Quality assessment of gasoline using comprehensive two-dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration.

PubMed

Parastar, Hadi; Mostafapour, Sara; Azimi, Gholamhasan

2016-01-01

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Feasibility of the simultaneous determination of polycyclic aromatic hydrocarbons based on two-dimensional fluorescence correlation spectroscopy

NASA Astrophysics Data System (ADS)

Yang, Renjie; Dong, Guimei; Sun, Xueshan; Yang, Yanrong; Yu, Yaping; Liu, Haixue; Zhang, Weiyu

2018-02-01

A new approach for quantitative determination of polycyclic aromatic hydrocarbons (PAHs) in environment was proposed based on two-dimensional (2D) fluorescence correlation spectroscopy in conjunction with multivariate method. 40 mixture solutions of anthracene and pyrene were prepared in the laboratory. Excitation-emission matrix (EEM) fluorescence spectra of all samples were collected. And 2D fluorescence correlation spectra were calculated under the excitation perturbation. The N-way partial least squares (N-PLS) models were developed based on 2D fluorescence correlation spectra, showing a root mean square error of calibration (RMSEC) of 3.50 μg L- 1 and root mean square error of prediction (RMSEP) of 4.42 μg L- 1 for anthracene and of 3.61 μg L- 1 and 4.29 μg L- 1 for pyrene, respectively. Also, the N-PLS models were developed for quantitative analysis of anthracene and pyrene using EEM fluorescence spectra. The RMSEC and RMSEP were 3.97 μg L- 1 and 4.63 μg L- 1 for anthracene, 4.46 μg L- 1 and 4.52 μg L- 1 for pyrene, respectively. It was found that the N-PLS model using 2D fluorescence correlation spectra could provide better results comparing with EEM fluorescence spectra because of its low RMSEC and RMSEP. The methodology proposed has the potential to be an alternative method for detection of PAHs in environment.

Computing Robust, Bootstrap-Adjusted Fit Indices for Use with Nonnormal Data

ERIC Educational Resources Information Center

Walker, David A.; Smith, Thomas J.

2017-01-01

Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…

Using Fit Indexes to Select a Covariance Model for Longitudinal Data

ERIC Educational Resources Information Center

Liu, Siwei; Rovine, Michael J.; Molenaar, Peter C. M.

2012-01-01

This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error…

[Integral assessment of learning subjects difficulties].

PubMed

Grebniak, N P; Shchudro, S A

2010-01-01

The integral criterion for subject difficulties in senior classes is substantiated in terms of progress in studies, variation coefficient, and subjective and expert appraisals of the difficulty of subjects. The compiled regression models adequately determine the difficulty of academic subjects. According to the root-mean-square deviation, all subjects were found to have 3 degrees of difficulty.

Analytical model of coincidence resolving time in TOF-PET

NASA Astrophysics Data System (ADS)

Wieczorek, H.; Thon, A.; Dey, T.; Khanin, V.; Rodnyi, P.

2016-06-01

The coincidence resolving time (CRT) of scintillation detectors is the parameter determining noise reduction in time-of-flight PET. We derive an analytical CRT model based on the statistical distribution of photons for two different prototype scintillators. For the first one, characterized by single exponential decay, CRT is proportional to the decay time and inversely proportional to the number of photons, with a square root dependence on the trigger level. For the second scintillator prototype, characterized by exponential rise and decay, CRT is proportional to the square root of the product of rise time and decay time divided by the doubled number of photons, and it is nearly independent of the trigger level. This theory is verified by measurements of scintillation time constants, light yield and CRT on scintillator sticks. Trapping effects are taken into account by defining an effective decay time. We show that in terms of signal-to-noise ratio, CRT is as important as patient dose, imaging time or PET system sensitivity. The noise reduction effect of better timing resolution is verified and visualized by Monte Carlo simulation of a NEMA image quality phantom.

Quantum cryptography: Security criteria reexamined

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

Kaszlikowski, Dagomir; Liang, Y.C.; Englert, Berthold-Georg

2004-09-01

We find that the generally accepted security criteria are flawed for a whole class of protocols for quantum cryptography. This is so because a standard assumption of the security analysis, namely that the so-called square-root measurement is optimal for eavesdropping purposes, is not true in general. There are rather large parameter regimes in which the optimal measurement extracts substantially more information than the square-root measurement.

Radial and tangential gravity rates from GRACE in areas of glacial isostatic adjustment

NASA Astrophysics Data System (ADS)

van der Wal, Wouter; Kurtenbach, Enrico; Kusche, Jürgen; Vermeersen, Bert

2011-11-01

In areas dominated by Glacial Isostatic Adjustment (GIA), the free-air gravity anomaly rate can be converted to uplift rate to good approximation by using a simple spectral relation. We provide quantitative comparisons between gravity rates derived from monthly gravity field solutions (GFZ Potsdam, CSR Texas, IGG Bonn) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission with uplift rates measured by GPS in these areas. The band-limited gravity data from the GRACE satellite mission can be brought to very good agreement with the point data from GPS by using scaling factors derived from a GIA model (the root-mean-square of differences is 0.55 mm yr-1 for a maximum uplift rate signal of 10 mm yr-1). The root-mean-square of the differences between GRACE derived uplift rates and GPS derived uplift rates decreases with increasing GRACE time period to a level below the uncertainty that is expected from GRACE observations, GPS measurements and the conversion from gravity rate to uplift rate. With the current length of time-series (more than 8 yr) applying filters and a hydrology correction to the GRACE data does not reduce the root-mean-square of differences significantly. The smallest root-mean-square was obtained with the GFZ solution in Fennoscandia and with the CSR solution in North America. With radial gravity rates in excellent agreement with GPS uplift rates, more information on the GIA process can be extracted from GRACE gravity field solutions in the form of tangential gravity rates, which are equivalent to a rate of change in the deflection of the vertical scaled by the magnitude of gravity rate vector. Tangential gravity rates derived from GRACE point towards the centre of the previously glaciated area, and are largest in a location close to the centre of the former ice sheet. Forward modelling showed that present day tangential gravity rates have maximum sensitivity between the centre and edge of the former ice sheet, while radial gravity rates are most sensitive in the centre of the former ice sheet. As a result, tangential gravity rates offer constraints on a two-layer mantle viscosity profile that are different from radial gravity rates, which can be exploited in future GIA studies.

Modelling of the batch biosorption system: study on exchange of protons with cell wall-bound mineral ions.

PubMed

Mishra, Vishal

2015-01-01

The interchange of the protons with the cell wall-bound calcium and magnesium ions at the interface of solution/bacterial cell surface in the biosorption system at various concentrations of protons has been studied in the present work. A mathematical model for establishing the correlation between concentration of protons and active sites was developed and optimized. The sporadic limited residence time reactor was used to titrate the calcium and magnesium ions at the individual data point. The accuracy of the proposed mathematical model was estimated using error functions such as nonlinear regression, adjusted nonlinear regression coefficient, the chi-square test, P-test and F-test. The values of the chi-square test (0.042-0.017), P-test (<0.001-0.04), sum of square errors (0.061-0.016), root mean square error (0.01-0.04) and F-test (2.22-19.92) reported in the present research indicated the suitability of the model over a wide range of proton concentrations. The zeta potential of the bacterium surface at various concentrations of protons was observed to validate the denaturation of active sites.

Diffusion in inhomogeneous polymer membranes

NASA Astrophysics Data System (ADS)

Kasargod, Sameer S.; Adib, Farhad; Neogi, P.

1995-10-01

The dual mode sorption solubility isotherms assume, and in instances Zimm-Lundberg analysis of the solubilities show, that glassy polymers are heterogeneous and that the distribution of the solute in the polymer is also inhomogeneous. Under some conditions, the heterogeneities cannot be represented as holes. A mathematical model describing diffusion in inhomogeneous polymer membranes is presented using Cahn and Hilliard's gradient theory. The fractional mass uptake is found to be proportional to the fourth root of time rather than the square root, predicted by Fickian diffusion. This type of diffusion is classified as pseudo-Fickian. The model is compared with one experimental result available. A negative value of the persistence factor is obtained and the results are interpreted.

Van Hove singularities in the paramagnetic phase of the Hubbard model: DMFT study

NASA Astrophysics Data System (ADS)

Žitko, Rok; Bonča, Janez; Pruschke, Thomas

2009-12-01

Using the dynamical mean-field theory (DMFT) with the numerical renormalization-group impurity solver we study the paramagnetic phase of the Hubbard model with the density of states (DOS) corresponding to the three-dimensional (3D) cubic lattice and the two-dimensional (2D) square lattice, as well as a DOS with inverse square-root singularity. We show that the electron correlations rapidly smooth out the square-root van Hove singularities (kinks) in the spectral function for the 3D lattice and that the Mott metal-insulator transition (MIT) as well as the magnetic-field-induced MIT differ only little from the well-known results for the Bethe lattice. The consequences of the logarithmic singularity in the DOS for the 2D lattice are more dramatic. At half filling, the divergence pinned at the Fermi level is not washed out, only its integrated weight decreases as the interaction is increased. While the Mott transition is still of the usual kind, the magnetic-field-induced MIT falls into a different universality class as there is no field-induced localization of quasiparticles. In the case of a power-law singularity in the DOS at the Fermi level, the power-law singularity persists in the presence of interaction, albeit with a different exponent, and the effective impurity model in the DMFT turns out to be a pseudogap Anderson impurity model with a hybridization function which vanishes at the Fermi level. The system is then a generalized Fermi liquid. At finite doping, regular Fermi-liquid behavior is recovered.

Students' motivational processes and their relationship to teacher ratings in school physical education: a self-determination theory approach.

PubMed

Standage, Martyn; Duda, Joan L; Ntoumanis, Nikos

2006-03-01

In the present study, we used a model of motivation grounded in self-determination theory (Deci & Ryan, 1985, 1991; Ryan & Deci, 2000a, 2000b, 2002) to examine the relationship between physical education (PE) students' motivational processes and ratings of their effort and persistence as provided by their PE teacher. Data were obtained from 394 British secondary school students (204 boys, 189 girls, 1 gender not specified; M age = 11.97 years; SD = .89; range = 11-14 years) who responded to a multisection inventory (tapping autonomy-support, autonomy, competence, relatedness, and self-determined motivation). The students' respective PE teachers subsequently provided ratings reflecting the effort and persistence each student exhibited in their PE classes. The hypothesized relationships among the study variables were examined via structural equation modeling analysis using latent factors. Results of maximum likelihood analysis using the bootstrapping method revealed the proposed model demonstrated a good fit to the data, chi-squared (292) = 632.68, p < .001; comparative fit index = .95; incremental fit index = .95, standardized root mean square residual = .077; root mean square error of approximation (RMSEA) = .054 (90% confidence interval of RMSEA = .049 -.060). Specifically, the model showed that students who perceived an autonomy supportive environment experienced greater levels of autonomy, competence, and relatedness and had higher scores on an index of self-determination. Student-reported levels of self-determined motivation positively predicted teacher ratings of effort and persistence in PE. The findings are discussed with regard to enhancing student motivation in PE settings.

A decentralized square root information filter/smoother

NASA Technical Reports Server (NTRS)

Bierman, G. J.; Belzer, M. R.

1985-01-01

A number of developments has recently led to a considerable interest in the decentralization of linear least squares estimators. The developments are partly related to the impending emergence of VLSI technology, the realization of parallel processing, and the need for algorithmic ways to speed the solution of dynamically decoupled, high dimensional estimation problems. A new method is presented for combining Square Root Information Filters (SRIF) estimates obtained from independent data sets. The new method involves an orthogonal transformation, and an information matrix filter 'homework' problem discussed by Schweppe (1973) is generalized. The employed SRIF orthogonal transformation methodology has been described by Bierman (1977).

Assessing Goodness of Fit in Item Response Theory with Nonparametric Models: A Comparison of Posterior Probabilities and Kernel-Smoothing Approaches

ERIC Educational Resources Information Center

Sueiro, Manuel J.; Abad, Francisco J.

2011-01-01

The distance between nonparametric and parametric item characteristic curves has been proposed as an index of goodness of fit in item response theory in the form of a root integrated squared error index. This article proposes to use the posterior distribution of the latent trait as the nonparametric model and compares the performance of an index…

Modeling protein conformational changes by iterative fitting of distance constraints using reoriented normal modes.

PubMed

Zheng, Wenjun; Brooks, Bernard R

2006-06-15

Recently we have developed a normal-modes-based algorithm that predicts the direction of protein conformational changes given the initial state crystal structure together with a small number of pairwise distance constraints for the end state. Here we significantly extend this method to accurately model both the direction and amplitude of protein conformational changes. The new protocol implements a multisteps search in the conformational space that is driven by iteratively minimizing the error of fitting the given distance constraints and simultaneously enforcing the restraint of low elastic energy. At each step, an incremental structural displacement is computed as a linear combination of the lowest 10 normal modes derived from an elastic network model, whose eigenvectors are reorientated to correct for the distortions caused by the structural displacements in the previous steps. We test this method on a list of 16 pairs of protein structures for which relatively large conformational changes are observed (root mean square deviation >3 angstroms), using up to 10 pairwise distance constraints selected by a fluctuation analysis of the initial state structures. This method has achieved a near-optimal performance in almost all cases, and in many cases the final structural models lie within root mean square deviation of 1 approximately 2 angstroms from the native end state structures.

Dynamic wind-tunnel tests of an aeromechanical gust-alleviation system using several different combinations of control surfaces

NASA Technical Reports Server (NTRS)

Stewart, E. C.; Doggett, R. V., Jr.

1978-01-01

Some experimental results are presented from wind tunnel studies of a dynamic model equipped with an aeromechanical gust alleviation system for reducing the normal acceleration response of light airplanes. The gust alleviation system consists of two auxiliary aerodynamic surfaces that deflect the wing flaps through mechanical linkages when a gust is encountered to maintain nearly constant airplane lift. The gust alleviation system was implemented on a 1/6-scale, rod mounted, free flying model that is geometrically and dynamically representative of small, four place, high wing, single engine, light airplanes. The effects of flaps with different spans, two size of auxiliary aerodynamic surfaces, plain and double hinged flaps, and a flap elevator interconnection were studied. The model test results are presented in terms of predicted root mean square response of the full scale airplane to atmospheric turbulence. The results show that the gust alleviation system reduces the root mean square normal acceleration response by 30 percent in comparison with the response in the flaps locked condition. Small reductions in pitch-rate response were also obtained. It is believed that substantially larger reductions in normal acceleration can be achieved by reducing the rather high levels of mechanical friction which were extant in the alleviation system of the present model.

Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell.

PubMed

Troyer, T W; Miller, K D

1997-07-01

To understand the interspike interval (ISI) variability displayed by visual cortical neurons (Softky & Koch, 1993), it is critical to examine the dynamics of their neuronal integration, as well as the variability in their synaptic input current. Most previous models have focused on the latter factor. We match a simple integrate-and-fire model to the experimentally measured integrative properties of cortical regular spiking cells (McCormick, Connors, Lighthall, & Prince, 1985). After setting RC parameters, the post-spike voltage reset is set to match experimental measurements of neuronal gain (obtained from in vitro plots of firing frequency versus injected current). Examination of the resulting model leads to an intuitive picture of neuronal integration that unifies the seemingly contradictory 1/square root of N and random walk pictures that have previously been proposed. When ISIs are dominated by postspike recovery, 1/square root of N arguments hold and spiking is regular; after the "memory" of the last spike becomes negligible, spike threshold crossing is caused by input variance around a steady state and spiking is Poisson. In integrate-and-fire neurons matched to cortical cell physiology, steady-state behavior is predominant, and ISIs are highly variable at all physiological firing rates and for a wide range of inhibitory and excitatory inputs.

Alternative approaches to predicting methane emissions from dairy cows.

PubMed

Mills, J A N; Kebreab, E; Yates, C M; Crompton, L A; Cammell, S B; Dhanoa, M S; Agnew, R E; France, J

2003-12-01

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

The Wording of a Proof: Hardy's Second "Elegant" Proof--The Pythagorean School's Irrationality of Square Root of 2

ERIC Educational Resources Information Center

Padula, Janice

2006-01-01

One of the most interesting and important proofs in the history of mathematics is the Pythagorean school's proof of the "irrationality" of the square root of 2. After a brief look at G. H. Hardy (1941) thoughts regarding it, two versions of the classic Pythagorean proof are examined and discussed in this article, one written by an American…

Comments on real tachyon vacuum solution without square roots

NASA Astrophysics Data System (ADS)

Arroyo, E. Aldo

2018-01-01

We analyze the consistency of a recently proposed real tachyon vacuum solution without square roots in open bosonic string field theory. We show that the equation of motion contracted with the solution itself is satisfied. Additionally, by expanding the solution in the basis of the curly ℒ0 and the traditional L 0 eigenstates, we evaluate numerically the vacuum energy and obtain a result in agreement with Sen's conjecture.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

PubMed

Miaw, Carolina Sheng Whei; Assis, Camila; Silva, Alessandro Rangel Carolino Sales; Cunha, Maria Luísa; Sena, Marcelo Martins; de Souza, Scheilla Vitorino Carvalho

2018-07-15

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated. Copyright © 2018 Elsevier Ltd. All rights reserved.

Due Date Assignment in a Dynamic Job Shop with the Orthogonal Kernel Least Squares Algorithm

NASA Astrophysics Data System (ADS)

Yang, D. H.; Hu, L.; Qian, Y.

2017-06-01

Meeting due dates is a key goal in the manufacturing industries. This paper proposes a method for due date assignment (DDA) by using the Orthogonal Kernel Least Squares Algorithm (OKLSA). A simulation model is built to imitate the production process of a highly dynamic job shop. Several factors describing job characteristics and system state are extracted as attributes to predict job flow-times. A number of experiments under conditions of varying dispatching rules and 90% shop utilization level have been carried out to evaluate the effectiveness of OKLSA applied for DDA. The prediction performance of OKLSA is compared with those of five conventional DDA models and back-propagation neural network (BPNN). The experimental results indicate that OKLSA is statistically superior to other DDA models in terms of mean absolute lateness and root mean squares lateness in most cases. The only exception occurs when the shortest processing time rule is used for dispatching jobs, the difference between OKLSA and BPNN is not statistically significant.

Accelerometer Data Analysis and Presentation Techniques

NASA Technical Reports Server (NTRS)

Rogers, Melissa J. B.; Hrovat, Kenneth; McPherson, Kevin; Moskowitz, Milton E.; Reckart, Timothy

1997-01-01

The NASA Lewis Research Center's Principal Investigator Microgravity Services project analyzes Orbital Acceleration Research Experiment and Space Acceleration Measurement System data for principal investigators of microgravity experiments. Principal investigators need a thorough understanding of data analysis techniques so that they can request appropriate analyses to best interpret accelerometer data. Accelerometer data sampling and filtering is introduced along with the related topics of resolution and aliasing. Specific information about the Orbital Acceleration Research Experiment and Space Acceleration Measurement System data sampling and filtering is given. Time domain data analysis techniques are discussed and example environment interpretations are made using plots of acceleration versus time, interval average acceleration versus time, interval root-mean-square acceleration versus time, trimmean acceleration versus time, quasi-steady three dimensional histograms, and prediction of quasi-steady levels at different locations. An introduction to Fourier transform theory and windowing is provided along with specific analysis techniques and data interpretations. The frequency domain analyses discussed are power spectral density versus frequency, cumulative root-mean-square acceleration versus frequency, root-mean-square acceleration versus frequency, one-third octave band root-mean-square acceleration versus frequency, and power spectral density versus frequency versus time (spectrogram). Instructions for accessing NASA Lewis Research Center accelerometer data and related information using the internet are provided.

Energy Performance Measurement and Simulation Modeling of Tactical Soft-Wall Shelters

DTIC Science & Technology

2015-07-01

was too low to measure was on the order of 5 hours. Because the research team did not have access to the site between 1700 and 0500 hours the...Basic for Applications ( VBA ). The objective function was the root mean square (RMS) errors between modeled and measured heating load and the modeled...References Phase Change Energy Solutions. (2013). BioPCM web page, http://phasechange.com/index.php/en/about/our-material. Accessed 16 September

Comparison of measured and modeled BRDF of natural targets

NASA Astrophysics Data System (ADS)

Boucher, Yannick; Cosnefroy, Helene; Petit, Alain D.; Serrot, Gerard; Briottet, Xavier

1999-07-01

The Bidirectional Reflectance Distribution Function (BRDF) plays a major role to evaluate or simulate the signatures of natural and artificial targets in the solar spectrum. A goniometer covering a large spectral and directional domain has been recently developed by the ONERA/DOTA. It was designed to allow both laboratory and outside measurements. The spectral domain ranges from 0.40 to 0.95 micrometer, with a resolution of 3 nm. The geometrical domain ranges 0 - 60 degrees for the zenith angle of the source and the sensor, and 0 - 180 degrees for the relative azimuth between the source and the sensor. The maximum target size for nadir measurements is 22 cm. The spatial target irradiance non-uniformity has been evaluated and then used to correct the raw measurements. BRDF measurements are calibrated thanks to a spectralon reference panel. Some BRDF measurements performed on sand and short grass and are presented here. Eight bidirectional models among the most popular models found in the literature have been tested on these measured data set. A code fitting the model parameters to the measured BRDF data has been developed. The comparative evaluation of the model performances is carried out, versus different criteria (root mean square error, root mean square relative error, correlation diagram . . .). The robustness of the models is evaluated with respect to the number of BRDF measurements, noise and interpolation.

[Application of wavelet transform-radial basis function neural network in NIRS for determination of rifampicin and isoniazide tablets].

PubMed

Lu, Jia-hui; Zhang, Yi-bo; Zhang, Zhuo-yong; Meng, Qing-fan; Guo, Wei-liang; Teng, Li-rong

2008-06-01

A calibration model (WT-RBFNN) combination of wavelet transform (WT) and radial basis function neural network (RBFNN) was proposed for synchronous and rapid determination of rifampicin and isoniazide in Rifampicin and Isoniazide tablets by near infrared reflectance spectroscopy (NIRS). The approximation coefficients were used for input data in RBFNN. The network parameters including the number of hidden layer neurons and spread constant (SC) were investigated. WT-RBFNN model which compressed the original spectra data, removed the noise and the interference of background, and reduced the randomness, the capabilities of prediction were well optimized. The root mean square errors of prediction (RMSEP) for the determination of rifampicin and isoniazide obtained from the optimum WT-RBFNN model are 0.00639 and 0.00587, and the root mean square errors of cross-calibration (RMSECV) for them are 0.00604 and 0.00457, respectively which are superior to those obtained by the optimum RBFNN and PLS models. Regression coefficient (R) between NIRS predicted values and RP-HPLC values for rifampicin and isoniazide are 0.99522 and 0.99392, respectively and the relative error is lower than 2.300%. It was verified that WT-RBFNN model is a suitable approach to dealing with NIRS. The proposed WT-RBFNN model is convenient, and rapid and with no pollution for the determination of rifampicin and isoniazide tablets.

A Model of Fatigue Following Traumatic Brain Injury.

PubMed

Ponsford, Jennie; Schönberger, Michael; Rajaratnam, Shantha M W

2015-01-01

Fatigue is one of the most frequent sequelae of traumatic brain injury (TBI), although its causes are poorly understood. This study investigated the interrelationships between fatigue and sleepiness, vigilance performance, depression, and anxiety, using a structural equation modeling approach. Seventy-two participants with moderate to severe TBI (78% males) were recruited a median of 305 days postinjury. They completed the Fatigue Severity Scale, a vigilance task, the Epworth Sleepiness Scale, and Hospital Anxiety and Depression Scale. A model of the interrelationships between the study variables was developed, tested, and modified with path analysis. The modified model had a good overall fit (χ2 = 1.3, P = .54; comparative fit index = 1.0; root-mean square error of approximation = 0.0; standardized root-mean square residual = 0.02). Most paths in this model were significant (P < .05). Fatigue predicted anxiety, depression, and daytime sleepiness. Depression predicted daytime sleepiness and poor vigilance, whereas anxiety tended to predict reduced daytime sleepiness. This model confirms the complexity of fatigue experience. It supports the hypothesis that fatigue after TBI is a cause, not a consequence, of anxiety, depression, and daytime sleepiness, which, in turn (especially depression), may exacerbate fatigue by affecting cognitive functioning. These findings suggest that to alleviate fatigue, it is important to address each of these factors. However, the findings need to be confirmed with a longitudinal research design.

Noncontact analysis of the fiber weight per unit area in prepreg by near-infrared spectroscopy.

PubMed

Jiang, B; Huang, Y D

2008-05-26

The fiber weight per unit area in prepreg is an important factor to ensure the quality of the composite products. Near-infrared spectroscopy (NIRS) technology together with a noncontact reflectance sources has been applied for quality analysis of the fiber weight per unit area. The range of the unit area fiber weight was 13.39-14.14mgcm(-2). The regression method was employed by partial least squares (PLS) and principal components regression (PCR). The calibration model was developed by 55 samples to determine the fiber weight per unit area in prepreg. The determination coefficient (R(2)), root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.82, 0.092, 0.099, respectively. The predicted values of the fiber weight per unit area in prepreg measured by NIRS technology were comparable to the values obtained by the reference method. For this technology, the noncontact reflectance sources focused directly on the sample with neither previous treatment nor manipulation. The results of the paired t-test revealed that there was no significant difference between the NIR method and the reference method. Besides, the prepreg could be analyzed one time within 20s without sample destruction.

[Seedling index of Salvia miltiorrhiza and its simulation model].

PubMed

Huang, Shu-Hua; Xu, Fu-Li; Wang, Wei-Ling; Du, Jun-Bo; Ru, Mei; Wang, Jing; Cao, Xian-Yan

2012-10-01

Through the correlation analysis on the quantitative traits and their ratios of Salvia miltiorrhiza seedlings and seedling quality, a series of representative indices reflecting the seedling quality of the plant species were determined, and the seedling index suitable to the S. miltiorrhiza seedlings was ascertained by correlation degree analysis. Meanwhile, based on the relationships between the seedling index and the air temperature, solar radiation and air humidity, a simulation model for the seedling index of S. miltiorrhiza was established. The experimental data of different test plots and planting dates were used to validate the model. The results showed that the root diameter, stem diameter, crown dry mass, root dry mass, and plant dry mass had significant positive relationships with the other traits, and could be used as the indicators of the seedling's health. The seedling index of S. miltiorrhiza could be calculated by (stem diameter/root diameter + root dry mass/crown dry mass) x plant dry mass. The stem diameter, root dry mass, crown dry mass and plant dry mass had higher correlations with the seedling index, and thus, the seedling index determined by these indicators could better reflect the seedling's quality. The coefficient of determination (R2) between the predicted and measured values based on 1:1 line was 0.95, and the root mean squared error (RMSE) was 0.15, indicating that the model established in this study could precisely reflect the quantitative relationships between the seedling index of S. miltiorrhiza and the environmental factors.

Modeling the language learning strategies and English language proficiency of pre-university students in UMS: A case study

NASA Astrophysics Data System (ADS)

Kiram, J. J.; Sulaiman, J.; Swanto, S.; Din, W. A.

2015-10-01

This study aims to construct a mathematical model of the relationship between a student's Language Learning Strategy usage and English Language proficiency. Fifty-six pre-university students of University Malaysia Sabah participated in this study. A self-report questionnaire called the Strategy Inventory for Language Learning was administered to them to measure their language learning strategy preferences before they sat for the Malaysian University English Test (MUET), the results of which were utilised to measure their English language proficiency. We attempted the model assessment specific to Multiple Linear Regression Analysis subject to variable selection using Stepwise regression. We conducted various assessments to the model obtained, including the Global F-test, Root Mean Square Error and R-squared. The model obtained suggests that not all language learning strategies should be included in the model in an attempt to predict Language Proficiency.

Gene expression programming approach for the estimation of moisture ratio in herbal plants drying with vacuum heat pump dryer

NASA Astrophysics Data System (ADS)

Dikmen, Erkan; Ayaz, Mahir; Gül, Doğan; Şahin, Arzu Şencan

2017-07-01

The determination of drying behavior of herbal plants is a complex process. In this study, gene expression programming (GEP) model was used to determine drying behavior of herbal plants as fresh sweet basil, parsley and dill leaves. Time and drying temperatures are input parameters for the estimation of moisture ratio of herbal plants. The results of the GEP model are compared with experimental drying data. The statistical values as mean absolute percentage error, root-mean-squared error and R-square are used to calculate the difference between values predicted by the GEP model and the values actually observed from the experimental study. It was found that the results of the GEP model and experimental study are in moderately well agreement. The results have shown that the GEP model can be considered as an efficient modelling technique for the prediction of moisture ratio of herbal plants.

Search for large extra dimensions in final states containing one photon or jet and large missing transverse energy produced in pp collisions at square root[s]=1.96 TeV.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Copic, K; Cordelli, M; Cortiana, G; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kusakabe, Y; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; Lecompte, T; Lee, E; Lee, H S; Lee, S W; Leone, S; Lewis, J D; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; Macqueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlok, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savard, P; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S

2008-10-31

We present the results of searches for large extra dimensions in samples of events with large missing transverse energy E_{T} and either a photon or a jet produced in pp[over ] collisions at sqrt[s]=1.96 TeV collected with the Collider Detector at Fermilab II. For gamma+E_{T} and jet+E_{T} candidate samples corresponding to 2.0 and 1.1 fb;{-1} of integrated luminosity, respectively, we observe good agreement with standard model expectations and obtain a combined lower limit on the fundamental parameter of the large extra dimensions model M_{D} as a function of the number of extra dimensions in the model.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods

NASA Astrophysics Data System (ADS)

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; Mello, Paola de Azevedo; Ferrão, Marco Flores; dos Santos, Maria de Fátima Pereira; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm-1). This model produced a RMSECV of 400 mg kg-1 S and RMSEP of 420 mg kg-1 S, showing a correlation coefficient of 0.990.

Evaluation of the Bitterness of Traditional Chinese Medicines using an E-Tongue Coupled with a Robust Partial Least Squares Regression Method

PubMed Central

Lin, Zhaozhou; Zhang, Qiao; Liu, Ruixin; Gao, Xiaojie; Zhang, Lu; Kang, Bingya; Shi, Junhan; Wu, Zidan; Gui, Xinjing; Li, Xuelin

2016-01-01

To accurately, safely, and efficiently evaluate the bitterness of Traditional Chinese Medicines (TCMs), a robust predictor was developed using robust partial least squares (RPLS) regression method based on data obtained from an electronic tongue (e-tongue) system. The data quality was verified by the Grubb’s test. Moreover, potential outliers were detected based on both the standardized residual and score distance calculated for each sample. The performance of RPLS on the dataset before and after outlier detection was compared to other state-of-the-art methods including multivariate linear regression, least squares support vector machine, and the plain partial least squares regression. Both R2 and root-mean-squares error (RMSE) of cross-validation (CV) were recorded for each model. With four latent variables, a robust RMSECV value of 0.3916 with bitterness values ranging from 0.63 to 4.78 were obtained for the RPLS model that was constructed based on the dataset including outliers. Meanwhile, the RMSECV, which was calculated using the models constructed by other methods, was larger than that of the RPLS model. After six outliers were excluded, the performance of all benchmark methods markedly improved, but the difference between the RPLS model constructed before and after outlier exclusion was negligible. In conclusion, the bitterness of TCM decoctions can be accurately evaluated with the RPLS model constructed using e-tongue data. PMID:26821026

An Investigation of the Sample Performance of Two Nonnormality Corrections for RMSEA

ERIC Educational Resources Information Center

Brosseau-Liard, Patricia E.; Savalei, Victoria; Li, Libo

2012-01-01

The root mean square error of approximation (RMSEA) is a popular fit index in structural equation modeling (SEM). Typically, RMSEA is computed using the normal theory maximum likelihood (ML) fit function. Under nonnormality, the uncorrected sample estimate of the ML RMSEA tends to be inflated. Two robust corrections to the sample ML RMSEA have…

In vitro evaluation of the sealing ability of three newly developed root canal sealers: A bacterial microleakage study

PubMed Central

Sobhani, Ehsan; Samadi-Kafil, Hossein; Pirzadeh, Ahmad; Jafari, Sanaz

2016-01-01

Background The purpose of this study was to compare the sealing ability of MTA Fillapex, Apatite Root Canal Sealer and AH26 sealers. Material and Methods The present in vitro study was carried out on 142 extracted single-rooted human mature teeth. The teeth were randomly divided into three experimental groups (n=44) and two control groups (n=5). Three root canal sealers were MTA Fillapex, Apatite Root Canal Sealer and AH26. The teeth in the control groups were either filled with no sealer or made completely impermeable. The root canals were prepared and obturated with gutta-percha and one of the sealers. The teeth were sterilized with ethylene oxide gas prior to the bacterial leakage assessment using Enterococcus faecalis. Leakage was evaluated every 24 hours for 90 days. Data were analyzed with descriptive statistical methods and chi-squared test. If the data were significant, a proper post hoc test was used. Statistical significance was set at P<0.05. Results The positive control specimens exhibited total bacterial penetration whilst the negative control specimens showed no evidence of bacterial penetration. At the end of the study, the analysis of microleakage with chi-squared test showed no significant differences between the experimental groups (P<0.05). The results of chi-squared test analyzing the pair-wise differences between the groups considering the numerical values for leakage day indicated the lowest leakage with AH26 and the highest with Apatite root sealer. Conclusions According to the results of the present study, sealing ability of AH26 was significantly higher than that of MTA Fillapex and Apatite Root Canal Sealer. Key words:Mineral Trioxide aggregate, root canal obturation, dental seal. PMID:27957271

Prediction of in situ root decomposition rates in an interspecific context from chemical and morphological traits

PubMed Central

Aulen, Maurice; Shipley, Bill; Bradley, Robert

2012-01-01

Background and Aims We quantitatively relate in situ root decomposition rates of a wide range of trees and herbs used in agroforestry to root chemical and morphological traits in order to better describe carbon fluxes from roots to the soil carbon pool across a diverse group of plant species. Methods In situ root decomposition rates were measured over an entire year by an intact core method on ten tree and seven herb species typical of agroforestry systems and were quantified using decay constants (k values) from Olson's single exponential model. Decay constants were related to root chemical (total carbon, nitrogen, soluble carbon, cellulose, hemicellulose, lignin) and morphological (specific root length, specific root length) traits. Traits were measured for both absorbing and non-absorbing roots. Key Results From 61 to 77 % of the variation in the different root traits and 63 % of that in root decomposition rates was interspecific. N was positively correlated, but total carbon and lignin were negatively correlated with k values. Initial root traits accounted for 75 % of the variation in interspecific decomposition rates using partial least squares regressions; partial slopes attributed to each trait were consistent with functional ecology expectations. Conclusions Easily measured initial root traits can be used to predict rates of root decomposition in soils in an interspecific context. PMID:22003237

Advances of the smooth variable structure filter: square-root and two-pass formulations

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Lee, Andrew S.

2017-01-01

The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilize a switching gain that brings an inherent amount of stability to the estimation process. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter's algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation, and the results are compared with the popular Kalman filter. In addition, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the Kalman-based two-pass smoother.

Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields*

PubMed Central

Sun, Jiaqi; Xie, Yuchen; Ye, Wenxing; Ho, Jeffrey; Entezari, Alireza; Blackband, Stephen J.

2013-01-01

In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data. PMID:24684004

Two Comments on Bond Angles

NASA Astrophysics Data System (ADS)

Glaister, P.

1997-09-01

Tetrahedral Bond Angle from Elementary Trigonometry The alternative approach of using the scalar (or dot) product of vectors enables the determination of the bond angle in a tetrahedral molecule in a simple way. There is, of course, an even more straightforward derivation suitable for students who are unfamiliar with vectors, or products thereof, but who do know some elementary trigonometry. The starting point is the figure showing triangle OAB. The point O is the center of a cube, and A and B are at opposite corners of a face of that cube in which fits a regular tetrahedron. The required bond angle alpha = AÔB; and using Pythagoras' theorem, AB = 2(square root 2) is the diagonal of a face of the cube. Hence from right-angled triangle OEB, tan(alpha/2) = (square root 2) and therefore alpha = 2tan-1(square root 2) is approx. 109° 28' (see Fig. 1).

Spiral tracing on a touchscreen is influenced by age, hand, implement, and friction.

PubMed

Heintz, Brittany D; Keenan, Kevin G

2018-01-01

Dexterity impairments are well documented in older adults, though it is unclear how these influence touchscreen manipulation. This study examined age-related differences while tracing on high- and low-friction touchscreens using the finger or stylus. 26 young and 24 older adults completed an Archimedes spiral tracing task on a touchscreen mounted on a force sensor. Root mean square error was calculated to quantify performance. Root mean square error increased by 29.9% for older vs. young adults using the fingertip, but was similar to young adults when using the stylus. Although other variables (e.g., touchscreen usage, sensation, and reaction time) differed between age groups, these variables were not related to increased error in older adults while using their fingertip. Root mean square error also increased on the low-friction surface for all subjects. These findings suggest that utilizing a stylus and increasing surface friction may improve touchscreen use in older adults.

Application of the Polynomial-Based Least Squares and Total Least Squares Models for the Attenuated Total Reflection Fourier Transform Infrared Spectra of Binary Mixtures of Hydroxyl Compounds.

PubMed

Shan, Peng; Peng, Silong; Zhao, Yuhui; Tang, Liang

2016-03-01

An analysis of binary mixtures of hydroxyl compound by Attenuated Total Reflection Fourier transform infrared spectroscopy (ATR FT-IR) and classical least squares (CLS) yield large model error due to the presence of unmodeled components such as H-bonded components. To accommodate these spectral variations, polynomial-based least squares (LSP) and polynomial-based total least squares (TLSP) are proposed to capture the nonlinear absorbance-concentration relationship. LSP is based on assuming that only absorbance noise exists; while TLSP takes both absorbance noise and concentration noise into consideration. In addition, based on different solving strategy, two optimization algorithms (limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm and Levenberg-Marquardt (LM) algorithm) are combined with TLSP and then two different TLSP versions (termed as TLSP-LBFGS and TLSP-LM) are formed. The optimum order of each nonlinear model is determined by cross-validation. Comparison and analyses of the four models are made from two aspects: absorbance prediction and concentration prediction. The results for water-ethanol solution and ethanol-ethyl lactate solution show that LSP, TLSP-LBFGS, and TLSP-LM can, for both absorbance prediction and concentration prediction, obtain smaller root mean square error of prediction than CLS. Additionally, they can also greatly enhance the accuracy of estimated pure component spectra. However, from the view of concentration prediction, the Wilcoxon signed rank test shows that there is no statistically significant difference between each nonlinear model and CLS. © The Author(s) 2016.

Estimating the Mechanical Behavior of the Knee Joint during Crouch Gait: Implications for Real-Time Motor Control of Robotic Knee Orthoses

PubMed Central

Damiano, Diane L.; Bulea, Thomas C.

2016-01-01

Individuals with cerebral palsy frequently exhibit crouch gait, a pathological walking pattern characterized by excessive knee flexion. Knowledge of the knee joint moment during crouch gait is necessary for the design and control of assistive devices used for treatment. Our goal was to 1) develop statistical models to estimate knee joint moment extrema and dynamic stiffness during crouch gait, and 2) use the models to estimate the instantaneous joint moment during weight-acceptance. We retrospectively computed knee moments from 10 children with crouch gait and used stepwise linear regression to develop statistical models describing the knee moment features. The models explained at least 90% of the response value variability: peak moment in early (99%) and late (90%) stance, and dynamic stiffness of weight-acceptance flexion (94%) and extension (98%). We estimated knee extensor moment profiles from the predicted dynamic stiffness and instantaneous knee angle. This approach captured the timing and shape of the computed moment (root-mean-squared error: 2.64 Nm); including the predicted early-stance peak moment as a correction factor improved model performance (root-mean-squared error: 1.37 Nm). Our strategy provides a practical, accurate method to estimate the knee moment during crouch gait, and could be used for real-time, adaptive control of robotic orthoses. PMID:27101612

Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment

DTIC Science & Technology

2009-01-01

reference point have the smallest unbiased RMSD value (Fig. 3). It would appear that the cluster of model points closest to the reference point may...total RMSD values. This is particularly the case for phyto- plankton absorption (Fig. 3B) where the cluster of points closest to the reference...pattern statistics and the bias (difference of mean values) each magnitude of the total Root-Mean-Square Difference ( RMSD ). An alternative skill score and

Application of Visible and Near-Infrared Hyperspectral Imaging to Determine Soluble Protein Content in Oilseed Rape Leaves

PubMed Central

Zhang, Chu; Liu, Fei; Kong, Wenwen; He, Yong

2015-01-01

Visible and near-infrared hyperspectral imaging covering spectral range of 380–1030 nm as a rapid and non-destructive method was applied to estimate the soluble protein content of oilseed rape leaves. Average spectrum (500–900 nm) of the region of interest (ROI) of each sample was extracted, and four samples out of 128 samples were defined as outliers by Monte Carlo-partial least squares (MCPLS). Partial least squares (PLS) model using full spectra obtained dependable performance with the correlation coefficient (rp) of 0.9441, root mean square error of prediction (RMSEP) of 0.1658 mg/g and residual prediction deviation (RPD) of 2.98. The weighted regression coefficient (Bw), successive projections algorithm (SPA) and genetic algorithm-partial least squares (GAPLS) selected 18, 15, and 16 sensitive wavelengths, respectively. SPA-PLS model obtained the best performance with rp of 0.9554, RMSEP of 0.1538 mg/g and RPD of 3.25. Distribution of protein content within the rape leaves were visualized and mapped on the basis of the SPA-PLS model. The overall results indicated that hyperspectral imaging could be used to determine and visualize the soluble protein content of rape leaves. PMID:26184198

[Suitability of four stomatal conductance models in agro-pastoral ecotone in North China: A case study for potato and oil sunflower.

PubMed

Huang, Ming Xia; Wang, Jing; Tang, Jian Zhao; Yu, Qiang; Zhang, Jun; Xue, Qing Yu; Chang, Qing; Tan, Mei Xiu

2016-11-18

The suitability of four popular empirical and semi-empirical stomatal conductance models (Jarvis model, Ball-Berry model, Leuning model and Medlyn model) was evaluated based on para-llel observation data of leaf stomatal conductance, leaf net photosynthetic rate and meteorological factors during the vigorous growing period of potato and oil sunflower at Wuchuan experimental station in agro-pastoral ecotone in North China. It was found that there was a significant linear relationship between leaf stomatal conductance and leaf net photosynthetic rate for potato, whereas the linear relationship appeared weaker for oil sunflower. The results of model evaluation showed that Ball-Berry model performed best in simulating leaf stomatal conductance of potato, followed by Leuning model and Medlyn model, while Jarvis model was the last in the performance rating. The root-mean-square error (RMSE) was 0.0331, 0.0371, 0.0456 and 0.0794 mol·m -2 ·s -1 , the normalized root-mean-square error (NRMSE) was 26.8%, 30.0%, 36.9% and 64.3%, and R-squared (R 2 ) was 0.96, 0.61, 0.91 and 0.88 between simulated and observed leaf stomatal conductance of potato for Ball-Berry model, Leuning model, Medlyn model and Jarvis model, respectively. For leaf stomatal conductance of oil sunflower, Jarvis model performed slightly better than Leuning model, Ball-Berry model and Medlyn model. RMSE was 0.2221, 0.2534, 0.2547 and 0.2758 mol·m -2 ·s -1 , NRMSE was 40.3%, 46.0%, 46.2% and 50.1%, and R 2 was 0.38, 0.22, 0.23 and 0.20 between simulated and observed leaf stomatal conductance of oil sunflower for Jarvis model, Leuning model, Ball-Berry model and Medlyn model, respectively. The path analysis was conducted to identify effects of specific meteorological factors on leaf stomatal conductance. The diurnal variation of leaf stomatal conductance was principally affected by vapour pressure saturation deficit for both potato and oil sunflower. The model evaluation suggested that the stomatal conductance models for oil sunflower are to be improved in further research.

Application of Adaptive Neuro-Fuzzy Inference System for Prediction of Neutron Yield of IR-IECF Facility in High Voltages

NASA Astrophysics Data System (ADS)

Adineh-Vand, A.; Torabi, M.; Roshani, G. H.; Taghipour, M.; Feghhi, S. A. H.; Rezaei, M.; Sadati, S. M.

2013-09-01

This paper presents a soft computing based artificial intelligent technique, adaptive neuro-fuzzy inference system (ANFIS) to predict the neutron production rate (NPR) of IR-IECF device in wide discharge current and voltage ranges. A hybrid learning algorithm consists of back-propagation and least-squares estimation is used for training the ANFIS model. The performance of the proposed ANFIS model is tested using the experimental data using four performance measures: correlation coefficient, mean absolute error, mean relative error percentage (MRE%) and root mean square error. The obtained results show that the proposed ANFIS model has achieved good agreement with the experimental results. In comparison to the experimental data the proposed ANFIS model has MRE% <1.53 and 2.85 % for training and testing data respectively. Therefore, this model can be used as an efficient tool to predict the NPR in the IR-IECF device.

[Study on the polarized reflectance-hyperspectral information fusion technology of tomato leaves nutrient diagnoses].

PubMed

Zhu, Wen-Jing; Mao, Han-Ping; Li, Qing-Lin; Liu, Hong-Yu; Sun, Jun; Zuo, Zhi-Yu; Chen, Yong

2014-09-01

With 25%, 50%, 75%, 100% and 150%, five levels of, nitrogen (N), phosphorus (P) and potassium (K) nutrition stress samples cultivated in Venlo type greenhouse soilless cultivation mode as the research object, polarized reflectance spectra and hyperspectral images of different nutrient deficiency greenhouse tomato leaves were acquired by using polarized reflectance spectroscopy system developed by our own research group and hyperspectral imaging system respectively. The relationship between a certain number of changes in the bump and texture of non-smooth surface of the nutrient stress leaf and the level of polarization reflected radiation was clarified by scanning electron microscopy (SEM). On the one hand, the polarization spectrum was converted into the degree of polarization through Stokes equation, and the four polarization characteristics between the polarization spectroscopy and reference measurement values of N, P and K respectively were extracted. On the other hand, the four characteristic wavelengths of N, P, K hyperspectral image data were determined respectively through the principal component analysis, followed by eight hyperspectral texture features extracted corresponding to the four characteristic wavelengths through correlation analysis. Polarization characteristics and hyperspectral texture features combined with each characteristics of N, P, K were extracted. These 12 characteristic variables were normalized by maximum-minimum value method. N, P, K nutrient levels quantitative diagnostic models were established by SVR. Results of models are as follows: the correlation coefficient of nitrogen r = 0.961 8, root mean square error RMSE= 0.451; correlation coefficient of phosphorus r = 0.916 3, root mean square error RMSE = 0.620; correlation coefficient of potassium r = 0.940 6, root mean square error RMSE = 0.494. The results show that high precision tomato leaves nutrition prediction model could be built by using polarized reflectance spectroscopy combined with high spectral information fusion technology and achieve good diagnoses effect. It has a great significance for the improvement of model accuracy and the development of special instruments. The research provides a new idea for the rapid detection of tomato nutrient content.

Distribution of Hydroxyl Groups in Kukersite Shale Oil: Quantitative Determination Using Fourier Transform Infrared (FT-IR) Spectroscopy.

PubMed

Baird, Zachariah Steven; Oja, Vahur; Järvik, Oliver

2015-05-01

This article describes the use of Fourier transform infrared (FT-IR) spectroscopy to quantitatively measure the hydroxyl concentrations among narrow boiling shale oil cuts. Shale oil samples were from an industrial solid heat carrier retort. Reference values were measured by titration and were used to create a partial least squares regression model from FT-IR data. The model had a root mean squared error (RMSE) of 0.44 wt% OH. This method was then used to study the distribution of hydroxyl groups among more than 100 shale oil cuts, which showed that hydroxyl content increased with the average boiling point of the cut up to about 350 °C and then leveled off and decreased.

[Application of near infrared spectroscopy in rapid and simultaneous determination of essential components in five varieties of anti-tuberculosis tablets].

PubMed

Teng, Le-sheng; Wang, Di; Song, Jia; Zhang, Yi-bo; Guo, Wei-liang; Teng, Li-rong

2008-08-01

Since 1980s, tuberculosis has become increasingly serious. Rifampicin tablets, isoniazide tablets, pyrazinamide tablets, rifampicin and isoniazide tablets and rifampicin isoniazide and pyrazinamide tablets are currently relatively efficacious antituberculosis drugs. In the present paper, near infrared spectroscopy (NIRS) with partial least squares (PLS) was applied to the simultaneous determination of rifampicin (RMP), isoniazide (INH) and pyrazinamide (PZA) contents in 5 varieties of anti-tuberculosis tablets. As the results showed, all of the models for the determination of RMP, INH and PZA contents applied the original NIR spectra. The most efficacious wavelength range for the determination of RMP contents was 1981-2195 nm, it was 1540-1717 nm and 2086-2197 nm for the determination of INH contents, and it was 1460-1537 nm, 1956-2022 nm and 2268-2393 nm for determination of PZA contents. The root mean square error of the calibration set obtained by cross-validation (RMSECV) of the optimum models for the quantitative analysis of RMP, INH and PZA contents was 0.0494, 0.0257 and 0.0307, respectively. Using these optimum models for the determination of RMP, INH and PZA contents in prediction set, the root mean square error of prediction set (RMSEP) was 0.0182, 0.0166 and 0.0134, respectively. The correlation coefficient (r(p)) between the predicted values and actual values was 0.9864, 0.9989 and 0.9993, respectively. These results demonstrated that this method was precise and reliable, and is significative for in situ measurement and the on-line quality control for anti-tuberculosis tablets production.

Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions.

PubMed

Feng, Chao-Hui; Makino, Yoshio; Yoshimura, Masatoshi; Thuyet, Dang Quoc; García-Martín, Juan Francisco

2018-02-01

The potential of hyperspectral imaging with wavelengths of 380 to 1000 nm was used to determine the pH of cooked sausages after different storage conditions (4 °C for 1 d, 35 °C for 1, 3, and 5 d). The mean spectra of the sausages were extracted from the hyperspectral images and partial least squares regression (PLSR) model was developed to relate spectral profiles with the pH of the cooked sausages. Eleven important wavelengths were selected based on the regression coefficient values. The PLSR model established using the optimal wavelengths showed good precision being the prediction coefficient of determination (R p 2 ) 0.909 and the root mean square error of prediction 0.035. The prediction map for illustrating pH indices in sausages was for the first time developed by R statistics. The overall results suggested that hyperspectral imaging combined with PLSR and R statistics are capable to quantify and visualize the sausages pH evolution under different storage conditions. In this paper, hyperspectral imaging is for the first time used to detect pH in cooked sausages using R statistics, which provides another useful information for the researchers who do not have the access to Matlab. Eleven optimal wavelengths were successfully selected, which were used for simplifying the PLSR model established based on the full wavelengths. This simplified model achieved a high R p 2 (0.909) and a low root mean square error of prediction (0.035), which can be useful for the design of multispectral imaging systems. © 2017 Institute of Food Technologists®.

Methods for estimating the magnitude and frequency of peak streamflows at ungaged sites in and near the Oklahoma Panhandle

USGS Publications Warehouse

Smith, S. Jerrod; Lewis, Jason M.; Graves, Grant M.

2015-09-28

Generalized-least-squares multiple-linear regression analysis was used to formulate regression relations between peak-streamflow frequency statistics and basin characteristics. Contributing drainage area was the only basin characteristic determined to be statistically significant for all percentage of annual exceedance probabilities and was the only basin characteristic used in regional regression equations for estimating peak-streamflow frequency statistics on unregulated streams in and near the Oklahoma Panhandle. The regression model pseudo-coefficient of determination, converted to percent, for the Oklahoma Panhandle regional regression equations ranged from about 38 to 63 percent. The standard errors of prediction and the standard model errors for the Oklahoma Panhandle regional regression equations ranged from about 84 to 148 percent and from about 76 to 138 percent, respectively. These errors were comparable to those reported for regional peak-streamflow frequency regression equations for the High Plains areas of Texas and Colorado. The root mean square errors for the Oklahoma Panhandle regional regression equations (ranging from 3,170 to 92,000 cubic feet per second) were less than the root mean square errors for the Oklahoma statewide regression equations (ranging from 18,900 to 412,000 cubic feet per second); therefore, the Oklahoma Panhandle regional regression equations produce more accurate peak-streamflow statistic estimates for the irrigated period of record in the Oklahoma Panhandle than do the Oklahoma statewide regression equations. The regression equations developed in this report are applicable to streams that are not substantially affected by regulation, impoundment, or surface-water withdrawals. These regression equations are intended for use for stream sites with contributing drainage areas less than or equal to about 2,060 square miles, the maximum value for the independent variable used in the regression analysis.

General solution for diffusion-controlled dissolution of spherical particles. 1. Theory.

PubMed

Wang, J; Flanagan, D R

1999-07-01

Three classical particle dissolution rate expressions are commonly used to interpret particle dissolution rate phenomena. Our analysis shows that an assumption used in the derivation of the traditional cube-root law may not be accurate under all conditions for diffusion-controlled particle dissolution. Mathematical analysis shows that the three classical particle dissolution rate expressions are approximate solutions to a general diffusion layer model. The cube-root law is most appropriate when particle size is much larger than the diffusion layer thickness, the two-thirds-root expression applies when the particle size is much smaller than the diffusion layer thickness. The square-root expression is intermediate between these two models. A general solution to the diffusion layer model for monodispersed spherical particles dissolution was derived for sink and nonsink conditions. Constant diffusion layer thickness was assumed in the derivation. Simulated dissolution data showed that the ratio between particle size and diffusion layer thickness (a0/h) is an important factor in controlling the shape of particle dissolution profiles. A new semiempirical general particle dissolution equation is also discussed which encompasses the three classical particle dissolution expressions. The success of the general equation in explaining limitations of traditional particle dissolution expressions demonstrates the usefulness of the general diffusion layer model.

Molecular dynamics simulation analysis of Focal Adhesive Kinase (FAK) docked with solanesol as an anti-cancer agent

PubMed Central

Daneial, Betty; Joseph, Jacob Paul Vazhappilly; Ramakrishna, Guruprasad

2017-01-01

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been associated in a series of cellular processes like cell migration and survival. FAK inhibition by an anti cancer agent is critical. Therefore, it is of interest to identify, modify, design, improve and develop molecules to inhibit FAK. Solanesol is known to have inhibitory activity towards FAK. However, the molecular principles of its binding with FAK is unknown. Solanesol is a highly flexible ligand (25 rotatable bonds). Hence, ligand-protein docking was completed using AutoDock with a modified contact based scoring function. The FAK-solanesol complex model was further energy minimized and simulated in GROMOS96 (53a6) force field followed by post simulation analysis such as Root mean square deviation (RMSD), root mean square fluctuations (RMSF) and solvent accessible surface area (SASA) calculations to explain solanesol-FAK binding. PMID:29081606

Molecular dynamics simulation analysis of Focal Adhesive Kinase (FAK) docked with solanesol as an anti-cancer agent.

PubMed

Daneial, Betty; Joseph, Jacob Paul Vazhappilly; Ramakrishna, Guruprasad

2017-01-01

Focal adhesion kinase (FAK) plays a primary role in regulating the activity of many signaling molecules. Increased FAK expression has been associated in a series of cellular processes like cell migration and survival. FAK inhibition by an anti cancer agent is critical. Therefore, it is of interest to identify, modify, design, improve and develop molecules to inhibit FAK. Solanesol is known to have inhibitory activity towards FAK. However, the molecular principles of its binding with FAK is unknown. Solanesol is a highly flexible ligand (25 rotatable bonds). Hence, ligand-protein docking was completed using AutoDock with a modified contact based scoring function. The FAK-solanesol complex model was further energy minimized and simulated in GROMOS96 (53a6) force field followed by post simulation analysis such as Root mean square deviation (RMSD), root mean square fluctuations (RMSF) and solvent accessible surface area (SASA) calculations to explain solanesol-FAK binding.

Is an intuitive convergence definition of molecular dynamics simulations solely based on the root mean square deviation possible?

PubMed

Knapp, B; Frantal, S; Cibena, M; Schreiner, W; Bauer, P

2011-08-01

Molecular dynamics is a commonly used technique in computational biology. One key issue of each molecular dynamics simulation is: When does this simulation reach equilibrium state? A widely used way to determine this is the visual and intuitive inspection of root mean square deviation (RMSD) plots of the simulation. Although this technique has been criticized several times, it is still often used. Therefore, we present a study proving that this method is not reliable at all. We conducted a survey with participants from the field in which we illustrated different RMSD plots to scientists in the field of molecular dynamics. These plots were randomized and repeated, using a statistical model and different variants of the plots. We show that there is no mutual consent about the point of equilibrium. The decisions are severely biased by different parameters. Therefore, we conclude that scientists should not discuss the equilibration of a molecular dynamics simulation on the basis of a RMSD plot.

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

NASA Technical Reports Server (NTRS)

Williams Colin P.

1999-01-01

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

Analysis of low flows and selected methods for estimating low-flow characteristics at partial-record and ungaged stream sites in western Washington

USGS Publications Warehouse

Curran, Christopher A.; Eng, Ken; Konrad, Christopher P.

2012-01-01

Regional low-flow regression models for estimating Q7,10 at ungaged stream sites are developed from the records of daily discharge at 65 continuous gaging stations (including 22 discontinued gaging stations) for the purpose of evaluating explanatory variables. By incorporating the base-flow recession time constant τ as an explanatory variable in the regression model, the root-mean square error for estimating Q7,10 at ungaged sites can be lowered to 72 percent (for known values of τ), which is 42 percent less than if only basin area and mean annual precipitation are used as explanatory variables. If partial-record sites are included in the regression data set, τ must be estimated from pairs of discharge measurements made during continuous periods of declining low flows. Eight measurement pairs are optimal for estimating τ at partial-record sites, and result in a lowering of the root-mean square error by 25 percent. A low-flow survey strategy that includes paired measurements at partial-record sites requires additional effort and planning beyond a standard strategy, but could be used to enhance regional estimates of τ and potentially reduce the error of regional regression models for estimating low-flow characteristics at ungaged sites.

Cross-Shore Exchange on Natural Beaches

DTIC Science & Technology

2014-09-01

87   Figure 2.   Wave conditions measured by the ADCP in 13 m water depth of (a) root- mean-square wave height Hrms...horizontal velocity, Umean, measured in the reference level, ∑Tsig,pulse T3−hour ∑Tsig,pulse T3−hour xi (e) local water depth, h, and (f) local root...mean-square wave height normalized by the local water depth, Hrms/h, measured by ADCPin (blue) and ADCPout (red) during the 3HRLTs. Colored lines

Flight Measurement of Wall-Pressure Fluctuations and Boundary-Layer Turbulence

NASA Technical Reports Server (NTRS)

Mull, Harold R.; Algranti, Joseph S.

1960-01-01

The results are presented for a flight test program using a fighter type jet aircraft flying at pressure altitudes of 10,000, 20,000, and 30,000 feet at Mach numbers from 0.3 to 0.8. Specially designed apparatus was used to measure and record the output of microphones and hot-wire anemometers mounted on the forward-fuselage section and wing of the airplane. Mean-velocity profiles in the boundary layers were obtained from total-pressure measurements. The ratio of the root-mean-square fluctuating wall pressure to the free-stream dynamic pressure is presented as a function of Reynolds number and Mach number. The longitudinal component of the turbulent-velocity fluctuations was measured, and the turbulence-intensity profiles are presented for the wing and forward-fuselage section. In general, the results are in agreement with wind-tunnel measurements which have been-reported in the literature. For example, the variation the square root of p(sup 2)/q times the square root of p(sup 2) is the root mean square of the wall-pressure fluctuation, and q is the free-stream dynamic pressure) with Reynolds number was found to be essentially constant for the forward-fuselage-section boundary layer, while variations at the wing station were probably unduly affected by the microphone diameter (5/8 in.), which was large compared with the boundary-layer thickness.

A least-squares minimization approach for model parameters estimate by using a new magnetic anomaly formula

NASA Astrophysics Data System (ADS)

Abo-Ezz, E. R.; Essa, K. S.

2016-04-01

A new linear least-squares approach is proposed to interpret magnetic anomalies of the buried structures by using a new magnetic anomaly formula. This approach depends on solving different sets of algebraic linear equations in order to invert the depth ( z), amplitude coefficient ( K), and magnetization angle ( θ) of buried structures using magnetic data. The utility and validity of the new proposed approach has been demonstrated through various reliable synthetic data sets with and without noise. In addition, the method has been applied to field data sets from USA and India. The best-fitted anomaly has been delineated by estimating the root-mean squared (rms). Judging satisfaction of this approach is done by comparing the obtained results with other available geological or geophysical information.

Response Surface Modeling Using Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2001-01-01

A nonlinear modeling technique was used to characterize response surfaces for non-dimensional longitudinal aerodynamic force and moment coefficients, based on wind tunnel data from a commercial jet transport model. Data were collected using two experimental procedures - one based on modem design of experiments (MDOE), and one using a classical one factor at a time (OFAT) approach. The nonlinear modeling technique used multivariate orthogonal functions generated from the independent variable data as modeling functions in a least squares context to characterize the response surfaces. Model terms were selected automatically using a prediction error metric. Prediction error bounds computed from the modeling data alone were found to be- a good measure of actual prediction error for prediction points within the inference space. Root-mean-square model fit error and prediction error were less than 4 percent of the mean response value in all cases. Efficacy and prediction performance of the response surface models identified from both MDOE and OFAT experiments were investigated.

Particle-number fluctuations and neutron-proton pairing effects on proton and neutron radii of even-even N Almost-Equal-To Z nuclei

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

Douici, M.; Allal, N. H.; Fellah, M.

The particle-number fluctuation effect on the root-mean-square (rms) proton and neutron radii of even-even N Almost-Equal-To Z nuclei is studied in the isovector neutron-proton (np) pairing case using an exact particle-number projection method and the Woods-Saxon model.

Measurement and Modeling of Ecosystem Risk and Recovery for In Situ Treatment of Contaminated Sediments

DTIC Science & Technology

2011-02-01

µECD Gas chromatography - micro electron capture detector HPAH high molecular weight polyaromatic hydrocarbon HOC Hydrophobic organic compound IR...hydrocarbon PCB Polychlorinated biphenyl PE Polyethylene PED Polyethylene devices PFC Perfluorinated chemical POM Polyoxymethylene PRC...Performance reference compound RMSE Root Mean Squared Error SPME Solid Phase Micro Extraction SERDP Strategic Environmental Research and Development

The mechanics of delamination in fiber-reinforced composite materials. Part 1: Stress singularities and solution structure

NASA Technical Reports Server (NTRS)

Wang, S. S.; Choi, I.

1983-01-01

The fundamental mechanics of delamination in fiber composite laminates is studied. Mathematical formulation of the problem is based on laminate anisotropic elasticity theory and interlaminar fracture mechanics concepts. Stress singularities and complete solution structures associated with general composite delaminations are determined. For a fully open delamination with traction-free surfaces, oscillatory stress singularities always appear, leading to physically inadmissible field solutions. A refined model is introduced by considering a partially closed delamination with crack surfaces in finite-length contact. Stress singularities associated with a partially closed delamination having frictional crack-surface contact are determined, and are found to be diferent from the inverse square-root one of the frictionless-contact case. In the case of a delamination with very small area of crack closure, a simplified model having a square-root stress singularity is employed by taking the limit of the partially closed delamination. The possible presence of logarithmic-type stress singularity is examined; no logarithmic singularity of any kind is found in the composite delamination problem. Numerical examples of dominant stress singularities are shown for delaminations having crack-tip closure with different frictional coefficients between general (1) and (2) graphite-epoxy composites.

Molecular Dynamics Simulation of Rap1 Myb-type domain in Saccharomyces cerevisiae

PubMed Central

Mukherjee, Koel; Pandey, Dev Mani; Vidyarthi, Ambarish Saran

2012-01-01

Telomere is a nucleoprotein complex that plays important role in stability and their maintenance and consists of random repeats of species specific motifs. In budding Saccharomyces cerevisiae, Repressor Activator Protein 1 (Rap1) is a sequence specific protein that involved in transcriptional regulation. Rap1 consist of three active domains like N-terminal BRCT-domain, DNA-binding domain and C-terminal RCT-domain. In this study the unknown 3D structure of Myb-type domain (having 61 residues) within DNAbinding domain was modeled by Modeller7, and verified using different online bioinformatics tools (ProCheck, WhatIf, Verify3D). Dynamics of Myb-type domain of Rap1was carried out through simulation studies using GROMACS software. Time dependent interactions among the molecules were analyzed by Root Mean Square Deviation (RMSD), Radius of Gyration (Rg) and Root Mean Square Fluctuation (RMSF) plots. Motional properties in reduced dimension were also performed by Principal Component Analysis (PCA). Result indicated that Rap1 interacts with DNA major groove through its Helix Turn Helix motifs. Helix 3 was rigid, less amount of fluctuation was found as it interacts with DNA major groove. Helix2 and N-terminal having considerable fluctuation in the time scale. PMID:23144544

Molecular Dynamics Simulation of Rap1 Myb-type domain in Saccharomyces cerevisiae.

PubMed

Mukherjee, Koel; Pandey, Dev Mani; Vidyarthi, Ambarish Saran

2012-01-01

Telomere is a nucleoprotein complex that plays important role in stability and their maintenance and consists of random repeats of species specific motifs. In budding Saccharomyces cerevisiae, Repressor Activator Protein 1 (Rap1) is a sequence specific protein that involved in transcriptional regulation. Rap1 consist of three active domains like N-terminal BRCT-domain, DNA-binding domain and C-terminal RCT-domain. In this study the unknown 3D structure of Myb-type domain (having 61 residues) within DNAbinding domain was modeled by Modeller7, and verified using different online bioinformatics tools (ProCheck, WhatIf, Verify3D). Dynamics of Myb-type domain of Rap1was carried out through simulation studies using GROMACS software. Time dependent interactions among the molecules were analyzed by Root Mean Square Deviation (RMSD), Radius of Gyration (Rg) and Root Mean Square Fluctuation (RMSF) plots. Motional properties in reduced dimension were also performed by Principal Component Analysis (PCA). Result indicated that Rap1 interacts with DNA major groove through its Helix Turn Helix motifs. Helix 3 was rigid, less amount of fluctuation was found as it interacts with DNA major groove. Helix2 and N-terminal having considerable fluctuation in the time scale.

Modeling of pathogen survival during simulated gastric digestion.

PubMed

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-02-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens.

Modeling of Pathogen Survival during Simulated Gastric Digestion ▿

PubMed Central

Koseki, Shige; Mizuno, Yasuko; Sotome, Itaru

2011-01-01

The objective of the present study was to develop a mathematical model of pathogenic bacterial inactivation kinetics in a gastric environment in order to further understand a part of the infectious dose-response mechanism. The major bacterial pathogens Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. were examined by using simulated gastric fluid adjusted to various pH values. To correspond to the various pHs in a stomach during digestion, a modified logistic differential equation model and the Weibull differential equation model were examined. The specific inactivation rate for each pathogen was successfully described by a square-root model as a function of pH. The square-root models were combined with the modified logistic differential equation to obtain a complete inactivation curve. Both the modified logistic and Weibull models provided a highly accurate fitting of the static pH conditions for every pathogen. However, while the residuals plots of the modified logistic model indicated no systematic bias and/or regional prediction problems, the residuals plots of the Weibull model showed a systematic bias. The modified logistic model appropriately predicted the pathogen behavior in the simulated gastric digestion process with actual food, including cut lettuce, minced tuna, hamburger, and scrambled egg. Although the developed model enabled us to predict pathogen inactivation during gastric digestion, its results also suggested that the ingested bacteria in the stomach would barely be inactivated in the real digestion process. The results of this study will provide important information on a part of the dose-response mechanism of bacterial pathogens. PMID:21131530

In silico approaches and proportional odds model towards identifying selective ADAM17 inhibitors from anti-inflammatory natural molecules.

PubMed

Borah, Pallab Kumar; Chakraborty, Sourav; Jha, Anupam N; Rajkhowa, Sanchaita; Duary, Raj Kumar

2016-11-01

ADAM metallopeptidase domain 17 (ADAM17) is an attractive target for the development of new anti-inflammatory drugs. We aimed to identify selective inhibitors of ADAM17 against matrix metalloproteinase enzymes (MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-13, and MMP-16) which have substantial structural similarity. Target proteins were docked with 29 anti-inflammatory natural molecule ligands and a known selective inhibitor IK682. The ligands were screened based on Lipinski rules, interaction with the ADAM17 active site cavity, and then ranked using the proportional odds model multinomial logistic regression. Silymarin was the most selective inhibitor of ADAM17 exhibiting H-bonding with Glu 406, Gly 349, Glu 398, Asn 447, Tyr 433, and Lys 432. Molecular dynamics simulations were carried out for 10ns. The root mean square deviation (RMSD), root mean squared fluctuations (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), and H-bonding indicated the induced metastability. A comparison of the principal component analysis revealed that the silymarin complex also explored lesser region compared to IK682 complex. A control study on ADAM17 protein (2OI0) is included. These observations present silymarin (widely present in plants such as milk thistle (Silybum maianum), wild artichokes (Cynara cardunculus), turmeric (Curcuma longa) roots, coriander (Coriandrum sativum) seeds, etc.) as a promising natural template for development of ADAM17 selective drugs. Copyright © 2016 Elsevier Inc. All rights reserved.

Fast Dating Using Least-Squares Criteria and Algorithms.

PubMed

To, Thu-Hien; Jung, Matthieu; Lycett, Samantha; Gascuel, Olivier

2016-01-01

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley-Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley-Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3. © The Author(s) 2015. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

Fast Dating Using Least-Squares Criteria and Algorithms

PubMed Central

To, Thu-Hien; Jung, Matthieu; Lycett, Samantha; Gascuel, Olivier

2016-01-01

Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodeficiency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here, we present very fast dating algorithms, based on a Gaussian model closely related to the Langley–Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least-squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal precedence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley–Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3. PMID:26424727

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS).

PubMed

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS. Copyright © 2013 Elsevier B.V. All rights reserved.

Improvement of near infrared spectroscopic (NIRS) analysis of caffeine in roasted Arabica coffee by variable selection method of stability competitive adaptive reweighted sampling (SCARS)

NASA Astrophysics Data System (ADS)

Zhang, Xuan; Li, Wei; Yin, Bin; Chen, Weizhong; Kelly, Declan P.; Wang, Xiaoxin; Zheng, Kaiyi; Du, Yiping

2013-10-01

Coffee is the most heavily consumed beverage in the world after water, for which quality is a key consideration in commercial trade. Therefore, caffeine content which has a significant effect on the final quality of the coffee products requires to be determined fast and reliably by new analytical techniques. The main purpose of this work was to establish a powerful and practical analytical method based on near infrared spectroscopy (NIRS) and chemometrics for quantitative determination of caffeine content in roasted Arabica coffees. Ground coffee samples within a wide range of roasted levels were analyzed by NIR, meanwhile, in which the caffeine contents were quantitative determined by the most commonly used HPLC-UV method as the reference values. Then calibration models based on chemometric analyses of the NIR spectral data and reference concentrations of coffee samples were developed. Partial least squares (PLS) regression was used to construct the models. Furthermore, diverse spectra pretreatment and variable selection techniques were applied in order to obtain robust and reliable reduced-spectrum regression models. Comparing the respective quality of the different models constructed, the application of second derivative pretreatment and stability competitive adaptive reweighted sampling (SCARS) variable selection provided a notably improved regression model, with root mean square error of cross validation (RMSECV) of 0.375 mg/g and correlation coefficient (R) of 0.918 at PLS factor of 7. An independent test set was used to assess the model, with the root mean square error of prediction (RMSEP) of 0.378 mg/g, mean relative error of 1.976% and mean relative standard deviation (RSD) of 1.707%. Thus, the results provided by the high-quality calibration model revealed the feasibility of NIR spectroscopy for at-line application to predict the caffeine content of unknown roasted coffee samples, thanks to the short analysis time of a few seconds and non-destructive advantages of NIRS.

Residual depressive symptoms, sleep disturbance and perceived cognitive impairment as determinants of functioning in patients with bipolar disorder.

PubMed

Samalin, Ludovic; Boyer, Laurent; Murru, Andrea; Pacchiarotti, Isabella; Reinares, María; Bonnin, Caterina Mar; Torrent, Carla; Verdolini, Norma; Pancheri, Corinna; de Chazeron, Ingrid; Boucekine, Mohamed; Geoffroy, Pierre-Alexis; Bellivier, Frank; Llorca, Pierre-Michel; Vieta, Eduard

2017-03-01

Many patients with bipolar disorder (BD) experience residual symptoms during their inter-episodic periods. The study aimed to analyse the relationship between residual depressive symptoms, sleep disturbances and self-reported cognitive impairment as determinants of psychosocial functioning in a large sample of euthymic BD patients. This was a cross-sectional study of 468 euthymic BD outpatients. We evaluated the residual depressive symptoms with the Bipolar Depression Rating Scale, the sleep disturbances with the Pittsburgh Sleep Quality Index, the perceived cognitive performance using visual analogic scales and functioning with the Functioning Assessment Short Test. Structural equation modelling (SEM) was used to describe the relationships among the residual depressive symptoms, sleep disturbances, perceived cognitive performance and functioning. SEM showed good fit with normed chi square=2.46, comparative fit index=0.94, root mean square error of approximation=0.05 and standardized root mean square residuals=0.06. This model revealed that residual depressive symptoms (path coefficient =0.37) and perceived cognitive performance (path coefficient=0.27) were the most important features significantly related to psychosocial functioning. Sleep disturbances were indirectly associated with functioning via residual depressive symptoms and perceived cognitive performance (path coefficient=0.23). This study contributes to a better understanding of the determinants of psychosocial functioning during the inter-episodic periods of BD patients. These findings should facilitate decision-making in therapeutics to improve the functional outcomes of BD during this period. Copyright © 2017 Elsevier B.V. All rights reserved.

Measuring implementation behaviour of menu guidelines in the childcare setting: confirmatory factor analysis of a theoretical domains framework questionnaire (TDFQ).

PubMed

Seward, Kirsty; Wolfenden, Luke; Wiggers, John; Finch, Meghan; Wyse, Rebecca; Oldmeadow, Christopher; Presseau, Justin; Clinton-McHarg, Tara; Yoong, Sze Lin

2017-04-04

While there are number of frameworks which focus on supporting the implementation of evidence based approaches, few psychometrically valid measures exist to assess constructs within these frameworks. This study aimed to develop and psychometrically assess a scale measuring each domain of the Theoretical Domains Framework for use in assessing the implementation of dietary guidelines within a non-health care setting (childcare services). A 75 item 14-domain Theoretical Domains Framework Questionnaire (TDFQ) was developed and administered via telephone interview to 202 centre based childcare service cooks who had a role in planning the service menu. Confirmatory factor analysis (CFA) was undertaken to assess the reliability, discriminant validity and goodness of fit of the 14-domain theoretical domain framework measure. For the CFA, five iterative processes of adjustment were undertaken where 14 items were removed, resulting in a final measure consisting of 14 domains and 61 items. For the final measure: the Chi-Square goodness of fit statistic was 3447.19; the Standardized Root Mean Square Residual (SRMR) was 0.070; the Root Mean Square Error of Approximation (RMSEA) was 0.072; and the Comparative Fit Index (CFI) had a value of 0.78. While only one of the three indices support goodness of fit of the measurement model tested, a 14-domain model with 61 items showed good discriminant validity and internally consistent items. Future research should aim to assess the psychometric properties of the developed TDFQ in other community-based settings.

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

PubMed

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

2014-01-01

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

Simulation of groundwater flow to assess future withdrawals associated with Base Realignment and Closure (BRAC) at Fort George G. Meade, Maryland

USGS Publications Warehouse

Raffensperger, Jeff P.; Fleming, Brandon J.; Banks, William S.L.; Horn, Marilee A.; Nardi, Mark R.; Andreasen, David C.

2010-01-01

Increased groundwater withdrawals from confined aquifers in the Maryland Coastal Plain to supply anticipated growth at Fort George G. Meade (Fort Meade) and surrounding areas resulting from the Department of Defense Base Realignment and Closure Program may have adverse effects in the outcrop or near-outcrop areas. Specifically, increased pumping from the Potomac Group aquifers (principally the Patuxent aquifer) could potentially reduce base flow in small streams below rates necessary for healthy biological functioning. Additionally, water levels may be lowered near, or possibly below, the top of the aquifer within the confined-unconfined transition zone near the outcrop area. A three-dimensional groundwater flow model was created to incorporate and analyze data on water withdrawals, streamflow, and hydraulic head in the region. The model is based on an earlier model developed to assess the effects of future withdrawals from well fields in Anne Arundel County, Maryland and surrounding areas, and includes some of the same features, including model extent, boundary conditions, and vertical discretization (layering). The resolution (horizontal grid discretization) of the earlier model limited its ability to simulate the effects of withdrawals on the outcrop and near-outcrop areas. The model developed for this study included a block-shaped higher-resolution local grid, referred to as the child model, centered on Fort Meade, which was coupled to the coarser-grid parent model using the shared node Local Grid Refinement capability of MODFLOW-LGR. A more detailed stream network was incorporated into the child model. In addition, for part of the transient simulation period, stress periods were reduced in length from 1 year to 3 months, to allow for simulation of the effects of seasonally varying withdrawals and recharge on the groundwater-flow system and simulated streamflow. This required revision of the database on withdrawals and estimation of seasonal variations in recharge represented in the earlier model. The calibrated model provides a tool for future forecasts of changes in the system under different management scenarios, and for simulating potential effects of withdrawals at Fort Meade and the surrounding area on water levels in the near-outcrop area and base flow in the outcrop area. Model error was assessed by comparing observed and simulated water levels from 62 wells (55 in the parent model and 7 in the child model). The root-mean-square error values for the parent and child model were 8.72 and 11.91 feet, respectively. Root-mean-square error values for the 55 parent model observation wells range from 0.95 to 30.31 feet; the range for the 7 child model observation wells is 5.00 to 24.17 feet. Many of the wells with higher root-mean-square error values occur at the perimeter of the child model and near large pumping centers, as well as updip in the confined aquifers. Root-mean-square error values decrease downdip and away from the large pumping centers. Both the parent and child models are sensitive to increasing withdrawal rates. The parent model is more sensitive than the child model to decreasing transmissivity of layers 3, 4, 5, and 6. The parent model is relatively insensitive to riverbed vertical conductance, however, the child model does exhibit some sensitivity to decreasing riverbed conductance. The overall water budget for the model included sources and sinks of water including recharge, surface-water bodies and rivers and streams, general-head boundaries, and withdrawals from permitted wells. Withdrawal from wells in 2005 was estimated to be equivalent to 8.5 percent of the total recharge rate.

Modeling number of claims and prediction of total claim amount

NASA Astrophysics Data System (ADS)

Acar, Aslıhan Şentürk; Karabey, Uǧur

2017-07-01

In this study we focus on annual number of claims of a private health insurance data set which belongs to a local insurance company in Turkey. In addition to Poisson model and negative binomial model, zero-inflated Poisson model and zero-inflated negative binomial model are used to model the number of claims in order to take into account excess zeros. To investigate the impact of different distributional assumptions for the number of claims on the prediction of total claim amount, predictive performances of candidate models are compared by using root mean square error (RMSE) and mean absolute error (MAE) criteria.

Anomalous Fluctuations in Autoregressive Models with Long-Term Memory

NASA Astrophysics Data System (ADS)

Sakaguchi, Hidetsugu; Honjo, Haruo

2015-10-01

An autoregressive model with a power-law type memory kernel is studied as a stochastic process that exhibits a self-affine-fractal-like behavior for a small time scale. We find numerically that the root-mean-square displacement Δ(m) for the time interval m increases with a power law as mα with α < 1/2 for small m but saturates at sufficiently large m. The exponent α changes with the power exponent of the memory kernel.

Particle-in-a-box model of exciton absorption and electroabsorption in conjugated polymers

NASA Astrophysics Data System (ADS)

Pedersen, Thomas G.

2000-12-01

The recently proposed particle-in-a-box model of one-dimensional excitons in conjugated polymers is applied in calculations of optical absorption and electroabsorption spectra. It is demonstrated that for polymers of long conjugation length a superposition of single exciton resonances produces a line shape characterized by a square-root singularity in agreement with experimental spectra near the absorption edge. The effects of finite conjugation length on both absorption and electroabsorption spectra are analyzed.

Family caregiver adjustment and stroke survivor impairment: A path analytic model.

PubMed

Pendergrass, Anna; Hautzinger, Martin; Elliott, Timothy R; Schilling, Oliver; Becker, Clemens; Pfeiffer, Klaus

2017-05-01

Depressive symptoms are a common problem among family caregivers of stroke survivors. The purpose of this study was to examine the association between care recipient's impairment and caregiver depression, and determine the possible mediating effects of caregiver negative problem-orientation, mastery, and leisure time satisfaction. The evaluated model was derived from Pearlin's stress process model of caregiver adjustment. We analyzed baseline data from 122 strained family members who were assisting stroke survivors in Germany for a minimum of 6 months and who consented to participate in a randomized clinical trial. Depressive symptoms were measured with the Center for Epidemiological Studies Depression Scale. The cross-sectional data were analyzed using path analysis. The results show an adequate fit of the model to the data, χ2(1, N = 122) = 0.17, p = .68; comparative fit index = 1.00; root mean square error of approximation: p < .01; standardized root mean square residual = 0.01. The model explained 49% of the variance in the caregiver depressive symptoms. Results indicate that caregivers at risk for depression reported a negative problem orientation, low caregiving mastery, and low leisure time satisfaction. The situation is particularly affected by the frequency of stroke survivor problematic behavior, and by the degree of their impairments in activities of daily living. The findings provide empirical support for the Pearlin's stress model and emphasize how important it is to target these mediators in health promotion interventions for family caregivers of stroke survivors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

An Empirical Assessment of REBT Models of Psychopathology and Psychological Health in the Prediction of Anxiety and Depression Symptoms.

PubMed

Oltean, Horea-Radu; Hyland, Philip; Vallières, Frédérique; David, Daniel Ovidiu

2017-11-01

This study aimed to assess the validity of two models which integrate the cognitive (satisfaction with life) and affective (symptoms of anxiety and depression) aspects of subjective well-being within the framework of rational emotive behaviour therapy (REBT) theory; specifically REBT's theory of psychopathology and theory of psychological health. 397 Irish and Northern Irish undergraduate students completed measures of rational/irrational beliefs, satisfaction with life, and anxiety/depression symptoms. Structural equation modelling techniques were used in order to test our hypothesis within a cross-sectional design. REBT's theory of psychopathology (χ2 = 373.78, d.f. = 163, p < .001; comparative fit index (CFI) = .92; Tucker Lewis index (TLI) = .91; root mean square error of approximation (RMSEA) = .06 (95% CI = .05 to .07); standardized root mean square residual (SRMR) = .07) and psychological health (χ2 = 371.89, d.f. = 181, p < .001; CFI = .93; TLI = .92; RMSEA = .05 (95% CI = .04 to .06); SRMR = .06) provided acceptable fit of the data. Moreover, the psychopathology model explained 34% of variance in levels of anxiety/depression, while the psychological health model explained 33% of variance. This study provides important findings linking the fields of clinical and positive psychology within a comprehensible framework for both researchers and clinicians. Findings are discussed in relation to the possibility of more effective interventions, incorporating and targeting not only negative outcomes, but also positive concepts within the same model.

Noninvasive and fast measurement of blood glucose in vivo by near infrared (NIR) spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Liming, Ye; Yufei, Liu; Chunyan, Li; Han, Chen

2017-05-01

This research was to develop a method for noninvasive and fast blood glucose assay in vivo. Near-infrared (NIR) spectroscopy, a more promising technique compared to other methods, was investigated in rats with diabetes and normal rats. Calibration models are generated by two different multivariate strategies: partial least squares (PLS) as linear regression method and artificial neural networks (ANN) as non-linear regression method. The PLS model was optimized individually by considering spectral range, spectral pretreatment methods and number of model factors, while the ANN model was studied individually by selecting spectral pretreatment methods, parameters of network topology, number of hidden neurons, and times of epoch. The results of the validation showed the two models were robust, accurate and repeatable. Compared to the ANN model, the performance of the PLS model was much better, with lower root mean square error of validation (RMSEP) of 0.419 and higher correlation coefficients (R) of 96.22%.

The effect of constraints on the analytical figures of merit achieved by extended multivariate curve resolution-alternating least-squares.

PubMed

Pellegrino Vidal, Rocío B; Allegrini, Franco; Olivieri, Alejandro C

2018-03-20

Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to chemically interpretable solutions, the use of constraints becomes essential. In this work, both simulated and experimental data have been analyzed by MCR-ALS, applying chemically reasonable constraints, and investigating the relationship between selectivity, analytical sensitivity (γ) and root mean square error of prediction (RMSEP). As the selectivity in the instrumental modes decreases, the estimated values for γ did not fully represent the predictive model capabilities, judged from the obtained RMSEP values. Since the available sensitivity expressions have been developed by error propagation theory in unconstrained systems, there is a need of developing new expressions or analytical indicators. They should not only consider the specific profiles retrieved by MCR-ALS, but also the constraints under which the latter ones have been obtained. Copyright © 2017 Elsevier B.V. All rights reserved.

Detection of Tetracycline in Milk using NIR Spectroscopy and Partial Least Squares

NASA Astrophysics Data System (ADS)

Wu, Nan; Xu, Chenshan; Yang, Renjie; Ji, Xinning; Liu, Xinyuan; Yang, Fan; Zeng, Ming

2018-02-01

The feasibility of measuring tetracycline in milk was investigated by near infrared (NIR) spectroscopic technique combined with partial least squares (PLS) method. The NIR transmittance spectra of 40 pure milk samples and 40 tetracycline adulterated milk samples with different concentrations (from 0.005 to 40 mg/L) were obtained. The pure milk and tetracycline adulterated milk samples were properly assigned to the categories with 100% accuracy in the calibration set, and the rate of correct classification of 96.3% was obtained in the prediction set. For the quantitation of tetracycline in adulterated milk, the root mean squares errors for calibration and prediction models were 0.61 mg/L and 4.22 mg/L, respectively. The PLS model had good fitting effect in calibration set, however its predictive ability was limited, especially for low tetracycline concentration samples. Totally, this approach can be considered as a promising tool for discrimination of tetracycline adulterated milk, as a supplement to high performance liquid chromatography.

Application of partial least squares near-infrared spectral classification in diabetic identification

NASA Astrophysics Data System (ADS)

Yan, Wen-juan; Yang, Ming; He, Guo-quan; Qin, Lin; Li, Gang

2014-11-01

In order to identify the diabetic patients by using tongue near-infrared (NIR) spectrum - a spectral classification model of the NIR reflectivity of the tongue tip is proposed, based on the partial least square (PLS) method. 39sample data of tongue tip's NIR spectra are harvested from healthy people and diabetic patients , respectively. After pretreatment of the reflectivity, the spectral data are set as the independent variable matrix, and information of classification as the dependent variables matrix, Samples were divided into two groups - i.e. 53 samples as calibration set and 25 as prediction set - then the PLS is used to build the classification model The constructed modelfrom the 53 samples has the correlation of 0.9614 and the root mean square error of cross-validation (RMSECV) of 0.1387.The predictions for the 25 samples have the correlation of 0.9146 and the RMSECV of 0.2122.The experimental result shows that the PLS method can achieve good classification on features of healthy people and diabetic patients.

Total sulfur determination in residues of crude oil distillation using FT-IR/ATR and variable selection methods.

PubMed

Müller, Aline Lima Hermes; Picoloto, Rochele Sogari; de Azevedo Mello, Paola; Ferrão, Marco Flores; de Fátima Pereira dos Santos, Maria; Guimarães, Regina Célia Lourenço; Müller, Edson Irineu; Flores, Erico Marlon Moraes

2012-04-01

Total sulfur concentration was determined in atmospheric residue (AR) and vacuum residue (VR) samples obtained from petroleum distillation process by Fourier transform infrared spectroscopy with attenuated total reflectance (FT-IR/ATR) in association with chemometric methods. Calibration and prediction set consisted of 40 and 20 samples, respectively. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). Different treatments and pre-processing steps were also evaluated for the development of models. The pre-treatment based on multiplicative scatter correction (MSC) and the mean centered data were selected for models construction. The use of siPLS as variable selection method provided a model with root mean square error of prediction (RMSEP) values significantly better than those obtained by PLS model using all variables. The best model was obtained using siPLS algorithm with spectra divided in 20 intervals and combinations of 3 intervals (911-824, 823-736 and 737-650 cm(-1)). This model produced a RMSECV of 400 mg kg(-1) S and RMSEP of 420 mg kg(-1) S, showing a correlation coefficient of 0.990. Copyright © 2011 Elsevier B.V. All rights reserved.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Determination of propranolol hydrochloride in pharmaceutical preparations using near infrared spectrometry with fiber optic probe and multivariate calibration methods.

PubMed

Marques Junior, Jucelino Medeiros; Muller, Aline Lima Hermes; Foletto, Edson Luiz; da Costa, Adilson Ben; Bizzi, Cezar Augusto; Irineu Muller, Edson

2015-01-01

A method for determination of propranolol hydrochloride in pharmaceutical preparation using near infrared spectrometry with fiber optic probe (FTNIR/PROBE) and combined with chemometric methods was developed. Calibration models were developed using two variable selection models: interval partial least squares (iPLS) and synergy interval partial least squares (siPLS). The treatments based on the mean centered data and multiplicative scatter correction (MSC) were selected for models construction. A root mean square error of prediction (RMSEP) of 8.2 mg g(-1) was achieved using siPLS (s2i20PLS) algorithm with spectra divided into 20 intervals and combination of 2 intervals (8501 to 8801 and 5201 to 5501 cm(-1)). Results obtained by the proposed method were compared with those using the pharmacopoeia reference method and significant difference was not observed. Therefore, proposed method allowed a fast, precise, and accurate determination of propranolol hydrochloride in pharmaceutical preparations. Furthermore, it is possible to carry out on-line analysis of this active principle in pharmaceutical formulations with use of fiber optic probe.

Rapid determination of crocins in saffron by near-infrared spectroscopy combined with chemometric techniques

NASA Astrophysics Data System (ADS)

Li, Shuailing; Shao, Qingsong; Lu, Zhonghua; Duan, Chengli; Yi, Haojun; Su, Liyang

2018-02-01

Saffron is an expensive spice. Its primary effective constituents are crocin I and II, and the contents of these compounds directly affect the quality and commercial value of saffron. In this study, near-infrared spectroscopy was combined with chemometric techniques for the determination of crocin I and II in saffron. Partial least squares regression models were built for the quantification of crocin I and II. By comparing different spectral ranges and spectral pretreatment methods (no pretreatment, vector normalization, subtract a straight line, multiplicative scatter correction, minimum-maximum normalization, eliminate the constant offset, first derivative, and second derivative), optimum models were developed. The root mean square error of cross-validation values of the best partial least squares models for crocin I and II were 1.40 and 0.30, respectively. The coefficients of determination for crocin I and II were 93.40 and 96.30, respectively. These results show that near-infrared spectroscopy can be combined with chemometric techniques to determine the contents of crocin I and II in saffron quickly and efficiently.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

PubMed

Hao, Z Q; Li, C M; Shen, M; Yang, X Y; Li, K H; Guo, L B; Li, X Y; Lu, Y F; Zeng, X Y

2015-03-23

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Determination of total phenolic compounds in compost by infrared spectroscopy.

PubMed

Cascant, M M; Sisouane, M; Tahiri, S; Krati, M El; Cervera, M L; Garrigues, S; de la Guardia, M

2016-06-01

Middle and near infrared (MIR and NIR) were applied to determine the total phenolic compounds (TPC) content in compost samples based on models built by using partial least squares (PLS) regression. The multiplicative scatter correction, standard normal variate and first derivative were employed as spectra pretreatment, and the number of latent variable were optimized by leave-one-out cross-validation. The performance of PLS-ATR-MIR and PLS-DR-NIR models was evaluated according to root mean square error of cross validation and prediction (RMSECV and RMSEP), the coefficient of determination for prediction (Rpred(2)) and residual predictive deviation (RPD) being obtained for this latter values of 5.83 and 8.26 for MIR and NIR, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Application of Fourier transform near-infrared spectroscopy combined with high-performance liquid chromatography in rapid and simultaneous determination of essential components in crude Radix Scrophulariae.

PubMed

Li, Xiaomeng; Fang, Dansi; Cong, Xiaodong; Cao, Gang; Cai, Hao; Cai, Baochang

2012-12-01

A method is described using rapid and sensitive Fourier transform near-infrared spectroscopy combined with high-performance liquid chromatography-diode array detection for the simultaneous identification and determination of four bioactive compounds in crude Radix Scrophulariae samples. Partial least squares regression is selected as the analysis type and multiplicative scatter correction, second derivative, and Savitzky-Golay filter were adopted for the spectral pretreatment. The correlation coefficients (R) of the calibration models were above 0.96 and the root mean square error of predictions were under 0.028. The developed models were applied to unknown samples with satisfactory results. The established method was validated and can be applied to the intrinsic quality control of crude Radix Scrophulariae.

Multivariate methods on the excitation emission matrix fluorescence spectroscopic data of diesel-kerosene mixtures: a comparative study.

PubMed

Divya, O; Mishra, Ashok K

2007-05-29

Quantitative determination of kerosene fraction present in diesel has been carried out based on excitation emission matrix fluorescence (EEMF) along with parallel factor analysis (PARAFAC) and N-way partial least squares regression (N-PLS). EEMF is a simple, sensitive and nondestructive method suitable for the analysis of multifluorophoric mixtures. Calibration models consisting of varying compositions of diesel and kerosene were constructed and their validation was carried out using leave-one-out cross validation method. The accuracy of the model was evaluated through the root mean square error of prediction (RMSEP) for the PARAFAC, N-PLS and unfold PLS methods. N-PLS was found to be a better method compared to PARAFAC and unfold PLS method because of its low RMSEP values.

Fourier transform infrared reflectance spectra of latent fingerprints: a biometric gauge for the age of an individual.

PubMed

Hemmila, April; McGill, Jim; Ritter, David

2008-03-01

To determine if changes in fingerprint infrared spectra linear with age can be found, partial least squares (PLS1) regression of 155 fingerprint infrared spectra against the person's age was constructed. The regression produced a linear model of age as a function of spectrum with a root mean square error of calibration of less than 4 years, showing an inflection at about 25 years of age. The spectral ranges emphasized by the regression do not correspond to the highest concentration constituents of the fingerprints. Separate linear regression models for old and young people can be constructed with even more statistical rigor. The success of the regression demonstrates that a combination of constituents can be found that changes linearly with age, with a significant shift around puberty.

Comparison of Nonlinear Filtering Techniques for Lunar Surface Roving Navigation

NASA Technical Reports Server (NTRS)

Kimber, Lemon; Welch, Bryan W.

2008-01-01

Leading up to the Apollo missions the Extended Kalman Filter, a modified version of the Kalman Filter, was developed to estimate the state of a nonlinear system. Throughout the Apollo missions, Potter's Square Root Filter was used for lunar navigation. Now that NASA is returning to the Moon, the filters used during the Apollo missions must be compared to the filters that have been developed since that time, the Bierman-Thornton Filter (UD) and the Unscented Kalman Filter (UKF). The UD Filter involves factoring the covariance matrix into UDUT and has similar accuracy to the Square Root Filter; however it requires less computation time. Conversely, the UKF, which uses sigma points, is much more computationally intensive than any of the filters; however it produces the most accurate results. The Extended Kalman Filter, Potter's Square Root Filter, the Bierman-Thornton UD Filter, and the Unscented Kalman Filter each prove to be the most accurate filter depending on the specific conditions of the navigation system.

Effect of temperature on microbial growth rate-mathematical analysis: the Arrhenius and Eyring-Polanyi connections.

PubMed

Huang, Lihan; Hwang, Andy; Phillips, John

2011-10-01

The objective of this work is to develop a mathematical model for evaluating the effect of temperature on the rate of microbial growth. The new mathematical model is derived by combination and modification of the Arrhenius equation and the Eyring-Polanyi transition theory. The new model, suitable for both suboptimal and the entire growth temperature ranges, was validated using a collection of 23 selected temperature-growth rate curves belonging to 5 groups of microorganisms, including Pseudomonas spp., Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Escherichia coli, from the published literature. The curve fitting is accomplished by nonlinear regression using the Levenberg-Marquardt algorithm. The resulting estimated growth rate (μ) values are highly correlated to the data collected from the literature (R(2) = 0.985, slope = 1.0, intercept = 0.0). The bias factor (B(f) ) of the new model is very close to 1.0, while the accuracy factor (A(f) ) ranges from 1.0 to 1.22 for most data sets. The new model is compared favorably with the Ratkowsky square root model and the Eyring equation. Even with more parameters, the Akaike information criterion, Bayesian information criterion, and mean square errors of the new model are not statistically different from the square root model and the Eyring equation, suggesting that the model can be used to describe the inherent relationship between temperature and microbial growth rates. The results of this work show that the new growth rate model is suitable for describing the effect of temperature on microbial growth rate. Practical Application:  Temperature is one of the most significant factors affecting the growth of microorganisms in foods. This study attempts to develop and validate a mathematical model to describe the temperature dependence of microbial growth rate. The findings show that the new model is accurate and can be used to describe the effect of temperature on microbial growth rate in foods. Journal of Food Science © 2011 Institute of Food Technologists® No claim to original US government works.

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process.

PubMed

Jahn, Patrick; Berg, Rune W; Hounsgaard, Jørn; Ditlevsen, Susanne

2011-11-01

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein-Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein-Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential.

[Modeling of sugar content based on NIRS during cider-making fermentation].

PubMed

Peng, Bang-Zhu; Yue, Tian-Li; Yuan, Ya-Hong; Gao, Zhen-Peng

2009-03-01

The sugar content and the matrix always are being changed during cider-making fermentation. In order to measure and monitor sugar content accurately and rapidly, it is necessary for the spectra to be sorted. Calibration models were established at different fermentation stages based on near infrared spectroscopy with artificial neural network. NIR spectral data were collected in the spectral region of 12 000-4 000 cm(-1) for the next analysis. After the different conditions for modeling sugar content were analyzed and discussed, the results indicated that the calibration models developed by the spectral data pretreatment of straight line subtraction(SLS) in the characteristic absorption spectra ranges of 7 502-6 472.1 cm(-1) at stage I and 6 102-5 446.2 cm(-1) at stage II were the best for sugar content. The result of comparison of different data pretreatment methods for establishing calibration model showed that the correlation coefficients of the models (R2) for stage I and II were 98.93% and 99.34% respectively and the root mean square errors of cross validation(RMSECV) for stage I and II were 4.42 and 1.21 g x L(-1) respectively. Then the models were tested and the results showed that the root mean square error of prediction (RMSEP) was 4.07 g x L(-1) and 1.13 g x L(-1) respectively. These demonstrated that the models the authors established are very well and can be applied to quick determination and monitoring of sugar content during cider-making fermentation.

Soil moisture depletion under simulated drought in the Amazon: impacts on deep root uptake.

PubMed

Markewitz, Daniel; Devine, Scott; Davidson, Eric A; Brando, Paulo; Nepstad, Daniel C

2010-08-01

*Deep root water uptake in tropical Amazonian forests has been a major discovery during the last 15 yr. However, the effects of extended droughts, which may increase with climate change, on deep soil moisture utilization remain uncertain. *The current study utilized a 1999-2005 record of volumetric water content (VWC) under a throughfall exclusion experiment to calibrate a one-dimensional model of the hydrologic system to estimate VWC, and to quantify the rate of root uptake through 11.5 m of soil. *Simulations with root uptake compensation had a relative root mean square error (RRMSE) of 11% at 0-40 cm and < 5% at 350-1150 cm. The simulated contribution of deep root uptake under the control was c. 20% of water demand from 250 to 550 cm and c. 10% from 550 to 1150 cm. Furthermore, in years 2 (2001) and 3 (2002) of throughfall exclusion, deep root uptake increased as soil moisture was available but then declined to near zero in deep layers in 2003 and 2004. *Deep root uptake was limited despite high VWC (i.e. > 0.30 cm(3) cm(-3)). This limitation may partly be attributable to high residual water contents (theta(r)) in these high-clay (70-90%) soils or due to high soil-to-root resistance. The ability of deep roots and soils to contribute increasing amounts of water with extended drought will be limited.

Effects of frequency and duration on psychometric functions for detection of increments and decrements in sinusoids in noise.

PubMed

Moore, B C; Peters, R W; Glasberg, B R

1999-12-01

Psychometric functions for detecting increments or decrements in level of sinusoidal pedestals were measured for increment and decrement durations of 5, 10, 20, 50, 100, and 200 ms and for frequencies of 250, 1000, and 4000 Hz. The sinusoids were presented in background noise intended to mask spectral splatter. A three-interval, three-alternative procedure was used. The results indicated that, for increments, the detectability index d' was approximately proportional to delta I/I. For decrements, d' was approximately proportional to delta L. The slopes of the psychometric functions increased (indicating better performance) with increasing frequency for both increments and decrements. For increments, the slopes increased with increasing increment duration up to 200 ms at 250 and 1000 Hz, but at 4000 Hz they increased only up to 50 ms. For decrements, the slopes increased for durations up to 50 ms, and then remained roughly constant, for all frequencies. For a center frequency of 250 Hz, the slopes of the psychometric functions for increment detection increased with duration more rapidly than predicted by a "multiple-looks" hypothesis, i.e., more rapidly than the square root of duration, for durations up to 50 ms. For center frequencies of 1000 and 4000 Hz, the slopes increased less rapidly than predicted by a multiple-looks hypothesis, for durations greater than about 20 ms. The slopes of the psychometric functions for decrement detection increased with decrement duration at a rate slightly greater than the square root of duration, for durations up to 50 ms, at all three frequencies. For greater durations, the increase in slope was less than proportional to the square root of duration. The results were analyzed using a model incorporating a simulated auditory filter, a compressive nonlinearity, a sliding temporal integrator, and a decision device based on a template mechanism. The model took into account the effects of both the external noise and an assumed internal noise. The model was able to account for the major features of the data for both increment and decrement detection.

Spectrophotometric determination of ternary mixtures of thiamin, riboflavin and pyridoxal in pharmaceutical and human plasma by least-squares support vector machines.

PubMed

Niazi, Ali; Zolgharnein, Javad; Afiuni-Zadeh, Somaie

2007-11-01

Ternary mixtures of thiamin, riboflavin and pyridoxal have been simultaneously determined in synthetic and real samples by applications of spectrophotometric and least-squares support vector machines. The calibration graphs were linear in the ranges of 1.0 - 20.0, 1.0 - 10.0 and 1.0 - 20.0 microg ml(-1) with detection limits of 0.6, 0.5 and 0.7 microg ml(-1) for thiamin, riboflavin and pyridoxal, respectively. The experimental calibration matrix was designed with 21 mixtures of these chemicals. The concentrations were varied between calibration graph concentrations of vitamins. The simultaneous determination of these vitamin mixtures by using spectrophotometric methods is a difficult problem, due to spectral interferences. The partial least squares (PLS) modeling and least-squares support vector machines were used for the multivariate calibration of the spectrophotometric data. An excellent model was built using LS-SVM, with low prediction errors and superior performance in relation to PLS. The root mean square errors of prediction (RMSEP) for thiamin, riboflavin and pyridoxal with PLS and LS-SVM were 0.6926, 0.3755, 0.4322 and 0.0421, 0.0318, 0.0457, respectively. The proposed method was satisfactorily applied to the rapid simultaneous determination of thiamin, riboflavin and pyridoxal in commercial pharmaceutical preparations and human plasma samples.

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

PubMed

Clark, D Angus; Bowles, Ryan P

2018-04-23

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Determining the Uncertainty of X-Ray Absorption Measurements

PubMed Central

Wojcik, Gary S.

2004-01-01

X-ray absorption (or more properly, x-ray attenuation) techniques have been applied to study the moisture movement in and moisture content of materials like cement paste, mortar, and wood. An increase in the number of x-ray counts with time at a location in a specimen may indicate a decrease in moisture content. The uncertainty of measurements from an x-ray absorption system, which must be known to properly interpret the data, is often assumed to be the square root of the number of counts, as in a Poisson process. No detailed studies have heretofore been conducted to determine the uncertainty of x-ray absorption measurements or the effect of averaging data on the uncertainty. In this study, the Poisson estimate was found to adequately approximate normalized root mean square errors (a measure of uncertainty) of counts for point measurements and profile measurements of water specimens. The Poisson estimate, however, was not reliable in approximating the magnitude of the uncertainty when averaging data from paste and mortar specimens. Changes in uncertainty from differing averaging procedures were well-approximated by a Poisson process. The normalized root mean square errors decreased when the x-ray source intensity, integration time, collimator size, and number of scanning repetitions increased. Uncertainties in mean paste and mortar count profiles were kept below 2 % by averaging vertical profiles at horizontal spacings of 1 mm or larger with counts per point above 4000. Maximum normalized root mean square errors did not exceed 10 % in any of the tests conducted. PMID:27366627

Gyre and gimble: a maximum-likelihood replacement for Patterson correlation refinement.

PubMed

McCoy, Airlie J; Oeffner, Robert D; Millán, Claudia; Sammito, Massimo; Usón, Isabel; Read, Randy J

2018-04-01

Descriptions are given of the maximum-likelihood gyre method implemented in Phaser for optimizing the orientation and relative position of rigid-body fragments of a model after the orientation of the model has been identified, but before the model has been positioned in the unit cell, and also the related gimble method for the refinement of rigid-body fragments of the model after positioning. Gyre refinement helps to lower the root-mean-square atomic displacements between model and target molecular-replacement solutions for the test case of antibody Fab(26-10) and improves structure solution with ARCIMBOLDO_SHREDDER.

Rapid investigation of α-glucosidase inhibitory activity of Phaleria macrocarpa extracts using FTIR-ATR based fingerprinting.

PubMed

Easmin, Sabina; Sarker, Md Zaidul Islam; Ghafoor, Kashif; Ferdosh, Sahena; Jaffri, Juliana; Ali, Md Eaqub; Mirhosseini, Hamed; Al-Juhaimi, Fahad Y; Perumal, Vikneswari; Khatib, Alfi

2017-04-01

Phaleria macrocarpa, known as "Mahkota Dewa", is a widely used medicinal plant in Malaysia. This study focused on the characterization of α-glucosidase inhibitory activity of P. macrocarpa extracts using Fourier transform infrared spectroscopy (FTIR)-based metabolomics. P. macrocarpa and its extracts contain thousands of compounds having synergistic effect. Generally, their variability exists, and there are many active components in meager amounts. Thus, the conventional measurement methods of a single component for the quality control are time consuming, laborious, expensive, and unreliable. It is of great interest to develop a rapid prediction method for herbal quality control to investigate the α-glucosidase inhibitory activity of P. macrocarpa by multicomponent analyses. In this study, a rapid and simple analytical method was developed using FTIR spectroscopy-based fingerprinting. A total of 36 extracts of different ethanol concentrations were prepared and tested on inhibitory potential and fingerprinted using FTIR spectroscopy, coupled with chemometrics of orthogonal partial least square (OPLS) at the 4000-400 cm -1 frequency region and resolution of 4 cm -1 . The OPLS model generated the highest regression coefficient with R 2 Y = 0.98 and Q 2 Y = 0.70, lowest root mean square error estimation = 17.17, and root mean square error of cross validation = 57.29. A five-component (1+4+0) predictive model was build up to correlate FTIR spectra with activity, and the responsible functional groups, such as -CH, -NH, -COOH, and -OH, were identified for the bioactivity. A successful multivariate model was constructed using FTIR-attenuated total reflection as a simple and rapid technique to predict the inhibitory activity. Copyright © 2016. Published by Elsevier B.V.

Application of FTIR-ATR spectroscopy coupled with multivariate analysis for rapid estimation of butter adulteration.

PubMed

Fadzlillah, Nurrulhidayah Ahmad; Rohman, Abdul; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2013-01-01

In dairy product sector, butter is one of the potential sources of fat soluble vitamins, namely vitamin A, D, E, K; consequently, butter is taken into account as high valuable price from other dairy products. This fact has attracted unscrupulous market players to blind butter with other animal fats to gain economic profit. Animal fats like mutton fat (MF) are potential to be mixed with butter due to the similarity in terms of fatty acid composition. This study focused on the application of FTIR-ATR spectroscopy in conjunction with chemometrics for classification and quantification of MF as adulterant in butter. The FTIR spectral region of 3910-710 cm⁻¹ was used for classification between butter and butter blended with MF at various concentrations with the aid of discriminant analysis (DA). DA is able to classify butter and adulterated butter without any mistakenly grouped. For quantitative analysis, partial least square (PLS) regression was used to develop a calibration model at the frequency regions of 3910-710 cm⁻¹. The equation obtained for the relationship between actual value of MF and FTIR predicted values of MF in PLS calibration model was y = 0.998x + 1.033, with the values of coefficient of determination (R²) and root mean square error of calibration are 0.998 and 0.046% (v/v), respectively. The PLS calibration model was subsequently used for the prediction of independent samples containing butter in the binary mixtures with MF. Using 9 principal components, root mean square error of prediction (RMSEP) is 1.68% (v/v). The results showed that FTIR spectroscopy can be used for the classification and quantification of MF in butter formulation for verification purposes.

Spinon-Holon Attraction in the Supersymmetric t-J Model with 1/r

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

Bernevig, B. A.; Giuliano, D.; Laughlin, R. B.

2001-10-22

We derive the coordinate representation of the one-spinon one-holon wave function for the supersymmetric t-J model with 1/r{sup 2} interaction. This result allows us to show that a spinon and a holon attract each other at short distance. The attraction gets stronger as the size of the system is increased and, in the thermodynamic limit, it is responsible for the square-root singularity in the hole spectral function [Y. Kato, Phys.Rev.Lett.81, 5402 (1998)].

Infrared radiometer for measuring thermophysical properties of wind tunnel models

NASA Technical Reports Server (NTRS)

Corwin, R. R.; Moorman, S. L.; Becker, E. C.

1978-01-01

An infrared radiometer is described which was developed to measure temperature rises of wind tunnel models undergoing transient heating over a temperature range of -17.8 C to 260 C. This radiometer interfaces directly with a system which measures the effective thermophysical property square root of rho ck. It has an output temperature fluctuation of 0.26 C at low temperatures and 0.07 C at high temperatures, and the output frequency response of the radiometer is from dc to 400 hertz.

Multi-Model Ensemble Approaches to Data Assimilation Using the 4D-Local Ensemble Transform Kalman Filter

DTIC Science & Technology

2013-09-30

accuracy of the analysis . Root mean square difference ( RMSD ) is much smaller for RIP than for either Simple Ocean Data Assimilation or Incremental... Analysis Update globally for temperature as well as salinity. Regionally the same results were found, with only one exception in which the salinity RMSD ...short-term forecast using a numerical model with the observations taken within the forecast time window. The resulting state is the so-called “ analysis

First measurement of the ratio of central-electron to forward-electron W partial cross sections in pp[over] collisions at (square root)s =1.96 TeV.

PubMed

Abulencia, A; Adelman, J; Affolder, T; Akimoto, T; Albrow, M G; Amerio, S; Amidei, D; Anastassov, A; Anikeev, K; Annovi, A; Antos, J; Aoki, M; Apollinari, G; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzi-Bacchetta, P; Azzurri, P; Bacchetta, N; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Behari, S; Bellettini, G; Bellinger, J; Belloni, A; Benjamin, D; Beretvas, A; Beringer, J; Berry, T; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bolshov, A; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carillo, S; Carlsmith, D; Carosi, R; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, I; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Cilijak, M; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Coca, M; Compostella, G; Convery, M E; Conway, J; Cooper, B; Copic, K; Cordelli, M; Cortiana, G; Crescioli, F; Almenar, C Cuenca; Cuevas, J; Culbertson, R; Cully, J C; DaRonco, S; Datta, M; D'Auria, S; Davies, T; Dagenhart, D; de Barbaro, P; De Cecco, S; Deisher, A; De Lentdecker, G; De Lorenzo, G; Dell'Orso, M; Delli Paoli, F; Demortier, L; Deng, J; Deninno, M; De Pedis, D; Derwent, P F; Giovanni, G P Di; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Dörr, C; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, I; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Field, R; Flanagan, G; Forrest, R; Forrester, S; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garcia, J E; Garberson, F; Garfinkel, A F; Gay, C; Gerberich, H; Gerdes, D; Giagu, S; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Goldstein, J; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Hamilton, A; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Holloway, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; Iyutin, B; James, E; Jang, D; Jayatilaka, B; Jeans, D; Jeon, E J; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Karchin, P E; Kato, Y; Kemp, Y; Kephart, R; Kerzel, U; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Klute, M; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kraan, A C; Kraus, J; Kreps, M; Kroll, J; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhlmann, S E; Kuhr, T; Kulkarni, N P; Kusakabe, Y; Kwang, S; Laasanen, A T; Lai, S; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; LeCompte, T; Lee, J; Lee, J; Lee, Y J; Lee, S W; Lefèvre, R; Leonardo, N; Leone, S; Levy, S; Lewis, J D; Lin, C; Lin, C S; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lu, R-S; Lucchesi, D; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis, A; Margaroli, F; Marginean, R; Marino, C; Marino, C P; Martin, A; Martin, M; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Matsunaga, H; Mattson, M E; Mazini, R; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzemer, S; Menzione, A; Merkel, P; Mesropian, C; Messina, A; Miao, T; Miladinovic, N; Miles, J; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyamoto, A; Moed, S; Moggi, N; Mohr, B; Moon, C S; Moore, R; Morello, M; Fernandez, P Movilla; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norniella, O; Nurse, E; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Oldeman, R; Orava, R; Osterberg, K; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Piedra, J; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Portell, X; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Salamanna, G; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savard, P; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, E E; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shapiro, M D; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soderberg, M; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spinella, F; Spreitzer, T; Squillacioti, P; Stanitzki, M; Staveris-Polykalas, A; Denis, R St; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Sun, H; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Tourneur, S; Trischuk, W; Tsuno, S; Tu, Y; Turini, N; Ukegawa, F; Uozumi, S; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vazquez, F; Velev, G; Vellidis, C; Veramendi, G; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vollrath, I; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner, J; Wagner, W; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Yagil, A; Yamamoto, K; Yamaoka, J; Yamashita, T; Yang, C; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zhou, J; Zucchelli, S

2007-06-22

We present a measurement of sigma(pp[over] --> W) x B(W --> e nu) at (square root)s = 1.96 TeV, using electrons identified in the forward region (1.2 < |eta| < 2.8) of the CDF II detector, in 223 pb(-1) of data. We measure sigma x B = 2796 +/- 13(stat)(-90)(+95)(syst) +/- 162(lum) pb. Combining this result with a previous CDF measurement obtained using electrons in the central region (|eta| approximately < 1), we present the first measurement of the ratio of central-electron to forward-electron W partial cross sections R(exp) = 0.925 +/- 0.006(stat) +/- 0.032(syst), consistent with theoretical predictions using Coordinated Theoretical-Experimental Project on QCD (CTEQ) and Martin-Roberts-Stirling-Thorne (MRST) parton distribution functions.

Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

PubMed

Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

2018-03-01

Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t prediction of saroglitazar sulfoxide, showed mean prediction error, mean absolute prediction error, and root mean square error <30% and correlation (r) was at least 0.9339 in the same pool of healthy subjects. A 3-concentration-time points limited sampling model predicts the exposure of saroglitazar (ie, AUC 0-t ) within predefined acceptable bias and imprecision limit. Same model was also used to predict AUC 0-∞ . The same limited sampling model was found to predict the exposure of saroglitazar sulfoxide within predefined criteria. This model can find utility during late-phase clinical development of saroglitazar in the patient population. Copyright © 2018 Elsevier HS Journals, Inc. All rights reserved.

Band head spin assignment of superdeformed bands in 133Pr using two-parameter formulae

NASA Astrophysics Data System (ADS)

Sharma, Honey; Mittal, H. M.

2018-03-01

The two-parameter formulae viz. the power index formula, the nuclear softness formula and the VMI model are adopted to accredit the band head spin (I0) of four superdeformed rotational bands in 133Pr. The technique of least square fitting is used to accredit the band head spin for four superdeformed rotational bands in 133Pr. The root mean deviation among the computed transition energies and well-known experimental transition energies are attained by extracting the model parameters from the two-parameter formulae. The determined transition energies are in excellent agreement with the experimental transition energies, whenever exact spins are accredited. The power index formula coincides well with the experimental data and provides minimum root mean deviation. So, the power index formula is more efficient tool than the nuclear softness formula and the VMI model. The deviation of dynamic moment of inertia J(2) against the rotational frequency is also examined.

Search for the associated production of the standard-model Higgs Boson in the all-hadronic channel.

PubMed

Aaltonen, T; Adelman, J; Akimoto, T; Albrow, M G; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Apresyan, A; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzurri, P; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Bednar, P; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Beringer, J; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Copic, K; Cordelli, M; Cortiana, G; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'Orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; Derwent, P F; di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Elagin, A; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Genser, K; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Group, R C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Knuteson, B; Ko, B R; Koay, S A; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhr, T; Kulkarni, N P; Kurata, M; Kusakabe, Y; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; LeCompte, T; Lee, E; Lee, S W; Leone, S; Lewis, J D; Lin, C S; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lovas, L; Lu, R-S; Lucchesi, D; Lueck, J; Luci, C; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Merkel, P; Mesropian, C; Miao, T; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moggi, N; Moon, C S; Moore, R; Morello, M J; Morlok, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spreitzer, T; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thompson, G A; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Tourneur, S; Tu, Y; Turini, N; Ukegawa, F; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vogel, M; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner-Kuhr, J; Wagner, W; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Xie, S; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zheng, Y; Zucchelli, S

2009-11-27

We report on a search for the standard-model Higgs boson in pp collisions at square root(s) = 1.96 TeV using an integrated luminosity of 2.0 fb(-1). We look for production of the Higgs boson decaying to a pair of bottom quarks in association with a vector boson V (W or Z) decaying to quarks, resulting in a four-jet final state. Two of the jets are required to have secondary vertices consistent with B-hadron decays. We set the first 95% confidence level upper limit on the VH production cross section with V(--> qq/qq')H(--> bb) decay for Higgs boson masses of 100-150 GeV/c2 using data from run II at the Fermilab Tevatron. For m(H) = 120 GeV/c2, we exclude cross sections larger than 38 times the standard-model prediction.

Light-regulated gravitropism in seedling roots of maize

NASA Technical Reports Server (NTRS)

Feldman, L. J.; Briggs, W. R.

1987-01-01

Red light-induced changes in the gravitropism of roots of Zea mays variety Merit is a very low fluence response with a threshold of 10(-9) moles per square meter and is not reversible by far red light. Blue light also affects root gravitropism but the sensitivity of roots to blue is 50 to 100 times less than to an equal fluence of red. In Z. mays Merit we conclude that phytochrome is the sole pigment associated with light-induced changes in root gravitropism.

PLS-LS-SVM based modeling of ATR-IR as a robust method in detection and qualification of alprazolam

NASA Astrophysics Data System (ADS)

Parhizkar, Elahehnaz; Ghazali, Mohammad; Ahmadi, Fatemeh; Sakhteman, Amirhossein

2017-02-01

According to the United States pharmacopeia (USP), Gold standard technique for Alprazolam determination in dosage forms is HPLC, an expensive and time-consuming method that is not easy to approach. In this study chemometrics assisted ATR-IR was introduced as an alternative method that produce similar results in fewer time and energy consumed manner. Fifty-eight samples containing different concentrations of commercial alprazolam were evaluated by HPLC and ATR-IR method. A preprocessing approach was applied to convert raw data obtained from ATR-IR spectra to normal matrix. Finally, a relationship between alprazolam concentrations achieved by HPLC and ATR-IR data was established using PLS-LS-SVM (partial least squares least squares support vector machines). Consequently, validity of the method was verified to yield a model with low error values (root mean square error of cross validation equal to 0.98). The model was able to predict about 99% of the samples according to R2 of prediction set. Response permutation test was also applied to affirm that the model was not assessed by chance correlations. At conclusion, ATR-IR can be a reliable method in manufacturing process in detection and qualification of alprazolam content.

Fast determination of total ginsenosides content in ginseng powder by near infrared reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Chen, Hua-cai; Chen, Xing-dan; Lu, Yong-jun; Cao, Zhi-qiang

2006-01-01

Near infrared (NIR) reflectance spectroscopy was used to develop a fast determination method for total ginsenosides in Ginseng (Panax Ginseng) powder. The spectra were analyzed with multiplicative signal correction (MSC) correlation method. The best correlative spectra region with the total ginsenosides content was 1660 nm~1880 nm and 2230nm~2380 nm. The NIR calibration models of ginsenosides were built with multiple linear regression (MLR), principle component regression (PCR) and partial least squares (PLS) regression respectively. The results showed that the calibration model built with PLS combined with MSC and the optimal spectrum region was the best one. The correlation coefficient and the root mean square error of correction validation (RMSEC) of the best calibration model were 0.98 and 0.15% respectively. The optimal spectrum region for calibration was 1204nm~2014nm. The result suggested that using NIR to rapidly determinate the total ginsenosides content in ginseng powder were feasible.

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

NASA Astrophysics Data System (ADS)

Mandal, Sukomal; Rao, Subba; N., Harish; Lokesha

2012-06-01

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

Exploratory and confirmatory factor analyses and demographic correlate models of the strategies for weight management measure for overweight or obese adults.

PubMed

Kolodziejczyk, Julia K; Norman, Gregory J; Roesch, Scott C; Rock, Cheryl L; Arredondo, Elva M; Madanat, Hala; Patrick, Kevin

2015-01-01

There is a need for a self-report measure that assesses use of recommended strategies related to weight management. Cross-sectional analysis. Universities, community. Exploratory factor analysis (EFA) involved data from 404 overweight/obese young adults (mean age = 22 years, 48% non-Hispanic white, 68% ethnic minority). Confirmatory factor analysis (CFA) involved data from 236 overweight/obese adults (mean age = 42 years, 63% non-Hispanic white, 84% ethnic minority). The Strategies for Weight Management (SWM) measure is a 35-item questionnaire that assesses use of recommended behavioral strategies for reducing energy intake and increasing energy expenditure in overweight/obese adults. EFA and CFA were conducted on the SWM. Correlate models assessed the associations between SWM factor/total scores and demographics by using linear regressions. EFA suggested a four-factor model: strategies categorized as targeting (1) energy intake, (2) energy expenditure, (3) self-monitoring, and (4) self-regulation. CFA indicated good model fit (χ(2)/df = 2.0, comparative fit index = .90, standardized root mean square residual = .06, and root mean square error of approximation = .07, confidence interval = .06-.08, R(2) = .11-.74). The fourth factor had the lowest loadings, possibly because the items cover a wide domain. The final model included 20 items. Correlate models revealed weak associations between the SWM scores and age, gender, Hispanic ethnicity, and relationship status in both samples, with the models explaining only 1% to 8% of the variance (betas = -.04 to .29, p < .05). The SWM has promising psychometric qualities in two diverse samples.

Mathematical modelling of growth of Listeria  monocytogenes in raw chilled pork.

PubMed

Ye, K; Wang, K; Liu, M; Liu, J; Zhu, L; Zhou, G

2017-04-01

The aim of this study was to analyse the growth kinetics of Listeria monocytogenes in naturally contaminated chilled pork. A cocktail of 26 meat-borne L. monocytogenes was inoculated to raw or sterile chilled pork to observe its growth at 4, 10, 16, 22 and 28°C respectively. The growth data were fitted by the Baranyi model and Ratkowsky square-root model. Results showed that the Baranyi model and Ratkowsky square-root model could describe the growth characteristics of L. monocytogenes at different temperatures reasonably well in raw chilled pork (1·0 ≤ Bf ≤ Af ≤ 1·1). Compared with the growth of L. monocytogenes in sterile chilled pork, the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The microbial predictive models developed in this study can be used to predict the growth of L. monocytogenes during natural spoilage, and construct quantitative risk assessments in chilled pork. This study simulated the actual growth of Listeria monocytogenes in chilled pork to the maximum extent, and described its growth characteristics of L. monocytogenes during natural spoilage. This study showed that the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The models developed in this study can be used to predict the growth of L. monocytogenes during refrigerated storage. © 2017 The Society for Applied Microbiology.

Testing a healthcare provider-patient communicative relationship quality model of pharmaceutical care in hospitals.

PubMed

Wang, Dan; Liu, Chenxi; Zhang, Zinan; Ye, Liping; Zhang, Xinping

2018-06-01

Background Patient-centeredness and participatory care is increasingly regarded as a proxy for high-quality interpersonal care. Considering the development of patient-centeredness and participatory care relationship model in pharmacist-patient domain, it is of great significance to explore the mechanism of how pharmacist and patient participative behaviors influence relationship quality and patient outcomes. Objective To validate pharmacist-patient relationship quality model in Chinese hospitals. Four tertiary hospitals in 2017. Methods The provision of pharmaceutical care was investigated. A cross-sectional questionnaire survey covering different constructs of communicative relationship quality model was conducted and the associations among pairs of the study constructs were explored. Based on the results of confirmatory factor analysis, path analysis was conducted to validate the proposed communicative relationship quality model. Main outcome measure Model fit indicators including Tucker-Lewis index (TLI), comparative fit index (CFI), root mean square error of approximation (RMSEA) and weighted root mean square residual(WRMR). Results There were 589 patients included in our study. The final path model had an excellent fit (TLI = 0.98, CFI = 0.98, RMSEA = 0.05; WRMR = 1.06). HCP participative behavior/patient-centeredness (β = 0.79, p < 0.001) and interpersonal communication (β = 0.13, p < 0.001) directly impact the communicative relationship quality. But patient participative behavior was not a predictor of either communicative relationship quality or patient satisfaction. Conclusion HCP participative behavior/patient-centeredness and interpersonal communication are positively related to relationship quality, and relationship quality is mediator between HCP participative behavior and interpersonal communication with patient satisfaction.

Modified locally weighted--partial least squares regression improving clinical predictions from infrared spectra of human serum samples.

PubMed

Perez-Guaita, David; Kuligowski, Julia; Quintás, Guillermo; Garrigues, Salvador; Guardia, Miguel de la

2013-03-30

Locally weighted partial least squares regression (LW-PLSR) has been applied to the determination of four clinical parameters in human serum samples (total protein, triglyceride, glucose and urea contents) by Fourier transform infrared (FTIR) spectroscopy. Classical LW-PLSR models were constructed using different spectral regions. For the selection of parameters by LW-PLSR modeling, a multi-parametric study was carried out employing the minimum root-mean square error of cross validation (RMSCV) as objective function. In order to overcome the effect of strong matrix interferences on the predictive accuracy of LW-PLSR models, this work focuses on sample selection. Accordingly, a novel strategy for the development of local models is proposed. It was based on the use of: (i) principal component analysis (PCA) performed on an analyte specific spectral region for identifying most similar sample spectra and (ii) partial least squares regression (PLSR) constructed using the whole spectrum. Results found by using this strategy were compared to those provided by PLSR using the same spectral intervals as for LW-PLSR. Prediction errors found by both, classical and modified LW-PLSR improved those obtained by PLSR. Hence, both proposed approaches were useful for the determination of analytes present in a complex matrix as in the case of human serum samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Green method by diffuse reflectance infrared spectroscopy and spectral region selection for the quantification of sulphamethoxazole and trimethoprim in pharmaceutical formulations.

PubMed

da Silva, Fabiana E B; Flores, Érico M M; Parisotto, Graciele; Müller, Edson I; Ferrão, Marco F

2016-03-01

An alternative method for the quantification of sulphametoxazole (SMZ) and trimethoprim (TMP) using diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) and partial least square regression (PLS) was developed. Interval Partial Least Square (iPLS) and Synergy Partial Least Square (siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Fifteen commercial tablet formulations and forty-nine synthetic samples were used. The ranges of concentration considered were 400 to 900 mg g-1SMZ and 80 to 240 mg g-1 TMP. Spectral data were recorded between 600 and 4000 cm-1 with a 4 cm-1 resolution by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS). The proposed procedure was compared to high performance liquid chromatography (HPLC). The results obtained from the root mean square error of prediction (RMSEP), during the validation of the models for samples of sulphamethoxazole (SMZ) and trimethoprim (TMP) using siPLS, demonstrate that this approach is a valid technique for use in quantitative analysis of pharmaceutical formulations. The selected interval algorithm allowed building regression models with minor errors when compared to the full spectrum PLS model. A RMSEP of 13.03 mg g-1for SMZ and 4.88 mg g-1 for TMP was obtained after the selection the best spectral regions by siPLS.

An augmented classical least squares method for quantitative Raman spectral analysis against component information loss.

PubMed

Zhou, Yan; Cao, Hui

2013-01-01

We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.

Soil sail content estimation in the yellow river delta with satellite hyperspectral data

USGS Publications Warehouse

Weng, Yongling; Gong, Peng; Zhu, Zhi-Liang

2008-01-01

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487-1527, 1971-1991, 2032-2092, and 2163-2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSEC = 0.753 (R2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSEV = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 (R2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area. ?? 2008 CASI.

A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model

NASA Astrophysics Data System (ADS)

Mehdizadeh, Saeid; Behmanesh, Javad; Khalili, Keivan

2017-11-01

Precipitation plays an important role in determining the climate of a region. Precise estimation of precipitation is required to manage and plan water resources, as well as other related applications such as hydrology, climatology, meteorology and agriculture. Time series of hydrologic variables such as precipitation are composed of deterministic and stochastic parts. Despite this fact, the stochastic part of the precipitation data is not usually considered in modeling of precipitation process. As an innovation, the present study introduces three new hybrid models by integrating soft computing methods including multivariate adaptive regression splines (MARS), Bayesian networks (BN) and gene expression programming (GEP) with a time series model, namely generalized autoregressive conditional heteroscedasticity (GARCH) for modeling of the monthly precipitation. For this purpose, the deterministic (obtained by soft computing methods) and stochastic (obtained by GARCH time series model) parts are combined with each other. To carry out this research, monthly precipitation data of Babolsar, Bandar Anzali, Gorgan, Ramsar, Tehran and Urmia stations with different climates in Iran were used during the period of 1965-2014. Root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute error (MAE) and determination coefficient (R2) were employed to evaluate the performance of conventional/single MARS, BN and GEP, as well as the proposed MARS-GARCH, BN-GARCH and GEP-GARCH hybrid models. It was found that the proposed novel models are more precise than single MARS, BN and GEP models. Overall, MARS-GARCH and BN-GARCH models yielded better accuracy than GEP-GARCH. The results of the present study confirmed the suitability of proposed methodology for precise modeling of precipitation.

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

A finite element method based microwave heat transfer modeling of frozen multi-component foods

NASA Astrophysics Data System (ADS)

Pitchai, Krishnamoorthy

Microwave heating is fast and convenient, but is highly non-uniform. Non-uniform heating in microwave cooking affects not only food quality but also food safety. Most food industries develop microwavable food products based on "cook-and-look" approach. This approach is time-consuming, labor intensive and expensive and may not result in optimal food product design that assures food safety and quality. Design of microwavable food can be realized through a simulation model which describes the physical mechanisms of microwave heating in mathematical expressions. The objective of this study was to develop a microwave heat transfer model to predict spatial and temporal profiles of various heterogeneous foods such as multi-component meal (chicken nuggets and mashed potato), multi-component and multi-layered meal (lasagna), and multi-layered food with active packages (pizza) during microwave heating. A microwave heat transfer model was developed by solving electromagnetic and heat transfer equations using finite element method in commercially available COMSOL Multiphysics v4.4 software. The microwave heat transfer model included detailed geometry of the cavity, phase change, and rotation of the food on the turntable. The predicted spatial surface temperature patterns and temporal profiles were validated against the experimental temperature profiles obtained using a thermal imaging camera and fiber-optic sensors. The predicted spatial surface temperature profile of different multi-component foods was in good agreement with the corresponding experimental profiles in terms of hot and cold spot patterns. The root mean square error values of temporal profiles ranged from 5.8 °C to 26.2 °C in chicken nuggets as compared 4.3 °C to 4.7 °C in mashed potatoes. In frozen lasagna, root mean square error values at six locations ranged from 6.6 °C to 20.0 °C for 6 min of heating. A microwave heat transfer model was developed to include susceptor assisted microwave heating of a frozen pizza. The root mean square error values of transient temperature profiles of five locations ranged from 5.0 °C to 12.6 °C. A methodology was developed to incorporate electromagnetic frequency spectrum in the coupled electromagnetic and heat transfer model. Implementing the electromagnetic frequency spectrum in the simulation improved the accuracy of temperature field pattern and transient temperature profile as compared to mono-chromatic frequency of 2.45 GHz. The bulk moisture diffusion coefficient of cooked pasta was calculated as a function of temperature at a constant water activity using desorption isotherms.

Vectorization of linear discrete filtering algorithms

NASA Technical Reports Server (NTRS)

Schiess, J. R.

1977-01-01

Linear filters, including the conventional Kalman filter and versions of square root filters devised by Potter and Carlson, are studied for potential application on streaming computers. The square root filters are known to maintain a positive definite covariance matrix in cases in which the Kalman filter diverges due to ill-conditioning of the matrix. Vectorization of the filters is discussed, and comparisons are made of the number of operations and storage locations required by each filter. The Carlson filter is shown to be the most efficient of the filters on the Control Data STAR-100 computer.

The Bidirectional Relationship Between Depressive Symptoms and Homebound Status Among Older Adults.

PubMed

Xiang, Xiaoling; An, Ruopeng; Oh, Hyunsung

2018-01-25

This study aimed to examine the bidirectional relationship between depressive symptoms and homebound status among older adults. The study sample included 7,603 community-dwelling older adults from the National Health and Aging Trends Study. A bivariate latent state-trait model of depressive symptoms and homebound status was estimated via structural equation modeling. The model fit the data well (Root Mean Square Error of Approximation = .02, Comparative Fit Index = .97, Standardized Root Mean Square Residual = .06). The relationship between homebound status and depressive symptoms can be decomposed into three parts: a moderate correlation between the stable trait components (r = .56, p <.001); a contemporary association of the state components (b = .17, p <.001); and bidirectional lagged effects between the state components. Change in homebound status was as a stronger predictor of depressive symptoms (b = .19, p < .001) than change in depressive symptoms was of homebound status (b = .06, p < .001; test of difference: Δ scaled χ2(1) = 24.2, p < .001). Homebound status and depressive symptoms form a feedback loop to influence each other. Improving the outdoor mobility of older adults may have immediate benefits for reducing depressive symptoms. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The self-transcendence scale: an investigation of the factor structure among nursing home patients.

PubMed

Haugan, Gørill; Rannestad, Toril; Garåsen, Helge; Hammervold, Randi; Espnes, Geir Arild

2012-09-01

Self-transcendence, the ability to expand personal boundaries in multiple ways, has been found to provide well-being. The purpose of this study was to examine the dimensionality of the Norwegian version of the Self-Transcendence Scale, which comprises 15 items. Reed's empirical nursing theory of self-transcendence provided the theoretical framework; self-transcendence includes an interpersonal, intrapersonal, transpersonal, and temporal dimension. Cross-sectional data were obtained from a sample of 202 cognitively intact elderly patients in 44 Norwegian nursing homes. Exploratory factor analysis revealed two and four internally consistent dimensions of self-transcendence, explaining 35.3% (two factors) and 50.7% (four factors) of the variance, respectively. Confirmatory factor analysis indicated that the hypothesized two- and four-factor models fitted better than the one-factor model (cx (2), root mean square error of approximation, standardized root mean square residual, normed fit index, nonnormed fit index, comparative fit index, goodness-of-fit index, and adjusted goodness-of-fit index). The findings indicate self-transcendence as a multifactorial construct; at present, we conclude that the two-factor model might be the most accurate and reasonable measure of self-transcendence. This research generates insights in the application of the widely used Self-Transcendence Scale by investigating its psychometric properties by applying a confirmatory factor analysis. It also generates new research-questions on the associations between self-transcendence and well-being.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

Augmented Reality Using Transurethral Ultrasound for Laparoscopic Radical Prostatectomy: Preclinical Evaluation.

PubMed

Lanchon, Cecilia; Custillon, Guillaume; Moreau-Gaudry, Alexandre; Descotes, Jean-Luc; Long, Jean-Alexandre; Fiard, Gaelle; Voros, Sandrine

2016-07-01

To guide the surgeon during laparoscopic or robot-assisted radical prostatectomy an innovative laparoscopic/ultrasound fusion platform was developed using a motorized 3-dimensional transurethral ultrasound probe. We present what is to our knowledge the first preclinical evaluation of 3-dimensional prostate visualization using transurethral ultrasound and the preliminary results of this new augmented reality. The transurethral probe and laparoscopic/ultrasound registration were tested on realistic prostate phantoms made of standard polyvinyl chloride. The quality of transurethral ultrasound images and the detection of passive markers placed on the prostate surface were evaluated on 2-dimensional dynamic views and 3-dimensional reconstructions. The feasibility, precision and reproducibility of laparoscopic/transurethral ultrasound registration was then determined using 4, 5, 6 and 7 markers to assess the optimal amount needed. The root mean square error was calculated for each registration and the median root mean square error and IQR were calculated according to the number of markers. The transurethral ultrasound probe was easy to manipulate and the prostatic capsule was well visualized in 2 and 3 dimensions. Passive markers could precisely be localized in the volume. Laparoscopic/transurethral ultrasound registration procedures were performed on 74 phantoms of various sizes and shapes. All were successful. The median root mean square error of 1.1 mm (IQR 0.8-1.4) was significantly associated with the number of landmarks (p = 0.001). The highest accuracy was achieved using 6 markers. However, prostate volume did not affect registration precision. Transurethral ultrasound provided high quality prostate reconstruction and easy marker detection. Laparoscopic/ultrasound registration was successful with acceptable mm precision. Further investigations are necessary to achieve sub mm accuracy and assess feasibility in a human model. Copyright © 2016 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Probing strong correlations with light scattering: Example of the quantum Ising model

DOE PAGES

Babujian, H. M.; Karowski, M.; Tsvelik, A. M.

2016-10-01

In this article we calculate the nonlinear susceptibility and the resonant Raman cross section for the paramagnetic phase of the ferromagnetic quantum Ising model in one dimension. In this region the spectrum of the Ising model has a gap m. The Raman cross section has a strong singularity when the energy of the outgoing photon is at the spectral gap ω f ≈ m and a square root threshold when the frequency difference between the incident and outgoing photons ω i₋ω f≈2m. Finally, the latter feature reflects the fermionic nature of the Ising model excitations.

Probing strong correlations with light scattering: Example of the quantum Ising model

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

Babujian, H. M.; Karowski, M.; Tsvelik, A. M.

In this article we calculate the nonlinear susceptibility and the resonant Raman cross section for the paramagnetic phase of the ferromagnetic quantum Ising model in one dimension. In this region the spectrum of the Ising model has a gap m. The Raman cross section has a strong singularity when the energy of the outgoing photon is at the spectral gap ω f ≈ m and a square root threshold when the frequency difference between the incident and outgoing photons ω i₋ω f≈2m. Finally, the latter feature reflects the fermionic nature of the Ising model excitations.

Generalized Jaynes-Cummings model as a quantum search algorithm

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

Romanelli, A.

2009-07-15

We propose a continuous time quantum search algorithm using a generalization of the Jaynes-Cummings model. In this model the states of the atom are the elements among which the algorithm realizes the search, exciting resonances between the initial and the searched states. This algorithm behaves like Grover's algorithm; the optimal search time is proportional to the square root of the size of the search set and the probability to find the searched state oscillates periodically in time. In this frame, it is possible to reinterpret the usual Jaynes-Cummings model as a trivial case of the quantum search algorithm.

Virtual reconstruction of modern and fossil hominoid crania: consequences of reference sample choice.

PubMed

Senck, Sascha; Bookstein, Fred L; Benazzi, Stefano; Kastner, Johann; Weber, Gerhard W

2015-05-01

Most hominin cranial fossils are incomplete and require reconstruction prior to subsequent analyses. Missing data can be estimated by geometric morphometrics using information from complete specimens, for example, by using thin-plate splines. In this study, we estimate missing data in several virtually fragmented models of hominoid crania (Homo, Pan, Pongo) and fossil hominins (e.g., Australopithecus africanus, Homo heidelbergensis). The aim is to investigate in which way different references influence estimations of cranial shape and how this information can be employed in the reconstruction of fossils. We used a sample of 64 three-dimensional digital models of complete human, chimpanzee, and orangutan crania and a set of 758 landmarks and semilandmarks. The virtually knocked out neurocranial and facial areas that were reconstructed corresponded to those of a real case found in A.L. 444-2 (A. afarensis) cranium. Accuracy of multiple intraspecies and interspecies reconstructions was computed as the maximum square root of the mean squared difference between the original and the reconstruction (root mean square). The results show that the uncertainty in reconstructions is a function of both the geometry of the knockout area and the dissimilarity between the reference sample and the specimen(s) undergoing reconstruction. We suggest that it is possible to estimate large missing cranial areas if the shape of the reference is similar enough to the shape of the specimen reconstructed, though caution must be exercised when employing these reconstructions in subsequent analyses. We provide a potential guide for the choice of the reference by means of bending energy. © 2015 Wiley Periodicals, Inc.

Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations

PubMed Central

Kish, Nicole E.; Helmuth, Brian; Wethey, David S.

2016-01-01

Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change. PMID:27729979

Growth modeling of Listeria monocytogenes in pasteurized liquid egg.

PubMed

Ohkochi, Miho; Koseki, Shigenobu; Kunou, Masaaki; Sugiura, Katsuaki; Tsubone, Hirokazu

2013-09-01

The growth kinetics of Listeria monocytogenes and natural flora in commercially produced pasteurized liquid egg was examined at 4.1 to 19.4°C, and a growth simulation model that can estimate the range of the number of L. monocytogenes bacteria was developed. The experimental kinetic data were fitted to the Baranyi model, and growth parameters, such as maximum specific growth rate (μ(max)), maximum population density (N(max)), and lag time (λ), were estimated. As a result of estimating these parameters, we found that L. monocytogenes can grow without spoilage below 12.2°C, and we then focused on storage temperatures below 12.2°C in developing our secondary models. The temperature dependency of the μ(max) was described by Ratkowsky's square root model. The N(max) of L. monocytogenes was modeled as a function of temperature, because the N(max) of L. monocytogenes decreased as storage temperature increased. A tertiary model of L. monocytogenes was developed using the Baranyi model and μ(max) and N(max) secondary models. The ranges of the numbers of L. monocytogenes bacteria were simulated using Monte Carlo simulations with an assumption that these parameters have variations that follow a normal distribution. Predictive simulations under both constant and fluctuating temperature conditions demonstrated a high accuracy, represented by root mean square errors of 0.44 and 0.34, respectively. The predicted ranges also seemed to show a reasonably good estimation, with 55.8 and 51.5% of observed values falling into the prediction range of the 25th to 75th percentile, respectively. These results suggest that the model developed here can be used to estimate the kinetics and range of L. monocytogenes growth in pasteurized liquid egg under refrigerated temperature.

The holographic dual of the Penrose transform

NASA Astrophysics Data System (ADS)

Neiman, Yasha

2018-01-01

We consider the holographic duality between type-A higher-spin gravity in AdS4 and the free U( N) vector model. In the bulk, linearized solutions can be translated into twistor functions via the Penrose transform. We propose a holographic dual to this transform, which translates between twistor functions and CFT sources and operators. We present a twistorial expression for the partition function, which makes global higher-spin symmetry manifest, and appears to automatically include all necessary contact terms. In this picture, twistor space provides a fully nonlocal, gauge-invariant description underlying both bulk and boundary spacetime pictures. While the bulk theory is handled at the linear level, our formula for the partition function includes the effects of bulk interactions. Thus, the CFT is used to solve the bulk, with twistors as a language common to both. A key ingredient in our result is the study of ordinary spacetime symmetries within the fundamental representation of higher-spin algebra. The object that makes these "square root" spacetime symmetries manifest becomes the kernel of our boundary/twistor transform, while the original Penrose transform is identified as a "square root" of CPT.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun

2009-10-01

To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.

The Stochastic X-Ray Variability of the Accreting Millisecond Pulsar MAXI J0911-655

NASA Technical Reports Server (NTRS)

Bult, Peter

2017-01-01

In this work, I report on the stochastic X-ray variability of the 340 hertz accreting millisecond pulsar MAXI J0911-655. Analyzing pointed observations of the XMM-Newton and NuSTAR observatories, I find that the source shows broad band-limited stochastic variability in the 0.01-10 hertz range with a total fractional variability of approximately 24 percent root mean square timing residuals in the 0.4 to 3 kiloelectronvolt energy band that increases to approximately 40 percent root mean square timing residuals in the 3 to 10 kiloelectronvolt band. Additionally, a pair of harmonically related quasi-periodic oscillations (QPOs) are discovered. The fundamental frequency of this harmonic pair is observed between frequencies of 62 and 146 megahertz. Like the band-limited noise, the amplitudes of the QPOs show a steep increase as a function of energy; this suggests that they share a similar origin, likely the inner accretion flow. Based on their energy dependence and frequency relation with respect to the noise terms, the QPOs are identified as low-frequency oscillations and discussed in terms of the Lense-Thirring precession model.

Oceanographic results from analysis of ERS-1 altimetry

NASA Technical Reports Server (NTRS)

Tapley, B. D.; Shum, C. K.; Chambers, D. P.; Peterson, G. E.; Ries, J. C.

1994-01-01

Large scale dynamic ocean topography and its variations were observed using ERS-1 radar altimeter measurements. The altimeter measurements analyzed are primarily from the ESA ocean product (OPR02) and from the Interim Geophysical Data Records (IGDR) generated by NOAA from the fast delivery (FD) data during the ERS-1 35 day repeat orbit phase. The precise orbits used for the dynamic topography solution are computed using dual satellite crossover measurements from ERS-1 and TOPEX (Topology Ocean Experiment)/Poseidon (T/P) as additional tracking data, and using improved models and constants which are consistent with T/P. Analysis of the ERS-1 dynamic topography solution indicates agreement with the T/P solution at the 5 cm root mean square level, with regional differences as large as 15 cm tide gauges at the 8 to 9 cm level. There are differences between the ERS-1 OPR02 and IGDR determined dynamic topography solutions on the order of 5 cm root mean square. Mesoscale oceanic variability time series obtained using collinear analysis of the ERS-1 altimeter data show good qualitative agreement when compared with the T/P results.

An evaluation of three growth and yield simulators for even-aged hardwood forests of the mid-Appalachian region

Treesearch

John R. Brooks; Gary W. Miller

2011-01-01

Data from even-aged hardwood stands in four ecoregions across the mid-Appalachian region were used to test projection accuracy for three available growth and yield software systems: SILVAH, the Forest Vegetation Simulator, and the Stand Damage Model. Average root mean squared error (RMSE) ranged from 20 to 140 percent of actual trees per acre while RMSE ranged from 2...

Examination of the Factor Structure of a Global Cognitive Function Battery across Race and Time

PubMed Central

Barnes, Lisa L.; Yumoto, Futoshi; Capuano, Ana; Wilson, Robert S.; Bennett, David A.; Tractenberg, Rochelle E.

2016-01-01

Older African Americans tend to perform more poorly on cognitive function tests than older Whites. One possible explanation for their poorer performance is that the tests used to assess cognition may not reflect the same construct in African Americans and Whites. Therefore, we tested measurement invariance, by race and over time, of a structured 18-test cognitive battery used in three epidemiologic cohort studies of diverse older adults. Multi-group confirmatory factor analyses were carried out with full-information maximum likelihood estimation in all models to capture as much information as was present in the observed data. Four different aspects of the data were fit to each model: comparative fit index (CFI), standardized root mean square residuals (SRMR), root mean square error of approximation (RMSEA), and model χ2. We found that the most constrained model fit the data well (CFI = 0.950; SRMR = 0.051; RMSEA = 0.057 (90% confidence interval: 0.056, 0.059); the model χ2 = 4600.68 on 862 df), supporting the characterization of this model of cognitive test scores as invariant over time and racial group. These results support the conclusion that the cognitive test battery used in the three studies is invariant across race and time and can be used to assess cognition among African Americans and Whites in longitudinal studies. Furthermore, the lower performance of African Americans on these tests is not due to bias in the tests themselves but rather likely reflect differences in social and environmental experiences over the life course. PMID:26563713

Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Jintao, Xue; Yufei, Liu; Liming, Ye; Chunyan, Li; Quanwei, Yang; Weiying, Wang; Yun, Jing; Minxiang, Zhang; Peng, Li

2018-01-01

Near-Infrared Spectroscopy (NIRS) was first used to develop a method for rapid and simultaneous determination of 5 active alkaloids (berberine, coptisine, palmatine, epiberberine and jatrorrhizine) in 4 parts (rhizome, fibrous root, stem and leaf) of Coptidis Rhizoma. A total of 100 samples from 4 main places of origin were collected and studied. With HPLC analysis values as calibration reference, the quantitative analysis of 5 marker components was performed by two different modeling methods, partial least-squares (PLS) regression as linear regression and artificial neural networks (ANN) as non-linear regression. The results indicated that the 2 types of models established were robust, accurate and repeatable for five active alkaloids, and the ANN models was more suitable for the determination of berberine, coptisine and palmatine while the PLS model was more suitable for the analysis of epiberberine and jatrorrhizine. The performance of the optimal models was achieved as follows: the correlation coefficient (R) for berberine, coptisine, palmatine, epiberberine and jatrorrhizine was 0.9958, 0.9956, 0.9959, 0.9963 and 0.9923, respectively; the root mean square error of validation (RMSEP) was 0.5093, 0.0578, 0.0443, 0.0563 and 0.0090, respectively. Furthermore, for the comprehensive exploitation and utilization of plant resource of Coptidis Rhizoma, the established NIR models were used to analysis the content of 5 active alkaloids in 4 parts of Coptidis Rhizoma and 4 main origin of places. This work demonstrated that NIRS may be a promising method as routine screening for off-line fast analysis or on-line quality assessment of traditional Chinese medicine (TCM).

The mechanics of delamination in fiber-reinforced composite materials. I - Stress singularities and solution structure

NASA Technical Reports Server (NTRS)

Wang, S. S.; Choi, I.

1983-01-01

The fundamental mechanics of delamination in fiber composite laminates is studied. Mathematical formulation of the problem is based on laminate anisotropic elasticity theory and interlaminar fracture mechanics concepts. Stress singularities and complete solution structures associated with general composite delaminations are determined. For a fully open delamination with traction-free surfaces, oscillatory stress singularities always appear, leading to physically inadmissible field solutions. A refined model is introduced by considering a partially closed delamination with crack surfaces in finite-length contact. Stress singularities associated with a partially closed delamination having frictional crack-surface contact are determined, and are found to be different from the inverse square-root one of the frictionless-contact case. In the case of a delamination with very small area of crack closure, a simplified model having a square-root stress singularity is employed by taking the limit of the partially closed delamination. The possible presence of logarithmic-type stress singularity is examined; no logarithmic singularity of any kind is found in the composite delamination problem. Numerical examples of dominant stress singularities are shown for delaminations having crack-tip closure with different frictional coefficients between general (1) and (2) graphite-epoxy composites. Previously announced in STAR as N84-13221

Quantitative assessment of copper proteinates used as animal feed additives using ATR-FTIR spectroscopy and powder X-ray diffraction (PXRD) analysis.

PubMed

Cantwell, Caoimhe A; Byrne, Laurann A; Connolly, Cathal D; Hynes, Michael J; McArdle, Patrick; Murphy, Richard A

2017-08-01

The aim of the present work was to establish a reliable analytical method to determine the degree of complexation in commercial metal proteinates used as feed additives in the solid state. Two complementary techniques were developed. Firstly, a quantitative attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopic method investigated modifications in vibrational absorption bands of the ligand on complex formation. Secondly, a powder X-ray diffraction (PXRD) method to quantify the amount of crystalline material in the proteinate product was developed. These methods were developed in tandem and cross-validated with each other. Multivariate analysis (MVA) was used to develop validated calibration and prediction models. The FTIR and PXRD calibrations showed excellent linearity (R 2  > 0.99). The diagnostic model parameters showed that the FTIR and PXRD methods were robust with a root mean square error of calibration RMSEC ≤3.39% and a root mean square error of prediction RMSEP ≤7.17% respectively. Comparative statistics show excellent agreement between the MVA packages assessed and between the FTIR and PXRD methods. The methods can be used to determine the degree of complexation in complexes of both protein hydrolysates and pure amino acids.

Psychometric evaluation of the Finnish version of the impact on participation and autonomy questionnaire in persons with multiple sclerosis.

PubMed

Karhula, Maarit E; Salminen, Anna-Liisa; Hämäläinen, Päivi I; Ruutiainen, Juhani; Era, Pertti; Tolvanen, Asko

2017-11-01

The objective of this study was to evaluate the psychometric properties of the impact on participation and autonomy (IPA) questionnaire. The Finnish version of IPA (IPAFin) was translated into Finnish using the protocol for linguistic validation for patient-reported outcomes instruments. A total of 194 persons with multiple sclerosis (MS) (mean age 50 years SD 9, 72% female) with moderate to severe disability participated in this study. A confirmatory factor analysis (CFA) was used to confirm the four factor structure of the IPAFin. The work and educational opportunities domain was excluded from analysis, because it was only applicable to 51 persons. Internal consistency was investigated by calculating Cronbach's alpha. CFA confirmed the construct validity of the IPA (standardized root mean square residual (SRMR) = 0.06, comparative fit index (CFI) = 0.93, Tucker-Lewis index =0.93, root mean square error of approximation (RMSEA) = 0.06), indicating a good fit to the model. There was no difference in the models for females and males. Cronbach's alpha for the domains ranged between 0.80 and 0.91, indicating good homogeneity. The construct validity and reliability of the IPAFin is acceptable. IPAFin is a suitable measure of participation in persons with MS.

Biologically active ligands for yersinia outer protein H (YopH): feature based pharmacophore screening, docking and molecular dynamics studies.

PubMed

Tamilvanan, Thangaraju; Hopper, Waheeta

2014-01-01

Yersinia pestis, a Gram negative bacillus, spreads via lymphatic to lymph nodes and to all organs through the bloodstream, causing plague. Yersinia outer protein H (YopH) is one of the important effector proteins, which paralyzes lymphocytes and macrophages by dephosphorylating critical tyrosine kinases and signal transduction molecules. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse sets of YopH inhibitors, which would be useful for designing of potential antitoxin. In this study, we have selected 60 biologically active inhibitors of YopH to perform Ligand based pharmacophore study to elucidate the important structural features responsible for biological activity. Pharmacophore model demonstrated the importance of two acceptors, one hydrophobic and two aromatic features toward the biological activity. Based on these features, different databases were screened to identify novel compounds and these ligands were subjected for docking, ADME properties and Binding energy prediction. Post docking validation was performed using molecular dynamics simulation for selected ligands to calculate the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The ligands, ASN03270114, Mol_252138, Mol_31073 and ZINC04237078 may act as inhibitors against YopH of Y. pestis.

Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages.

PubMed

Yu, Peigen; Low, Mei Yin; Zhou, Weibiao

2018-01-01

In order to develop products that would be preferred by consumers, the effects of the chemical compositions of ready-to-drink green tea beverages on consumer liking were studied through regression analyses. Green tea model systems were prepared by dosing solutions of 0.1% green tea extract with differing concentrations of eight flavour keys deemed to be important for green tea aroma and taste, based on a D-optimal experimental design, before undergoing commercial sterilisation. Sensory evaluation of the green tea model system was carried out using an untrained consumer panel to obtain hedonic liking scores of the samples. Regression models were subsequently trained to objectively predict the consumer liking scores of the green tea model systems. A linear partial least squares (PLS) regression model was developed to describe the effects of the eight flavour keys on consumer liking, with a coefficient of determination (R 2 ) of 0.733, and a root-mean-square error (RMSE) of 3.53%. The PLS model was further augmented with an artificial neural network (ANN) to establish a PLS-ANN hybrid model. The established hybrid model was found to give a better prediction of consumer liking scores, based on its R 2 (0.875) and RMSE (2.41%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluating the validity of the Work Role Functioning Questionnaire (Canadian French version) using classical test theory and item response theory.

PubMed

Hong, Quan Nha; Coutu, Marie-France; Berbiche, Djamal

2017-01-01

The Work Role Functioning Questionnaire (WRFQ) was developed to assess workers' perceived ability to perform job demands and is used to monitor presenteeism. Still few studies on its validity can be found in the literature. The purpose of this study was to assess the items and factorial composition of the Canadian French version of the WRFQ (WRFQ-CF). Two measurement approaches were used to test the WRFQ-CF: Classical Test Theory (CTT) and non-parametric Item Response Theory (IRT). A total of 352 completed questionnaires were analyzed. A four-factor and three-factor model models were tested and shown respectively good fit with 14 items (Root Mean Square Error of Approximation (RMSEA) = 0.06, Standardized Root Mean Square Residual (SRMR) = 0.04, Bentler Comparative Fit Index (CFI) = 0.98) and with 17 items (RMSEA = 0.059, SRMR = 0.048, CFI = 0.98). Using IRT, 13 problematic items were identified, of which 9 were common with CTT. This study tested different models with fewer problematic items found in a three-factor model. Using a non-parametric IRT and CTT for item purification gave complementary results. IRT is still scarcely used and can be an interesting alternative method to enhance the quality of a measurement instrument. More studies are needed on the WRFQ-CF to refine its items and factorial composition.

Ensemble forecasting of short-term system scale irrigation demands using real-time flow data and numerical weather predictions

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; Robertson, David E.; George, Biju; Nawarathna, Bandara

2016-06-01

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real-time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short-term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash-Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

Quantitative comparison of the application accuracy between NDI and IGT tracking systems

NASA Astrophysics Data System (ADS)

Li, Qinghang; Zamorano, Lucia J.; Jiang, Charlie Z. W.; Gong, JianXing; Diaz, Fernando

1999-07-01

The application accuracy is a crucial factor for the stereotactic surgical localization system in which space digitization system is one of the most important part of equipment. In this study we compared the application accuracy of using the OPTOTRAK space digitization system (OPTOTRAK 3020, Northern Digital, Waterloo, CAN) and FlashPoint Model 3000 and 5000 3-D digitizer systems (FlashPoint Model 3000 and 5000, Image Guided Surgery Technology Inc., Boulder, CO 80301, USA) for interactive localization of intracranial lesions. A phantom was mounted with the implantable frameless marker system (Fischer- Leibinger, Freiburg, Germany) which randomly distributed markers on the surface of the phantom. The target point was digitized and the coordinates were recorded and compared with reference points. The differences from the reference points were used as the deviation from the `true point'. The mean square root was calculated to show the sum of vectors. A paired t-test was used to analyze results. The results of the phantom showed that the mean square roots were 0.76 +/- 0.54 mm for the OPTOTRAK system and 1.23 +/- 0.53 mm for FlashPoint Model 3000 3-D digitizer system and 1.00 +/- 0.42 mm for FlashPoint Model 3000 3-D digitizer system in the 1 mm sections of CT scan. This preliminary results showed that there is no significant difference between two tracking systems. Both of them can be used for image guided surgery procedure.

Predictors of FIFA 11+ Implementation Intention in Female Adolescent Soccer: An Application of the Health Action Process Approach (HAPA) Model

PubMed Central

McKay, Carly D.; Merrett, Charlotte K.; Emery, Carolyn A.

2016-01-01

The Fédération Internationale de Football (FIFA) 11+ warm-up program is efficacious at preventing lower limb injury in youth soccer; however, there has been poor adoption of the program in the community. The purpose of this study was to determine the utility of the Health Action Process Approach (HAPA) behavior change model in predicting intention to use the FIFA 11+ in a sample of 12 youth soccer teams (coaches n = 10; 12–16 year old female players n = 200). A bespoke cross-sectional questionnaire measured pre-season risk perceptions, outcome expectancies, task self-efficacy, facilitators, barriers, and FIFA 11+ implementation intention. Most coaches (90.0%) and players (80.0%) expected the program to reduce injury risk but reported limited intention to use it. Player data demonstrated an acceptable fit to the hypothesized model (standardized root mean square residual (SRMR) = 0.08; root mean square of error of approximation (RMSEA) = 0.06 (0.047–0.080); comparative fit index (CFI) = 0.93; Tucker Lewis index (TLI) = 0.91) Task self-efficacy (β = 0.53, p ≤ 0.01) and outcome expectancies (β = 0.13 p ≤ 0.05) were positively associated with intention, but risk perceptions were not (β = −0.02). The findings suggest that the HAPA model is appropriate for use in this context, and highlight the need to target task self-efficacy and outcome expectancies in FIFA 11+ implementation strategies. PMID:27399746

Agent-based models for latent liquidity and concave price impact

NASA Astrophysics Data System (ADS)

Mastromatteo, Iacopo; Tóth, Bence; Bouchaud, Jean-Philippe

2014-04-01

We revisit the "ɛ-intelligence" model of Tóth et al. [Phys. Rev. X 1, 021006 (2011), 10.1103/PhysRevX.1.021006], which was proposed as a minimal framework to understand the square-root dependence of the impact of meta-orders on volume in financial markets. The basic idea is that most of the daily liquidity is "latent" and furthermore vanishes linearly around the current price, as a consequence of the diffusion of the price itself. However, the numerical implementation of Tóth et al. (2011) was criticized as being unrealistic, in particular because all the "intelligence" was conferred to market orders, while limit orders were passive and random. In this work, we study various alternative specifications of the model, for example, allowing limit orders to react to the order flow or changing the execution protocols. By and large, our study lends strong support to the idea that the square-root impact law is a very generic and robust property that requires very few ingredients to be valid. We also show that the transition from superdiffusion to subdiffusion reported in Tóth et al. (2011) is in fact a crossover but that the original model can be slightly altered in order to give rise to a genuine phase transition, which is of interest on its own. We finally propose a general theoretical framework to understand how a nonlinear impact may appear even in the limit where the bias in the order flow is vanishingly small.

How do physicians become medical experts? A test of three competing theories: distinct domains, independent influence and encapsulation models.

PubMed

Violato, Claudio; Gao, Hong; O'Brien, Mary Claire; Grier, David; Shen, E

2018-05-01

The distinction between basic sciences and clinical knowledge which has led to a theoretical debate on how medical expertise is developed has implications for medical school and lifelong medical education. This longitudinal, population based observational study was conducted to test the fit of three theories-knowledge encapsulation, independent influence, distinct domains-of the development of medical expertise employing structural equation modelling. Data were collected from 548 physicians (292 men-53.3%; 256 women-46.7%; mean age = 24.2 years on admission) who had graduated from medical school 2009-2014. They included (1) Admissions data of undergraduate grade point average and Medical College Admission Test sub-test scores, (2) Course performance data from years 1, 2, and 3 of medical school, and (3) Performance on the NBME exams (i.e., Step 1, Step 2 CK, and Step 3). Statistical fit indices (Goodness of Fit Index-GFI; standardized root mean squared residual-SRMR; root mean squared error of approximation-RSMEA) and comparative fit [Formula: see text] of three theories of cognitive development of medical expertise were used to assess model fit. There is support for the knowledge encapsulation three factor model of clinical competency (GFI = 0.973, SRMR = 0.043, RSMEA = 0.063) which had superior fit indices to both the independent influence and distinct domains theories ([Formula: see text] vs [Formula: see text] [[Formula: see text

Cohesion and coordination effects on transition metal surface energies

NASA Astrophysics Data System (ADS)

Ruvireta, Judit; Vega, Lorena; Viñes, Francesc

2017-10-01

Here we explore the accuracy of Stefan equation and broken-bond model semiempirical approaches to obtain surface energies on transition metals. Cohesive factors are accounted for either via the vaporization enthalpies, as proposed in Stefan equation, or via cohesive energies, as employed in the broken-bond model. Coordination effects are considered including the saturation degree, as suggested in Stefan equation, employing Coordination Numbers (CN), or as the ratio of broken bonds, according to the bond-cutting model, considering as well the square root dependency of the bond strength on CN. Further, generalized coordination numbers CN bar are contemplated as well, exploring a total number of 12 semiempirical formulations on the three most densely packed surfaces of 3d, 4d, and 5d Transition Metals (TMs) displaying face-centered cubic (fcc), body-centered cubic (bcc), or hexagonal close-packed (hcp) crystallographic structures. Estimates are compared to available experimental surface energies obtained extrapolated to zero temperature. Results reveal that Stefan formula cohesive and coordination dependencies are only qualitative suited, but unadvised for quantitative discussion, as surface energies are highly overestimated, favoring in addition the stability of under-coordinated surfaces. Broken-bond cohesion and coordination dependencies are a suited basis for quantitative comparison, where square-root dependencies on CN to account for bond weakening are sensibly worse. An analysis using Wulff shaped averaged surface energies suggests the employment of broken-bond model using CN to gain surface energies for TMs, likely applicable to other metals.

Agent-based models for latent liquidity and concave price impact.

PubMed

Mastromatteo, Iacopo; Tóth, Bence; Bouchaud, Jean-Philippe

2014-04-01

We revisit the "ɛ-intelligence" model of Tóth et al. [Phys. Rev. X 1, 021006 (2011)], which was proposed as a minimal framework to understand the square-root dependence of the impact of meta-orders on volume in financial markets. The basic idea is that most of the daily liquidity is "latent" and furthermore vanishes linearly around the current price, as a consequence of the diffusion of the price itself. However, the numerical implementation of Tóth et al. (2011) was criticized as being unrealistic, in particular because all the "intelligence" was conferred to market orders, while limit orders were passive and random. In this work, we study various alternative specifications of the model, for example, allowing limit orders to react to the order flow or changing the execution protocols. By and large, our study lends strong support to the idea that the square-root impact law is a very generic and robust property that requires very few ingredients to be valid. We also show that the transition from superdiffusion to subdiffusion reported in Tóth et al. (2011) is in fact a crossover but that the original model can be slightly altered in order to give rise to a genuine phase transition, which is of interest on its own. We finally propose a general theoretical framework to understand how a nonlinear impact may appear even in the limit where the bias in the order flow is vanishingly small.

Inclusive search for standard model Higgs boson production in the WW decay channel using the CDF II detector.

PubMed

Aaltonen, T; Adelman, J; Alvarez González, B; Amerio, S; Amidei, D; Anastassov, A; Annovi, A; Antos, J; Apollinari, G; Appel, J; Apresyan, A; Arisawa, T; Artikov, A; Asaadi, J; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Barria, P; Bartos, P; Bauer, G; Beauchemin, P-H; Bedeschi, F; Beecher, D; Behari, S; Bellettini, G; Bellinger, J; Benjamin, D; Beretvas, A; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Bridgeman, A; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Calancha, C; Camarda, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carls, B; Carlsmith, D; Carosi, R; Carrillo, S; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavaliere, V; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, K; Chokheli, D; Chou, J P; Chung, K; Chung, W H; Chung, Y S; Chwalek, T; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Compostella, G; Convery, M E; Conway, J; Corbo, M; Cordelli, M; Cox, C A; Cox, D J; Crescioli, F; Cuenca Almenar, C; Cuevas, J; Culbertson, R; Cully, J C; Dagenhart, D; d'Ascenzo, N; Datta, M; Davies, T; de Barbaro, P; De Cecco, S; Deisher, A; De Lorenzo, G; Dell'Orso, M; Deluca, C; Demortier, L; Deng, J; Deninno, M; d'Errico, M; Di Canto, A; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Donati, S; Dong, P; Dorigo, T; Dube, S; Ebina, K; Elagin, A; Erbacher, R; Errede, D; Errede, S; Ershaidat, N; Eusebi, R; Fang, H C; Farrington, S; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Ferrazza, C; Field, R; Flanagan, G; Forrest, R; Frank, M J; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garberson, F; Garcia, J E; Garfinkel, A F; Garosi, P; Gerberich, H; Gerdes, D; Gessler, A; Giagu, S; Giakoumopoulou, V; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C M; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Group, R C; Grundler, U; Guimaraes da Costa, J; Gunay-Unalan, Z; Haber, C; Hahn, S R; Halkiadakis, E; Han, B-Y; Han, J Y; Happacher, F; Hara, K; Hare, D; Hare, M; Harr, R F; Hartz, M; Hatakeyama, K; Hays, C; Heck, M; Heinrich, J; Herndon, M; Heuser, J; Hewamanage, S; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Hou, S; Houlden, M; Hsu, S-C; Hughes, R E; Hurwitz, M; Husemann, U; Hussein, M; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; James, E; Jang, D; Jayatilaka, B; Jeon, E J; Jha, M K; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Kar, D; Karchin, P E; Kato, Y; Kephart, R; Ketchum, W; Keung, J; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, H W; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kreps, M; Kroll, J; Krop, D; Krumnack, N; Kruse, M; Krutelyov, V; Kuhr, T; Kulkarni, N P; Kurata, M; Kwang, S; Laasanen, A T; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; LeCompte, T; Lee, E; Lee, H S; Lee, J S; Lee, S W; Leone, S; Lewis, J D; Lin, C-J; Linacre, J; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, C; Liu, T; Lockyer, N S; Loginov, A; Lovas, L; Lucchesi, D; Lueck, J; Lujan, P; Lukens, P; Lungu, G; Lys, J; Lysak, R; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maksimovic, P; Malde, S; Malik, S; Manca, G; Manousakis-Katsikakis, A; Margaroli, F; Marino, C; Marino, C P; Martin, A; Martin, V; Martínez, M; Martínez-Ballarín, R; Mastrandrea, P; Mathis, M; Mattson, M E; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzione, A; Mesropian, C; Miao, T; Mietlicki, D; Miladinovic, N; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyake, H; Moed, S; Moggi, N; Mondragon, M N; Moon, C S; Moore, R; Morello, M J; Morlock, J; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Murat, P; Mussini, M; Nachtman, J; Nagai, Y; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Nett, J; Neu, C; Neubauer, M S; Neubauer, S; Nielsen, J; Nodulman, L; Norman, M; Norniella, O; Nurse, E; Oakes, L; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Orava, R; Osterberg, K; Pagan Griso, S; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramanov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Peiffer, T; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Pianori, E; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Potamianos, K; Poukhov, O; Prokoshin, F; Pronko, A; Ptohos, F; Pueschel, E; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Renton, P; Renz, M; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rodriguez, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Rutherford, B; Saarikko, H; Safonov, A; Sakumoto, W K; Santi, L; Sartori, L; Sato, K; Saveliev, V; Savoy-Navarro, A; Schlabach, P; Schmidt, A; Schmidt, E E; Schmidt, M A; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sforza, F; Sfyrla, A; Shalhout, S Z; Shears, T; Shepard, P F; Shimojima, M; Shiraishi, S; Shochet, M; Shon, Y; Shreyber, I; Simonenko, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soha, A; Somalwar, S; Sorin, V; Squillacioti, P; Stanitzki, M; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Strycker, G L; Suh, J S; Sukhanov, A; Suslov, I; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tang, J; Tecchio, M; Teng, P K; Thom, J; Thome, J; Thompson, G A; Thomson, E; Tipton, P; Ttito-Guzmán, P; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Totaro, P; Trovato, M; Tsai, S-Y; Tu, Y; Turini, N; Ukegawa, F; Uozumi, S; van Remortel, N; Varganov, A; Vataga, E; Vázquez, F; Velev, G; Vellidis, C; Vidal, M; Vila, I; Vilar, R; Vogel, M; Volobouev, I; Volpi, G; Wagner, P; Wagner, R G; Wagner, R L; Wagner, W; Wagner-Kuhr, J; Wakisaka, T; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Weinelt, J; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Wilbur, S; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wolfe, H; Wright, T; Wu, X; Würthwein, F; Yagil, A; Yamamoto, K; Yamaoka, J; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yi, K; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanetti, A; Zeng, Y; Zhang, X; Zheng, Y; Zucchelli, S

2010-02-12

We present a search for standard model (SM) Higgs boson production using pp collision data at square root(s) = 1.96 TeV, collected with the CDF II detector and corresponding to an integrated luminosity of 4.8 fb(-1). We search for Higgs bosons produced in all processes with a significant production rate and decaying to two W bosons. We find no evidence for SM Higgs boson production and place upper limits at the 95% confidence level on the SM production cross section (sigma(H)) for values of the Higgs boson mass (sigma(H)) in the range from 110 to 200 GeV. These limits are the most stringent for m(H) > 130 GeV and are 1.29 above the predicted value of sigma(H) for c = 165 GeV.

Search for heavy long-lived particles that decay to photons at CDF II.

PubMed

Abulencia, A; Adelman, J; Affolder, T; Akimoto, T; Albrow, M G; Amerio, S; Amidei, D; Anastassov, A; Anikeev, K; Annovi, A; Antos, J; Aoki, M; Apollinari, G; Arisawa, T; Artikov, A; Ashmanskas, W; Attal, A; Aurisano, A; Azfar, F; Azzi-Bacchetta, P; Azzurri, P; Bacchetta, N; Badgett, W; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Bartsch, V; Bauer, G; Beauchemin, P-H; Bedeschi, F; Behari, S; Bellettini, G; Bellinger, J; Belloni, A; Benjamin, D; Beretvas, A; Beringer, J; Berry, T; Bhatti, A; Binkley, M; Bisello, D; Bizjak, I; Blair, R E; Blocker, C; Blumenfeld, B; Bocci, A; Bodek, A; Boisvert, V; Bolla, G; Bolshov, A; Bortoletto, D; Boudreau, J; Boveia, A; Brau, B; Brigliadori, L; Bromberg, C; Brubaker, E; Budagov, J; Budd, H S; Budd, S; Burkett, K; Busetto, G; Bussey, P; Buzatu, A; Byrum, K L; Cabrera, S; Campanelli, M; Campbell, M; Canelli, F; Canepa, A; Carillo, S; Carlsmith, D; Carosi, R; Carron, S; Casal, B; Casarsa, M; Castro, A; Catastini, P; Cauz, D; Cavalli-Sforza, M; Cerri, A; Cerrito, L; Chang, S H; Chen, Y C; Chertok, M; Chiarelli, G; Chlachidze, G; Chlebana, F; Cho, I; Cho, K; Chokheli, D; Chou, J P; Choudalakis, G; Chuang, S H; Chung, K; Chung, W H; Chung, Y S; Cilijak, M; Ciobanu, C I; Ciocci, M A; Clark, A; Clark, D; Coca, M; Compostella, G; Convery, M E; Conway, J; Cooper, B; Copic, K; Cordelli, M; Cortiana, G; Crescioli, F; Almenar, C Cuenca; Cuevas, J; Culbertson, R; Cully, J C; Daronco, S; Datta, M; D'Auria, S; Davies, T; Dagenhart, D; de Barbaro, P; De Cecco, S; Deisher, A; De Lentdecker, G; De Lorenzo, G; Dell'Orso, M; Delli Paoli, F; Demortier, L; Deng, J; Deninno, M; De Pedis, D; Derwent, P F; Di Giovanni, G P; Dionisi, C; Di Ruzza, B; Dittmann, J R; D'Onofrio, M; Dörr, C; Donati, S; Dong, P; Donini, J; Dorigo, T; Dube, S; Efron, J; Erbacher, R; Errede, D; Errede, S; Eusebi, R; Fang, H C; Farrington, S; Fedorko, I; Fedorko, W T; Feild, R G; Feindt, M; Fernandez, J P; Field, R; Flanagan, G; Forrest, R; Forrester, S; Franklin, M; Freeman, J C; Furic, I; Gallinaro, M; Galyardt, J; Garcia, J E; Garberson, F; Garfinkel, A F; Gay, C; Gerberich, H; Gerdes, D; Giagu, S; Giannetti, P; Gibson, K; Gimmell, J L; Ginsburg, C; Giokaris, N; Giordani, M; Giromini, P; Giunta, M; Giurgiu, G; Glagolev, V; Glenzinski, D; Gold, M; Goldschmidt, N; Goldstein, J; Golossanov, A; Gomez, G; Gomez-Ceballos, G; Goncharov, M; González, O; Gorelov, I; Goshaw, A T; Goulianos, K; Gresele, A; Grinstein, S; Grosso-Pilcher, C; Grundler, U; da Costa, J Guimaraes; Gunay-Unalan, Z; Haber, C; Hahn, K; Hahn, S R; Halkiadakis, E; Hamilton, A; Han, B-Y; Han, J Y; Handler, R; Happacher, F; Hara, K; Hare, D; Hare, M; Harper, S; Harr, R F; Harris, R M; Hartz, M; Hatakeyama, K; Hauser, J; Hays, C; Heck, M; Heijboer, A; Heinemann, B; Heinrich, J; Henderson, C; Herndon, M; Heuser, J; Hidas, D; Hill, C S; Hirschbuehl, D; Hocker, A; Holloway, A; Hou, S; Houlden, M; Hsu, S-C; Huffman, B T; Hughes, R E; Husemann, U; Huston, J; Incandela, J; Introzzi, G; Iori, M; Ivanov, A; Iyutin, B; James, E; Jang, D; Jayatilaka, B; Jeans, D; Jeon, E J; Jindariani, S; Johnson, W; Jones, M; Joo, K K; Jun, S Y; Jung, J E; Junk, T R; Kamon, T; Karchin, P E; Kato, Y; Kemp, Y; Kephart, R; Kerzel, U; Khotilovich, V; Kilminster, B; Kim, D H; Kim, H S; Kim, J E; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kimura, N; Kirsch, L; Klimenko, S; Klute, M; Knuteson, B; Ko, B R; Kondo, K; Kong, D J; Konigsberg, J; Korytov, A; Kotwal, A V; Kraan, A C; Kraus, J; Kreps, M; Kroll, J; Krumnack, N; Kruse, M; Krutelyov, V; Kubo, T; Kuhlmann, S E; Kuhr, T; Kulkarni, N P; Kusakabe, Y; Kwang, S; Laasanen, A T; Lai, S; Lami, S; Lammel, S; Lancaster, M; Lander, R L; Lannon, K; Lath, A; Latino, G; Lazzizzera, I; LeCompte, T; Lee, E; Lee, J; Lee, J; Lee, Y J; Lee, S W; Lefèvre, R; Leonardo, N; Leone, S; Levy, S; Lewis, J D; Lin, C; Lin, C S; Lindgren, M; Lipeles, E; Lister, A; Litvintsev, D O; Liu, T; Lockyer, N S; Loginov, A; Loreti, M; Lu, R-S; Lucchesi, D; Lujan, P; Lukens, P; Lungu, G; Lyons, L; Lys, J; Lysak, R; Lytken, E; Mack, P; MacQueen, D; Madrak, R; Maeshima, K; Makhoul, K; Maki, T; Maksimovic, P; Malde, S; Malik, S; Manca, G; Margaroli, F; Marginean, R; Marino, C; Marino, C P; Martin, A; Martin, M; Martin, V; Martínez, M; Martínez-Ballarín, R; Maruyama, T; Mastrandrea, P; Masubuchi, T; Matsunaga, H; Mattson, M E; Mazini, R; Mazzanti, P; McFarland, K S; McIntyre, P; McNulty, R; Mehta, A; Mehtala, P; Menzemer, S; Menzione, A; Merkel, P; Mesropian, C; Messina, A; Miao, T; Miladinovic, N; Miles, J; Miller, R; Mills, C; Milnik, M; Mitra, A; Mitselmakher, G; Miyamoto, A; Moed, S; Moggi, N; Mohr, B; Moon, C S; Moore, R; Morello, M; Movilla Fernandez, P; Mülmenstädt, J; Mukherjee, A; Muller, Th; Mumford, R; Murat, P; Mussini, M; Nachtman, J; Nagano, A; Naganoma, J; Nakamura, K; Nakano, I; Napier, A; Necula, V; Neu, C; Neubauer, M S; Nielsen, J; Nodulman, L; Norniella, O; Nurse, E; Oh, S H; Oh, Y D; Oksuzian, I; Okusawa, T; Oldeman, R; Orava, R; Osterberg, K; Pagliarone, C; Palencia, E; Papadimitriou, V; Papaikonomou, A; Paramonov, A A; Parks, B; Pashapour, S; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pellett, D E; Penzo, A; Phillips, T J; Piacentino, G; Piedra, J; Pinera, L; Pitts, K; Plager, C; Pondrom, L; Portell, X; Poukhov, O; Pounder, N; Prakoshyn, F; Pronko, A; Proudfoot, J; Ptohos, F; Punzi, G; Pursley, J; Rademacker, J; Rahaman, A; Ramakrishnan, V; Ranjan, N; Redondo, I; Reisert, B; Rekovic, V; Renton, P; Rescigno, M; Richter, S; Rimondi, F; Ristori, L; Robson, A; Rodrigo, T; Rogers, E; Rolli, S; Roser, R; Rossi, M; Rossin, R; Roy, P; Ruiz, A; Russ, J; Rusu, V; Saarikko, H; Safonov, A; Sakumoto, W K; Salamanna, G; Saltó, O; Santi, L; Sarkar, S; Sartori, L; Sato, K; Savard, P; Savoy-Navarro, A; Scheidle, T; Schlabach, P; Schmidt, E E; Schmidt, M P; Schmitt, M; Schwarz, T; Scodellaro, L; Scott, A L; Scribano, A; Scuri, F; Sedov, A; Seidel, S; Seiya, Y; Semenov, A; Sexton-Kennedy, L; Sfyrla, A; Shalhout, S Z; Shapiro, M D; Shears, T; Shepard, P F; Sherman, D; Shimojima, M; Shochet, M; Shon, Y; Shreyber, I; Sidoti, A; Sinervo, P; Sisakyan, A; Slaughter, A J; Slaunwhite, J; Sliwa, K; Smith, J R; Snider, F D; Snihur, R; Soderberg, M; Soha, A; Somalwar, S; Sorin, V; Spalding, J; Spinella, F; Spreitzer, T; Squillacioti, P; Stanitzki, M; Staveris-Polykalas, A; St Denis, R; Stelzer, B; Stelzer-Chilton, O; Stentz, D; Strologas, J; Stuart, D; Suh, J S; Sukhanov, A; Sun, H; Suslov, I; Suzuki, T; Taffard, A; Takashima, R; Takeuchi, Y; Tanaka, R; Tecchio, M; Teng, P K; Terashi, K; Thom, J; Thompson, A S; Thomson, E; Tipton, P; Tiwari, V; Tkaczyk, S; Toback, D; Tokar, S; Tollefson, K; Tomura, T; Tonelli, D; Torre, S; Torretta, D; Tourneur, S; Trischuk, W; Tsuno, S; Tu, Y; Turini, N; Ukegawa, F; Uozumi, S; Vallecorsa, S; van Remortel, N; Varganov, A; Vataga, E; Vazquez, F; Velev, G; Veramendi, G; Veszpremi, V; Vidal, M; Vidal, R; Vila, I; Vilar, R; Vine, T; Vollrath, I; Volobouev, I; Volpi, G; Würthwein, F; Wagner, P; Wagner, R G; Wagner, R L; Wagner, J; Wagner, W; Wallny, R; Wang, S M; Warburton, A; Waters, D; Weinberger, M; Wester, W C; Whitehouse, B; Whiteson, D; Wicklund, A B; Wicklund, E; Williams, G; Williams, H H; Wilson, P; Winer, B L; Wittich, P; Wolbers, S; Wolfe, C; Wright, T; Wu, X; Wynne, S M; Yagil, A; Yamamoto, K; Yamaoka, J; Yamashita, T; Yang, C; Yang, U K; Yang, Y C; Yao, W M; Yeh, G P; Yoh, J; Yorita, K; Yoshida, T; Yu, G B; Yu, I; Yu, S S; Yun, J C; Zanello, L; Zanetti, A; Zaw, I; Zhang, X; Zhou, J; Zucchelli, S

2007-09-21

We present the first search for heavy, long-lived particles that decay to photons at a hadron collider. We use a sample of gamma + jet + missing transverse energy events in pp[over] collisions at square root[s] = 1.96 TeV taken with the CDF II detector. Candidate events are selected based on the arrival time of the photon at the detector. Using an integrated luminosity of 570 pb(-1) of collision data, we observe 2 events, consistent with the background estimate of 1.3+/-0.7 events. While our search strategy does not rely on model-specific dynamics, we set cross section limits in a supersymmetric model with [Formula: see text] and place the world-best 95% C.L. lower limit on the [Formula: see text] mass of 101 GeV/c(2) at [Formula: see text].

Geometrical Solutions of Some Quadratic Equations with Non-Real Roots

ERIC Educational Resources Information Center

Pathak, H. K.; Grewal, A. S.

2002-01-01

This note gives geometrical/graphical methods of finding solutions of the quadratic equation ax[squared] + bx + c = 0, a [not equal to] 0, with non-real roots. Three different cases which give rise to non-real roots of the quadratic equation have been discussed. In case I a geometrical construction and its proof for finding the solutions of the…

Degradation trend estimation of slewing bearing based on LSSVM model

NASA Astrophysics Data System (ADS)

Lu, Chao; Chen, Jie; Hong, Rongjing; Feng, Yang; Li, Yuanyuan

2016-08-01

A novel prediction method is proposed based on least squares support vector machine (LSSVM) to estimate the slewing bearing's degradation trend with small sample data. This method chooses the vibration signal which contains rich state information as the object of the study. Principal component analysis (PCA) was applied to fuse multi-feature vectors which could reflect the health state of slewing bearing, such as root mean square, kurtosis, wavelet energy entropy, and intrinsic mode function (IMF) energy. The degradation indicator fused by PCA can reflect the degradation more comprehensively and effectively. Then the degradation trend of slewing bearing was predicted by using the LSSVM model optimized by particle swarm optimization (PSO). The proposed method was demonstrated to be more accurate and effective by the whole life experiment of slewing bearing. Therefore, it can be applied in engineering practice.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

Validation of Core Temperature Estimation Algorithm

DTIC Science & Technology

2016-01-29

plot of observed versus estimated core temperature with the line of identity (dashed) and the least squares regression line (solid) and line equation...estimated PSI with the line of identity (dashed) and the least squares regression line (solid) and line equation in the top left corner. (b) Bland...for comparison. The root mean squared error (RMSE) was also computed, as given by Equation 2.

Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model

NASA Astrophysics Data System (ADS)

Khaki, M.; Hoteit, I.; Kuhn, M.; Awange, J.; Forootan, E.; van Dijk, A. I. J. M.; Schumacher, M.; Pattiaratchi, C.

2017-09-01

The time-variable terrestrial water storage (TWS) products from the Gravity Recovery And Climate Experiment (GRACE) have been increasingly used in recent years to improve the simulation of hydrological models by applying data assimilation techniques. In this study, for the first time, we assess the performance of the most popular data assimilation sequential techniques for integrating GRACE TWS into the World-Wide Water Resources Assessment (W3RA) model. We implement and test stochastic and deterministic ensemble-based Kalman filters (EnKF), as well as Particle filters (PF) using two different resampling approaches of Multinomial Resampling and Systematic Resampling. These choices provide various opportunities for weighting observations and model simulations during the assimilation and also accounting for error distributions. Particularly, the deterministic EnKF is tested to avoid perturbing observations before assimilation (that is the case in an ordinary EnKF). Gaussian-based random updates in the EnKF approaches likely do not fully represent the statistical properties of the model simulations and TWS observations. Therefore, the fully non-Gaussian PF is also applied to estimate more realistic updates. Monthly GRACE TWS are assimilated into W3RA covering the entire Australia. To evaluate the filters performances and analyze their impact on model simulations, their estimates are validated by independent in-situ measurements. Our results indicate that all implemented filters improve the estimation of water storage simulations of W3RA. The best results are obtained using two versions of deterministic EnKF, i.e. the Square Root Analysis (SQRA) scheme and the Ensemble Square Root Filter (EnSRF), respectively, improving the model groundwater estimations errors by 34% and 31% compared to a model run without assimilation. Applying the PF along with Systematic Resampling successfully decreases the model estimation error by 23%.

Computational intelligence models to predict porosity of tablets using minimum features

PubMed Central

Khalid, Mohammad Hassan; Kazemi, Pezhman; Perez-Gandarillas, Lucia; Michrafy, Abderrahim; Szlęk, Jakub; Jachowicz, Renata; Mendyk, Aleksander

2017-01-01

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space. PMID:28138223

Computational intelligence models to predict porosity of tablets using minimum features.

PubMed

Khalid, Mohammad Hassan; Kazemi, Pezhman; Perez-Gandarillas, Lucia; Michrafy, Abderrahim; Szlęk, Jakub; Jachowicz, Renata; Mendyk, Aleksander

2017-01-01

The effects of different formulations and manufacturing process conditions on the physical properties of a solid dosage form are of importance to the pharmaceutical industry. It is vital to have in-depth understanding of the material properties and governing parameters of its processes in response to different formulations. Understanding the mentioned aspects will allow tighter control of the process, leading to implementation of quality-by-design (QbD) practices. Computational intelligence (CI) offers an opportunity to create empirical models that can be used to describe the system and predict future outcomes in silico. CI models can help explore the behavior of input parameters, unlocking deeper understanding of the system. This research endeavor presents CI models to predict the porosity of tablets created by roll-compacted binary mixtures, which were milled and compacted under systematically varying conditions. CI models were created using tree-based methods, artificial neural networks (ANNs), and symbolic regression trained on an experimental data set and screened using root-mean-square error (RMSE) scores. The experimental data were composed of proportion of microcrystalline cellulose (MCC) (in percentage), granule size fraction (in micrometers), and die compaction force (in kilonewtons) as inputs and porosity as an output. The resulting models show impressive generalization ability, with ANNs (normalized root-mean-square error [NRMSE] =1%) and symbolic regression (NRMSE =4%) as the best-performing methods, also exhibiting reliable predictive behavior when presented with a challenging external validation data set (best achieved symbolic regression: NRMSE =3%). Symbolic regression demonstrates the transition from the black box modeling paradigm to more transparent predictive models. Predictive performance and feature selection behavior of CI models hints at the most important variables within this factor space.

Does the covariance structure matter in longitudinal modelling for the prediction of future CD4 counts?

PubMed

Taylor, J M; Law, N

1998-10-30

We investigate the importance of the assumed covariance structure for longitudinal modelling of CD4 counts. We examine how individual predictions of future CD4 counts are affected by the covariance structure. We consider four covariance structures: one based on an integrated Ornstein-Uhlenbeck stochastic process; one based on Brownian motion, and two derived from standard linear and quadratic random-effects models. Using data from the Multicenter AIDS Cohort Study and from a simulation study, we show that there is a noticeable deterioration in the coverage rate of confidence intervals if we assume the wrong covariance. There is also a loss in efficiency. The quadratic random-effects model is found to be the best in terms of correctly calibrated prediction intervals, but is substantially less efficient than the others. Incorrectly specifying the covariance structure as linear random effects gives too narrow prediction intervals with poor coverage rates. Fitting using the model based on the integrated Ornstein-Uhlenbeck stochastic process is the preferred one of the four considered because of its efficiency and robustness properties. We also use the difference between the future predicted and observed CD4 counts to assess an appropriate transformation of CD4 counts; a fourth root, cube root and square root all appear reasonable choices.

Field, laboratory and numerical approaches to studying flow through mangrove pneumatophores

NASA Astrophysics Data System (ADS)

Chua, V. P.

2014-12-01

The circulation of water in riverine mangrove swamps is expected to be influenced by mangrove roots, which in turn affect the nutrients, pollutants and sediments transport in these systems. Field studies were carried out in mangrove areas along the coastline of Singapore where Avicennia marina and Sonneratia alba pneumatophore species are found. Geometrical properties, such as height, diameter and spatial density of the mangrove roots were assessed through the use of photogrammetric methods. Samples of these roots were harvested from mangrove swamps and their material properties, such as bending strength and Young's modulus were determined in the laboratory. It was found that the pneumatophores under hydrodynamic loadings in a mangrove environment could be regarded as fairly rigid. Artificial root models of pneumatophores were fabricated from downscaling based on field observations of mangroves. Flume experiments were performed and measurements of mean flow velocities, Reynolds stress and turbulent kinetic energy were made. The boundary layer formed over the vegetation patch is fully developed after x = 6 m with a linear mean velocity profile. High shear stresses and turbulent kinetic energy were observed at the interface between the top portion of the roots and the upper flow. The experimental data was employed to calibrate and validate three-dimensional simulations of flow in pneumatophores. The simulations were performed with the Delft3D-FLOW model, where the vegetation effect is introduced by adding a depth-distributed resistance force and modifying the k-ɛ turbulence model. The model-predicted profiles for mean velocity, turbulent kinetic energy and concentration were compared with experimental data. The model calibration is performed by adjusting the horizontal and vertical eddy viscosities and diffusivities. A skill assessment of the model is performed using statistical measures that include the Pearson correlation coefficient (r), the mean absolute error (MAE), and the root-mean-squared error (RMSE).

Predicting Soil Organic Carbon and Total Nitrogen in the Russian Chernozem from Depth and Wireless Color Sensor Measurements

NASA Astrophysics Data System (ADS)

Mikhailova, E. A.; Stiglitz, R. Y.; Post, C. J.; Schlautman, M. A.; Sharp, J. L.; Gerard, P. D.

2017-12-01

Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but the applicability of these technologies may vary by soil type. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix Pro™ color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. Eleven samples were randomly selected to be used to construct prediction models and the remaining ten samples were set aside for cross validation. The root mean squared error (RMSE) was calculated to determine each model's prediction error. The data from the eleven soil samples were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), mean squared prediction error (MSPE) and coefficients of determination ( R 2, adjusted R 2) were used to assess model fit for each of the SOC and total N prediction models. Final models were fit using all soil samples, which included depth and color variables, for lnSOC ( R 2 = 0.987, Adj. R 2 = 0.981, RMSE = 0.003, p-value < 0.001, MSPE = 0.182) and lnTN ( R 2 = 0.980 Adj. R 2 = 0.972, RMSE = 0.004, p-value < 0.001, MSPE = 0.001). Additionally, final models were fit for all soil samples, which included only color variables, for lnSOC ( R 2 = 0.959 Adj. R 2 = 0.949, RMSE = 0.007, p-value < 0.001, MSPE = 0.536) and lnTN ( R 2 = 0.912 Adj. R 2 = 0.890, RMSE = 0.015, p-value < 0.001, MSPE = 0.001). The results suggest that soil color may be used for rapid assessment of SOC and TN in these agriculturally important soils.

Fast Measurement of Soluble Solid Content in Mango Based on Visible and Infrared Spectroscopy Technique

NASA Astrophysics Data System (ADS)

Yu, Jiajia; He, Yong

Mango is a kind of popular tropical fruit, and the soluble solid content is an important in this study visible and short-wave near-infrared spectroscopy (VIS/SWNIR) technique was applied. For sake of investigating the feasibility of using VIS/SWNIR spectroscopy to measure the soluble solid content in mango, and validating the performance of selected sensitive bands, for the calibration set was formed by 135 mango samples, while the remaining 45 mango samples for the prediction set. The combination of partial least squares and backpropagation artificial neural networks (PLS-BP) was used to calculate the prediction model based on raw spectrum data. Based on PLS-BP, the determination coefficient for prediction (Rp) was 0.757 and root mean square and the process is simple and easy to operate. Compared with the Partial least squares (PLS) result, the performance of PLS-BP is better.

Convex lattice polygons of fixed area with perimeter-dependent weights.

PubMed

Rajesh, R; Dhar, Deepak

2005-01-01

We study fully convex polygons with a given area, and variable perimeter length on square and hexagonal lattices. We attach a weight tm to a convex polygon of perimeter m and show that the sum of weights of all polygons with a fixed area s varies as s(-theta(conv))eK(t)square root(s) for large s and t less than a critical threshold tc, where K(t) is a t-dependent constant, and theta(conv) is a critical exponent which does not change with t. Using heuristic arguments, we find that theta(conv) is 1/4 for the square lattice, but -1/4 for the hexagonal lattice. The reason for this unexpected nonuniversality of theta(conv) is traced to existence of sharp corners in the asymptotic shape of these polygons.

Light-Regulated Gravitropism in Seedling Roots of Maize 1

PubMed Central

Feldman, Lewis J.; Briggs, Winslow R.

1987-01-01

Red light-induced changes in the gravitropism of roots of Zea mays variety Merit is a very low fluence response with a threshold of 10−9 moles per square meter and is not reversible by far red light. Blue light also affects root gravitropism but the sensitivity of roots to blue is 50 to 100 times less than to an equal fluence of red. In Z. mays Merit we conclude that phytochrome is the sole pigment associated with light-induced changes in root gravitropism. PMID:11539030

Method of measuring cross-flow vortices by use of an array of hot-film sensors

NASA Technical Reports Server (NTRS)

Agarwal, Aval K. (Inventor); Maddalon, Dal V. (Inventor); Mangalam, Siva M. (Inventor)

1993-01-01

The invention is a method for measuring the wavelength of cross-flow vortices of air flow having streamlines of flow traveling across a swept airfoil. The method comprises providing a plurality of hot-film sensors. Each hot-film sensor provides a signal which can be processed, and each hot-film sensor is spaced in a straight-line array such that the distance between successive hot-film sensors is less than the wavelength of the cross-flow vortices being measured. The method further comprises determining the direction of travel of the streamlines across the airfoil and positioning the straight-line array of hot film sensors perpendicular to the direction of travel of the streamlines, such that each sensor has a spanwise location. The method further comprises processing the signals provided by the sensors to provide root-mean-square values for each signal, plotting each root-mean-square value as a function of its spanwise location, and determining the wavelength of the cross-flow vortices by noting the distance between two maxima or two minima of root-mean-square values.

Expected distributions of root-mean-square positional deviations in proteins.

PubMed

Pitera, Jed W

2014-06-19

The atom positional root-mean-square deviation (RMSD) is a standard tool for comparing the similarity of two molecular structures. It is used to characterize the quality of biomolecular simulations, to cluster conformations, and as a reaction coordinate for conformational changes. This work presents an approximate analytic form for the expected distribution of RMSD values for a protein or polymer fluctuating about a stable native structure. The mean and maximum of the expected distribution are independent of chain length for long chains and linearly proportional to the average atom positional root-mean-square fluctuations (RMSF). To approximate the RMSD distribution for random-coil or unfolded ensembles, numerical distributions of RMSD were generated for ensembles of self-avoiding and non-self-avoiding random walks. In both cases, for all reference structures tested for chains more than three monomers long, the distributions have a maximum distant from the origin with a power-law dependence on chain length. The purely entropic nature of this result implies that care must be taken when interpreting stable high-RMSD regions of the free-energy landscape as "intermediates" or well-defined stable states.

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

PubMed Central

Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-01-01

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets’ state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems. PMID:29113085

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association.

PubMed

Liu, Yu; Liu, Jun; Li, Gang; Qi, Lin; Li, Yaowen; He, You

2017-11-05

This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets' state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems.

Association of auricular pressing and heart rate variability in pre-exam anxiety students.

PubMed

Wu, Wocao; Chen, Junqi; Zhen, Erchuan; Huang, Huanlin; Zhang, Pei; Wang, Jiao; Ou, Yingyi; Huang, Yong

2013-03-25

A total of 30 students scoring between 12 and 20 on the Test Anxiety Scale who had been exhibiting an anxious state > 24 hours, and 30 normal control students were recruited. Indices of heart rate variability were recorded using an Actiheart electrocardiogram recorder at 10 minutes before auricular pressing, in the first half of stimulation and in the second half of stimulation. The results revealed that the standard deviation of all normal to normal intervals and the root mean square of standard deviation of normal to normal intervals were significantly increased after stimulation. The heart rate variability triangular index, very-low-frequency power, low-frequency power, and the ratio of low-frequency to high-frequency power were increased to different degrees after stimulation. Compared with normal controls, the root mean square of standard deviation of normal to normal intervals was significantly increased in anxious students following auricular pressing. These results indicated that auricular pressing can elevate heart rate variability, especially the root mean square of standard deviation of normal to normal intervals in students with pre-exam anxiety.

Association of auricular pressing and heart rate variability in pre-exam anxiety students

PubMed Central

Wu, Wocao; Chen, Junqi; Zhen, Erchuan; Huang, Huanlin; Zhang, Pei; Wang, Jiao; Ou, Yingyi; Huang, Yong

2013-01-01

A total of 30 students scoring between 12 and 20 on the Test Anxiety Scale who had been exhibiting an anxious state > 24 hours, and 30 normal control students were recruited. Indices of heart rate variability were recorded using an Actiheart electrocardiogram recorder at 10 minutes before auricular pressing, in the first half of stimulation and in the second half of stimulation. The results revealed that the standard deviation of all normal to normal intervals and the root mean square of standard deviation of normal to normal intervals were significantly increased after stimulation. The heart rate variability triangular index, very-low-frequency power, low-frequency power, and the ratio of low-frequency to high-frequency power were increased to different degrees after stimulation. Compared with normal controls, the root mean square of standard deviation of normal to normal intervals was significantly increased in anxious students following auricular pressing. These results indicated that auricular pressing can elevate heart rate variability, especially the root mean square of standard deviation of normal to normal intervals in students with pre-exam anxiety. PMID:25206734

Validation of drying models and rehydration characteristics of betel (Piper betel L.) leaves.

PubMed

Balasubramanian, S; Sharma, R; Gupta, R K; Patil, R T

2011-12-01

Effect of temperature on drying behaviour of betel leaves at drying air temperatures of 50, 60 and 70°C was investigated in tunnel as well as cabinet dryer. The L* and b* values increased whereas, a* values decreased, as the drying air temperature increased from 50 to 70°C in both the dryers, but the colour values remained higher for cabinet dryer than tunnel dryer in all cases. Eleven different drying models were compared according to their coefficients of determination (R(2)), root mean square error (RMSE) and chi square (χ (2)) to estimate drying curves. The results indicated that, logarithmic model and modified Page model could satisfactorily describe the drying curve of betel leaves for tunnel drying and cabinet dryer, respectively. In terms of colour quality, drying of betel leaves at 60°C in tunnel dryer and at 50°C in cabinet dryer was found optimum whereas, rehydration at 40°C produced the best acceptable product.

Physicochemical characterization of Lavandula spp. honey with FT-Raman spectroscopy.

PubMed

Anjos, Ofélia; Santos, António J A; Paixão, Vasco; Estevinho, Letícia M

2018-02-01

This study aimed to evaluate the potential of FT-Raman spectroscopy in the prediction of the chemical composition of Lavandula spp. monofloral honey. Partial Least Squares (PLS) regression models were performed for the quantitative estimation and the results were correlated with those obtained using reference methods. Good calibration models were obtained for electrical conductivity, ash, total acidity, pH, reducing sugars, hydroxymethylfurfural (HMF), proline, diastase index, apparent sucrose, total flavonoids content and total phenol content. On the other hand, the model was less accurate for pH determination. The calibration models had high r 2 (ranging between 92.8% and 99.9%), high residual prediction deviation - RPD (ranging between 4.2 and 26.8) and low root mean square errors. These results confirm the hypothesis that FT-Raman is a useful technique for the quality control and chemical properties' evaluation of Lavandula spp honey. Its application may allow improving the efficiency, speed and cost of the current laboratory analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Determining particle size and water content by near-infrared spectroscopy in the granulation of naproxen sodium.

PubMed

Bär, David; Debus, Heiko; Brzenczek, Sina; Fischer, Wolfgang; Imming, Peter

2018-03-20

Near-infrared spectroscopy is frequently used by the pharmaceutical industry to monitor and optimize several production processes. In combination with chemometrics, a mathematical-statistical technique, the following advantages of near-infrared spectroscopy can be applied: It is a fast, non-destructive, non-invasive, and economical analytical method. One of the most advanced and popular chemometric technique is the partial least square algorithm with its best applicability in routine and its results. The required reference analytic enables the analysis of various parameters of interest, for example, moisture content, particle size, and many others. Parameters like the correlation coefficient, root mean square error of prediction, root mean square error of calibration, and root mean square error of validation have been used for evaluating the applicability and robustness of these analytical methods developed. This study deals with investigating a Naproxen Sodium granulation process using near-infrared spectroscopy and the development of water content and particle-size methods. For the water content method, one should consider a maximum water content of about 21% in the granulation process, which must be confirmed by the loss on drying. Further influences to be considered are the constantly changing product temperature, rising to about 54 °C, the creation of hydrated states of Naproxen Sodium when using a maximum of about 21% water content, and the large quantity of about 87% Naproxen Sodium in the formulation. It was considered to use a combination of these influences in developing the near-infrared spectroscopy method for the water content of Naproxen Sodium granules. The "Root Mean Square Error" was 0.25% for calibration dataset and 0.30% for the validation dataset, which was obtained after different stages of optimization by multiplicative scatter correction and the first derivative. Using laser diffraction, the granules have been analyzed for particle sizes and obtaining the summary sieve sizes of >63 μm and >100 μm. The following influences should be considered for application in routine production: constant changes in water content up to 21% and a product temperature up to 54 °C. The different stages of optimization result in a "Root Mean Square Error" of 2.54% for the calibration data set and 3.53% for the validation set by using the Kubelka-Munk conversion and first derivative for the near-infrared spectroscopy method for a particle size >63 μm. For the near-infrared spectroscopy method using a particle size >100 μm, the "Root Mean Square Error" was 3.47% for the calibration data set and 4.51% for the validation set, while using the same pre-treatments. - The robustness and suitability of this methodology has already been demonstrated by its recent successful implementation in a routine granulate production process. Copyright © 2018 Elsevier B.V. All rights reserved.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I + II + III supernatant in human albumin separation

NASA Astrophysics Data System (ADS)

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-01

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I + II + III (FI + II + III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (Rp2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501 g/L, 0.465 g/L and 5.57 for TP, and 0.969, 0.530 g/L, 0.341 g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI + II + III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS.

Near infrared spectroscopy combined with multivariate analysis for monitoring the ethanol precipitation process of fraction I+II+III supernatant in human albumin separation.

PubMed

Li, Can; Wang, Fei; Zang, Lixuan; Zang, Hengchang; Alcalà, Manel; Nie, Lei; Wang, Mingyu; Li, Lian

2017-03-15

Nowadays, as a powerful process analytical tool, near infrared spectroscopy (NIRS) has been widely applied in process monitoring. In present work, NIRS combined with multivariate analysis was used to monitor the ethanol precipitation process of fraction I+II+III (FI+II+III) supernatant in human albumin (HA) separation to achieve qualitative and quantitative monitoring at the same time and assure the product's quality. First, a qualitative model was established by using principal component analysis (PCA) with 6 of 8 normal batches samples, and evaluated by the remaining 2 normal batches and 3 abnormal batches. The results showed that the first principal component (PC1) score chart could be successfully used for fault detection and diagnosis. Then, two quantitative models were built with 6 of 8 normal batches to determine the content of the total protein (TP) and HA separately by using partial least squares regression (PLS-R) strategy, and the models were validated by 2 remaining normal batches. The determination coefficient of validation (R p 2 ), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and ratio of performance deviation (RPD) were 0.975, 0.501g/L, 0.465g/L and 5.57 for TP, and 0.969, 0.530g/L, 0.341g/L and 5.47 for HA, respectively. The results showed that the established models could give a rapid and accurate measurement of the content of TP and HA. The results of this study indicated that NIRS is an effective tool and could be successfully used for qualitative and quantitative monitoring the ethanol precipitation process of FI+II+III supernatant simultaneously. This research has significant reference value for assuring the quality and improving the recovery ratio of HA in industrialization scale by using NIRS. Copyright © 2016 Elsevier B.V. All rights reserved.

Combination of multiple model population analysis and mid-infrared technology for the estimation of copper content in Tegillarca granosa

NASA Astrophysics Data System (ADS)

Hu, Meng-Han; Chen, Xiao-Jing; Ye, Peng-Chao; Chen, Xi; Shi, Yi-Jian; Zhai, Guang-Tao; Yang, Xiao-Kang

2016-11-01

The aim of this study was to use mid-infrared spectroscopy coupled with multiple model population analysis based on Monte Carlo-uninformative variable elimination for rapidly estimating the copper content of Tegillarca granosa. Copper-specific wavelengths were first extracted from the whole spectra, and subsequently, a least square-support vector machine was used to develop the prediction models. Compared with the prediction model based on full wavelengths, models that used 100 multiple MC-UVE selected wavelengths without and with bin operation showed comparable performances with Rp (root mean square error of Prediction) of 0.97 (14.60 mg/kg) and 0.94 (20.85 mg/kg) versus 0.96 (17.27 mg/kg), as well as ratio of percent deviation (number of wavelength) of 2.77 (407) and 1.84 (45) versus 2.32 (1762). The obtained results demonstrated that the mid-infrared technique could be used for estimating copper content in T. granosa. In addition, the proposed multiple model population analysis can eliminate uninformative, weakly informative and interfering wavelengths effectively, that substantially reduced the model complexity and computation time.

Active Vertical Tail Buffeting Alleviation on an F/A-18 Model in a Wind Tunnel

NASA Technical Reports Server (NTRS)

Moses, Robert W.

1999-01-01

A 1/6-scale F-18 wind-tunnel model was tested in the Transonic Dynamics Tunnel at the NASA Langley Research Center as part of the Actively Controlled Response Of Buffet-Affected Tails (ACROBAT) program to assess the use of active controls in reducing vertical tail buffeting. The starboard vertical tail was equipped with an active rudder and other aerodynamic devices, and the port vertical tail was equipped with piezoelectric actuators. The tunnel conditions were atmospheric air at a dynamic pressure of 14 psf. By using single-input-single-output control laws at gains well below the physical limits of the control effectors, the power spectral density of the root strains at the frequency of the first bending mode of the vertical tail was reduced by as much as 60 percent up to angles of attack of 37 degrees. Root mean square (RMS) values of root strain were reduced by as much as 19 percent. Stability margins indicate that a constant gain setting in the control law may be used throughout the range of angle of attack tested.

Modeling variations in the cedi/dollar exchange rate in Ghana: an autoregressive conditional heteroscedastic (ARCH) models.

PubMed

Techie Quaicoe, Michael; Twenefour, Frank B K; Baah, Emmanuel M; Nortey, Ezekiel N N

2015-01-01

This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box-Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.

Storage and computationally efficient permutations of factorized covariance and square-root information arrays

NASA Technical Reports Server (NTRS)

Muellerschoen, R. J.

1988-01-01

A unified method to permute vector stored Upper triangular Diagonal factorized covariance and vector stored upper triangular Square Root Information arrays is presented. The method involves cyclic permutation of the rows and columns of the arrays and retriangularization with fast (slow) Givens rotations (reflections). Minimal computation is performed, and a one dimensional scratch array is required. To make the method efficient for large arrays on a virtual memory machine, computations are arranged so as to avoid expensive paging faults. This method is potentially important for processing large volumes of radio metric data in the Deep Space Network.

Thermal Noise Limit in Frequency Stabilization of Lasers with Rigid Cavities

NASA Technical Reports Server (NTRS)

Numata, Kenji; Kemery, Amy; Camp, Jordan

2004-01-01

We evaluated thermal noise (Brownian motion) in a rigid reference cavity used for frequency stabilization of lasers, based on the mechanical loss of cavity materials and the numerical analysis of the mirror-spacer mechanics with t.he direct application of the fluctuation dissipation theorem. This noise sets a fundamental limit for the frequency stability achieved with a rigid frequency- reference cavity of order 1 Hz/square root Hz(0.01 Hz/square root Hz) at 10 mHz (100 Hz) at room temperature. This level coincides with the world-highest level stabilization results.

Longitudinal double-spin asymmetry and cross section for inclusive jet production in polarized proton collisions at square root of s = 200 GeV.

PubMed

Abelev, B I; Aggarwal, M M; Ahammed, Z; Amonett, J; Anderson, B D; Anderson, M; Arkhipkin, D; Averichev, G S; Bai, Y; Balewski, J; Barannikova, O; Barnby, L S; Baudot, J; Bekele, S; Belaga, V V; Bellingeri-Laurikainen, A; Bellwied, R; Benedosso, F; Bhardwaj, S; Bhasin, A; Bhati, A K; Bichsel, H; Bielcik, J; Bielcikova, J; Bland, L C; Blyth, S-L; Bonner, B E; Botje, M; Bouchet, J; Brandin, A V; Bravar, A; Burton, T P; Bystersky, M; Cadman, R V; Cai, X Z; Caines, H; Sánchez, M Calderón de la Barca; Castillo, J; Catu, O; Cebra, D; Chajecki, Z; Chaloupka, P; Chattopadhyay, S; Chen, H F; Chen, J H; Cheng, J; Cherney, M; Chikanian, A; Christie, W; Coffin, J P; Cormier, T M; Cosentino, M R; Cramer, J G; Crawford, H J; Das, D; Das, S; Dash, S; Daugherity, M; de Moura, M M; Dedovich, T G; Dephillips, M; Derevschikov, A A; Didenko, L; Dietel, T; Djawotho, P; Dogra, S M; Dong, W J; Dong, X; Draper, J E; Du, F; Dunin, V B; Dunlop, J C; Mazumdar, M R Dutta; Eckardt, V; Edwards, W R; Efimov, L G; Emelianov, V; Engelage, J; Eppley, G; Erazmus, B; Estienne, M; Fachini, P; Fatemi, R; Fedorisin, J; Filip, P; Finch, E; Fine, V; Fisyak, Y; Fu, J; Gagliardi, C A; Gaillard, L; Ganti, M S; Ghazikhanian, V; Ghosh, P; Gonzalez, J E; Gorbunov, Y G; Gos, H; Grebenyuk, O; Grosnick, D; Guertin, S M; Guimaraes, K S F F; Gupta, N; Gutierrez, T D; Haag, B; Hallman, T J; Hamed, A; Harris, J W; He, W; Heinz, M; Henry, T W; Hepplemann, S; Hippolyte, B; Hirsch, A; Hjort, E; Hoffman, A M; Hoffmann, G W; Horner, M J; Huang, H Z; Huang, S L; Hughes, E W; Humanic, T J; Igo, G; Jacobs, P; Jacobs, W W; Jakl, P; Jia, F; Jiang, H; Jones, P G; Judd, E G; Kabana, S; Kang, K; Kapitan, J; Kaplan, M; Keane, D; Kechechyan, A; Khodyrev, V Yu; Kim, B C; Kiryluk, J; Kisiel, A; Kislov, E M; Klein, S R; Kocoloski, A; Koetke, D D; Kollegger, T; Kopytine, M; Kotchenda, L; Kouchpil, V; Kowalik, K L; Kramer, M; Kravtsov, P; Kravtsov, V I; Krueger, K; Kuhn, C; Kulikov, A I; Kumar, A; Kuznetsov, A A; Lamont, M A C; Landgraf, J M; Lange, S; LaPointe, S; Laue, F; Lauret, J; Lebedev, A; Lednicky, R; Lee, C-H; Lehocka, S; LeVine, M J; Li, C; Li, Q; Li, Y; Lin, G; Lin, X; Lindenbaum, S J; Lisa, M A; Liu, F; Liu, H; Liu, J; Liu, L; Liu, Z; Ljubicic, T; Llope, W J; Long, H; Longacre, R S; Love, W A; Lu, Y; Ludlam, T; Lynn, D; Ma, G L; Ma, J G; Ma, Y G; Magestro, D; Mahapatra, D P; Majka, R; Mangotra, L K; Manweiler, R; Margetis, S; Markert, C; Martin, L; Matis, H S; Matulenko, Yu A; McClain, C J; McShane, T S; Melnick, Yu; Meschanin, A; Millane, J; Miller, M L; Minaev, N G; Mioduszewski, S; Mironov, C; Mischke, A; Mishra, D K; Mitchell, J; Mohanty, B; Molnar, L; Moore, C F; Morozov, D A; Munhoz, M G; Nandi, B K; Nattrass, C; Nayak, T K; Nelson, J M; Netrakanti, P K; Nogach, L V; Nurushev, S B; Odyniec, G; Ogawa, A; Okorokov, V; Oldenburg, M; Olson, D; Pachr, M; Pal, S K; Panebratsev, Y; Panitkin, S Y; Pavlinov, A I; Pawlak, T; Peitzmann, T; Perevoztchikov, V; Perkins, C; Peryt, W; Phatak, S C; Picha, R; Planinic, M; Pluta, J; Poljak, N; Porile, N; Porter, J; Poskanzer, A M; Potekhin, M; Potrebenikova, E; Potukuchi, B V K S; Prindle, D; Pruneau, C; Putschke, J; Rakness, G; Raniwala, R; Raniwala, S; Ray, R L; Razin, S V; Reinnarth, J; Relyea, D; Ridiger, A; Ritter, H G; Roberts, J B; Rogachevskiy, O V; Romero, J L; Rose, A; Roy, C; Ruan, L; Russcher, M J; Sahoo, R; Sakuma, T; Salur, S; Sandweiss, J; Sarsour, M; Sazhin, P S; Schambach, J; Scharenberg, R P; Schmitz, N; Seger, J; Selyuzhenkov, I; Seyboth, P; Shabetai, A; Shahaliev, E; Shao, M; Sharma, M; Shen, W Q; Shimanskiy, S S; Sichtermann, E P; Simon, F; Singaraju, R N; Smirnov, N; Snellings, R; Sood, G; Sorensen, P; Sowinski, J; Speltz, J; Spinka, H M; Srivastava, B; Stadnik, A; Stanislaus, T D S; Stock, R; Stolpovsky, A; Strikhanov, M; Stringfellow, B; Suaide, A A P; Sugarbaker, E; Sumbera, M; Sun, Z; Surrow, B; Swanger, M; Symons, T J M; Szanto de Toledo, A; Tai, A; Takahashi, J; Tang, A H; Tarnowsky, T; Thein, D; Thomas, J H; Timmins, A R; Timoshenko, S; Tokarev, M; Trainor, T A; Trentalange, S; Tribble, R E; Tsai, O D; Ulery, J; Ullrich, T; Underwood, D G; Buren, G Van; van der Kolk, N; van Leeuwen, M; Molen, A M Vander; Varma, R; Vasilevski, I M; Vasiliev, A N; Vernet, R; Vigdor, S E; Viyogi, Y P; Vokal, S; Voloshin, S A; Waggoner, W T; Wang, F; Wang, G; Wang, J S; Wang, X L; Wang, Y; Watson, J W; Webb, J C; Westfall, G D; Wetzler, A; Whitten, C; Wieman, H; Wissink, S W; Witt, R; Wood, J; Wu, J; Xu, N; Xu, Q H; Xu, Z; Yepes, P; Yoo, I-K; Yurevich, V I; Zhan, W; Zhang, H; Zhang, W M; Zhang, Y; Zhang, Z P; Zhao, Y; Zhong, C; Zoulkarneev, R; Zoulkarneeva, Y; Zubarev, A N; Zuo, J X

2006-12-22

We report a measurement of the longitudinal double-spin asymmetry A(LL) and the differential cross section for inclusive midrapidity jet production in polarized proton collisions at square root of s = 200 GeV. The cross section data cover transverse momenta 5 < pT < 50 GeV/c and agree with next-to-leading order perturbative QCD evaluations. The A(LL) data cover 5 < pT < 17 GeV/c and disfavor at 98% C.L. maximal positive gluon polarization in the polarized nucleon.

Tough cryogenic alloys from the Fe-Mn and Fe-Mn-Cr systems

NASA Technical Reports Server (NTRS)

Schanfein, M. J.; Zackay, V. F.; Morris, J. W., Jr.

1974-01-01

By adjusting composition, metastable gamma (austenite) and epsilon (hexagonal) martensite may be retained in Fe-Mn and Fe-Mn-Cr alloys and used to impact toughness through the TRIP mechanism. The resulting alloys have excellent toughness at cryogenic temperatures. The best alloys obtained to date are: Fe-20Mn, with sigma (sub y) = 79ksi and K sub IC = 275ksi square root of (in) at 77 K, and Fc-16Mn-8Cr, with sigma sub y = 85ksi and K sub IC = 72ksi square root of (in) at 77 K.

Can generic knee joint models improve the measurement of osteoarthritic knee kinematics during squatting activity?

PubMed

Clément, Julien; Dumas, Raphaël; Hagemeister, Nicola; de Guise, Jaques A

2017-01-01

Knee joint kinematics derived from multi-body optimisation (MBO) still requires evaluation. The objective of this study was to corroborate model-derived kinematics of osteoarthritic knees obtained using four generic knee joint models used in musculoskeletal modelling - spherical, hinge, degree-of-freedom coupling curves and parallel mechanism - against reference knee kinematics measured by stereo-radiography. Root mean square errors ranged from 0.7° to 23.4° for knee rotations and from 0.6 to 9.0 mm for knee displacements. Model-derived knee kinematics computed from generic knee joint models was inaccurate. Future developments and experiments should improve the reliability of osteoarthritic knee models in MBO and musculoskeletal modelling.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

PubMed

Wang, Hui; Qin, Feng; Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

PubMed Central

Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. PMID:27128464

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

A nonlinear model of gold production in Malaysia

NASA Astrophysics Data System (ADS)

Ramli, Norashikin; Muda, Nora; Umor, Mohd Rozi

2014-06-01

Malaysia is a country which is rich in natural resources and one of it is a gold. Gold has already become an important national commodity. This study is conducted to determine a model that can be well fitted with the gold production in Malaysia from the year 1995-2010. Five nonlinear models are presented in this study which are Logistic model, Gompertz, Richard, Weibull and Chapman-Richard model. These model are used to fit the cumulative gold production in Malaysia. The best model is then selected based on the model performance. The performance of the fitted model is measured by sum squares error, root mean squares error, coefficient of determination, mean relative error, mean absolute error and mean absolute percentage error. This study has found that a Weibull model is shown to have significantly outperform compare to the other models. To confirm that Weibull is the best model, the latest data are fitted to the model. Once again, Weibull model gives the lowest readings at all types of measurement error. We can concluded that the future gold production in Malaysia can be predicted according to the Weibull model and this could be important findings for Malaysia to plan their economic activities.

Inviscid to turbulent transition of trailing vortices

NASA Technical Reports Server (NTRS)

Iversen, J. D.

1974-01-01

The characteristics of the plateau region in the vortex system which trails from a lifting wing are discussed. The decay of the vortex due to viscous or turbulent shear is very slow in the plateau so that the maximum tangential speed in the vortices remains nearly constant for some distance downstream of roll-up and then begins to decrease, becoming inversely proportional to the square root of the distance downstream. Mathematical models are developed to analyze the structure of the plateau area. Solutions are obtained for both constant and variable eddy viscosity models.

Simulation Study Using a New Type of Sample Variance

NASA Technical Reports Server (NTRS)

Howe, D. A.; Lainson, K. J.

1996-01-01

We evaluate with simulated data a new type of sample variance for the characterization of frequency stability. The new statistic (referred to as TOTALVAR and its square root TOTALDEV) is a better predictor of long-term frequency variations than the present sample Allan deviation. The statistical model uses the assumption that a time series of phase or frequency differences is wrapped (periodic) with overall frequency difference removed. We find that the variability at long averaging times is reduced considerably for the five models of power-law noise commonly encountered with frequency standards and oscillators.

Bio-inspired optimization algorithms for optical parameter extraction of dielectric materials: A comparative study

NASA Astrophysics Data System (ADS)

Ghulam Saber, Md; Arif Shahriar, Kh; Ahmed, Ashik; Hasan Sagor, Rakibul

2016-10-01

Particle swarm optimization (PSO) and invasive weed optimization (IWO) algorithms are used for extracting the modeling parameters of materials useful for optics and photonics research community. These two bio-inspired algorithms are used here for the first time in this particular field to the best of our knowledge. The algorithms are used for modeling graphene oxide and the performances of the two are compared. Two objective functions are used for different boundary values. Root mean square (RMS) deviation is determined and compared.

IMPROVED ALGORITHMS FOR RADAR-BASED RECONSTRUCTION OF ASTEROID SHAPES

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

Greenberg, Adam H.; Margot, Jean-Luc

We describe our implementation of a global-parameter optimizer and Square Root Information Filter into the asteroid-modeling software shape. We compare the performance of our new optimizer with that of the existing sequential optimizer when operating on various forms of simulated data and actual asteroid radar data. In all cases, the new implementation performs substantially better than its predecessor: it converges faster, produces shape models that are more accurate, and solves for spin axis orientations more reliably. We discuss potential future changes to improve shape's fitting speed and accuracy.

Shock wave oscillation driven by turbulent boundary layer fluctuations

NASA Technical Reports Server (NTRS)

Plotkin, K. J.

1972-01-01

Pressure fluctuations due to the interaction of a shock wave with a turbulent boundary layer were investigated. A simple model is proposed in which the shock wave is convected from its mean position by velocity fluctuations in the turbulent boundary layer. Displacement of the shock is assumed limited by a linear restoring mechanism. Predictions of peak root mean square pressure fluctuation and spectral density are in excellent agreement with available experimental data.

Kinetic Behavior of Escherichia coli on Various Cheeses under Constant and Dynamic Temperature.

PubMed

Kim, K; Lee, H; Gwak, E; Yoon, Y

2014-07-01

In this study, we developed kinetic models to predict the growth of pathogenic Escherichia coli on cheeses during storage at constant and changing temperatures. A five-strain mixture of pathogenic E. coli was inoculated onto natural cheeses (Brie and Camembert) and processed cheeses (sliced Mozzarella and sliced Cheddar) at 3 to 4 log CFU/g. The inoculated cheeses were stored at 4, 10, 15, 25, and 30°C for 1 to 320 h, with a different storage time being used for each temperature. Total bacteria and E. coli cells were enumerated on tryptic soy agar and MacConkey sorbitol agar, respectively. E. coli growth data were fitted to the Baranyi model to calculate the maximum specific growth rate (μ max; log CFU/g/h), lag phase duration (LPD; h), lower asymptote (log CFU/g), and upper asymptote (log CFU/g). The kinetic parameters were then analyzed as a function of storage temperature, using the square root model, polynomial equation, and linear equation. A dynamic model was also developed for varying temperature. The model performance was evaluated against observed data, and the root mean square error (RMSE) was calculated. At 4°C, E. coli cell growth was not observed on any cheese. However, E. coli growth was observed at 10°C to 30°C with a μ max of 0.01 to 1.03 log CFU/g/h, depending on the cheese. The μ max values increased as temperature increased, while LPD values decreased, and μ max and LPD values were different among the four types of cheese. The developed models showed adequate performance (RMSE = 0.176-0.337), indicating that these models should be useful for describing the growth kinetics of E. coli on various cheeses.

Determinants of Antibiotic Consumption - Development of a Model using Partial Least Squares Regression based on Data from India.

PubMed

Tamhankar, Ashok J; Karnik, Shreyasee S; Stålsby Lundborg, Cecilia

2018-04-23

Antibiotic resistance, a consequence of antibiotic use, is a threat to health, with severe consequences for resource constrained settings. If determinants for human antibiotic use in India, a lower middle income country, with one of the highest antibiotic consumption in the world could be understood, interventions could be developed, having implications for similar settings. Year wise data for India, for potential determinants and antibiotic consumption, was sourced from publicly available databases for the years 2000-2010. Data was analyzed using Partial Least Squares regression and correlation between determinants and antibiotic consumption was evaluated, formulating 'Predictors' and 'Prediction models'. The 'prediction model' with the statistically most significant predictors (root mean square errors of prediction for train set-377.0 and test set-297.0) formulated from a combination of Health infrastructure + Surface transport infrastructure (HISTI), predicted antibiotic consumption within 95% confidence interval and estimated an antibiotic consumption of 11.6 standard units/person (14.37 billion standard units totally; standard units = number of doses sold in the country; a dose being a pill, capsule, or ampoule) for India for 2014. The HISTI model may become useful in predicting antibiotic consumption for countries/regions having circumstances and data similar to India, but without resources to measure actual data of antibiotic consumption.

Implementation of neural network for color properties of polycarbonates

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Criterion for evaluating the predictive ability of nonlinear regression models without cross-validation.

PubMed

Kaneko, Hiromasa; Funatsu, Kimito

2013-09-23

We propose predictive performance criteria for nonlinear regression models without cross-validation. The proposed criteria are the determination coefficient and the root-mean-square error for the midpoints between k-nearest-neighbor data points. These criteria can be used to evaluate predictive ability after the regression models are updated, whereas cross-validation cannot be performed in such a situation. The proposed method is effective and helpful in handling big data when cross-validation cannot be applied. By analyzing data from numerical simulations and quantitative structural relationships, we confirm that the proposed criteria enable the predictive ability of the nonlinear regression models to be appropriately quantified.

Water-quality models to assess algal community dynamics, water quality, and fish habitat suitability for two agricultural land-use dominated lakes in Minnesota, 2014

USGS Publications Warehouse

Smith, Erik A.; Kiesling, Richard L.; Ziegeweid, Jeffrey R.

2017-07-20

Fish habitat can degrade in many lakes due to summer blue-green algal blooms. Predictive models are needed to better manage and mitigate loss of fish habitat due to these changes. The U.S. Geological Survey (USGS), in cooperation with the Minnesota Department of Natural Resources, developed predictive water-quality models for two agricultural land-use dominated lakes in Minnesota—Madison Lake and Pearl Lake, which are part of Minnesota’s sentinel lakes monitoring program—to assess algal community dynamics, water quality, and fish habitat suitability of these two lakes under recent (2014) meteorological conditions. The interaction of basin processes to these two lakes, through the delivery of nutrient loads, were simulated using CE-QUAL-W2, a carbon-based, laterally averaged, two-dimensional water-quality model that predicts distribution of temperature and oxygen from interactions between nutrient cycling, primary production, and trophic dynamics.The CE-QUAL-W2 models successfully predicted water temperature and dissolved oxygen on the basis of the two metrics of mean absolute error and root mean square error. For Madison Lake, the mean absolute error and root mean square error were 0.53 and 0.68 degree Celsius, respectively, for the vertical temperature profile comparisons; for Pearl Lake, the mean absolute error and root mean square error were 0.71 and 0.95 degree Celsius, respectively, for the vertical temperature profile comparisons. Temperature and dissolved oxygen were key metrics for calibration targets. These calibrated lake models also simulated algal community dynamics and water quality. The model simulations presented potential explanations for persistently large total phosphorus concentrations in Madison Lake, key differences in nutrient concentrations between these lakes, and summer blue-green algal bloom persistence.Fish habitat suitability simulations for cool-water and warm-water fish indicated that, in general, both lakes contained a large proportion of good-growth habitat and a sustained period of optimal growth habitat in the summer, without any periods of lethal oxythermal habitat. For Madison and Pearl Lakes, examples of important cool-water fish, particularly game fish, include northern pike (Esox lucius), walleye (Sander vitreus), and black crappie (Pomoxis nigromaculatus); examples of important warm-water fish include bluegill (Lepomis macrochirus), largemouth bass (Micropterus salmoides), and smallmouth bass (Micropterus dolomieu). Sensitivity analyses were completed to understand lake response effects through the use of controlled departures on certain calibrated model parameters and input nutrient loads. These sensitivity analyses also operated as land-use change scenarios because alterations in agricultural practices, for example, could potentially increase or decrease nutrient loads.

[Confirmative study of a French version of the Exercise Dependence Scale-revised with a French population].

PubMed

Allegre, B; Therme, P

2008-10-01

Since the first writings on excessive exercise, there has been an increased interest in exercise dependence. One of the major consequences of this increased interest has been the development of several definitions and measures of exercise dependence. The work of Veale [Does primary exercise dependence really exist? In: Annet J, Cripps B, Steinberg H, editors. Exercise addiction: Motivation for participation in sport and exercise.Leicester, UK: Br Psychol Soc; 1995. p. 1-5.] provides an advance for the definition and measure of exercise dependence. These studies have adapted the DSM-IV criteria for substance dependence to measure exercise dependence. The Exercise Dependence Scale-Revised is based on these diagnostic criteria, which are: tolerance; withdrawal effects; intention effect; lack of control; time; reductions in other activities; continuance. Confirmatory factor analyses of EDS-R provided support to present a measurement model (21 items loaded in seven factors) of EDS-R (Comparative Fit Index=0.97; Root mean Square Error of Approximation=0.05; Tucker-Lewis Index=0.96). The aim of this study was to examine the psychometric properties of a French version of the EDS-R [Factorial validity and psychometric examination of the exercise dependence scale-revised. Meas Phys Educ Exerc Sci 2004;8(4):183-201.] to test the stability of the seven-factor model of the original version with a French population. A total of 516 half-marathoners ranged in age from 17 to 74 years old (Mean age=39.02 years, ET=10.64), with 402 men (77.9%) and 114 women (22.1%) participated in the study. The principal component analysis results in a six-factor structure, which accounts for 68.60% of the total variance. Because principal component analysis presents a six-factor structure differing from the original seven-factor structure, two models were tested, using confirmatory factor analysis. The first model is the seven-factor model of the original version of the EDS-R and the second is the model produced by the principal component analysis. The results of confirmatory factor analysis presented the original model (with a seven-factor structure) as a good model and fit indices were good (X(2)/ddl=2.89, Root Mean Square Error of Approximation (RMSEA)=0.061, Expected Cross Validation Index (ECVI)=1.20, Goodness-of-Fit Index (GFI)=0.92, Comparative Fit Index (CFI)=0.94, Standardized Root Mean Square (SRMS)=0.048). These results showed that the French version of EDS-R has an identical factor structure to the original. Therefore, the French version of EDS-R was an acceptable scale to measure exercise dependence and can be used on a French population.

Sound diffraction at wall impedance discontinuities in a circular cylinder, investigated using Wiener-Hopf technique

NASA Technical Reports Server (NTRS)

Cho, Y. C.

1983-01-01

Rigorous solutions are presented for sound diffraction in a circular cylinder with axial discontinuities of the wall admittance (or impedance). Analytical expressions are derived for the reflection and the transmission coefficients for duct modes. The results are discussed quantitatively in the limits of small admittance shifts (delta) and of low frequencies (ka). One of the results is the low frequency behavior of the reflection coefficient R(o) sub 00 of the fundamental mode. For the mode of a hardwall duct reflected from the junction with a softwall duct, (R(o) sub oo yields - (1-square root of (ka) square root of (2/i delta)); this result is in contrast to the frequency dependence of the reflection from the open end of a hardwall duct, for which R(o) sub oo yields - 1-(ka) squared/2 .

A component prediction method for flue gas of natural gas combustion based on nonlinear partial least squares method.

PubMed

Cao, Hui; Yan, Xingyu; Li, Yaojiang; Wang, Yanxia; Zhou, Yan; Yang, Sanchun

2014-01-01

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.

Effect of root planing on surface topography: an in-vivo randomized experimental trial.

PubMed

Rosales-Leal, J I; Flores, A B; Contreras, T; Bravo, M; Cabrerizo-Vílchez, M A; Mesa, F

2015-04-01

The root surface topography exerts a major influence on clinical attachment and bacterial recolonization after root planing. In-vitro topographic studies have yielded variable results, and clinical studies are necessary to compare root surface topography after planing with current ultrasonic devices and with traditional manual instrumentation. The aim of this study was to compare the topography of untreated single-rooted teeth planed in vivo with a curette, a piezoelectric ultrasonic (PU) scraper or a vertically oscillating ultrasonic (VOU) scraper. In a randomized experimental trial of 19 patients, 44 single-rooted teeth were randomly assigned to one of four groups for: no treatment; manual root planing with a curette; root planing with a PU scraper; or root planing with a VOU scraper. Post-treatment, the teeth were extracted and their topography was analyzed in 124 observations with white-light confocal microscopy, measuring the roughness parameters arithmetic average height, root-mean-square roughness, maximum height of peaks, maximum depth of valleys, absolute height, skewness and kurtosis. The roughness values arithmetic average height and root-mean-square roughness were similar after each treatment and lower than after no treatment ( p < 0.05). Absolute height was lower in the VOU group than in the untreated ( p = 0.0026) and PU (p = 0.045) groups. Surface morphology was similar after the three treatments and was less irregular than in the untreated group. Values for the remaining roughness parameters were similar among all treatment groups ( p > 0.05). Both ultrasonic devices reduce the roughness, producing a similar topography to that observed after manual instrumentation with a curette, to which they appear to represent a valid alternative. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prediction of pH of cola beverage using Vis/NIR spectroscopy and least squares-support vector machine

NASA Astrophysics Data System (ADS)

Liu, Fei; He, Yong

2008-02-01

Visible and near infrared (Vis/NIR) transmission spectroscopy and chemometric methods were utilized to predict the pH values of cola beverages. Five varieties of cola were prepared and 225 samples (45 samples for each variety) were selected for the calibration set, while 75 samples (15 samples for each variety) for the validation set. The smoothing way of Savitzky-Golay and standard normal variate (SNV) followed by first-derivative were used as the pre-processing methods. Partial least squares (PLS) analysis was employed to extract the principal components (PCs) which were used as the inputs of least squares-support vector machine (LS-SVM) model according to their accumulative reliabilities. Then LS-SVM with radial basis function (RBF) kernel function and a two-step grid search technique were applied to build the regression model with a comparison of PLS regression. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias were 0.961, 0.040 and 0.012 for PLS, while 0.975, 0.031 and 4.697x10 -3 for LS-SVM, respectively. Both methods obtained a satisfying precision. The results indicated that Vis/NIR spectroscopy combined with chemometric methods could be applied as an alternative way for the prediction of pH of cola beverages.

Detection of Glutamic Acid in Oilseed Rape Leaves Using Near Infrared Spectroscopy and the Least Squares-Support Vector Machine

PubMed Central

Bao, Yidan; Kong, Wenwen; Liu, Fei; Qiu, Zhengjun; He, Yong

2012-01-01

Amino acids are quite important indices to indicate the growth status of oilseed rape under herbicide stress. Near infrared (NIR) spectroscopy combined with chemometrics was applied for fast determination of glutamic acid in oilseed rape leaves. The optimal spectral preprocessing method was obtained after comparing Savitzky-Golay smoothing, standard normal variate, multiplicative scatter correction, first and second derivatives, detrending and direct orthogonal signal correction. Linear and nonlinear calibration methods were developed, including partial least squares (PLS) and least squares-support vector machine (LS-SVM). The most effective wavelengths (EWs) were determined by the successive projections algorithm (SPA), and these wavelengths were used as the inputs of PLS and LS-SVM model. The best prediction results were achieved by SPA-LS-SVM (Raw) model with correlation coefficient r = 0.9943 and root mean squares error of prediction (RMSEP) = 0.0569 for prediction set. These results indicated that NIR spectroscopy combined with SPA-LS-SVM was feasible for the fast and effective detection of glutamic acid in oilseed rape leaves. The selected EWs could be used to develop spectral sensors, and the important and basic amino acid data were helpful to study the function mechanism of herbicide. PMID:23203052

Carbon Nanotubes' Effect on Mitochondrial Oxygen Flux Dynamics: Polarography Experimental Study and Machine Learning Models using Star Graph Trace Invariants of Raman Spectra.

PubMed

González-Durruthy, Michael; Monserrat, Jose M; Rasulev, Bakhtiyor; Casañola-Martín, Gerardo M; Barreiro Sorrivas, José María; Paraíso-Medina, Sergio; Maojo, Víctor; González-Díaz, Humberto; Pazos, Alejandro; Munteanu, Cristian R

2017-11-11

This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux ( J m ) under three experimental conditions. New experimental results and a new methodology are reported for the first time and they are based on CNT Raman spectra star graph transform (spectral moments) and perturbation theory. The experimental measures of J m showed that no tested CNT family can inhibit the oxygen consumption profiles of mitochondria. The best model for the prediction of J m for other CNTs was provided by random forest using eight features, obtaining test R-squared ( R ²) of 0.863 and test root-mean-square error (RMSE) of 0.0461. The results demonstrate the capability of encoding CNT information into spectral moments of the Raman star graphs (SG) transform with a potential applicability as predictive tools in nanotechnology and material risk assessments.

Revised tephra volumes for Cascade Range volcanoes

NASA Astrophysics Data System (ADS)

Nathenson, Manuel

2017-07-01

Isopach maps from tephra eruptions from Mount St. Helens were reported in Carey et al. (1995) and for tephra eruptions from Glacier Peak in Gardner et al. (1998). For exponential thinning, the isopach data only define a single slope on a log thickness versus square root of area plot. Carey et al. (1995) proposed a model that was used to estimate a second slope, and volumes were presented in both studies using this model. A study by Sulpizio (2005) for estimating the second slope and square root of area where the lines intersect involves a systematic analysis of many eruptions to provide correlation equations. The purpose of this paper is to recalculate the volumes of Cascades eruptions and compare results from the two methods. In order to gain some perspective on the methods for estimating the second slope, we use data for thickness versus distance beyond the last isopach that are available for some of the larger eruptions in the Cascades. The thickness versus square root of area method is extended to thickness versus distance by developing an approximate relation between the two assuming elliptical isopachs with the source at one of the foci. Based on the comparisons made between the Carey et al. (1995) and Sulpizio (2005) methods, it is felt that the later method provides a better estimate of the second slope. For Mount St. Helens, the estimates of total volume using the Sulpizio (2005) method are generally smaller than those using the Carey et al. (1995) method. For the volume estimates of Carey et al. (1995), the volume of the May 18, 1980, eruption of Mount St. Helens is smaller than six of the eight previous eruptions. With the new volumes using the Sulpizio (2005) method, the 1980 eruption is smaller in volume than the upper end of the range for only three of the layers (Wn, Ye, and Yn) and is the same size as layer We. Thus the 1980 eruption becomes representative of the mid-range of volumes rather than being in the lower range.

Foveal Curvature and Asymmetry Assessed Using Optical Coherence Tomography.

PubMed

VanNasdale, Dean A; Eilerman, Amanda; Zimmerman, Aaron; Lai, Nicky; Ramsey, Keith; Sinnott, Loraine T

2017-06-01

The aims of this study were to use cross-sectional optical coherence tomography imaging and custom curve fitting software to evaluate and model the foveal curvature as a spherical surface and to compare the radius of curvature in the horizontal and vertical meridians and test the sensitivity of this technique to anticipated meridional differences. Six 30-degree foveal-centered radial optical coherence tomography cross-section scans were acquired in the right eye of 20 clinically normal subjects. Cross sections were manually segmented, and custom curve fitting software was used to determine foveal pit radius of curvature using the central 500, 1000, and 1500 μm of the foveal contour. Radius of curvature was compared across different fitting distances. Root mean square error was used to determine goodness of fit. The radius of curvature was compared between the horizontal and vertical meridians for each fitting distance. There radius of curvature was significantly different when comparing each of the three fitting distances (P < .01 for each comparison). The average radii of curvature were 970 μm (95% confidence interval [CI], 913 to 1028 μm), 1386 μm (95% CI, 1339 to 1439 μm), and 2121 μm (95% CI, 2066 to 2183) for the 500-, 1000-, and 1500-μm fitting distances, respectively. Root mean square error was also significantly different when comparing each fitting distance (P < .01 for each comparison). The average root mean square errors were 2.48 μm (95% CI, 2.41 to 2.53 μm), 6.22 μm (95% CI, 5.77 to 6.60 μm), and 13.82 μm (95% CI, 12.93 to 14.58 μm) for the 500-, 1000-, and 1500-μm fitting distances, respectively. The radius of curvature between the horizontal and vertical meridian radii was statistically different only in the 1000- and 1500-μm fitting distances (P < .01 for each), with the horizontal meridian being flatter than the vertical. The foveal contour can be modeled as a sphere with low curve fitting error over a limited distance and capable of detecting subtle foveal contour differences between meridians.

Revised tephra volumes for Cascade Range volcanoes

USGS Publications Warehouse

Nathenson, Manuel

2017-01-01

Isopach maps from tephra eruptions from Mount St. Helens were reported in Carey et al. (1995) and for tephra eruptions from Glacier Peak in Gardner et al. (1998). For exponential thinning, the isopach data only define a single slope on a log thickness versus square root of area plot. Carey et al. (1995) proposed a model that was used to estimate a second slope, and volumes were presented in both studies using this model. A study by Sulpizio (2005) for estimating the second slope and square root of area where the lines intersect involves a systematic analysis of many eruptions to provide correlation equations. The purpose of this paper is to recalculate the volumes of Cascades eruptions and compare results from the two methods. In order to gain some perspective on the methods for estimating the second slope, we use data for thickness versus distance beyond the last isopach that are available for some of the larger eruptions in the Cascades. The thickness versus square root of area method is extended to thickness versus distance by developing an approximate relation between the two assuming elliptical isopachs with the source at one of the foci. Based on the comparisons made between the Carey et al. (1995) and Sulpizio (2005) methods, it is felt that the later method provides a better estimate of the second slope. For Mount St. Helens, the estimates of total volume using the Sulpizio (2005) method are generally smaller than those using the Carey et al. (1995) method. For the volume estimates of Carey et al. (1995), the volume of the May 18, 1980, eruption of Mount St. Helens is smaller than six of the eight previous eruptions. With the new volumes using the Sulpizio (2005) method, the 1980 eruption is smaller in volume than the upper end of the range for only three of the layers (Wn, Ye, and Yn) and is the same size as layer We. Thus the 1980 eruption becomes representative of the mid-range of volumes rather than being in the lower range.

Analysis of S-box in Image Encryption Using Root Mean Square Error Method

NASA Astrophysics Data System (ADS)

Hussain, Iqtadar; Shah, Tariq; Gondal, Muhammad Asif; Mahmood, Hasan

2012-07-01

The use of substitution boxes (S-boxes) in encryption applications has proven to be an effective nonlinear component in creating confusion and randomness. The S-box is evolving and many variants appear in literature, which include advanced encryption standard (AES) S-box, affine power affine (APA) S-box, Skipjack S-box, Gray S-box, Lui J S-box, residue prime number S-box, Xyi S-box, and S8 S-box. These S-boxes have algebraic and statistical properties which distinguish them from each other in terms of encryption strength. In some circumstances, the parameters from algebraic and statistical analysis yield results which do not provide clear evidence in distinguishing an S-box for an application to a particular set of data. In image encryption applications, the use of S-boxes needs special care because the visual analysis and perception of a viewer can sometimes identify artifacts embedded in the image. In addition to existing algebraic and statistical analysis already used for image encryption applications, we propose an application of root mean square error technique, which further elaborates the results and enables the analyst to vividly distinguish between the performances of various S-boxes. While the use of the root mean square error analysis in statistics has proven to be effective in determining the difference in original data and the processed data, its use in image encryption has shown promising results in estimating the strength of the encryption method. In this paper, we show the application of the root mean square error analysis to S-box image encryption. The parameters from this analysis are used in determining the strength of S-boxes

In Silico Molecular Modeling and Docking Studies on Novel Mutants (E229V, H225P and D230C) of the Nucleotide-Binding Domain of Homo sapiens Hsp70.

PubMed

Elengoe, Asita; Hamdan, Salehhuddin

2017-12-01

In this study, we explored the possibility of determining the synergistic interactions between nucleotide-binding domain (NBD) of Homo sapiens heat-shock 70 kDa protein (Hsp70) and E1A 32 kDa of adenovirus serotype 5 motif (PNLVP) in the efficiency of killing of tumor cells in cancer treatment. At present, the protein interaction between NBD and PNLVP motif is still unknown, but believed to enhance the rate of virus replication in tumor cells. Three mutant models (E229V, H225P and D230C) were built and simulated, and their interactions with PNLVP motif were studied. The PNLVP motif showed the binding energy and intermolecular energy values with the novel E229V mutant at -7.32 and -11.2 kcal/mol. The E229V mutant had the highest number of hydrogen bonds (7). Based on the root mean square deviation, root mean square fluctuation, hydrogen bonds, salt bridge, secondary structure, surface-accessible solvent area, potential energy and distance matrices analyses, it was proved that the E229V had the strongest and most stable interaction with the PNLVP motif among all the four protein-ligand complex structures. The knowledge of this protein-ligand complex model would help in designing Hsp70 structure-based drug for cancer therapy.

Improving Marine Corps Assignment of SDAP Levels

DTIC Science & Technology

2013-03-01

Nettles , Colonel, USMC, M&RA (MPO). 15 Information Paper SDAP. 17 for a job or being able to fill the position at all, in addition to the potential to...Total 7850672.93 60132 130.557323 Root MSE = 6.2241... Root MSE = .26638 Adj R-squared = 0.0097 Residual 4577.29534 64507 .070958118

On Nth roots of positive operators

NASA Technical Reports Server (NTRS)

Brown, D. R.; Omalley, M. J.

1978-01-01

A bounded operator A on a Hilbert space H was positive. These operators were symmetric, and as such constitute a natural generalization of nonnegative real diagonal matrices. The following result is thus both well known and not surprising: A positive operator has a unique positive square root (under operator composition).

Quality aspects of ex vivo root canal treatments done by undergraduate dental students using four different endodontic treatment systems.

PubMed

Jungnickel, Luise; Kruse, Casper; Vaeth, Michael; Kirkevang, Lise-Lotte

2018-04-01

To evaluate factors associated with treatment quality of ex vivo root canal treatments performed by undergraduate dental students using different endodontic treatment systems. Four students performed root canal treatment on 80 extracted human teeth using four endodontic treatment systems in designated treatment order following a Latin square design. Lateral seal and length of root canal fillings was radiographically assessed; for lateral seal, a graded visual scale was used. Treatment time was measured separately for access preparation, biomechanical root canal preparation, obturation and for the total procedure. Mishaps were registered. An ANOVA mirroring the Latin square design was performed. Use of machine-driven nickel-titanium systems resulted in overall better quality scores for lateral seal than use of the manual stainless-steel system. Among systems with machine-driven files, scores did not significantly differ. Use of machine-driven instruments resulted in shorter treatment time than manual instrumentation. Machine-driven systems with few files achieved shorter treatment times. With increasing number of treatments, root canal-filling quality increased, treatment time decreased; a learning curve was plotted. No root canal shaping file separated. The use of endodontic treatment systems with machine-driven files led to higher quality lateral seal compared to the manual system. The three contemporary machine-driven systems delivered comparable results regarding quality of root canal fillings; they were safe to use and provided a more efficient workflow than the manual technique. Increasing experience had a positive impact on the quality of root canal fillings while treatment time decreased.

Research on the infiltration processes of lawn soils of the Babao River in the Qilian Mountain.

PubMed

Li, GuangWen; Feng, Qi; Zhang, FuPing; Cheng, AiFang

2014-01-01

Using a Guelph Permeameter, the soil water infiltration processes were analyzed in the Babao River of the Qilian Mountain in China. The results showed that the average soil initial infiltration and the steady infiltration rates in the upstream reaches of the Babao River are 1.93 and 0.99 cm/min, whereas those of the middle area are 0.48 cm/min and 0.21 cm/min, respectively. The infiltration processes can be divided into three stages: the rapidly changing stage (0-10 min), the slowly changing stage (10-30 min) and the stabilization stage (after 30 min). We used field data collected from lawn soils and evaluated the performances of the infiltration models of Philip, Kostiakov and Horton with the sum of squared error, the root mean square error, the coefficient of determination, the mean error, the model efficiency and Willmott's index of agreement. The results indicated that the Kostiakov model was most suitable for studying the infiltration process in the alpine lawn soils.

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

NASA Astrophysics Data System (ADS)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Detection and quantification of adulteration of sesame oils with vegetable oils using gas chromatography and multivariate data analysis.

PubMed

Peng, Dan; Bi, Yanlan; Ren, Xiaona; Yang, Guolong; Sun, Shangde; Wang, Xuede

2015-12-01

This study was performed to develop a hierarchical approach for detection and quantification of adulteration of sesame oil with vegetable oils using gas chromatography (GC). At first, a model was constructed to discriminate the difference between authentic sesame oils and adulterated sesame oils using support vector machine (SVM) algorithm. Then, another SVM-based model is developed to identify the type of adulterant in the mixed oil. At last, prediction models for sesame oil were built for each kind of oil using partial least square method. To validate this approach, 746 samples were prepared by mixing authentic sesame oils with five types of vegetable oil. The prediction results show that the detection limit for authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction range from 1.19% to 4.29%, meaning that this approach is a valuable tool to detect and quantify the adulteration of sesame oil. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe.

PubMed

Ebtehaj, Isa; Bonakdari, Hossein

2014-01-01

The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.

The prediction of food additives in the fruit juice based on electronic nose with chemometrics.

PubMed

Qiu, Shanshan; Wang, Jun

2017-09-01

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R 2 s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring. Copyright © 2017 Elsevier Ltd. All rights reserved.

The role of critical ethnic awareness and social support in the discrimination-depression relationship among Asian Americans: path analysis.

PubMed

Kim, Isok

2014-01-01

This study used a path analytic technique to examine associations among critical ethnic awareness, racial discrimination, social support, and depressive symptoms. Using a convenience sample from online survey of Asian American adults (N = 405), the study tested 2 main hypotheses: First, based on the empowerment theory, critical ethnic awareness would be positively associated with racial discrimination experience; and second, based on the social support deterioration model, social support would partially mediate the relationship between racial discrimination and depressive symptoms. The result of the path analysis model showed that the proposed path model was a good fit based on global fit indices, χ²(2) = 4.70, p = .10; root mean square error of approximation = 0.06; comparative fit index = 0.97; Tucker-Lewis index = 0.92; and standardized root mean square residual = 0.03. The examinations of study hypotheses demonstrated that critical ethnic awareness was directly associated (b = .11, p < .05) with the racial discrimination experience, whereas social support had a significant indirect effect (b = .48; bias-corrected 95% confidence interval [0.02, 1.26]) between the racial discrimination experience and depressive symptoms. The proposed path model illustrated that both critical ethnic awareness and social support are important mechanisms for explaining the relationship between racial discrimination and depressive symptoms among this sample of Asian Americans. This study highlights the usefulness of the critical ethnic awareness concept as a way to better understand how Asian Americans might perceive and recognize racial discrimination experiences in relation to its mental health consequences.

Over length quantification of the multiaxial mechanical properties of the ascending, descending and abdominal aorta using Digital Image Correlation.

PubMed

Peña, Juan A; Corral, Victoria; Martínez, Miguel A; Peña, Estefanía

2018-01-01

In this paper, we hypothesize that the biaxial mechanical properties of the aorta may be dependent on arterial location. To demonstrate any possible position-related difference, our study analyzed and compared the biaxial mechanical properties of the ascending thoracic aorta, descending thoracic aorta and infrarenal abdominal aorta stemming from the same porcine subjects, and reported values of constitutive parameters for well-known strain energy functions, showing how these mechanical properties are affected by location along the aorta. When comparing ascending thoracic aorta, descending thoracic aorta and infrarenal abdominal aorta, abdominal tissues were found to be stiffer and highly anisotropic. We found that the aorta changed from a more isotropic to a more anisotropic tissue and became progressively less compliant and stiffer with the distance to the heart. We observed substantial differences in the anisotropy parameter between aortic samples where abdominal samples were more anisotropic and nonlinear than the thoracic samples. The phenomenological model was not able to capture the passive biaxial properties of each specific porcine aorta over a wide range of biaxial deformations, showing the best prediction root mean square error ε=0.2621 for ascending thoracic samples and, especially, the worst for the infrarenal abdominal samples ε=0.3780. The micro-structured model with Bingham orientation density function was able to better predict biaxial deformations (ε=0.1372 for ascending thoracic aorta samples). The root mean square error of the micro-structural model and the micro-structured model with von Mises orientation density function were similar for all positions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Building global models for fat and total protein content in raw milk based on historical spectroscopic data in the visible and short-wave near infrared range.

PubMed

Melenteva, Anastasiia; Galyanin, Vladislav; Savenkova, Elena; Bogomolov, Andrey

2016-07-15

A large set of fresh cow milk samples collected from many suppliers over a large geographical area in Russia during a year has been analyzed by optical spectroscopy in the range 400-1100 nm in accordance with previously developed scatter-based technique. The global (i.e. resistant to seasonal, genetic, regional and other variations of the milk composition) models for fat and total protein content, which were built using partial least-squares (PLS) regression, exhibit satisfactory prediction performances enabling their practical application in the dairy. The root mean-square errors of prediction (RMSEP) were 0.09 and 0.10 for fat and total protein content, respectively. The issues of raw milk analysis and multivariate modelling based on the historical spectroscopic data have been considered and approaches to the creation of global models and their transfer between the instruments have been proposed. Availability of global models should significantly facilitate the dissemination of optical spectroscopic methods for the laboratory and in-line quantitative milk analysis. Copyright © 2016. Published by Elsevier Ltd.

A QSAR study of integrase strand transfer inhibitors based on a large set of pyrimidine, pyrimidone, and pyridopyrazine carboxamide derivatives

NASA Astrophysics Data System (ADS)

de Campos, Luana Janaína; de Melo, Eduardo Borges

2017-08-01

In the present study, 199 compounds derived from pyrimidine, pyrimidone and pyridopyrazine carboxamides with inhibitory activity against HIV-1 integrase were modeled. Subsequently, a multivariate QSAR study was conducted with 54 molecules employed by Ordered Predictors Selection (OPS) and Partial Least Squares (PLS) for the selection of variables and model construction, respectively. Topological, electrotopological, geometric, and molecular descriptors were used. The selected real model was robust and free from chance correlation; in addition, it demonstrated favorable internal and external statistical quality. Once statistically validated, the training model was used to predict the activity of a second data set (n = 145). The root mean square deviation (RMSD) between observed and predicted values was 0.698. Although it is a value outside of the standards, only 15 (10.34%) of the samples exhibited higher residual values than 1 log unit, a result considered acceptable. Results of Williams and Euclidean applicability domains relative to the prediction showed that the predictions did not occur by extrapolation and that the model is representative of the chemical space of test compounds.

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

NASA Astrophysics Data System (ADS)

Parmar, Kulwinder Singh; Bhardwaj, Rashmi

2014-12-01

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

Computing daily mean streamflow at ungaged locations in Iowa by using the Flow Anywhere and Flow Duration Curve Transfer statistical methods

USGS Publications Warehouse

Linhart, S. Mike; Nania, Jon F.; Sanders, Curtis L.; Archfield, Stacey A.

2012-01-01

The U.S. Geological Survey (USGS) maintains approximately 148 real-time streamgages in Iowa for which daily mean streamflow information is available, but daily mean streamflow data commonly are needed at locations where no streamgages are present. Therefore, the USGS conducted a study as part of a larger project in cooperation with the Iowa Department of Natural Resources to develop methods to estimate daily mean streamflow at locations in ungaged watersheds in Iowa by using two regression-based statistical methods. The regression equations for the statistical methods were developed from historical daily mean streamflow and basin characteristics from streamgages within the study area, which includes the entire State of Iowa and adjacent areas within a 50-mile buffer of Iowa in neighboring states. Results of this study can be used with other techniques to determine the best method for application in Iowa and can be used to produce a Web-based geographic information system tool to compute streamflow estimates automatically. The Flow Anywhere statistical method is a variation of the drainage-area-ratio method, which transfers same-day streamflow information from a reference streamgage to another location by using the daily mean streamflow at the reference streamgage and the drainage-area ratio of the two locations. The Flow Anywhere method modifies the drainage-area-ratio method in order to regionalize the equations for Iowa and determine the best reference streamgage from which to transfer same-day streamflow information to an ungaged location. Data used for the Flow Anywhere method were retrieved for 123 continuous-record streamgages located in Iowa and within a 50-mile buffer of Iowa. The final regression equations were computed by using either left-censored regression techniques with a low limit threshold set at 0.1 cubic feet per second (ft3/s) and the daily mean streamflow for the 15th day of every other month, or by using an ordinary-least-squares multiple linear regression method and the daily mean streamflow for the 15th day of every other month. The Flow Duration Curve Transfer method was used to estimate unregulated daily mean streamflow from the physical and climatic characteristics of gaged basins. For the Flow Duration Curve Transfer method, daily mean streamflow quantiles at the ungaged site were estimated with the parameter-based regression model, which results in a continuous daily flow-duration curve (the relation between exceedance probability and streamflow for each day of observed streamflow) at the ungaged site. By the use of a reference streamgage, the Flow Duration Curve Transfer is converted to a time series. Data used in the Flow Duration Curve Transfer method were retrieved for 113 continuous-record streamgages in Iowa and within a 50-mile buffer of Iowa. The final statewide regression equations for Iowa were computed by using a weighted-least-squares multiple linear regression method and were computed for the 0.01-, 0.05-, 0.10-, 0.15-, 0.20-, 0.30-, 0.40-, 0.50-, 0.60-, 0.70-, 0.80-, 0.85-, 0.90-, and 0.95-exceedance probability statistics determined from the daily mean streamflow with a reporting limit set at 0.1 ft3/s. The final statewide regression equation for Iowa computed by using left-censored regression techniques was computed for the 0.99-exceedance probability statistic determined from the daily mean streamflow with a low limit threshold and a reporting limit set at 0.1 ft3/s. For the Flow Anywhere method, results of the validation study conducted by using six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 1,016 to 138 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 1,690 to 237 ft3/s. Values of the percent root-mean-square error ranged from 115 percent to 26.2 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 13.0 to 5.3 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.80 to 0.40. Percent-bias values ranged from 25.4 to 4.0 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.35. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.86 to 0.56. For the streamgage with the best agreement between observed and estimated streamflow, higher streamflows appear to be underestimated. For the streamgage with the worst agreement between observed and estimated streamflow, low flows appear to be overestimated whereas higher flows seem to be underestimated. Estimated cumulative streamflows for the period October 1, 2004, to September 30, 2009, are underestimated by -25.8 and -7.4 percent for the closest and poorest comparisons, respectively. For the Flow Duration Curve Transfer method, results of the validation study conducted by using the same six streamgages show that differences between the root-mean-square error and the mean absolute error ranged from 437 to 93.9 ft3/s, with the larger value signifying a greater occurrence of outliers between observed and estimated streamflows. Root-mean-square-error values ranged from 906 to 169 ft3/s. Values of the percent root-mean-square-error ranged from 67.0 to 25.6 percent. The logarithm (base 10) streamflow percent root-mean-square error ranged from 12.5 to 4.4 percent. Root-mean-square-error observations standard-deviation-ratio values ranged from 0.79 to 0.40. Percent-bias values ranged from 22.7 to 0.94 percent. Untransformed streamflow Nash-Sutcliffe efficiency values ranged from 0.84 to 0.38. The logarithm (base 10) streamflow Nash-Sutcliffe efficiency values ranged from 0.89 to 0.48. For the streamgage with the closest agreement between observed and estimated streamflow, there is relatively good agreement between observed and estimated streamflows. For the streamgage with the poorest agreement between observed and estimated streamflow, streamflows appear to be substantially underestimated for much of the time period. Estimated cumulative streamflow for the period October 1, 2004, to September 30, 2009, are underestimated by -9.3 and -22.7 percent for the closest and poorest comparisons, respectively.

Descendant root volume varies as a function of root type: estimation of root biomass lost during uprooting in Pinus pinaster.

PubMed

Danjon, Frédéric; Caplan, Joshua S; Fortin, Mathieu; Meredieu, Céline

2013-01-01

Root systems of woody plants generally display a strong relationship between the cross-sectional area or cross-sectional diameter (CSD) of a root and the dry weight of biomass (DWd) or root volume (Vd) that has grown (i.e., is descendent) from a point. Specification of this relationship allows one to quantify root architectural patterns and estimate the amount of material lost when root systems are extracted from the soil. However, specifications of this relationship generally do not account for the fact that root systems are comprised of multiple types of roots. We assessed whether the relationship between CSD and Vd varies as a function of root type. Additionally, we sought to identify a more accurate and time-efficient method for estimating missing root volume than is currently available. We used a database that described the 3D root architecture of Pinus pinaster root systems (5, 12, or 19 years) from a stand in southwest France. We determined the relationship between CSD and Vd for 10,000 root segments from intact root branches. Models were specified that did and did not account for root type. The relationships were then applied to the diameters of 11,000 broken root ends to estimate the volume of missing roots. CSD was nearly linearly related to the square root of Vd, but the slope of the curve varied greatly as a function of root type. Sinkers and deep roots tapered rapidly, as they were limited by available soil depth. Distal shallow roots tapered gradually, as they were less limited spatially. We estimated that younger trees lost an average of 17% of root volume when excavated, while older trees lost 4%. Missing volumes were smallest in the central parts of root systems and largest in distal shallow roots. The slopes of the curves for each root type are synthetic parameters that account for differentiation due to genetics, soil properties, or mechanical stimuli. Accounting for this differentiation is critical to estimating root loss accurately.

Descendant root volume varies as a function of root type: estimation of root biomass lost during uprooting in Pinus pinaster

PubMed Central

Danjon, Frédéric; Caplan, Joshua S.; Fortin, Mathieu; Meredieu, Céline

2013-01-01

Root systems of woody plants generally display a strong relationship between the cross-sectional area or cross-sectional diameter (CSD) of a root and the dry weight of biomass (DWd) or root volume (Vd) that has grown (i.e., is descendent) from a point. Specification of this relationship allows one to quantify root architectural patterns and estimate the amount of material lost when root systems are extracted from the soil. However, specifications of this relationship generally do not account for the fact that root systems are comprised of multiple types of roots. We assessed whether the relationship between CSD and Vd varies as a function of root type. Additionally, we sought to identify a more accurate and time-efficient method for estimating missing root volume than is currently available. We used a database that described the 3D root architecture of Pinus pinaster root systems (5, 12, or 19 years) from a stand in southwest France. We determined the relationship between CSD and Vd for 10,000 root segments from intact root branches. Models were specified that did and did not account for root type. The relationships were then applied to the diameters of 11,000 broken root ends to estimate the volume of missing roots. CSD was nearly linearly related to the square root of Vd, but the slope of the curve varied greatly as a function of root type. Sinkers and deep roots tapered rapidly, as they were limited by available soil depth. Distal shallow roots tapered gradually, as they were less limited spatially. We estimated that younger trees lost an average of 17% of root volume when excavated, while older trees lost 4%. Missing volumes were smallest in the central parts of root systems and largest in distal shallow roots. The slopes of the curves for each root type are synthetic parameters that account for differentiation due to genetics, soil properties, or mechanical stimuli. Accounting for this differentiation is critical to estimating root loss accurately. PMID:24167506

Psychometric Properties and Correlates of the Beck Hopelessness Scale in Family Caregivers of Nigerian Patients with Psychiatric Disorders in Southwestern Nigeria

PubMed Central

Aloba, Olutayo; Ajao, Olayinka; Alimi, Taiwo; Esan, Olufemi

2016-01-01

Objectives: To examine the construct and correlates of hopelessness among family caregivers of Nigerian psychiatric patients. Materials and Methods: This is a cross-sectional, descriptive study involving 264 family caregiver-patients’ dyads recruited from two university teaching hospitals psychiatric clinics in Southwestern Nigeria. Results: Exploratory factor analysis revealed a two-factor 9-item model of the Beck Hopelessness Scale (BHS) among the family caregivers. Confirmatory factor analysis of the model revealed satisfactory indices of fitness (goodness of fit index = 0.97, comparative fit index = 0.96, Chi-square/degree of freedom (CMIN/DF) = 1.60, root mean square error of approximation = 0.048, expected cross-validation index = 0.307, and standardized root mean residual = 0.005). Reliability of the scale was modestly satisfactory (Cronbach's alpha 0.72). Construct validity of scale was supported by significant correlations with the family caregivers’ scores on the Zarit Burden Interview, mini international neuropsychiatric interview suicidality module, General Health Questionnaire-12 (GHQ-12), and Patient Health Questionnaire-9. The greatest variance in the family caregivers’ scores on the BHS was contributed by their scores on the psychological distress scale (GHQ-12). Conclusions: The BHS has adequate psychometric properties among Nigerian psychiatric patients’ family caregivers. There is the need to pay attention to the psychological well-being of the family caregivers of Nigerian psychiatric patients. PMID:28163498

Poor quality of life, depressed mood, and memory impairment may be mediated by sleep disruption in patients with Addison's disease

PubMed Central

Henry, Michelle; Wolf, Pedro S.A.; Ross, Ian L.; Thomas, Kevin G.F.

2015-01-01

Standard replacement therapy for Addison's disease (AD) does not restore a normal circadian rhythm. In fact, hydrocortisone replacement in AD patients likely induces disrupted sleep. Given that healthy sleep plays an important role in improving quality of life, optimizing cognition, and ensuring affect regulation, the aim of this study was to investigate whether poor quality of life, mood alterations, and memory complaints reported by AD patients are associated with their disrupted sleep patterns. Sixty patients with AD and 60 matched healthy controls completed a battery of self-report questionnaires assessing perceived physical and mental health (Short-Form 36), mood (Beck Depression Inventory—II), sleep quality (Pittsburgh Sleep Quality Index), and cognition (Cognitive Failures Questionnaire). A latent variable model revealed that although AD had a significant direct effect on quality of life, the indirect effect of sleep was significantly greater. Furthermore, although AD had no direct effect on cognitive functioning, the indirect effect of sleep was significant. The overall model showed a good fit (comparative fit index = 0.91, root mean square of approximation = 0.09, and standardized root mean square residual = 0.05). Our findings suggest that disrupted sleep, and not the disease per se, may induce poor quality of life, memory impairment, and affect dysregulation in patients with AD. We think that improving sleep architecture may improve cognitive, affective, and physical functioning. PMID:26256520

Simultaneous Estimation of Microphysical Parameters and Atmospheric State Variables With Radar Data and Ensemble Square-root Kalman Filter

NASA Astrophysics Data System (ADS)

Tong, M.; Xue, M.

2006-12-01

An important source of model error for convective-scale data assimilation and prediction is microphysical parameterization. This study investigates the possibility of estimating up to five fundamental microphysical parameters, which are closely involved in the definition of drop size distribution of microphysical species in a commonly used single-moment ice microphysics scheme, using radar observations and the ensemble Kalman filter method. The five parameters include the intercept parameters for rain, snow and hail/graupel, and the bulk densities of hail/graupel and snow. Parameter sensitivity and identifiability are first examined. The ensemble square-root Kalman filter (EnSRF) is employed for simultaneous state and parameter estimation. OSS experiments are performed for a model-simulated supercell storm, in which the five microphysical parameters are estimated individually or in different combinations starting from different initial guesses. When error exists in only one of the microphysical parameters, the parameter can be successfully estimated without exception. The estimation of multiple parameters is found to be less robust, with end results of estimation being sensitive to the realization of the initial parameter perturbation. This is believed to be because of the reduced parameter identifiability and the existence of non-unique solutions. The results of state estimation are, however, always improved when simultaneous parameter estimation is performed, even when the estimated parameters values are not accurate.

Comparison of spatial interpolation methods for soil moisture and its application for monitoring drought.

PubMed

Chen, Hui; Fan, Li; Wu, Wei; Liu, Hong-Bin

2017-09-26

Soil moisture data can reflect valuable information on soil properties, terrain features, and drought condition. The current study compared and assessed the performance of different interpolation methods for estimating soil moisture in an area with complex topography in southwest China. The approaches were inverse distance weighting, multifarious forms of kriging, regularized spline with tension, and thin plate spline. The 5-day soil moisture observed at 167 stations and daily temperature recorded at 33 stations during the period of 2010-2014 were used in the current work. Model performance was tested with accuracy indicators of determination coefficient (R 2 ), mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and modeling efficiency (ME). The results indicated that inverse distance weighting had the best performance with R 2 , MAPE, RMSE, RRMSE, and ME of 0.32, 14.37, 13.02%, 0.16, and 0.30, respectively. Based on the best method, a spatial database of soil moisture was developed and used to investigate drought condition over the study area. The results showed that the distribution of drought was characterized by evidently regional difference. Besides, drought mainly occurred in August and September in the 5 years and was prone to happening in the western and central parts rather than in the northeastern and southeastern areas.

A validation study of the Brazilian version of the pornography consumption inventory (PCI) in a sample of female university students.

PubMed

Baltieri, Danilo Antonio; Luísa de Souza Gatti, Ana; Henrique de Oliveira, Vitor; Junqueira Aguiar, Ana Saito; Almeida de Souza Aranha e Silva, Renata

2016-02-01

Although men constitute the widest consumer group of pornography, the Internet has facilitated both the production of and access to pornographic material by women as well. However, few measures are available to examine pornography-use constructs, which can compromise the reliability of statements regarding the harmful use of pornography. Our study aimed to confirm the factorial validity and internal consistency of the Pornography Consumption Inventory (PCI) in a sample of female university students in Brazil. The PCI is a four-factor, 15-item, five-point Likert-type scale. After translation and back-translation of the PCI, it was administered to 105 female medical students. Exploratory and confirmatory factor analyses were conducted to examine the construct validity. The results supported the four-factor model of the PCI. The model showed adequate internal reliability and good fit indices (comparative fit index (CFI) = 0.95, Tucker-Lewis index (TLI) = 0.94, root mean square error of approximation (RMSEA) = 0.07 (95% confidence interval (CI) = 0.04-0.09), and standardized root mean square residual (SRMR) = 0.08). Overall, the findings from this study support the use of the PCI in Portuguese-speaking women. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Estimates of Ionospheric Transport and Ion Loss at Mars

NASA Astrophysics Data System (ADS)

Cravens, T. E.; Hamil, O.; Houston, S.; Bougher, S.; Ma, Y.; Brain, D.; Ledvina, S.

2017-10-01

Ion loss from the topside ionosphere of Mars associated with the solar wind interaction makes an important contribution to the loss of volatiles from this planet. Data from NASA's Mars Atmosphere and Volatile Evolution mission combined with theoretical modeling are now helping us to understand the processes involved in the ion loss process. Given the complexity of the solar wind interaction, motivation exists for considering a simple approach to this problem and for understanding how the loss rates might scale with solar wind conditions and solar extreme ultraviolet irradiance. This paper reviews the processes involved in the ionospheric dynamics. Simple analytical and semiempirical expressions for ion flow speeds and ion loss are derived. In agreement with more sophisticated models and with purely empirical studies, it is found that the oxygen loss rate from ion transport is about 5% (i.e., global O ion loss rate of Qion ≈ 4 × 1024 s-1) of the total oxygen loss rate. The ion loss is found to approximately scale as the square root of the solar ionizing photon flux and also as the square root of the solar wind dynamic pressure. Typical ion flow speeds are found to be about 1 km/s in the topside ionosphere near an altitude of 300 km on the dayside. Not surprisingly, the plasma flow speed is found to increase with altitude due to the decreasing ion-neutral collision frequency.

Application of square-root filtering for spacecraft attitude control

NASA Technical Reports Server (NTRS)

Sorensen, J. A.; Schmidt, S. F.; Goka, T.

1978-01-01

Suitable digital algorithms are developed and tested for providing on-board precision attitude estimation and pointing control for potential use in the Landsat-D spacecraft. These algorithms provide pointing accuracy of better than 0.01 deg. To obtain necessary precision with efficient software, a six state-variable square-root Kalman filter combines two star tracker measurements to update attitude estimates obtained from processing three gyro outputs. The validity of the estimation and control algorithms are established, and the sensitivity of their performance to various error sources and software parameters are investigated by detailed digital simulation. Spacecraft computer memory, cycle time, and accuracy requirements are estimated.

J/Psi production in pp collisions at square root = 200 GeV at the BNL relativistic heavy ion collider.

PubMed

Cooper, Fred; Liu, Ming X; Nayak, Gouranga C

2004-10-22

We study J/psi production in pp collisions at BNL Relativistic Heavy Ion Collider (RHIC) within the PHENIX detector acceptance range using the color singlet and color octet mechanism which are based on perturbative QCD and nonrelativistic QCD. Here we show that the color octet mechanism reproduces the RHIC data for J/psi production in pp collisions with respect to the p(T) distribution, the rapidity distribution, and the total cross section at square root = 200 GeV. The color singlet mechanism leads to a relatively small contribution to the total cross section when compared to the octet contribution.

Advanced optical system for scanning-spot photorefractive keratectomy (PRK)

NASA Astrophysics Data System (ADS)

Mrochen, Michael; Wullner, Christian; Semchishen, Vladimir A.; Seiler, Theo

1999-06-01

Purpose: The goal of this presentation is to discuss the use of the Light Shaping Beam Homogenizer in an optical system for scanning-spot PRK. Methods: The basic principle of the LSBH is the transformation of any incident intensity distribution by light scattering on an irregular microlens structure z = f(x,y). The relief of this microlens structure is determined by a defined statistical function, i.e. it is defined by the mean root-squared tilt σ of the surface relief. Therefore, the beam evolution after the LSBH and in the focal plane of an imaging lens was measured for various root-squared tilts. Beside this, an optical setup for scanning-spot PRK was assembled according to the theoretical and experimental results. Results: The divergence, homogeneity and the Gaussian radius of the intensity distribution in the treatment plane of the scanning-spot PRK laser system is mainly characterized by dependent on root-mean-square tilt σ of the LSBH, as it will be explained by the theoretical description of the LSBH. Conclusions: The LSBH represents a simple, low cost beam homogenizer with low energy losses, for scanning-spot excimer laser systems.

Month-to-month and year-to-year reproducibility of high frequency QRS ECG signals

NASA Technical Reports Server (NTRS)

Batdorf, Niles J.; Feiveson, Alan H.; Schlegel, Todd T.

2004-01-01

High frequency electrocardiography analyzing the entire QRS complex in the frequency range of 150 to 250 Hz may prove useful in the detection of coronary artery disease, yet the long-term stability of these waveforms has not been fully characterized. Therefore, we prospectively investigated the reproducibility of the root mean squared voltage, kurtosis, and the presence versus absence of reduced amplitude zones in signal averaged 12-lead high frequency QRS recordings acquired in the supine position one month apart in 16 subjects and one year apart in 27 subjects. Reproducibility of root mean squared voltage and kurtosis was excellent over these time intervals in the limb leads, and acceptable in the precordial leads using both the V-lead and CR-lead derivations. The relative error of root mean squared voltage was 12% month-to-month and 16% year-to-year in the serial recordings when averaged over all 12 leads. Reduced amplitude zones were also reproducible up to a rate of 87% and 81%, respectively, for the month-to-month and year-to-year recordings. We conclude that 12-lead high frequency QRS electrocardiograms are sufficiently reproducible for clinical use.

Enhancing prediction power of chemometric models through manipulation of the fed spectrophotometric data: A comparative study

NASA Astrophysics Data System (ADS)

Saad, Ahmed S.; Hamdy, Abdallah M.; Salama, Fathy M.; Abdelkawy, Mohamed

2016-10-01

Effect of data manipulation in preprocessing step proceeding construction of chemometric models was assessed. The same set of UV spectral data was used for construction of PLS and PCR models directly and after mathematically manipulation as per well known first and second derivatives of the absorption spectra, ratio spectra and first and second derivatives of the ratio spectra spectrophotometric methods, meanwhile the optimal working wavelength ranges were carefully selected for each model and the models were constructed. Unexpectedly, number of latent variables used for models' construction varied among the different methods. The prediction power of the different models was compared using a validation set of 8 mixtures prepared as per the multilevel multifactor design and results were statistically compared using two-way ANOVA test. Root mean squares error of prediction (RMSEP) was used for further comparison of the predictability among different constructed models. Although no significant difference was found between results obtained using Partial Least Squares (PLS) and Principal Component Regression (PCR) models, however, discrepancies among results was found to be attributed to the variation in the discrimination power of adopted spectrophotometric methods on spectral data.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling.

PubMed

Soni, Kirti; Parmar, Kulwinder Singh; Kapoor, Sangeeta; Kumar, Nishant

2016-05-15

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend. Copyright © 2016 Elsevier B.V. All rights reserved.

Data assimilation for groundwater flow modelling using Unbiased Ensemble Square Root Filter: Case study in Guantao, North China Plain

NASA Astrophysics Data System (ADS)

Li, N.; Kinzelbach, W.; Li, H.; Li, W.; Chen, F.; Wang, L.

2017-12-01

Data assimilation techniques are widely used in hydrology to improve the reliability of hydrological models and to reduce model predictive uncertainties. This provides critical information for decision makers in water resources management. This study aims to evaluate a data assimilation system for the Guantao groundwater flow model coupled with a one-dimensional soil column simulation (Hydrus 1D) using an Unbiased Ensemble Square Root Filter (UnEnSRF) originating from the Ensemble Kalman Filter (EnKF) to update parameters and states, separately or simultaneously. To simplify the coupling between unsaturated and saturated zone, a linear relationship obtained from analyzing inputs to and outputs from Hydrus 1D is applied in the data assimilation process. Unlike EnKF, the UnEnSRF updates parameter ensemble mean and ensemble perturbations separately. In order to keep the ensemble filter working well during the data assimilation, two factors are introduced in the study. One is called damping factor to dampen the update amplitude of the posterior ensemble mean to avoid nonrealistic values. The other is called inflation factor to relax the posterior ensemble perturbations close to prior to avoid filter inbreeding problems. The sensitivities of the two factors are studied and their favorable values for the Guantao model are determined. The appropriate observation error and ensemble size were also determined to facilitate the further analysis. This study demonstrated that the data assimilation of both model parameters and states gives a smaller model prediction error but with larger uncertainty while the data assimilation of only model states provides a smaller predictive uncertainty but with a larger model prediction error. Data assimilation in a groundwater flow model will improve model prediction and at the same time make the model converge to the true parameters, which provides a successful base for applications in real time modelling or real time controlling strategies in groundwater resources management.

l/f Noise in the Superconducting Transition of a MgB2 Thin Film

NASA Technical Reports Server (NTRS)

Lakew, B.; Aslam, S.; Jones, H.; Stevenson, T.; Cao, N.

2010-01-01

The noise voltage spectral density in the superconducting transition of a MgB2 thin film on a SiN-coated Si thick substrate was measured over the frequency range 1 Hz-to-1 KHz. Using established bolometer noise theory the theoretical noise components due to Johnson, 1/f(excess) and phonon noise are modeled to the measured data. It is shown that for the case of a MgB2 thin film in the vicinity of the mid-point of transition, coupled to a heat sink via a fairly high thermal conductance (approximately equal to 10(sup -1) W/K)) that the measured noise voltage spectrum is 1/f limited and exhibits lit dependence with a varying between 0.3 and 0.5 in the measured frequency range. At a video frame rate frequency of 30 Hz the measured noise voltage density in the film is approximately equal to 61 nV /the square root of HZ, using this value an upper limit of electrical NEP approximately equal to 0.67pW / the square root of Hz is implied for a practical MgB2 bolometer operating at 36.1 K.

Molecular Dynamic Simulation of Space and Earth-Grown Crystal Structures of Thermostable T1 Lipase Geobacillus zalihae Revealed a Better Structure.

PubMed

Ishak, Siti Nor Hasmah; Aris, Sayangku Nor Ariati Mohamad; Halim, Khairul Bariyyah Abd; Ali, Mohd Shukuri Mohamad; Leow, Thean Chor; Kamarudin, Nor Hafizah Ahmad; Masomian, Malihe; Rahman, Raja Noor Zaliha Raja Abd

2017-09-25

Less sedimentation and convection in a microgravity environment has become a well-suited condition for growing high quality protein crystals. Thermostable T1 lipase derived from bacterium Geobacillus zalihae has been crystallized using the counter diffusion method under space and earth conditions. Preliminary study using YASARA molecular modeling structure program for both structures showed differences in number of hydrogen bond, ionic interaction, and conformation. The space-grown crystal structure contains more hydrogen bonds as compared with the earth-grown crystal structure. A molecular dynamics simulation study was used to provide insight on the fluctuations and conformational changes of both T1 lipase structures. The analysis of root mean square deviation (RMSD), radius of gyration, and root mean square fluctuation (RMSF) showed that space-grown structure is more stable than the earth-grown structure. Space-structure also showed more hydrogen bonds and ion interactions compared to the earth-grown structure. Further analysis also revealed that the space-grown structure has long-lived interactions, hence it is considered as the more stable structure. This study provides the conformational dynamics of T1 lipase crystal structure grown in space and earth condition.

Development of New Open-Shell Perturbation and Coupled-Cluster Theories Based on Symmetric Spin Orbitals

NASA Technical Reports Server (NTRS)

Lee, Timothy J.; Arnold, James O. (Technical Monitor)

1994-01-01

A new spin orbital basis is employed in the development of efficient open-shell coupled-cluster and perturbation theories that are based on a restricted Hartree-Fock (RHF) reference function. The spin orbital basis differs from the standard one in the spin functions that are associated with the singly occupied spatial orbital. The occupied orbital (in the spin orbital basis) is assigned the delta(+) = 1/square root of 2(alpha+Beta) spin function while the unoccupied orbital is assigned the delta(-) = 1/square root of 2(alpha-Beta) spin function. The doubly occupied and unoccupied orbitals (in the reference function) are assigned the standard alpha and Beta spin functions. The coupled-cluster and perturbation theory wave functions based on this set of "symmetric spin orbitals" exhibit much more symmetry than those based on the standard spin orbital basis. This, together with interacting space arguments, leads to a dramatic reduction in the computational cost for both coupled-cluster and perturbation theory. Additionally, perturbation theory based on "symmetric spin orbitals" obeys Brillouin's theorem provided that spin and spatial excitations are both considered. Other properties of the coupled-cluster and perturbation theory wave functions and models will be discussed.

Viscoelastic properties and efficient acoustic damping in confined polymer nano-layers at GHz frequencies

PubMed Central

Hettich, Mike; Jacob, Karl; Ristow, Oliver; Schubert, Martin; Bruchhausen, Axel; Gusev, Vitalyi; Dekorsy, Thomas

2016-01-01

We investigate the viscoelastic properties of confined molecular nano-layers by time resolved optical pump-probe measurements. Access to the elastic properties is provided by the damping time of acoustic eigenmodes of thin metal films deposited on the molecular nano-layers which show a strong dependence on the molecular layer thickness and on the acoustic eigen-mode frequencies. An analytical model including the viscoelastic properties of the molecular layer allows us to obtain the longitudinal sound velocity as well as the acoustic absorption coefficient of the layer. Our experiments and theoretical analysis indicate for the first time that the molecular nano-layers are much more viscous than elastic in the investigated frequency range from 50 to 120 GHz and thus show pronounced acoustic absorption. The longitudinal acoustic wavenumber has nearly equal real and imaginary parts, both increasing proportional to the square root of the frequency. Thus, both acoustic velocity and acoustic absorption are proportional to the square root of frequency and the propagation of compressional/dilatational acoustic waves in the investigated nano-layers is of the diffusional type, similar to the propagation of shear waves in viscous liquids and thermal waves in solids. PMID:27633351

System and Method for Determining Rate of Rotation Using Brushless DC Motor

NASA Technical Reports Server (NTRS)

Howard, David E. (Inventor); Smith, Dennis A. (Inventor)

2000-01-01

A system and method are provided for measuring rate of rotation. A brushless DC motor is rotated and produces a back electromagnetic force (emf) on each winding thereof. Each winding's back-emf is squared. The squared outputs associated with each winding are combined, with the square root being taken of such combination, to produce a DC output proportional only to the rate of rotation of the motor's shaft.

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data.

PubMed

Groenendijk, Piet; Heinen, Marius; Klammler, Gernot; Fank, Johann; Kupfersberger, Hans; Pisinaras, Vassilios; Gemitzi, Alexandra; Peña-Haro, Salvador; García-Prats, Alberto; Pulido-Velazquez, Manuel; Perego, Alessia; Acutis, Marco; Trevisan, Marco

2014-11-15

The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test for 2005-2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009-2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement, model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates. Copyright © 2014 Elsevier B.V. All rights reserved.

Input Forces Estimation for Nonlinear Systems by Applying a Square-Root Cubature Kalman Filter.

PubMed

Song, Xuegang; Zhang, Yuexin; Liang, Dakai

2017-10-10

This work presents a novel inverse algorithm to estimate time-varying input forces in nonlinear beam systems. With the system parameters determined, the input forces can be estimated in real-time from dynamic responses, which can be used for structural health monitoring. In the process of input forces estimation, the Runge-Kutta fourth-order algorithm was employed to discretize the state equations; a square-root cubature Kalman filter (SRCKF) was employed to suppress white noise; the residual innovation sequences, a priori state estimate, gain matrix, and innovation covariance generated by SRCKF were employed to estimate the magnitude and location of input forces by using a nonlinear estimator. The nonlinear estimator was based on the least squares method. Numerical simulations of a large deflection beam and an experiment of a linear beam constrained by a nonlinear spring were employed. The results demonstrated accuracy of the nonlinear algorithm.