional shielding calculation--to the design thickness of the shield. It has been also found that in common cases of shield design, where any necessity of minimizing the shield thickness does not exist, the tendency to minimize the value of this deviation is hardly substantiable

Improved iterative oscillation tests for first-order deviating differential equations

Directory of Open Access Journals (Sweden)

George E. Chatzarakis

2018-01-01

Full Text Available In this paper, improved oscillation conditions are established for the oscillation of all solutions of differential equations with non-monotone deviating arguments and nonnegative coefficients. They lead to a procedure that checks for oscillations by iteratively computing \\(\\lim \\sup\\ and \\(\\lim \\inf\\ on terms recursively defined on the equation's coefficients and deviating argument. This procedure significantly improves all known oscillation criteria. The results and the improvement achieved over the other known conditions are illustrated by two examples, numerically solved in MATLAB.

Optimization of neural network algorithm of the land market description

Directory of Open Access Journals (Sweden)

M. A. Karpovich

2016-01-01

Full Text Available The advantages of neural network technology is shown in comparison of traditional descriptions of dynamically changing systems, which include a modern land market. The basic difficulty arising in the practical implementation of neural network models of the land market and construction products is revealed It is the formation of a representative set of training and test examples. The requirements which are necessary for the correct description of the current economic situation has been determined, it consists in the fact that Train-paid-set in the feature space should not has the ranges with a low density of observations. The methods of optimization of empirical array, which allow to avoid the long-range extrapolation of data from range of concentration of the set of examples are formulated. It is shown that a radical method of optimization a set of training and test examples enclosing to collect supplemantary information, is associated with significant costs time and resources for the economic problems and the ratio of cost / efficiency is less efficient than an algorithm optimization neural network models the earth market fixed set of empirical data. Algorithm of optimization based on the transformation of arrays of information which represents the expansion of the ranges of concentration of the set of examples and compression the ranges of low density of observations is analyzed in details. The significant reduction in the relative error of land price description is demonstrated on the specific example of Voronezh region market of lands which intend for road construction, it makes the using of radical method of empirical optimization of the array costeffective with accounting the significant absolute value of the land. The high economic efficiency of the proposed algorithms is demonstrated.

A universal multilingual weightless neural network tagger via quantitative linguistics.

Science.gov (United States)

Carneiro, Hugo C C; Pedreira, Carlos E; França, Felipe M G; Lima, Priscila M V

2017-07-01

In the last decade, given the availability of corpora in several distinct languages, research on multilingual part-of-speech tagging started to grow. Amongst the novelties there is mWANN-Tagger (multilingual weightless artificial neural network tagger), a weightless neural part-of-speech tagger capable of being used for mostly-suffix-oriented languages. The tagger was subjected to corpora in eight languages of quite distinct natures and had a remarkable accuracy with very low sample deviation in every one of them, indicating the robustness of weightless neural systems for part-of-speech tagging tasks. However, mWANN-Tagger needed to be tuned for every new corpus, since each one required a different parameter configuration. For mWANN-Tagger to be truly multilingual, it should be usable for any new language with no need of parameter tuning. This article proposes a study that aims to find a relation between the lexical diversity of a language and the parameter configuration that would produce the best performing mWANN-Tagger instance. Preliminary analyses suggested that a single parameter configuration may be applied to the eight aforementioned languages. The mWANN-Tagger instance produced by this configuration was as accurate as the language-dependent ones obtained through tuning. Afterwards, the weightless neural tagger was further subjected to new corpora in languages that range from very isolating to polysynthetic ones. The best performing instances of mWANN-Tagger are again the ones produced by the universal parameter configuration. Hence, mWANN-Tagger can be applied to new corpora with no need of parameter tuning, making it a universal multilingual part-of-speech tagger. Further experiments with Universal Dependencies treebanks reveal that mWANN-Tagger may be extended and that it has potential to outperform most state-of-the-art part-of-speech taggers if better word representations are provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artificial neural network for research reactor safety status monitoring

International Nuclear Information System (INIS)

Varde, P.V.

2001-01-01

During reactor upset/abnormal conditions, emphasis is placed on plant operator's ability to quickly identify the problem and perform diagnosis and initiate recovery action to ensure safety of the plant. However, the reliability of human action is adversely affected at the time of crisis, due to the time stress and psychological factors. Availability of operational aids capable of monitoring the status of the plant and quickly identifying the deviation from normal operation is expected to significantly improve the operator reliability. Artificial Neural Network (based on Back Propagation Algorithm) has been developed and applied for reactor safety status monitoring, as part of an Operator Support System. ANN has been trained for 14 different plant states using 42 input symptom patterns. Recall tests performed on the ANN show that the system was able to identify the plant state with reasonable accuracy. (author)

Neural tissue-spheres

DEFF Research Database (Denmark)

Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

2007-01-01

By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

Absolute photoionization cross-section of the propargyl radical

Energy Technology Data Exchange (ETDEWEB)

Savee, John D.; Welz, Oliver; Taatjes, Craig A.; Osborn, David L. [Sandia National Laboratories, Combustion Research Facility, Livermore, California 94551 (United States); Soorkia, Satchin [Institut des Sciences Moleculaires d' Orsay, Universite Paris-Sud 11, Orsay (France); Selby, Talitha M. [Department of Chemistry, University of Wisconsin, Washington County Campus, West Bend, Wisconsin 53095 (United States)

2012-04-07

Using synchrotron-generated vacuum-ultraviolet radiation and multiplexed time-resolved photoionization mass spectrometry we have measured the absolute photoionization cross-section for the propargyl (C{sub 3}H{sub 3}) radical, {sigma}{sub propargyl}{sup ion}(E), relative to the known absolute cross-section of the methyl (CH{sub 3}) radical. We generated a stoichiometric 1:1 ratio of C{sub 3}H{sub 3} : CH{sub 3} from 193 nm photolysis of two different C{sub 4}H{sub 6} isomers (1-butyne and 1,3-butadiene). Photolysis of 1-butyne yielded values of {sigma}{sub propargyl}{sup ion}(10.213 eV)=(26.1{+-}4.2) Mb and {sigma}{sub propargyl}{sup ion}(10.413 eV)=(23.4{+-}3.2) Mb, whereas photolysis of 1,3-butadiene yielded values of {sigma}{sub propargyl}{sup ion}(10.213 eV)=(23.6{+-}3.6) Mb and {sigma}{sub propargyl}{sup ion}(10.413 eV)=(25.1{+-}3.5) Mb. These measurements place our relative photoionization cross-section spectrum for propargyl on an absolute scale between 8.6 and 10.5 eV. The cross-section derived from our results is approximately a factor of three larger than previous determinations.

Design and analysis of control charts for standard deviation with estimated parameters

NARCIS (Netherlands)

Schoonhoven, M.; Riaz, M.; Does, R.J.M.M.

2011-01-01

This paper concerns the design and analysis of the standard deviation control chart with estimated limits. We consider an extensive range of statistics to estimate the in-control standard deviation (Phase I) and design the control chart for real-time process monitoring (Phase II) by determining the

[Roaming through methodology. XXXVIII. Common misconceptions involving standard deviation and standard error

NARCIS (Netherlands)

Mokkink, H.G.A.

2002-01-01

Standard deviation and standard error have a clear mutual relationship, but at the same time they differ strongly in the type of information they supply. This can lead to confusion and misunderstandings. Standard deviation describes the variability in a sample of measures of a variable, for instance

14 CFR 121.360 - Ground proximity warning-glide slope deviation alerting system.

Science.gov (United States)

2010-01-01

... deviation alerting system. 121.360 Section 121.360 Aeronautics and Space FEDERAL AVIATION ADMINISTRATION... Equipment Requirements § 121.360 Ground proximity warning-glide slope deviation alerting system. (a) No... system that meets the performance and environmental standards of TSO-C92 (available from the FAA, 800...

Combining neural networks and genetic algorithms for hydrological flow forecasting

Science.gov (United States)

Neruda, Roman; Srejber, Jan; Neruda, Martin; Pascenko, Petr

2010-05-01

We present a neural network approach to rainfall-runoff modeling for small size river basins based on several time series of hourly measured data. Different neural networks are considered for short time runoff predictions (from one to six hours lead time) based on runoff and rainfall data observed in previous time steps. Correlation analysis shows that runoff data, short time rainfall history, and aggregated API values are the most significant data for the prediction. Neural models of multilayer perceptron and radial basis function networks with different numbers of units are used and compared with more traditional linear time series predictors. Out of possible 48 hours of relevant history of all the input variables, the most important ones are selected by means of input filters created by a genetic algorithm. The genetic algorithm works with population of binary encoded vectors defining input selection patterns. Standard genetic operators of two-point crossover, random bit-flipping mutation, and tournament selection were used. The evaluation of objective function of each individual consists of several rounds of building and testing a particular neural network model. The whole procedure is rather computational exacting (taking hours to days on a desktop PC), thus a high-performance mainframe computer has been used for our experiments. Results based on two years worth data from the Ploucnice river in Northern Bohemia suggest that main problems connected with this approach to modeling are ovetraining that can lead to poor generalization, and relatively small number of extreme events which makes it difficult for a model to predict the amplitude of the event. Thus, experiments with both absolute and relative runoff predictions were carried out. In general it can be concluded that the neural models show about 5 per cent improvement in terms of efficiency coefficient over liner models. Multilayer perceptrons with one hidden layer trained by back propagation algorithm and

A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

Directory of Open Access Journals (Sweden)

Ludi Wang

2018-01-01

Full Text Available The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM is used for feature extraction, and an artificial neural network (ANN is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

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

Science.gov (United States)

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.

Characterizing absolute piezoelectric microelectromechanical system displacement using an atomic force microscope

International Nuclear Information System (INIS)

Evans, J.; Chapman, S.

2014-01-01

Piezoresponse Force Microscopy (PFM) is a popular tool for the study of ferroelectric and piezoelectric materials at the nanometer level. Progress in the development of piezoelectric MEMS fabrication is highlighting the need to characterize absolute displacement at the nanometer and Ångstrom scales, something Atomic Force Microscopy (AFM) might do but PFM cannot. Absolute displacement is measured by executing a polarization measurement of the ferroelectric or piezoelectric capacitor in question while monitoring the absolute vertical position of the sample surface with a stationary AFM cantilever. Two issues dominate the execution and precision of such a measurement: (1) the small amplitude of the electrical signal from the AFM at the Ångstrom level and (2) calibration of the AFM. The authors have developed a calibration routine and test technique for mitigating the two issues, making it possible to use an atomic force microscope to measure both the movement of a capacitor surface as well as the motion of a micro-machine structure actuated by that capacitor. The theory, procedures, pitfalls, and results of using an AFM for absolute piezoelectric measurement are provided

Characterizing absolute piezoelectric microelectromechanical system displacement using an atomic force microscope

Energy Technology Data Exchange (ETDEWEB)

Evans, J., E-mail: radiant@ferrodevices.com; Chapman, S., E-mail: radiant@ferrodevices.com [Radiant Technologies, Inc., 2835C Pan American Fwy NE, Albuquerque, New Mexico 87107 (United States)

2014-08-14

Piezoresponse Force Microscopy (PFM) is a popular tool for the study of ferroelectric and piezoelectric materials at the nanometer level. Progress in the development of piezoelectric MEMS fabrication is highlighting the need to characterize absolute displacement at the nanometer and Ångstrom scales, something Atomic Force Microscopy (AFM) might do but PFM cannot. Absolute displacement is measured by executing a polarization measurement of the ferroelectric or piezoelectric capacitor in question while monitoring the absolute vertical position of the sample surface with a stationary AFM cantilever. Two issues dominate the execution and precision of such a measurement: (1) the small amplitude of the electrical signal from the AFM at the Ångstrom level and (2) calibration of the AFM. The authors have developed a calibration routine and test technique for mitigating the two issues, making it possible to use an atomic force microscope to measure both the movement of a capacitor surface as well as the motion of a micro-machine structure actuated by that capacitor. The theory, procedures, pitfalls, and results of using an AFM for absolute piezoelectric measurement are provided.

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

Science.gov (United States)

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.

Geometry of river networks. I. Scaling, fluctuations, and deviations

International Nuclear Information System (INIS)

Dodds, Peter Sheridan; Rothman, Daniel H.

2001-01-01

This paper is the first in a series of three papers investigating the detailed geometry of river networks. Branching networks are a universal structure employed in the distribution and collection of material. Large-scale river networks mark an important class of two-dimensional branching networks, being not only of intrinsic interest but also a pervasive natural phenomenon. In the description of river network structure, scaling laws are uniformly observed. Reported values of scaling exponents vary, suggesting that no unique set of scaling exponents exists. To improve this current understanding of scaling in river networks and to provide a fuller description of branching network structure, here we report a theoretical and empirical study of fluctuations about and deviations from scaling. We examine data for continent-scale river networks such as the Mississippi and the Amazon and draw inspiration from a simple model of directed, random networks. We center our investigations on the scaling of the length of a subbasin's dominant stream with its area, a characterization of basin shape known as Hack's law. We generalize this relationship to a joint probability density, and provide observations and explanations of deviations from scaling. We show that fluctuations about scaling are substantial, and grow with system size. We find strong deviations from scaling at small scales which can be explained by the existence of a linear network structure. At intermediate scales, we find slow drifts in exponent values, indicating that scaling is only approximately obeyed and that universality remains indeterminate. At large scales, we observe a breakdown in scaling due to decreasing sample space and correlations with overall basin shape. The extent of approximate scaling is significantly restricted by these deviations, and will not be improved by increases in network resolution

A Note on Standard Deviation and Standard Error

Science.gov (United States)

Hassani, Hossein; Ghodsi, Mansoureh; Howell, Gareth

2010-01-01

Many students confuse the standard deviation and standard error of the mean and are unsure which, if either, to use in presenting data. In this article, we endeavour to address these questions and cover some related ambiguities about these quantities.

A vibration correction method for free-fall absolute gravimeters

Science.gov (United States)

Qian, J.; Wang, G.; Wu, K.; Wang, L. J.

2018-02-01

An accurate determination of gravitational acceleration, usually approximated as 9.8 m s-2, has been playing an important role in the areas of metrology, geophysics, and geodetics. Absolute gravimetry has been experiencing rapid developments in recent years. Most absolute gravimeters today employ a free-fall method to measure gravitational acceleration. Noise from ground vibration has become one of the most serious factors limiting measurement precision. Compared to vibration isolators, the vibration correction method is a simple and feasible way to reduce the influence of ground vibrations. A modified vibration correction method is proposed and demonstrated. A two-dimensional golden section search algorithm is used to search for the best parameters of the hypothetical transfer function. Experiments using a T-1 absolute gravimeter are performed. It is verified that for an identical group of drop data, the modified method proposed in this paper can achieve better correction effects with much less computation than previous methods. Compared to vibration isolators, the correction method applies to more hostile environments and even dynamic platforms, and is expected to be used in a wider range of applications.

Wide-field absolute transverse blood flow velocity mapping in vessel centerline

Science.gov (United States)

Wu, Nanshou; Wang, Lei; Zhu, Bifeng; Guan, Caizhong; Wang, Mingyi; Han, Dingan; Tan, Haishu; Zeng, Yaguang

2018-02-01

We propose a wide-field absolute transverse blood flow velocity measurement method in vessel centerline based on absorption intensity fluctuation modulation effect. The difference between the light absorption capacities of red blood cells and background tissue under low-coherence illumination is utilized to realize the instantaneous and average wide-field optical angiography images. The absolute fuzzy connection algorithm is used for vessel centerline extraction from the average wide-field optical angiography. The absolute transverse velocity in the vessel centerline is then measured by a cross-correlation analysis according to instantaneous modulation depth signal. The proposed method promises to contribute to the treatment of diseases, such as those related to anemia or thrombosis.

Neural network based daily precipitation generator (NNGEN-P)

Energy Technology Data Exchange (ETDEWEB)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

On determining absolute entropy without quantum theory or the third law of thermodynamics

Science.gov (United States)

Steane, Andrew M.

2016-04-01

We employ classical thermodynamics to gain information about absolute entropy, without recourse to statistical methods, quantum mechanics or the third law of thermodynamics. The Gibbs-Duhem equation yields various simple methods to determine the absolute entropy of a fluid. We also study the entropy of an ideal gas and the ionization of a plasma in thermal equilibrium. A single measurement of the degree of ionization can be used to determine an unknown constant in the entropy equation, and thus determine the absolute entropy of a gas. It follows from all these examples that the value of entropy at absolute zero temperature does not need to be assigned by postulate, but can be deduced empirically.

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.

Science.gov (United States)

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.

Relationship and significance of gait deviations associated with limb length discrepancy: A systematic review.

Science.gov (United States)

Khamis, Sam; Carmeli, Eli

2017-09-01

Controversy still exists as to the clinical significance of leg length discrepancy (LLD) in spite of the fact that further evidence has been emerging regarding the relationship between several clinical conditions and LLD. The objectives of our study were to review the available research with regard to LLD as a cause of clinically significant gait deviations, to determine if there is a relationship between the magnitude of LLD and the presence of gait deviations and to identify the most common gait deviations associated with LLD. In line with the PRISMA guidelines, a literature search was carried out throughout the Medline, CINAHL and EMBASE databases. Twelve articles met the predetermined inclusion criteria and were included in the review. Quality assessment using the Methodological Index for Non-Randomized Studies (MINORS) scale was completed for all included studies. Two main methodologies were found in 4 studies evaluating gait asymmetry in patients or healthy participants with anatomic LLD and 8 studies evaluating gait deviations while simulating LLD by employing artificial lifts of 1-5cm on healthy subjects. A significant relationship was found between anatomic LLD and gait deviation. Evidence suggests that gait deviations may occur with discrepancies of >1cm, with greater impact seen as the discrepancy increases. Compensatory strategies were found to occur in both the shorter and longer limb, throughout the lower limb. As the discrepancy increases, more compensatory strategies occur. Sagittal plane deviations seem to be the most effective deviations, although, frontal plane compensations also occur in the pelvis, hip and foot. Copyright © 2017 Elsevier B.V. All rights reserved.

40 CFR 60.2780 - What must I include in the deviation report?

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What must I include in the deviation... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emissions Guidelines and... the deviation report? In each report required under § 60.2775, for any pollutant or parameter that...

40 CFR 60.2958 - What must I include in the deviation report?

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What must I include in the deviation... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Operator Training and Qualification Recordkeeping and Reporting § 60.2958 What must I include in the deviation report? In each report...

Fingerprints of flower absolutes using supercritical fluid chromatography hyphenated with high resolution mass spectrometry.

Science.gov (United States)

Santerre, Cyrille; Vallet, Nadine; Touboul, David

2018-06-02

Supercritical fluid chromatography hyphenated with high resolution mass spectrometry (SFC-HRMS) was developed for fingerprint analysis of different flower absolutes commonly used in cosmetics field, especially in perfumes. Supercritical fluid chromatography-atmospheric pressure photoionization-high resolution mass spectrometry (SFC-APPI-HRMS) technique was employed to identify the components of the fingerprint. The samples were separated with a porous graphitic carbon (PGC) Hypercarb™ column (100 mm × 2.1 mm, 3 μm) by gradient elution using supercritical CO 2 and ethanol (0.0-20.0 min (2-30% B), 20.0-25.0 min (30% B), 25.0-26.0 min (30-2% B) and 26.0-30.0 min (2% B)) as mobile phase at a flow rate of 1.5 mL/min. In order to compare the SFC fingerprints between five different flower absolutes: Jasminum grandiflorum absolutes, Jasminum sambac absolutes, Narcissus jonquilla absolutes, Narcissus poeticus absolutes, Lavandula angustifolia absolutes from different suppliers and batches, the chemometric procedure including principal component analysis (PCA) was applied to classify the samples according to their genus and their species. Consistent results were obtained to show that samples could be successfully discriminated. Copyright © 2018 Elsevier B.V. All rights reserved.

Telling in-tune from out-of-tune: widespread evidence for implicit absolute intonation.

Science.gov (United States)

Van Hedger, Stephen C; Heald, Shannon L M; Huang, Alex; Rutstein, Brooke; Nusbaum, Howard C

2017-04-01

Absolute pitch (AP) is the rare ability to name or produce an isolated musical note without the aid of a reference note. One skill thought to be unique to AP possessors is the ability to provide absolute intonation judgments (e.g., classifying an isolated note as "in-tune" or "out-of-tune"). Recent work has suggested that absolute intonation perception among AP possessors is not crystallized in a critical period of development, but is dynamically maintained by the listening environment, in which the vast majority of Western music is tuned to a specific cultural standard. Given that all listeners of Western music are constantly exposed to this specific cultural tuning standard, our experiments address whether absolute intonation perception extends beyond AP possessors. We demonstrate that non-AP listeners are able to accurately judge the intonation of completely isolated notes. Both musicians and nonmusicians showed evidence for absolute intonation recognition when listening to familiar timbres (piano and violin). When testing unfamiliar timbres (triangle and inverted sine waves), only musicians showed weak evidence of absolute intonation recognition (Experiment 2). Overall, these results highlight a previously unknown similarity between AP and non-AP possessors' long-term musical note representations, including evidence of sensitivity to frequency.

FEMORAL NECK FRACTURES GARDEN I AND II: EVALUATION OF THE DEVIATION IN LATERAL VIEW.

Science.gov (United States)

Leonhardt, Natália Zalc; Melo, Lucas da Ponte; Nordon, David Gonçalves; Silva, Fernando Brandão de Andrade E; Kojima, Kodi Edson; Silva, Jorge Santos

2017-01-01

To evaluate the rate of deviation in the lateral radiographic incidence in patients with femoral neck fracture classified as non-diverted in the anteroposterior view (Garden I and II). Nineteen selected patients with femoral neck fractures classified as Garden I and II were retrospectively evaluated, estimating the degree of deviation in the lateral view. Fifteen cases (79%) presented deviations in lateral view, with a mean of 18.6 degrees (±15.5). Most fractures of the femoral neck classified as Garden I and II present some degree of posterior deviation in the X-ray lateral view. Level of Evidence III, Retrospective Comparative Study.

Method for solving fully fuzzy linear programming problems using deviation degree measure

Institute of Scientific and Technical Information of China (English)

Haifang Cheng; Weilai Huang; Jianhu Cai

2013-01-01

A new ful y fuzzy linear programming (FFLP) prob-lem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crispδ-parametric linear programming (LP) problem. Giving the value of deviation degree in each constraint, the δ-fuzzy optimal so-lution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict: to improve the va-lues of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to il ustrate the proposed method.

Graded versus ungraded inferior oblique anterior transposition in patients with asymmetric dissociated vertical deviation.

Science.gov (United States)

Rajavi, Zhale; Feizi, Mohadeseh; Naderi, Ali; Sabbaghi, Hamideh; Behradfar, Narges; Yaseri, Mehdi; Faghihi, Mohammad

2017-12-01

To report the surgical outcomes of graded versus ungraded inferior oblique anterior transposition (IOAT) in treatment of patients with asymmetric dissociated vertical deviation (DVD) and bilateral inferior oblique overaction (IOOA). A total of 74 eyes of 37 patients with asymmetric DVD (interocular difference of ≥5 Δ ) and bilateral IOOA of > +1 were included in this randomized clinical trial. In the ungraded group (n = 18), both inferior oblique muscles were sutured at the inferior rectus level; in the graded group (n = 19), the inferior oblique muscles of eyes with more DVD were sutured at the level of the inferior rectus and inferior oblique muscles of eyes with less DVD were sutured 2 mm posterior to the level of the inferior rectus muscle. DVD was significantly reduced in each group (P < 0.001 for both). Although the postoperative mean difference of asymmetry of DVD was less in the ungraded group compared to the graded group (1.2 ± 1.9 vs 3.2 ± 1.2 [P = 0.001]), the absolute amounts of reduction of DVD asymmetry were similar (4.3 ± 2.3 vs 4.4 ± 3.1 [P = 0.78]). IOOA and V patterns were also reduced postoperatively. Each method of IOAT was effective in reducing DVD, asymmetry, IOOA, and V patterns. Copyright © 2017 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.

40 CFR 60.2220 - What must I include in the deviation report?

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What must I include in the deviation... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Standards of Performance for... Recordkeeping and Reporting § 60.2220 What must I include in the deviation report? In each report required under...

40 CFR 60.3053 - What must I include in the deviation report?

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What must I include in the deviation... PROGRAMS (CONTINUED) STANDARDS OF PERFORMANCE FOR NEW STATIONARY SOURCES Emission Guidelines and Compliance... Model Rule-Recordkeeping and Reporting § 60.3053 What must I include in the deviation report? In each...

Effects of Fish Oil Supplementation during the Suckling Period on Auditory Neural Conduction in n-3 Fatty Acid-Deficient Rat Pups

Directory of Open Access Journals (Sweden)

vida rahimi

2014-07-01

Full Text Available Abstract Introduction: Omega 3 fatty acid especially in the form of fish oil, has structural and biological role in the body's various systems especially nervous system. Numerous studies have tried to research about it. Auditory is one of the affected systems. Omega 3 deficiency can have devastating effects on the nervous system and auditory. This study aimed to evaluate neural conduction in n-3 fatty acid-deficient rat pups following the supplementation of fish oil consumption during the suckling period Materials and Methods: In this interventional and experimental study, one sources of omega3 fatty acid (fish oil were fed to rat pups of n-3 PUFA-deficient dams to compare changes in their auditory neural conduction with that of control and n-3 PUFA-deficient groups, using Auditory Brainstem Response (ABR. The parameters of interest were P1, P3, P4 absolute latency, P1-P3, P1-P4 and P3-P4 IPL , P4/P1 amplitude ratio . The rat pups were given oral fish oil, 5 Ml /g weight for 17 days, between the age of 5 and 21 days. Results There were no significant group differences in P1 and P3 absolute latency (p > 0.05. but the result in P4 was significant(P ≤ 0.05 . The n-3 PUFA deficient +vehicle had the most prolonged (the worst P1-P4 IPL and P3-P4 IPL compared with control and n-3 PUFA deficient + FO groups. There was no significant difference in P1-P4 IPL and P3-P4 IPL between n-3 PUFA deficient + FO and control groups (p > 0.05.There was a significant effect of diet on P1-P4 IPL and P3-P4 IPL between groups (P ≤ 0.05. Conclusion: The results of present study showed the effect of omega3 deficiency on auditory neural structure during pregnancy and lactation period. Additionally, we observed the reduced devastating effects on neural conduction in n-3 fatty acid-deficient rat pups following the supplementation of fish oil during the suckling period

The Absolute Immanence in Deleuze

OpenAIRE

Park, Daeseung

2013-01-01

The absolute immanence in Deleuze Daeseung Park Abstract The plane of immanence is not unique. Deleuze and Guattari suppose a multiplicity of planes. Each great philosopher draws new planes on his own way, and these planes constitute the "time of philosophy". We can, therefore, "present the entire history of philosophy from the viewpoint of the institution of a plane of immanence" or present the time of philosophy from the viewpoint of the superposition and of the coexistence of planes. Howev...

On the absolute measure of Beta activities

International Nuclear Information System (INIS)

Sanchez del Rio, C.; Jimenez Reynaldo, O.; Rodriguez Mayquez, E.

1956-01-01

A new method for absolute beta counting of solid samples is given. The mea surements is made with an inside Geiger-Muller tube of new construction. The backscattering correction when using an infinite thick mounting is discussed and results for different materials given. (Author)

Hazards and preventive measures of well deviation in well construction of in-situ leaching

International Nuclear Information System (INIS)

Zou Wenjie; Chen Shihe

2006-01-01

Whether the in-situ leaching method is successful depends on the quality of borehole engineering to a great extent. There are lots of factors that affect the quality, and the well deviation is one of notable problems. The hazards and causes of the well deviation are analyzed. The preventive measures and the methods of rectifying the deviation are put forward. (authors)

A geometrically exact beam element based on the absolute nodal coordinate formulation

International Nuclear Information System (INIS)

Gerstmayr, Johannes; Matikainen, Marko K.; Mikkola, Aki M.

2008-01-01

In this study, Reissner's classical nonlinear rod formulation, as implemented by Simo and Vu-Quoc by means of the large rotation vector approach, is implemented into the framework of the absolute nodal coordinate formulation. The implementation is accomplished in the planar case accounting for coupled axial, bending, and shear deformation. By employing the virtual work of elastic forces similarly to Simo and Vu-Quoc in the absolute nodal coordinate formulation, the numerical results of the formulation are identical to those of the large rotation vector formulation. It is noteworthy, however, that the material definition in the absolute nodal coordinate formulation can differ from the material definition used in Reissner's beam formulation. Based on an analytical eigenvalue analysis, it turns out that the high frequencies of cross section deformation modes in the absolute nodal coordinate formulation are only slightly higher than frequencies of common shear modes, which are present in the classical large rotation vector formulation of Simo and Vu-Quoc, as well. Thus, previous claims that the absolute nodal coordinate formulation is inefficient or would lead to ill-conditioned finite element matrices, as compared to classical approaches, could be refuted. In the introduced beam element, locking is prevented by means of reduced integration of certain parts of the elastic forces. Several classical large deformation static and dynamic examples as well as an eigenvalue analysis document the equivalence of classical nonlinear rod theories and the absolute nodal coordinate formulation for the case of appropriate material definitions. The results also agree highly with those computed in commercial finite element codes

Standard deviation index for stimulated Brillouin scattering suppression with different homogeneities.

Science.gov (United States)

Ran, Yang; Su, Rongtao; Ma, Pengfei; Wang, Xiaolin; Zhou, Pu; Si, Lei

2016-05-10

We present a new quantitative index of standard deviation to measure the homogeneity of spectral lines in a fiber amplifier system so as to find the relation between the stimulated Brillouin scattering (SBS) threshold and the homogeneity of the corresponding spectral lines. A theoretical model is built and a simulation framework has been established to estimate the SBS threshold when input spectra with different homogeneities are set. In our experiment, by setting the phase modulation voltage to a constant value and the modulation frequency to different values, spectral lines with different homogeneities can be obtained. The experimental results show that the SBS threshold increases negatively with the standard deviation of the modulated spectrum, which is in good agreement with the theoretical results. When the phase modulation voltage is confined to 10 V and the modulation frequency is set to 80 MHz, the standard deviation of the modulated spectrum equals 0.0051, which is the lowest value in our experiment. Thus, at this time, the highest SBS threshold has been achieved. This standard deviation can be a good quantitative index in evaluating the power scaling potential in a fiber amplifier system, which is also a design guideline in suppressing the SBS to a better degree.

The neural processing of foreign-accented speech and its relationship to listener bias

Directory of Open Access Journals (Sweden)

Han-Gyol eYi

2014-10-01

Full Text Available 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.

Wavelength selection method with standard deviation: application to pulse oximetry.

Science.gov (United States)

Vazquez-Jaccaud, Camille; Paez, Gonzalo; Strojnik, Marija

2011-07-01

Near-infrared spectroscopy provides useful biological information after the radiation has penetrated through the tissue, within the therapeutic window. One of the significant shortcomings of the current applications of spectroscopic techniques to a live subject is that the subject may be uncooperative and the sample undergoes significant temporal variations, due to his health status that, from radiometric point of view, introduce measurement noise. We describe a novel wavelength selection method for monitoring, based on a standard deviation map, that allows low-noise sensitivity. It may be used with spectral transillumination, transmission, or reflection signals, including those corrupted by noise and unavoidable temporal effects. We apply it to the selection of two wavelengths for the case of pulse oximetry. Using spectroscopic data, we generate a map of standard deviation that we propose as a figure-of-merit in the presence of the noise introduced by the living subject. Even in the presence of diverse sources of noise, we identify four wavelength domains with standard deviation, minimally sensitive to temporal noise, and two wavelengths domains with low sensitivity to temporal noise.

Utilization of 1H NMR in the determination of absolute configuration of alcohols

International Nuclear Information System (INIS)

Barreiros, Marizeth L.; David, Jorge M.; David, Juceni P. . E-juceni@ufba.br

2005-01-01

This review reports the determination of absolute configuration of primary and secondary alcohols by 1 H NMR spectroscopy, using the Mosher method. This method consists in the derivatization of an alcohol possessing unknown absolute configuration with one or both enantiomers of an auxiliary reagent. The resulting diastereoisomer spectra are registered and compared, and the chemical shift differences (Δδ R,S = δ R - δ S ) are measured. The determination of the absolute configuration of the alcohol molecule is based on the correlation between its chiral center and the auxiliary reagent's chiral center. Therefore, the determination of the absolute configuration depends on aromatic ring shielding effects on the substituents of the alcohol as evidenced by the 1 H NMR spectrum. (author)

On the absolute meaning of motion

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H. Edwards

Full Text Available The present manuscript aims to clarify why motion causes matter to age slower in a comparable sense, and how this relates to relativistic effects caused by motion. A fresh analysis of motion, build on first axiom, delivers proof with its result, from which significant new understanding and computational power is gained.A review of experimental results demonstrates, that unaccelerated motion causes matter to age slower in a comparable, observer independent sense. Whilst focusing on this absolute effect, the present manuscript clarifies its context to relativistic effects, detailing their relationship and incorporating both into one consistent picture. The presented theoretical results make new predictions and are testable through suggested experiment of a novel nature. The manuscript finally arrives at an experimental tool and methodology, which as far as motion in ungravitated space is concerned or gravity appreciated, enables us to find the absolute observer independent picture of reality, which is reflected in the comparable display of atomic clocks.The discussion of the theoretical results, derives a physical causal understanding of gravity, a mathematical formulation of which, will be presented. Keywords: Kinematics, Gravity, Atomic clocks, Cosmic microwave background

Linear ultrasonic motor for absolute gravimeter.

Science.gov (United States)

Jian, Yue; Yao, Zhiyuan; Silberschmidt, Vadim V

2017-05-01

Thanks to their compactness and suitability for vacuum applications, linear ultrasonic motors are considered as substitutes for classical electromagnetic motors as driving elements in absolute gravimeters. Still, their application is prevented by relatively low power output. To overcome this limitation and provide better stability, a V-type linear ultrasonic motor with a new clamping method is proposed for a gravimeter. In this paper, a mechanical model of stators with flexible clamping components is suggested, according to a design criterion for clamps of linear ultrasonic motors. After that, an effect of tangential and normal rigidity of the clamping components on mechanical output is studied. It is followed by discussion of a new clamping method with sufficient tangential rigidity and a capability to facilitate pre-load. Additionally, a prototype of the motor with the proposed clamping method was fabricated and the performance tests in vertical direction were implemented. Experimental results show that the suggested motor has structural stability and high dynamic performance, such as no-load speed of 1.4m/s and maximal thrust of 43N, meeting the requirements for absolute gravimeters. Copyright © 2017 Elsevier B.V. All rights reserved.

Standardization of the cumulative absolute velocity

International Nuclear Information System (INIS)

O'Hara, T.F.; Jacobson, J.P.

1991-12-01

EPRI NP-5930, ''A Criterion for Determining Exceedance of the Operating Basis Earthquake,'' was published in July 1988. As defined in that report, the Operating Basis Earthquake (OBE) is exceeded when both a response spectrum parameter and a second damage parameter, referred to as the Cumulative Absolute Velocity (CAV), are exceeded. In the review process of the above report, it was noted that the calculation of CAV could be confounded by time history records of long duration containing low (nondamaging) acceleration. Therefore, it is necessary to standardize the method of calculating CAV to account for record length. This standardized methodology allows consistent comparisons between future CAV calculations and the adjusted CAV threshold value based upon applying the standardized methodology to the data set presented in EPRI NP-5930. The recommended method to standardize the CAV calculation is to window its calculation on a second-by-second basis for a given time history. If the absolute acceleration exceeds 0.025g at any time during each one second interval, the earthquake records used in EPRI NP-5930 have been reanalyzed and the adjusted threshold of damage for CAV was found to be 0.16g-set