Applications of principal component analysis to breath air absorption spectra profiles classification

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Y.

2015-12-01

The results of numerical simulation of application principal component analysis to absorption spectra of breath air of patients with pulmonary diseases are presented. Various methods of experimental data preprocessing are analyzed.

Application of principal component regression and partial least squares regression in ultraviolet spectrum water quality detection

NASA Astrophysics Data System (ADS)

Li, Jiangtong; Luo, Yongdao; Dai, Honglin

2018-01-01

Water is the source of life and the essential foundation of all life. With the development of industrialization, the phenomenon of water pollution is becoming more and more frequent, which directly affects the survival and development of human. Water quality detection is one of the necessary measures to protect water resources. Ultraviolet (UV) spectral analysis is an important research method in the field of water quality detection, which partial least squares regression (PLSR) analysis method is becoming predominant technology, however, in some special cases, PLSR's analysis produce considerable errors. In order to solve this problem, the traditional principal component regression (PCR) analysis method was improved by using the principle of PLSR in this paper. The experimental results show that for some special experimental data set, improved PCR analysis method performance is better than PLSR. The PCR and PLSR is the focus of this paper. Firstly, the principal component analysis (PCA) is performed by MATLAB to reduce the dimensionality of the spectral data; on the basis of a large number of experiments, the optimized principal component is extracted by using the principle of PLSR, which carries most of the original data information. Secondly, the linear regression analysis of the principal component is carried out with statistic package for social science (SPSS), which the coefficients and relations of principal components can be obtained. Finally, calculating a same water spectral data set by PLSR and improved PCR, analyzing and comparing two results, improved PCR and PLSR is similar for most data, but improved PCR is better than PLSR for data near the detection limit. Both PLSR and improved PCR can be used in Ultraviolet spectral analysis of water, but for data near the detection limit, improved PCR's result better than PLSR.

[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].

PubMed

Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin

2007-07-01

Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.

Interpreting the results of chemical stone analysis in the era of modern stone analysis techniques

PubMed Central

Gilad, Ron; Williams, James C.; Usman, Kalba D.; Holland, Ronen; Golan, Shay; Ruth, Tor; Lifshitz, David

2017-01-01

Introduction and Objective Stone analysis should be performed in all first-time stone formers. The preferred analytical procedures are Fourier-transform infrared spectroscopy (FT-IR) or X-ray diffraction (XRD). However, due to limited resources, chemical analysis (CA) is still in use throughout the world. The aim of the study was to compare FT-IR and CA in well matched stone specimens and characterize the pros and cons of CA. Methods In a prospective bi-center study, urinary stones were retrieved from 60 consecutive endoscopic procedures. In order to assure that identical stone samples were sent for analyses, the samples were analyzed initially by micro-computed tomography to assess uniformity of each specimen before submitted for FTIR and CA. Results Overall, the results of CA did not match with the FTIR results in 56% of the cases. In 16% of the cases CA missed the major stone component and in 40% the minor stone component. 37 of the 60 specimens contained CaOx as major component by FTIR, and CA reported major CaOx in 47/60, resulting in high sensitivity, but very poor specificity. CA was relatively accurate for UA and cystine. CA missed struvite and calcium phosphate as a major component in all cases. In mixed stones the sensitivity of CA for the minor component was poor, generally less than 50%. Conclusions Urinary stone analysis using CA provides only limited data that should be interpreted carefully. Urinary stone analysis using CA is likely to result in clinically significant errors in its assessment of stone composition. Although the monetary costs of CA are relatively modest, this method does not provide the level of analytical specificity required for proper management of patients with metabolic stones. PMID:26956131

[Preliminary study on effective components of Tripterygium wilfordii for liver toxicity based on spectrum-effect correlation analysis].

PubMed

Zhao, Xiao-Mei; Pu, Shi-Biao; Zhao, Qing-Guo; Gong, Man; Wang, Jia-Bo; Ma, Zhi-Jie; Xiao, Xiao-He; Zhao, Kui-Jun

2016-08-01

In this paper, the spectrum-effect correlation analysis method was used to explore the main effective components of Tripterygium wilfordii for liver toxicity, and provide reference for promoting the quality control of T. wilfordii. Chinese medicine T.wilfordii was taken as the study object, and LC-Q-TOF-MS was used to characterize the chemical components in T. wilfordii samples from different areas, and their main components were initially identified after referring to the literature. With the normal human hepatocytes (LO2 cell line)as the carrier, acetaminophen as positive medicine, and cell inhibition rate as testing index, the simple correlation analysis and multivariate linear correlation analysis methods were used to screen the main components of T. wilfordii for liver toxicity. As a result, 10 kinds of main components were identified, and the spectrum-effect correlation analysis showed that triptolide may be the toxic component, which was consistent with previous results of traditional literature. Meanwhile it was found that tripterine and demethylzeylasteral may greatly contribute to liver toxicity in multivariate linear correlation analysis. T. wilfordii samples of different varieties or different origins showed large difference in quality, and the T. wilfordii from southwest China showed lower liver toxicity, while those from Hunan and Anhui province showed higher liver toxicity. This study will provide data support for further rational use of T. wilfordii and research on its liver toxicity ingredients. Copyright© by the Chinese Pharmaceutical Association.

Lattice Independent Component Analysis for Mobile Robot Localization

NASA Astrophysics Data System (ADS)

Villaverde, Ivan; Fernandez-Gauna, Borja; Zulueta, Ekaitz

This paper introduces an approach to appearance based mobile robot localization using Lattice Independent Component Analysis (LICA). The Endmember Induction Heuristic Algorithm (EIHA) is used to select a set of Strong Lattice Independent (SLI) vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Selected endmembers are used to compute the linear unmixing of the robot's acquired images. The resulting mixing coefficients are used as feature vectors for view recognition through classification. We show on a sample path experiment that our approach can recognise the localization of the robot and we compare the results with the Independent Component Analysis (ICA).

Reliability analysis of component-level redundant topologies for solid-state fault current limiter

NASA Astrophysics Data System (ADS)

Farhadi, Masoud; Abapour, Mehdi; Mohammadi-Ivatloo, Behnam

2018-04-01

Experience shows that semiconductor switches in power electronics systems are the most vulnerable components. One of the most common ways to solve this reliability challenge is component-level redundant design. There are four possible configurations for the redundant design in component level. This article presents a comparative reliability analysis between different component-level redundant designs for solid-state fault current limiter. The aim of the proposed analysis is to determine the more reliable component-level redundant configuration. The mean time to failure (MTTF) is used as the reliability parameter. Considering both fault types (open circuit and short circuit), the MTTFs of different configurations are calculated. It is demonstrated that more reliable configuration depends on the junction temperature of the semiconductor switches in the steady state. That junction temperature is a function of (i) ambient temperature, (ii) power loss of the semiconductor switch and (iii) thermal resistance of heat sink. Also, results' sensitivity to each parameter is investigated. The results show that in different conditions, various configurations have higher reliability. The experimental results are presented to clarify the theory and feasibility of the proposed approaches. At last, levelised costs of different configurations are analysed for a fair comparison.

Long-life mission reliability for outer planet atmospheric entry probes

NASA Technical Reports Server (NTRS)

Mccall, M. T.; Rouch, L.; Maycock, J. N.

1976-01-01

The results of a literature analysis on the effects of prolonged exposure to deep space environment on the properties of outer planet atmospheric entry probe components are presented. Materials considered included elastomers and plastics, pyrotechnic devices, thermal control components, metal springs and electronic components. The rates of degradation of each component were determined and extrapolation techniques were used to predict the effects of exposure for up to eight years to deep space. Pyrotechnic devices were aged under accelerated conditions to an equivalent of eight years in space and functionally tested. Results of the literature analysis of the selected components and testing of the devices indicated that no severe degradation should be expected during an eight year space mission.

Periodic component analysis as a spatial filter for SSVEP-based brain-computer interface.

PubMed

Kiran Kumar, G R; Reddy, M Ramasubba

2018-06-08

Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise. The πCA can separate out components corresponding to a given frequency of interest from the background electroencephalogram (EEG) by capturing the temporal information and does not generalize SSVEP based on rigid templates. Data from ten test subjects were used to evaluate the proposed method and the results demonstrate that the periodic component analysis acts as a reliable spatial filter for SSVEP extraction. Statistical tests were performed to validate the results. The experimental results show that πCA provides significant improvement in accuracy compared to standard CCA and MEC in low SNR conditions. The results demonstrate that πCA provides better detection accuracy compared to CCA and on par with that of MEC at a lower computational cost. Hence πCA is a reliable and efficient alternative detection algorithm for SSVEP based brain-computer interface (BCI). Copyright © 2018. Published by Elsevier B.V.

Assessment of Supportive, Conflicted, and Controlling Dimensions of Family Functioning: A Principal Components Analysis of Family Environment Scale Subscales in a College Sample.

ERIC Educational Resources Information Center

Kronenberger, William G.; Thompson, Robert J., Jr.; Morrow, Catherine

1997-01-01

A principal components analysis of the Family Environment Scale (FES) (R. Moos and B. Moos, 1994) was performed using 113 undergraduates. Research supported 3 broad components encompassing the 10 FES subscales. These results supported previous research and the generalization of the FES to college samples. (SLD)

Component isolation for multi-component signal analysis using a non-parametric gaussian latent feature model

NASA Astrophysics Data System (ADS)

Yang, Yang; Peng, Zhike; Dong, Xingjian; Zhang, Wenming; Clifton, David A.

2018-03-01

A challenge in analysing non-stationary multi-component signals is to isolate nonlinearly time-varying signals especially when they are overlapped in time and frequency plane. In this paper, a framework integrating time-frequency analysis-based demodulation and a non-parametric Gaussian latent feature model is proposed to isolate and recover components of such signals. The former aims to remove high-order frequency modulation (FM) such that the latter is able to infer demodulated components while simultaneously discovering the number of the target components. The proposed method is effective in isolating multiple components that have the same FM behavior. In addition, the results show that the proposed method is superior to generalised demodulation with singular-value decomposition-based method, parametric time-frequency analysis with filter-based method and empirical model decomposition base method, in recovering the amplitude and phase of superimposed components.

Is Stacking Intervention Components Cost-Effective? An Analysis of the Incredible Years Program

ERIC Educational Resources Information Center

Foster, E. Michael; Olchowski, Allison E.; Webster-Stratton, Carolyn H.

2007-01-01

The cost-effectiveness of delivering stacked multiple intervention components for children is compared to implementing single intervention by analyzing the Incredible Years Series program. The result suggests multiple intervention components are more cost-effective than single intervention components.

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

PubMed

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA. Copyright © 2017 Elsevier B.V. All rights reserved.

Analysis of complex elastic structures by a Rayleigh-Ritz component modes method using Lagrange multipliers. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Klein, L. R.

1974-01-01

The free vibrations of elastic structures of arbitrary complexity were analyzed in terms of their component modes. The method was based upon the use of the normal unconstrained modes of the components in a Rayleigh-Ritz analysis. The continuity conditions were enforced by means of Lagrange Multipliers. Examples of the structures considered are: (1) beams with nonuniform properties; (2) airplane structures with high or low aspect ratio lifting surface components; (3) the oblique wing airplane; and (4) plate structures. The method was also applied to the analysis of modal damping of linear elastic structures. Convergence of the method versus the number of modes per component and/or the number of components is discussed and compared to more conventional approaches, ad-hoc methods, and experimental results.

Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

PubMed Central

Maneshi, Mona; Vahdat, Shahabeddin; Gotman, Jean; Grova, Christophe

2016-01-01

Independent component analysis (ICA) has been widely used to study functional magnetic resonance imaging (fMRI) connectivity. However, the application of ICA in multi-group designs is not straightforward. We have recently developed a new method named “shared and specific independent component analysis” (SSICA) to perform between-group comparisons in the ICA framework. SSICA is sensitive to extract those components which represent a significant difference in functional connectivity between groups or conditions, i.e., components that could be considered “specific” for a group or condition. Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences. We examined SSICA robustness with respect to the number of allowable extracted specific components and between-group orthogonality assumptions. Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. We also evaluated the consistency and specificity of the extracted specific components by SSICA. The results on realistic simulated and real fMRI data showed that SSICA outperforms the regular group ICA approach in terms of reconstruction and classification performance. We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state fMRI. Our findings in task fMRI show that SSICA confirms results of the general linear model (GLM) analysis and when combined with clustering analysis, it complements GLM findings by providing additional information regarding the reliability and specificity of networks. PMID:27729843

Development of STS/Centaur failure probabilities liftoff to Centaur separation

NASA Technical Reports Server (NTRS)

Hudson, J. M.

1982-01-01

The results of an analysis to determine STS/Centaur catastrophic vehicle response probabilities for the phases of vehicle flight from STS liftoff to Centaur separation from the Orbiter are presented. The analysis considers only category one component failure modes as contributors to the vehicle response mode probabilities. The relevant component failure modes are grouped into one of fourteen categories of potential vehicle behavior. By assigning failure rates to each component, for each of its failure modes, the STS/Centaur vehicle response probabilities in each phase of flight can be calculated. The results of this study will be used in a DOE analysis to ascertain the hazard from carrying a nuclear payload on the STS.

Technical Note: Introduction of variance component analysis to setup error analysis in radiotherapy

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

Matsuo, Yukinori, E-mail: ymatsuo@kuhp.kyoto-u.ac.

Purpose: The purpose of this technical note is to introduce variance component analysis to the estimation of systematic and random components in setup error of radiotherapy. Methods: Balanced data according to the one-factor random effect model were assumed. Results: Analysis-of-variance (ANOVA)-based computation was applied to estimate the values and their confidence intervals (CIs) for systematic and random errors and the population mean of setup errors. The conventional method overestimates systematic error, especially in hypofractionated settings. The CI for systematic error becomes much wider than that for random error. The ANOVA-based estimation can be extended to a multifactor model considering multiplemore » causes of setup errors (e.g., interpatient, interfraction, and intrafraction). Conclusions: Variance component analysis may lead to novel applications to setup error analysis in radiotherapy.« less

Spectroscopic and Chemometric Analysis of Binary and Ternary Edible Oil Mixtures: Qualitative and Quantitative Study.

PubMed

Jović, Ozren; Smolić, Tomislav; Primožič, Ines; Hrenar, Tomica

2016-04-19

The aim of this study was to investigate the feasibility of FTIR-ATR spectroscopy coupled with the multivariate numerical methodology for qualitative and quantitative analysis of binary and ternary edible oil mixtures. Four pure oils (extra virgin olive oil, high oleic sunflower oil, rapeseed oil, and sunflower oil), as well as their 54 binary and 108 ternary mixtures, were analyzed using FTIR-ATR spectroscopy in combination with principal component and discriminant analysis, partial least-squares, and principal component regression. It was found that the composition of all 166 samples can be excellently represented using only the first three principal components describing 98.29% of total variance in the selected spectral range (3035-2989, 1170-1140, 1120-1100, 1093-1047, and 930-890 cm(-1)). Factor scores in 3D space spanned by these three principal components form a tetrahedral-like arrangement: pure oils being at the vertices, binary mixtures at the edges, and ternary mixtures on the faces of a tetrahedron. To confirm the validity of results, we applied several cross-validation methods. Quantitative analysis was performed by minimization of root-mean-square error of cross-validation values regarding the spectral range, derivative order, and choice of method (partial least-squares or principal component regression), which resulted in excellent predictions for test sets (R(2) > 0.99 in all cases). Additionally, experimentally more demanding gas chromatography analysis of fatty acid content was carried out for all specimens, confirming the results obtained by FTIR-ATR coupled with principal component analysis. However, FTIR-ATR provided a considerably better model for prediction of mixture composition than gas chromatography, especially for high oleic sunflower oil.

Comparing sugar components of cereal and pseudocereal flour by GC-MS analysis.

PubMed

Ačanski, Marijana M; Vujić, Djura N

2014-02-15

Gas chromatography with mass spectrometry was used for carrying out a qualitative analysis of the ethanol soluble flour extract of different types of cereals bread wheat and spelt and pseudocereals (amaranth and buckwheat). TMSI (trimethylsilylimidazole) was used as a reagent for the derivatisation of carbohydrates into trimethylsilyl ethers. All samples were first defatted with hexane. (In our earlier investigations, hexane extracts were used for the analysis of fatty acid of lipid components.) Many components of pentoses, hexoses and disaccharides were identified using 73 and 217 Da mass and the Wiley Online Library search. The aim of this paper is not to identify and find new components, but to compare sugar components of tested samples of flour of cereals bread wheat and spelt and pseudocereals (amarnath and buckwheat). Results were analysed using descriptive statistics (dendrograms and PCA). The results show that this method can be used for making a distinction among different types of flour. Copyright © 2013 Elsevier Ltd. All rights reserved.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

On the Extraction of Components and the Applicability of the Factor Model.

ERIC Educational Resources Information Center

Dziuban, Charles D.; Harris, Chester W.

A reanalysis of Shaycroft's matrix of intercorrelations of 10 test variables plus 4 random variables is discussed. Three different procedures were used in the reanalysis: (1) Image Component Analysis, (2) Uniqueness Rescaling Factor Analysis, and (3) Alpha Factor Analysis. The results of these analyses are presented in tables. It is concluded from…

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES.

PubMed

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D

2008-05-12

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component's discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies.

Non-Newtonian Liquid Flow through Small Diameter Piping Components: CFD Analysis

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Tarun Kanti; Das, Sudip Kumar

2016-10-01

Computational Fluid Dynamics (CFD) analysis have been carried out to evaluate the frictional pressure drop across the horizontal pipeline and different piping components, like elbows, orifices, gate and globe valves for non-Newtonian liquid through 0.0127 m pipe line. The mesh generation is done using GAMBIT 6.3 and FLUENT 6.3 is used for CFD analysis. The CFD results are verified with our earlier published experimental data. The CFD results show the very good agreement with the experimental values.

Function Invariant and Parameter Scale-Free Transformation Methods

ERIC Educational Resources Information Center

Bentler, P. M.; Wingard, Joseph A.

1977-01-01

A scale-invariant simple structure function of previously studied function components for principal component analysis and factor analysis is defined. First and second partial derivatives are obtained, and Newton-Raphson iterations are utilized. The resulting solutions are locally optimal and subjectively pleasing. (Author/JKS)

Design Optimization Method for Composite Components Based on Moment Reliability-Sensitivity Criteria

NASA Astrophysics Data System (ADS)

Sun, Zhigang; Wang, Changxi; Niu, Xuming; Song, Yingdong

2017-08-01

In this paper, a Reliability-Sensitivity Based Design Optimization (RSBDO) methodology for the design of the ceramic matrix composites (CMCs) components has been proposed. A practical and efficient method for reliability analysis and sensitivity analysis of complex components with arbitrary distribution parameters are investigated by using the perturbation method, the respond surface method, the Edgeworth series and the sensitivity analysis approach. The RSBDO methodology is then established by incorporating sensitivity calculation model into RBDO methodology. Finally, the proposed RSBDO methodology is applied to the design of the CMCs components. By comparing with Monte Carlo simulation, the numerical results demonstrate that the proposed methodology provides an accurate, convergent and computationally efficient method for reliability-analysis based finite element modeling engineering practice.

SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.

PubMed

Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan

2017-09-01

With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group. Copyright © 2017 Elsevier B.V. All rights reserved.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Estimating the number of pure chemical components in a mixture by X-ray absorption spectroscopy.

PubMed

Manceau, Alain; Marcus, Matthew; Lenoir, Thomas

2014-09-01

Principal component analysis (PCA) is a multivariate data analysis approach commonly used in X-ray absorption spectroscopy to estimate the number of pure compounds in multicomponent mixtures. This approach seeks to describe a large number of multicomponent spectra as weighted sums of a smaller number of component spectra. These component spectra are in turn considered to be linear combinations of the spectra from the actual species present in the system from which the experimental spectra were taken. The dimension of the experimental dataset is given by the number of meaningful abstract components, as estimated by the cascade or variance of the eigenvalues (EVs), the factor indicator function (IND), or the F-test on reduced EVs. It is shown on synthetic and real spectral mixtures that the performance of the IND and F-test critically depends on the amount of noise in the data, and may result in considerable underestimation or overestimation of the number of components even for a signal-to-noise (s/n) ratio of the order of 80 (σ = 20) in a XANES dataset. For a given s/n ratio, the accuracy of the component recovery from a random mixture depends on the size of the dataset and number of components, which is not known in advance, and deteriorates for larger datasets because the analysis picks up more noise components. The scree plot of the EVs for the components yields one or two values close to the significant number of components, but the result can be ambiguous and its uncertainty is unknown. A new estimator, NSS-stat, which includes the experimental error to XANES data analysis, is introduced and tested. It is shown that NSS-stat produces superior results compared with the three traditional forms of PCA-based component-number estimation. A graphical user-friendly interface for the calculation of EVs, IND, F-test and NSS-stat from a XANES dataset has been developed under LabVIEW for Windows and is supplied in the supporting information. Its possible application to EXAFS data is discussed, and several XANES and EXAFS datasets are also included for download.

Error analysis and correction of discrete solutions from finite element codes

NASA Technical Reports Server (NTRS)

Thurston, G. A.; Stein, P. A.; Knight, N. F., Jr.; Reissner, J. E.

1984-01-01

Many structures are an assembly of individual shell components. Therefore, results for stresses and deflections from finite element solutions for each shell component should agree with the equations of shell theory. This paper examines the problem of applying shell theory to the error analysis and the correction of finite element results. The general approach to error analysis and correction is discussed first. Relaxation methods are suggested as one approach to correcting finite element results for all or parts of shell structures. Next, the problem of error analysis of plate structures is examined in more detail. The method of successive approximations is adapted to take discrete finite element solutions and to generate continuous approximate solutions for postbuckled plates. Preliminary numerical results are included.

A comparison of the usefulness of canonical analysis, principal components analysis, and band selection for extraction of features from TMS data for landcover analysis

NASA Technical Reports Server (NTRS)

Boyd, R. K.; Brumfield, J. O.; Campbell, W. J.

1984-01-01

Three feature extraction methods, canonical analysis (CA), principal component analysis (PCA), and band selection, have been applied to Thematic Mapper Simulator (TMS) data in order to evaluate the relative performance of the methods. The results obtained show that CA is capable of providing a transformation of TMS data which leads to better classification results than provided by all seven bands, by PCA, or by band selection. A second conclusion drawn from the study is that TMS bands 2, 3, 4, and 7 (thermal) are most important for landcover classification.

Multiple Component Event-Related Potential (mcERP) Estimation

NASA Technical Reports Server (NTRS)

Knuth, K. H.; Clanton, S. T.; Shah, A. S.; Truccolo, W. A.; Ding, M.; Bressler, S. L.; Trejo, L. J.; Schroeder, C. E.; Clancy, Daniel (Technical Monitor)

2002-01-01

We show how model-based estimation of the neural sources responsible for transient neuroelectric signals can be improved by the analysis of single trial data. Previously, we showed that a multiple component event-related potential (mcERP) algorithm can extract the responses of individual sources from recordings of a mixture of multiple, possibly interacting, neural ensembles. McERP also estimated single-trial amplitudes and onset latencies, thus allowing more accurate estimation of ongoing neural activity during an experimental trial. The mcERP algorithm is related to informax independent component analysis (ICA); however, the underlying signal model is more physiologically realistic in that a component is modeled as a stereotypic waveshape varying both in amplitude and onset latency from trial to trial. The result is a model that reflects quantities of interest to the neuroscientist. Here we demonstrate that the mcERP algorithm provides more accurate results than more traditional methods such as factor analysis and the more recent ICA. Whereas factor analysis assumes the sources are orthogonal and ICA assumes the sources are statistically independent, the mcERP algorithm makes no such assumptions thus allowing investigators to examine interactions among components by estimating the properties of single-trial responses.

Rapidly differentiating grape seeds from different sources based on characteristic fingerprints using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics.

PubMed

Song, Yuqiao; Liao, Jie; Dong, Junxing; Chen, Li

2015-09-01

The seeds of grapevine (Vitis vinifera) are a byproduct of wine production. To examine the potential value of grape seeds, grape seeds from seven sources were subjected to fingerprinting using direct analysis in real time coupled with time-of-flight mass spectrometry combined with chemometrics. Firstly, we listed all reported components (56 components) from grape seeds and calculated the precise m/z values of the deprotonated ions [M-H](-) . Secondly, the experimental conditions were systematically optimized based on the peak areas of total ion chromatograms of the samples. Thirdly, the seven grape seed samples were examined using the optimized method. Information about 20 grape seed components was utilized to represent characteristic fingerprints. Finally, hierarchical clustering analysis and principal component analysis were performed to analyze the data. Grape seeds from seven different sources were classified into two clusters; hierarchical clustering analysis and principal component analysis yielded similar results. The results of this study lay the foundation for appropriate utilization and exploitation of grape seed samples. Due to the absence of complicated sample preparation methods and chromatographic separation, the method developed in this study represents one of the simplest and least time-consuming methods for grape seed fingerprinting. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Independent EEG Sources Are Dipolar

PubMed Central

Delorme, Arnaud; Palmer, Jason; Onton, Julie; Oostenveld, Robert; Makeig, Scott

2012-01-01

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition ‘dipolarity’ defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison). PMID:22355308

Space tug propulsion system failure mode, effects and criticality analysis

NASA Technical Reports Server (NTRS)

Boyd, J. W.; Hardison, E. P.; Heard, C. B.; Orourke, J. C.; Osborne, F.; Wakefield, L. T.

1972-01-01

For purposes of the study, the propulsion system was considered as consisting of the following: (1) main engine system, (2) auxiliary propulsion system, (3) pneumatic system, (4) hydrogen feed, fill, drain and vent system, (5) oxygen feed, fill, drain and vent system, and (6) helium reentry purge system. Each component was critically examined to identify possible failure modes and the subsequent effect on mission success. Each space tug mission consists of three phases: launch to separation from shuttle, separation to redocking, and redocking to landing. The analysis considered the results of failure of a component during each phase of the mission. After the failure modes of each component were tabulated, those components whose failure would result in possible or certain loss of mission or inability to return the Tug to ground were identified as critical components and a criticality number determined for each. The criticality number of a component denotes the number of mission failures in one million missions due to the loss of that component. A total of 68 components were identified as critical with criticality numbers ranging from 1 to 2990.

Analysis of components of variance in multiple-reader studies of computer-aided diagnosis with different tasks

NASA Astrophysics Data System (ADS)

Beiden, Sergey V.; Wagner, Robert F.; Campbell, Gregory; Metz, Charles E.; Chan, Heang-Ping; Nishikawa, Robert M.; Schnall, Mitchell D.; Jiang, Yulei

2001-06-01

In recent years, the multiple-reader, multiple-case (MRMC) study paradigm has become widespread for receiver operating characteristic (ROC) assessment of systems for diagnostic imaging and computer-aided diagnosis. We review how MRMC data can be analyzed in terms of the multiple components of the variance (case, reader, interactions) observed in those studies. Such information is useful for the design of pivotal studies from results of a pilot study and also for studying the effects of reader training. Recently, several of the present authors have demonstrated methods to generalize the analysis of multiple variance components to the case where unaided readers of diagnostic images are compared with readers who receive the benefit of a computer assist (CAD). For this case it is necessary to model the possibility that several of the components of variance might be reduced when readers incorporate the computer assist, compared to the unaided reading condition. We review results of this kind of analysis on three previously published MRMC studies, two of which were applications of CAD to diagnostic mammography and one was an application of CAD to screening mammography. The results for the three cases are seen to differ, depending on the reader population sampled and the task of interest. Thus, it is not possible to generalize a particular analysis of variance components beyond the tasks and populations actually investigated.

Combination of PCA and LORETA for sources analysis of ERP data: an emotional processing study

NASA Astrophysics Data System (ADS)

Hu, Jin; Tian, Jie; Yang, Lei; Pan, Xiaohong; Liu, Jiangang

2006-03-01

The purpose of this paper is to study spatiotemporal patterns of neuronal activity in emotional processing by analysis of ERP data. 108 pictures (categorized as positive, negative and neutral) were presented to 24 healthy, right-handed subjects while 128-channel EEG data were recorded. An analysis of two steps was applied to the ERP data. First, principal component analysis was performed to obtain significant ERP components. Then LORETA was applied to each component to localize their brain sources. The first six principal components were extracted, each of which showed different spatiotemporal patterns of neuronal activity. The results agree with other emotional study by fMRI or PET. The combination of PCA and LORETA can be used to analyze spatiotemporal patterns of ERP data in emotional processing.

EXTRACTING PRINCIPLE COMPONENTS FOR DISCRIMINANT ANALYSIS OF FMRI IMAGES

PubMed Central

Liu, Jingyu; Xu, Lai; Caprihan, Arvind; Calhoun, Vince D.

2009-01-01

This paper presents an approach for selecting optimal components for discriminant analysis. Such an approach is useful when further detailed analyses for discrimination or characterization requires dimensionality reduction. Our approach can accommodate a categorical variable such as diagnosis (e.g. schizophrenic patient or healthy control), or a continuous variable like severity of the disorder. This information is utilized as a reference for measuring a component’s discriminant power after principle component decomposition. After sorting each component according to its discriminant power, we extract the best components for discriminant analysis. An application of our reference selection approach is shown using a functional magnetic resonance imaging data set in which the sample size is much less than the dimensionality. The results show that the reference selection approach provides an improved discriminant component set as compared to other approaches. Our approach is general and provides a solid foundation for further discrimination and classification studies. PMID:20582334

Estimating the vibration level of an L-shaped beam using power flow techniques

NASA Technical Reports Server (NTRS)

Cuschieri, J. M.; Mccollum, M.; Rassineux, J. L.; Gilbert, T.

1986-01-01

The response of one component of an L-shaped beam, with point force excitation on the other component, is estimated using the power flow method. The transmitted power from the source component to the receiver component is expressed in terms of the transfer and input mobilities at the excitation point and the joint. The response is estimated both in narrow frequency bands, using the exact geometry of the beams, and as a frequency averaged response using infinite beam models. The results using this power flow technique are compared to the results obtained using finite element analysis (FEA) of the L-shaped beam for the low frequency response and to results obtained using statistical energy analysis (SEA) for the high frequencies. The agreement between the FEA results and the power flow method results at low frequencies is very good. SEA results are in terms of frequency averaged levels and these are in perfect agreement with the results obtained using the infinite beam models in the power flow method. The narrow frequency band results from the power flow method also converge to the SEA results at high frequencies. The advantage of the power flow method is that detail of the response can be retained while reducing computation time, which will allow the narrow frequency band analysis of the response to be extended to higher frequencies.

Check-Standard Testing Across Multiple Transonic Wind Tunnels with the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

Deloach, Richard

2012-01-01

This paper reports the result of an analysis of wind tunnel data acquired in support of the Facility Analysis Verification & Operational Reliability (FAVOR) project. The analysis uses methods referred to collectively at Langley Research Center as the Modern Design of Experiments (MDOE). These methods quantify the total variance in a sample of wind tunnel data and partition it into explained and unexplained components. The unexplained component is further partitioned in random and systematic components. This analysis was performed on data acquired in similar wind tunnel tests executed in four different U.S. transonic facilities. The measurement environment of each facility was quantified and compared.

A first application of independent component analysis to extracting structure from stock returns.

PubMed

Back, A D; Weigend, A S

1997-08-01

This paper explores the application of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

Extracting functional components of neural dynamics with Independent Component Analysis and inverse Current Source Density.

PubMed

Lęski, Szymon; Kublik, Ewa; Swiejkowski, Daniel A; Wróbel, Andrzej; Wójcik, Daniel K

2010-12-01

Local field potentials have good temporal resolution but are blurred due to the slow spatial decay of the electric field. For simultaneous recordings on regular grids one can reconstruct efficiently the current sources (CSD) using the inverse Current Source Density method (iCSD). It is possible to decompose the resultant spatiotemporal information about the current dynamics into functional components using Independent Component Analysis (ICA). We show on test data modeling recordings of evoked potentials on a grid of 4 × 5 × 7 points that meaningful results are obtained with spatial ICA decomposition of reconstructed CSD. The components obtained through decomposition of CSD are better defined and allow easier physiological interpretation than the results of similar analysis of corresponding evoked potentials in the thalamus. We show that spatiotemporal ICA decompositions can perform better for certain types of sources but it does not seem to be the case for the experimental data studied. Having found the appropriate approach to decomposing neural dynamics into functional components we use the technique to study the somatosensory evoked potentials recorded on a grid spanning a large part of the forebrain. We discuss two example components associated with the first waves of activation of the somatosensory thalamus. We show that the proposed method brings up new, more detailed information on the time and spatial location of specific activity conveyed through various parts of the somatosensory thalamus in the rat.

Simultaneous quantitation of 14 active components in Yinchenhao decoction by using ultra high performance liquid chromatography with diode array detection: Method development and ingredient analysis of different commonly prepared samples.

PubMed

Yi, YaXiong; Zhang, Yong; Ding, Yue; Lu, Lu; Zhang, Tong; Zhao, Yuan; Xu, XiaoJun; Zhang, YuXin

2016-11-01

We developed a novel quantitative analysis method based on ultra high performance liquid chromatography coupled with diode array detection for the simultaneous determination of the 14 main active components in Yinchenhao decoction. All components were separated on an Agilent SB-C18 column by using a gradient solvent system of acetonitrile/0.1% phosphoric acid solution at a flow rate of 0.4 mL/min for 35 min. Subsequently, linearity, precision, repeatability, and accuracy tests were implemented to validate the method. Furthermore, the method has been applied for compositional difference analysis of 14 components in eight normal-extraction Yinchenhao decoction samples, accompanied by hierarchical clustering analysis and similarity analysis. The result that all samples were divided into three groups based on different contents of components demonstrated that extraction methods of decocting, refluxing, ultrasonication and extraction solvents of water or ethanol affected component differentiation, and should be related to its clinical applications. The results also indicated that the sample prepared by patients in the family by using water extraction employing a casserole was almost same to that prepared using a stainless-steel kettle, which is mostly used in pharmaceutical factories. This research would help patients to select the best and most convenient method for preparing Yinchenhao decoction. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Order-crossing removal in Gabor order tracking by independent component analysis

NASA Astrophysics Data System (ADS)

Guo, Yu; Tan, Kok Kiong

2009-08-01

Order-crossing problems in Gabor order tracking (GOT) of rotating machinery often occur when noise due to power-frequency interference, local structure resonance, etc., is prominent in applications. They can render the analysis results and the waveform-reconstruction tasks in GOT inaccurate or even meaningless. An approach is proposed in this paper to address the order-crossing problem by independent component analysis (ICA). With the approach, accurate order analysis results can be obtained and the waveforms of the order components of interest can be reconstructed or extracted from the recorded noisy data series. In addition, the ambiguities (permutation and scaling) of ICA results are also solved with the approach. The approach is amenable to applications in condition monitoring and fault diagnosis of rotating machinery. The evaluation of the approach is presented in detail based on simulations and an experiment on a rotor test rig. The results obtained using the proposed approach are compared with those obtained using the standard GOT. The comparison shows that the presented approach is more effective to solve order-crossing problems in GOT.

The use of principal component and cluster analysis to differentiate banana peel flours based on their starch and dietary fibre components.

PubMed

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-08-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food.

The Use of Principal Component and Cluster Analysis to Differentiate Banana Peel Flours Based on Their Starch and Dietary Fibre Components

PubMed Central

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-01-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food. PMID:24575193

Recovery of a spectrum based on a compressive-sensing algorithm with weighted principal component analysis

NASA Astrophysics Data System (ADS)

Dafu, Shen; Leihong, Zhang; Dong, Liang; Bei, Li; Yi, Kang

2017-07-01

The purpose of this study is to improve the reconstruction precision and better copy the color of spectral image surfaces. A new spectral reflectance reconstruction algorithm based on an iterative threshold combined with weighted principal component space is presented in this paper, and the principal component with weighted visual features is the sparse basis. Different numbers of color cards are selected as the training samples, a multispectral image is the testing sample, and the color differences in the reconstructions are compared. The channel response value is obtained by a Mega Vision high-accuracy, multi-channel imaging system. The results show that spectral reconstruction based on weighted principal component space is superior in performance to that based on traditional principal component space. Therefore, the color difference obtained using the compressive-sensing algorithm with weighted principal component analysis is less than that obtained using the algorithm with traditional principal component analysis, and better reconstructed color consistency with human eye vision is achieved.

Systems Perturbation Analysis of a Large-Scale Signal Transduction Model Reveals Potentially Influential Candidates for Cancer Therapeutics

PubMed Central

Puniya, Bhanwar Lal; Allen, Laura; Hochfelder, Colleen; Majumder, Mahbubul; Helikar, Tomáš

2016-01-01

Dysregulation in signal transduction pathways can lead to a variety of complex disorders, including cancer. Computational approaches such as network analysis are important tools to understand system dynamics as well as to identify critical components that could be further explored as therapeutic targets. Here, we performed perturbation analysis of a large-scale signal transduction model in extracellular environments that stimulate cell death, growth, motility, and quiescence. Each of the model’s components was perturbed under both loss-of-function and gain-of-function mutations. Using 1,300 simulations under both types of perturbations across various extracellular conditions, we identified the most and least influential components based on the magnitude of their influence on the rest of the system. Based on the premise that the most influential components might serve as better drug targets, we characterized them for biological functions, housekeeping genes, essential genes, and druggable proteins. The most influential components under all environmental conditions were enriched with several biological processes. The inositol pathway was found as most influential under inactivating perturbations, whereas the kinase and small lung cancer pathways were identified as the most influential under activating perturbations. The most influential components were enriched with essential genes and druggable proteins. Moreover, known cancer drug targets were also classified in influential components based on the affected components in the network. Additionally, the systemic perturbation analysis of the model revealed a network motif of most influential components which affect each other. Furthermore, our analysis predicted novel combinations of cancer drug targets with various effects on other most influential components. We found that the combinatorial perturbation consisting of PI3K inactivation and overactivation of IP3R1 can lead to increased activity levels of apoptosis-related components and tumor-suppressor genes, suggesting that this combinatorial perturbation may lead to a better target for decreasing cell proliferation and inducing apoptosis. Finally, our approach shows a potential to identify and prioritize therapeutic targets through systemic perturbation analysis of large-scale computational models of signal transduction. Although some components of the presented computational results have been validated against independent gene expression data sets, more laboratory experiments are warranted to more comprehensively validate the presented results. PMID:26904540

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

Priority of VHS Development Based in Potential Area using Principal Component Analysis

NASA Astrophysics Data System (ADS)

Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

Application of principal component analysis (PCA) as a sensory assessment tool for fermented food products.

PubMed

Ghosh, Debasree; Chattopadhyay, Parimal

2012-06-01

The objective of the work was to use the method of quantitative descriptive analysis (QDA) to describe the sensory attributes of the fermented food products prepared with the incorporation of lactic cultures. Panellists were selected and trained to evaluate various attributes specially color and appearance, body texture, flavor, overall acceptability and acidity of the fermented food products like cow milk curd and soymilk curd, idli, sauerkraut and probiotic ice cream. Principal component analysis (PCA) identified the six significant principal components that accounted for more than 90% of the variance in the sensory attribute data. Overall product quality was modelled as a function of principal components using multiple least squares regression (R (2) = 0.8). The result from PCA was statistically analyzed by analysis of variance (ANOVA). These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring the fermented food product attributes that are important for consumer acceptability.

Regional assessment of trends in vegetation change dynamics using principal component analysis

NASA Astrophysics Data System (ADS)

Osunmadewa, B. A.; Csaplovics, E.; R. A., Majdaldin; Adeofun, C. O.; Aralova, D.

2016-10-01

Vegetation forms the basis for the existence of animal and human. Due to changes in climate and human perturbation, most of the natural vegetation of the world has undergone some form of transformation both in composition and structure. Increased anthropogenic activities over the last decades had pose serious threat on the natural vegetation in Nigeria, many vegetated areas are either transformed to other land use such as deforestation for agricultural purpose or completely lost due to indiscriminate removal of trees for charcoal, fuelwood and timber production. This study therefore aims at examining the rate of change in vegetation cover, the degree of change and the application of Principal Component Analysis (PCA) in the dry sub-humid region of Nigeria using Normalized Difference Vegetation Index (NDVI) data spanning from 1983-2011. The method used for the analysis is the T-mode orientation approach also known as standardized PCA, while trends are examined using ordinary least square, median trend (Theil-Sen) and monotonic trend. The result of the trend analysis shows both positive and negative trend in vegetation change dynamics over the 29 years period examined. Five components were used for the Principal Component Analysis. The results of the first component explains about 98 % of the total variance of the vegetation (NDVI) while components 2-5 have lower variance percentage (< 1%). Two ancillary land use land cover data of 2000 and 2009 from European Space Agency (ESA) were used to further explain changes observed in the Normalized Difference Vegetation Index. The result of the land use data shows changes in land use pattern which can be attributed to anthropogenic activities such as cutting of trees for charcoal production, fuelwood and agricultural practices. The result of this study shows the ability of remote sensing data for monitoring vegetation change in the dry-sub humid region of Nigeria.

Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

2015-11-01

The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

Evaluation of skin melanoma in spectral range 450-950 nm using principal component analysis

NASA Astrophysics Data System (ADS)

Jakovels, D.; Lihacova, I.; Kuzmina, I.; Spigulis, J.

2013-06-01

Diagnostic potential of principal component analysis (PCA) of multi-spectral imaging data in the wavelength range 450- 950 nm for distant skin melanoma recognition is discussed. Processing of the measured clinical data by means of PCA resulted in clear separation between malignant melanomas and pigmented nevi.

Simplified Phased-Mission System Analysis for Systems with Independent Component Repairs

NASA Technical Reports Server (NTRS)

Somani, Arun K.

1996-01-01

Accurate analysis of reliability of system requires that it accounts for all major variations in system's operation. Most reliability analyses assume that the system configuration, success criteria, and component behavior remain the same. However, multiple phases are natural. We present a new computationally efficient technique for analysis of phased-mission systems where the operational states of a system can be described by combinations of components states (such as fault trees or assertions). Moreover, individual components may be repaired, if failed, as part of system operation but repairs are independent of the system state. For repairable systems Markov analysis techniques are used but they suffer from state space explosion. That limits the size of system that can be analyzed and it is expensive in computation. We avoid the state space explosion. The phase algebra is used to account for the effects of variable configurations, repairs, and success criteria from phase to phase. Our technique yields exact (as opposed to approximate) results. We demonstrate our technique by means of several examples and present numerical results to show the effects of phases and repairs on the system reliability/availability.

[Applications of three-dimensional fluorescence spectrum of dissolved organic matter to identification of red tide algae].

PubMed

Lü, Gui-Cai; Zhao, Wei-Hong; Wang, Jiang-Tao

2011-01-01

The identification techniques for 10 species of red tide algae often found in the coastal areas of China were developed by combining the three-dimensional fluorescence spectra of fluorescence dissolved organic matter (FDOM) from the cultured red tide algae with principal component analysis. Based on the results of principal component analysis, the first principal component loading spectrum of three-dimensional fluorescence spectrum was chosen as the identification characteristic spectrum for red tide algae, and the phytoplankton fluorescence characteristic spectrum band was established. Then the 10 algae species were tested using Bayesian discriminant analysis with a correct identification rate of more than 92% for Pyrrophyta on the level of species, and that of more than 75% for Bacillariophyta on the level of genus in which the correct identification rates were more than 90% for the phaeodactylum and chaetoceros. The results showed that the identification techniques for 10 species of red tide algae based on the three-dimensional fluorescence spectra of FDOM from the cultured red tide algae and principal component analysis could work well.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures.

PubMed

González-Vidal, Juan José; Pérez-Pueyo, Rosanna; Soneira, María José; Ruiz-Moreno, Sergio

2015-03-01

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.

Vibration signature analysis of multistage gear transmission

NASA Technical Reports Server (NTRS)

Choy, F. K.; Tu, Y. K.; Savage, M.; Townsend, D. P.

1989-01-01

An analysis is presented for multistage multimesh gear transmission systems. The analysis predicts the overall system dynamics and the transmissibility to the gear box or the enclosed structure. The modal synthesis approach of the analysis treats the uncoupled lateral/torsional model characteristics of each stage or component independently. The vibration signature analysis evaluates the global dynamics coupling in the system. The method synthesizes the interaction of each modal component or stage with the nonlinear gear mesh dynamics and the modal support geometry characteristics. The analysis simulates transient and steady state vibration events to determine the resulting torque variations, speeds, changes, rotor imbalances, and support gear box motion excitations. A vibration signature analysis examines the overall dynamic characteristics of the system, and the individual model component responses. The gear box vibration analysis also examines the spectral characteristics of the support system.

On 3-D inelastic analysis methods for hot section components. Volume 1: Special finite element models

NASA Technical Reports Server (NTRS)

Nakazawa, S.

1988-01-01

This annual status report presents the results of work performed during the fourth year of the 3-D Inelastic Analysis Methods for Hot Section Components program (NASA Contract NAS3-23697). The objective of the program is to produce a series of new computer codes permitting more accurate and efficient 3-D analysis of selected hot section components, i.e., combustor liners, turbine blades and turbine vanes. The computer codes embody a progression of math models and are streamlined to take advantage of geometrical features, loading conditions, and forms of material response that distinguish each group of selected components. Volume 1 of this report discusses the special finite element models developed during the fourth year of the contract.

Principal components analysis in clinical studies.

PubMed

Zhang, Zhongheng; Castelló, Adela

2017-09-01

In multivariate analysis, independent variables are usually correlated to each other which can introduce multicollinearity in the regression models. One approach to solve this problem is to apply principal components analysis (PCA) over these variables. This method uses orthogonal transformation to represent sets of potentially correlated variables with principal components (PC) that are linearly uncorrelated. PCs are ordered so that the first PC has the largest possible variance and only some components are selected to represent the correlated variables. As a result, the dimension of the variable space is reduced. This tutorial illustrates how to perform PCA in R environment, the example is a simulated dataset in which two PCs are responsible for the majority of the variance in the data. Furthermore, the visualization of PCA is highlighted.

Non-linear principal component analysis applied to Lorenz models and to North Atlantic SLP

NASA Astrophysics Data System (ADS)

Russo, A.; Trigo, R. M.

2003-04-01

A non-linear generalisation of Principal Component Analysis (PCA), denoted Non-Linear Principal Component Analysis (NLPCA), is introduced and applied to the analysis of three data sets. Non-Linear Principal Component Analysis allows for the detection and characterisation of low-dimensional non-linear structure in multivariate data sets. This method is implemented using a 5-layer feed-forward neural network introduced originally in the chemical engineering literature (Kramer, 1991). The method is described and details of its implementation are addressed. Non-Linear Principal Component Analysis is first applied to a data set sampled from the Lorenz attractor (1963). It is found that the NLPCA approximations are more representative of the data than are the corresponding PCA approximations. The same methodology was applied to the less known Lorenz attractor (1984). However, the results obtained weren't as good as those attained with the famous 'Butterfly' attractor. Further work with this model is underway in order to assess if NLPCA techniques can be more representative of the data characteristics than are the corresponding PCA approximations. The application of NLPCA to relatively 'simple' dynamical systems, such as those proposed by Lorenz, is well understood. However, the application of NLPCA to a large climatic data set is much more challenging. Here, we have applied NLPCA to the sea level pressure (SLP) field for the entire North Atlantic area and the results show a slight imcrement of explained variance associated. Finally, directions for future work are presented.%}

Tracing and separating plasma components causing matrix effects in hydrophilic interaction chromatography-electrospray ionization mass spectrometry.

PubMed

Ekdahl, Anja; Johansson, Maria C; Ahnoff, Martin

2013-04-01

Matrix effects on electrospray ionization were investigated for plasma samples analysed by hydrophilic interaction chromatography (HILIC) in gradient elution mode, and HILIC columns of different chemistries were tested for separation of plasma components and model analytes. By combining mass spectral data with post-column infusion traces, the following components of protein-precipitated plasma were identified and found to have significant effect on ionization: urea, creatinine, phosphocholine, lysophosphocholine, sphingomyelin, sodium ion, chloride ion, choline and proline betaine. The observed effect on ionization was both matrix-component and analyte dependent. The separation of identified plasma components and model analytes on eight columns was compared, using pair-wise linear correlation analysis and principal component analysis (PCA). Large changes in selectivity could be obtained by change of column, while smaller changes were seen when the mobile phase buffer was changed from ammonium formate pH 3.0 to ammonium acetate pH 4.5. While results from PCA and linear correlation analysis were largely in accord, linear correlation analysis was judged to be more straight-forward in terms of conduction and interpretation.

Response of basic structural elements and B-52 structural components to simulated nuclear overpressure. Volume I-program description and results (basic structural elements). Final report, 1 June 1977-30 September 1979

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

Syring, R.P.; Grubb, R.L.

1979-09-30

This document reports on the following: (1) experimental determination of the response of 16 basic structural elements and 7 B-52 components to simulated nuclear overpressure environments (utilizing Sandia Corporation's Thunderpipe Shock Tube), (2) analysis of these test specimens utilizing the NOVA-2 computer program, and (3) correlation of test and analysis results.

Airborne electromagnetic data levelling using principal component analysis based on flight line difference

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Hyperspectral functional imaging of the human brain

NASA Astrophysics Data System (ADS)

Toronov, Vladislav; Schelkanova, Irina

2013-03-01

We performed the independent component analysis of the hyperspectral functional near-infrared data acquired on humans during exercise and rest. We found that the hyperspectral functional data acquired on the human brain requires only two physiologically meaningful components to cover more than 50% o the temporal variance in hundreds of wavelengths. The analysis of the spectra of independent components showed that these components could be interpreted as results of changes in the cerebral blood volume and blood flow. Also, we found significant contributions of water and cytochrome c oxydase into changes associated with the independent components. Another remarkable effect of ICA was its good performance in terms of the filtering of the data noise.

Detection of increase in corneal irregularity due to pterygium using Fourier series harmonic analyses with multiple diameters.

PubMed

Minami, Keiichiro; Miyata, Kazunori; Otani, Atsushi; Tokunaga, Tadatoshi; Tokuda, Shouta; Amano, Shiro

2018-05-01

To determine steep increase of corneal irregularity induced by advancement of pterygium. A total of 456 eyes from 456 consecutive patients with primary pterygia were examined for corneal topography and advancement of pterygium with respect to the corneal diameter. Corneal irregularity induced by the pterygium advancement was evaluated by Fourier harmonic analyses of the topographic data that were modified for a series of analysis diameters from 1 mm to 6 mm. Incidences of steep increases in the asymmetry or higher-order irregularity components (inflection points) were determined by using segmented regression analysis for each analysis diameter. The pterygium advancement ranged from 2% to 57%, with a mean of 22.0%. Both components showed steep increases from the inflection points. The inflection points in the higher-order irregularity component altered with the analysis diameter (14.0%-30.6%), while there was no alternation in the asymmetry components (35.5%-36.8%). For the former component, the values at the inflection points were obtained in a range of 0.16 to 0.25 D. The Fourier harmonic analyses for a series of analysis diameters revealed that the higher-order irregularity component increased with the pterygium advancement. The analysis results confirmed the precedence of corneal irregularity due to pterygium advancement.

Independent Component Analysis-motivated Approach to Classificatory Decomposition of Cortical Evoked Potentials

PubMed Central

Smolinski, Tomasz G; Buchanan, Roger; Boratyn, Grzegorz M; Milanova, Mariofanna; Prinz, Astrid A

2006-01-01

Background Independent Component Analysis (ICA) proves to be useful in the analysis of neural activity, as it allows for identification of distinct sources of activity. Applied to measurements registered in a controlled setting and under exposure to an external stimulus, it can facilitate analysis of the impact of the stimulus on those sources. The link between the stimulus and a given source can be verified by a classifier that is able to "predict" the condition a given signal was registered under, solely based on the components. However, the ICA's assumption about statistical independence of sources is often unrealistic and turns out to be insufficient to build an accurate classifier. Therefore, we propose to utilize a novel method, based on hybridization of ICA, multi-objective evolutionary algorithms (MOEA), and rough sets (RS), that attempts to improve the effectiveness of signal decomposition techniques by providing them with "classification-awareness." Results The preliminary results described here are very promising and further investigation of other MOEAs and/or RS-based classification accuracy measures should be pursued. Even a quick visual analysis of those results can provide an interesting insight into the problem of neural activity analysis. Conclusion We present a methodology of classificatory decomposition of signals. One of the main advantages of our approach is the fact that rather than solely relying on often unrealistic assumptions about statistical independence of sources, components are generated in the light of a underlying classification problem itself. PMID:17118151

A Cost-Utility Model of Care for Peristomal Skin Complications

PubMed Central

Inglese, Gary; Manson, Andrea; Townshend, Arden

2016-01-01

PURPOSE: The aim of this study was to evaluate the economic and humanistic implications of using ostomy components to prevent subsequent peristomal skin complications (PSCs) in individuals who experience an initial, leakage-related PSC event. DESIGN: Cost-utility analysis. METHODS: We developed a simple decision model to consider, from a payer's perspective, PSCs managed with and without the use of ostomy components over 1 year. The model evaluated the extent to which outcomes associated with the use of ostomy components (PSC events avoided; quality-adjusted life days gained) offset the costs associated with their use. RESULTS: Our base case analysis of 1000 hypothetical individuals over 1 year assumes that using ostomy components following a first PSC reduces recurrent events versus PSC management without components. In this analysis, component acquisition costs were largely offset by lower resource use for ostomy supplies (barriers; pouches) and lower clinical utilization to manage PSCs. The overall annual average resource use for individuals using components was about 6.3% ($139) higher versus individuals not using components. Each PSC event avoided yielded, on average, 8 additional quality-adjusted life days over 1 year. CONCLUSIONS: In our analysis, (1) acquisition costs for ostomy components were offset in whole or in part by the use of fewer ostomy supplies to manage PSCs and (2) use of ostomy components to prevent PSCs produced better outcomes (fewer repeat PSC events; more health-related quality-adjusted life days) over 1 year compared to not using components. PMID:26633166

Storytelling Leadership: A Semiotics Theories Qualitative Inquiry into the Components Forming an Oral Story

ERIC Educational Resources Information Center

Cater, Earl F.

2015-01-01

Using semiotics theories as a guide, the qualitative examination of storytelling literature and current storytelling practitioners provides research support for a list of storytelling components. Analysis of story building components discovered from literature in comparison to the results from research questionnaire responses by current…

NEXT GENERATION ANALYSIS SOFTWARE FOR COMPONENT EVALUATION - Results of Rotational Seismometer Evaluation

NASA Astrophysics Data System (ADS)

Hart, D. M.; Merchant, B. J.; Abbott, R. E.

2012-12-01

The Component Evaluation project at Sandia National Laboratories supports the Ground-based Nuclear Explosion Monitoring program by performing testing and evaluation of the components that are used in seismic and infrasound monitoring systems. In order to perform this work, Component Evaluation maintains a testing facility called the FACT (Facility for Acceptance, Calibration, and Testing) site, a variety of test bed equipment, and a suite of software tools for analyzing test data. Recently, Component Evaluation has successfully integrated several improvements to its software analysis tools and test bed equipment that have substantially improved our ability to test and evaluate components. The software tool that is used to analyze test data is called TALENT: Test and AnaLysis EvaluatioN Tool. TALENT is designed to be a single, standard interface to all test configuration, metadata, parameters, waveforms, and results that are generated in the course of testing monitoring systems. It provides traceability by capturing everything about a test in a relational database that is required to reproduce the results of that test. TALENT provides a simple, yet powerful, user interface to quickly acquire, process, and analyze waveform test data. The software tool has also been expanded recently to handle sensors whose output is proportional to rotation angle, or rotation rate. As an example of this new processing capability, we show results from testing the new ATA ARS-16 rotational seismometer. The test data was collected at the USGS ASL. Four datasets were processed: 1) 1 Hz with increasing amplitude, 2) 4 Hz with increasing amplitude, 3) 16 Hz with increasing amplitude and 4) twenty-six discrete frequencies between 0.353 Hz to 64 Hz. The results are compared to manufacture-supplied data sheets.

Effect of noise in principal component analysis with an application to ozone pollution

NASA Astrophysics Data System (ADS)

Tsakiri, Katerina G.

This thesis analyzes the effect of independent noise in principal components of k normally distributed random variables defined by a covariance matrix. We prove that the principal components as well as the canonical variate pairs determined from joint distribution of original sample affected by noise can be essentially different in comparison with those determined from the original sample. However when the differences between the eigenvalues of the original covariance matrix are sufficiently large compared to the level of the noise, the effect of noise in principal components and canonical variate pairs proved to be negligible. The theoretical results are supported by simulation study and examples. Moreover, we compare our results about the eigenvalues and eigenvectors in the two dimensional case with other models examined before. This theory can be applied in any field for the decomposition of the components in multivariate analysis. One application is the detection and prediction of the main atmospheric factor of ozone concentrations on the example of Albany, New York. Using daily ozone, solar radiation, temperature, wind speed and precipitation data, we determine the main atmospheric factor for the explanation and prediction of ozone concentrations. A methodology is described for the decomposition of the time series of ozone and other atmospheric variables into the global term component which describes the long term trend and the seasonal variations, and the synoptic scale component which describes the short term variations. By using the Canonical Correlation Analysis, we show that solar radiation is the only main factor between the atmospheric variables considered here for the explanation and prediction of the global and synoptic scale component of ozone. The global term components are modeled by a linear regression model, while the synoptic scale components by a vector autoregressive model and the Kalman filter. The coefficient of determination, R2, for the prediction of the synoptic scale ozone component was found to be the highest when we consider the synoptic scale component of the time series for solar radiation and temperature. KEY WORDS: multivariate analysis; principal component; canonical variate pairs; eigenvalue; eigenvector; ozone; solar radiation; spectral decomposition; Kalman filter; time series prediction

[Principal component analysis and cluster analysis of inorganic elements in sea cucumber Apostichopus japonicus].

PubMed

Liu, Xiao-Fang; Xue, Chang-Hu; Wang, Yu-Ming; Li, Zhao-Jie; Xue, Yong; Xu, Jie

2011-11-01

The present study is to investigate the feasibility of multi-elements analysis in determination of the geographical origin of sea cucumber Apostichopus japonicus, and to make choice of the effective tracers in sea cucumber Apostichopus japonicus geographical origin assessment. The content of the elements such as Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Hg and Pb in sea cucumber Apostichopus japonicus samples from seven places of geographical origin were determined by means of ICP-MS. The results were used for the development of elements database. Cluster analysis(CA) and principal component analysis (PCA) were applied to differentiate the sea cucumber Apostichopus japonicus geographical origin. Three principal components which accounted for over 89% of the total variance were extracted from the standardized data. The results of Q-type cluster analysis showed that the 26 samples could be clustered reasonably into five groups, the classification results were significantly associated with the marine distribution of the sea cucumber Apostichopus japonicus samples. The CA and PCA were the effective methods for elements analysis of sea cucumber Apostichopus japonicus samples. The content of the mineral elements in sea cucumber Apostichopus japonicus samples was good chemical descriptors for differentiating their geographical origins.

[Evaluate drug interaction of multi-components in Morus alba leaves based on α-glucosidase inhibitory activity].

PubMed

Ji, Tao; Su, Shu-Lan; Guo, Sheng; Qian, Da-Wei; Ouyang, Zhen; Duan, Jin-Ao

2016-06-01

Column chromatography was used for enrichment and separation of flavonoids, alkaloids and polysaccharides from the extracts of Morus alba leaves; glucose oxidase method was used with sucrose as the substrate to evaluate the multi-components of M. alba leaves in α-glucosidase inhibitory models; isobole method, Chou-Talalay combination index analysis and isobolographic analysis were used to evaluate the interaction effects and dose-effect characteristics of two components, providing scientific basis for revealing the hpyerglycemic mechanism of M. alba leaves. The components analysis showed that flavonoid content was 5.3%; organic phenolic acids content was 10.8%; DNJ content was 39.4%; and polysaccharide content was 18.9%. Activity evaluation results demonstrated that flavonoids, alkaloids and polysaccharides of M. alba leaves had significant inhibitory effects on α-glucosidase, and the inhibitory rate was increased with the increasing concentration. Alkaloids showed most significant inhibitory effects among these three components. Both compatibility of alkaloids and flavonoids, and the compatibility of alkaloids and polysaccharides demonstrated synergistic effects, but the compatibility of flavonoids and polysaccharides showed no obvious synergistic effects. The results have confirmed the interaction of multi-components from M. alba leaves to regulate blood sugar, and provided scientific basis for revealing hpyerglycemic effectiveness and mechanism of the multi-components from M. alba leaves. Copyright© by the Chinese Pharmaceutical Association.

Conceptual model of iCAL4LA: Proposing the components using comparative analysis

NASA Astrophysics Data System (ADS)

Ahmad, Siti Zulaiha; Mutalib, Ariffin Abdul

2016-08-01

This paper discusses an on-going study that initiates an initial process in determining the common components for a conceptual model of interactive computer-assisted learning that is specifically designed for low achieving children. This group of children needs a specific learning support that can be used as an alternative learning material in their learning environment. In order to develop the conceptual model, this study extracts the common components from 15 strongly justified computer assisted learning studies. A comparative analysis has been conducted to determine the most appropriate components by using a set of specific indication classification to prioritize the applicability. The results of the extraction process reveal 17 common components for consideration. Later, based on scientific justifications, 16 of them were selected as the proposed components for the model.

The Importance of Engine External's Health

NASA Technical Reports Server (NTRS)

Stoner, Barry L.

2006-01-01

Engine external components include all the fluid carrying, electron carrying, and support devices that are needed to operate the propulsion system. These components are varied and include: pumps, valves, actuators, solenoids, sensors, switches, heat exchangers, electrical generators, electrical harnesses, tubes, ducts, clamps and brackets. The failure of any component to perform its intended function will result in a maintenance action, a dispatch delay, or an engine in flight shutdown. The life of each component, in addition to its basic functional design, is closely tied to its thermal and dynamic environment .Therefore, to reach a mature design life, the component's thermal and dynamic environment must be understood and controlled, which can only be accomplished by attention to design analysis and testing. The purpose of this paper is to review analysis and test techniques toward achieving good component health.

Impact of Measurement Uncertainties on Receptor Modeling of Speciated Atmospheric Mercury.

PubMed

Cheng, I; Zhang, L; Xu, X

2016-02-09

Gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurement uncertainties could potentially affect the analysis and modeling of atmospheric mercury. This study investigated the impact of GOM measurement uncertainties on Principal Components Analysis (PCA), Absolute Principal Component Scores (APCS), and Concentration-Weighted Trajectory (CWT) receptor modeling results. The atmospheric mercury data input into these receptor models were modified by combining GOM and PBM into a single reactive mercury (RM) parameter and excluding low GOM measurements to improve the data quality. PCA and APCS results derived from RM or excluding low GOM measurements were similar to those in previous studies, except for a non-unique component and an additional component extracted from the RM dataset. The percent variance explained by the major components from a previous study differed slightly compared to RM and excluding low GOM measurements. CWT results were more sensitive to the input of RM than GOM excluding low measurements. Larger discrepancies were found between RM and GOM source regions than those between RM and PBM. Depending on the season, CWT source regions of RM differed by 40-61% compared to GOM from a previous study. No improvement in correlations between CWT results and anthropogenic mercury emissions were found.

Impact of Measurement Uncertainties on Receptor Modeling of Speciated Atmospheric Mercury

PubMed Central

Cheng, I.; Zhang, L.; Xu, X.

2016-01-01

Gaseous oxidized mercury (GOM) and particle-bound mercury (PBM) measurement uncertainties could potentially affect the analysis and modeling of atmospheric mercury. This study investigated the impact of GOM measurement uncertainties on Principal Components Analysis (PCA), Absolute Principal Component Scores (APCS), and Concentration-Weighted Trajectory (CWT) receptor modeling results. The atmospheric mercury data input into these receptor models were modified by combining GOM and PBM into a single reactive mercury (RM) parameter and excluding low GOM measurements to improve the data quality. PCA and APCS results derived from RM or excluding low GOM measurements were similar to those in previous studies, except for a non-unique component and an additional component extracted from the RM dataset. The percent variance explained by the major components from a previous study differed slightly compared to RM and excluding low GOM measurements. CWT results were more sensitive to the input of RM than GOM excluding low measurements. Larger discrepancies were found between RM and GOM source regions than those between RM and PBM. Depending on the season, CWT source regions of RM differed by 40–61% compared to GOM from a previous study. No improvement in correlations between CWT results and anthropogenic mercury emissions were found. PMID:26857835

Analysis and Evaluation of the Characteristic Taste Components in Portobello Mushroom.

PubMed

Wang, Jinbin; Li, Wen; Li, Zhengpeng; Wu, Wenhui; Tang, Xueming

2018-05-10

To identify the characteristic taste components of the common cultivated mushroom (brown; Portobello), Agaricus bisporus, taste components in the stipe and pileus of Portobello mushroom harvested at different growth stages were extracted and identified, and principal component analysis (PCA) and taste active value (TAV) were used to reveal the characteristic taste components during the each of the growth stages of Portobello mushroom. In the stipe and pileus, 20 and 14 different principal taste components were identified, respectively, and they were considered as the principal taste components of Portobello mushroom fruit bodies, which included most amino acids and 5'-nucleotides. Some taste components that were found at high levels, such as lactic acid and citric acid, were not detected as Portobello mushroom principal taste components through PCA. However, due to their high content, Portobello mushroom could be used as a source of organic acids. The PCA and TAV results revealed that 5'-GMP, glutamic acid, malic acid, alanine, proline, leucine, and aspartic acid were the characteristic taste components of Portobello mushroom fruit bodies. Portobello mushroom was also found to be rich in protein and amino acids, so it might also be useful in the formulation of nutraceuticals and functional food. The results in this article could provide a theoretical basis for understanding and regulating the characteristic flavor components synthesis process of Portobello mushroom. © 2018 Institute of Food Technologists®.

Dynamic analysis for shuttle design verification

NASA Technical Reports Server (NTRS)

Fralich, R. W.; Green, C. E.; Rheinfurth, M. H.

1972-01-01

Two approaches that are used for determining the modes and frequencies of space shuttle structures are discussed. The first method, direct numerical analysis, involves finite element mathematical modeling of the space shuttle structure in order to use computer programs for dynamic structural analysis. The second method utilizes modal-coupling techniques of experimental verification made by vibrating only spacecraft components and by deducing modes and frequencies of the complete vehicle from results obtained in the component tests.

Radiation hardening of components and systems for nuclear rocket vehicle applications

NASA Technical Reports Server (NTRS)

Greenhow, W. A.; Cheever, P. R.

1972-01-01

The results of the analysis of the S-2 and S-4B components, although incomplete, indicate that many Saturn 5 components and subsystems, e.g., pumps, valves, etc., can be radiation hardened to meet NRV requirements by material substitution and minor design modifications. Results of these analyses include (1) recommended radiation tolerance limits for over 100 material applications; (2) design data which describes the components of each system; (3) presentation of radiation hardening examples of systems; and (4) designing radiation effects tests to supply data for selecting materials.

Temperament and problem solving in a population of adolescent guide dogs.

PubMed

Bray, Emily E; Sammel, Mary D; Seyfarth, Robert M; Serpell, James A; Cheney, Dorothy L

2017-09-01

It is often assumed that measures of temperament within individuals are more correlated to one another than to measures of problem solving. However, the exact relationship between temperament and problem-solving tasks remains unclear because large-scale studies have typically focused on each independently. To explore this relationship, we tested 119 prospective adolescent guide dogs on a battery of 11 temperament and problem-solving tasks. We then summarized the data using both confirmatory factor analysis and exploratory principal components analysis. Results of confirmatory analysis revealed that a priori separation of tests as measuring either temperament or problem solving led to weak results, poor model fit, some construct validity, and no predictive validity. In contrast, results of exploratory analysis were best summarized by principal components that mixed temperament and problem-solving traits. These components had both construct and predictive validity (i.e., association with success in the guide dog training program). We conclude that there is complex interplay between tasks of "temperament" and "problem solving" and that the study of both together will be more informative than approaches that consider either in isolation.

Laser-induced breakdown spectroscopy is a reliable method for urinary stone analysis

PubMed Central

Mutlu, Nazım; Çiftçi, Seyfettin; Gülecen, Turgay; Öztoprak, Belgin Genç; Demir, Arif

2016-01-01

Objective We compared laser-induced breakdown spectroscopy (LIBS) with the traditionally used and recommended X-ray diffraction technique (XRD) for urinary stone analysis. Material and methods In total, 65 patients with urinary calculi were enrolled in this prospective study. Stones were obtained after surgical or extracorporeal shockwave lithotripsy procedures. All stones were divided into two equal pieces. One sample was analyzed by XRD and the other by LIBS. The results were compared by the kappa (κ) and Spearman’s correlation coefficient (rho) tests. Results Using LIBS, 95 components were identified from 65 stones, while XRD identified 88 components. LIBS identified 40 stones with a single pure component, 20 stones with two different components, and 5 stones with three components. XRD demonstrated 42 stones with a single component, 22 stones with two different components, and only 1 stone with three different components. There was a strong relationship in the detection of stone types between LIBS and XRD for stones components (Spearman rho, 0.866; p<0.001). There was excellent agreement between the two techniques among 38 patients with pure stones (κ index, 0.910; Spearman rho, 0.916; p<0.001). Conclusion Our study indicates that LIBS is a valid and reliable technique for determining urinary stone composition. Moreover, it is a simple, low-cost, and nondestructive technique. LIBS can be safely used in routine daily practice if our results are supported by studies with larger numbers of patients. PMID:27011877

Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

NASA Astrophysics Data System (ADS)

Wojciechowski, Adam

2017-04-01

In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity were distinguished: hypsometric component (PC1), deciduous forest habitats component (PC2), river valleys and alder habitats component (PC3), and lakes component (PC4). The distinguished factors characterise natural qualities of postglacial area and reflect well the role of the four most important groups of environment components in shaping ecodiversity of the area under study. The map of ecodiversity of Debnica Kaszubska commune was created on the basis of the first four principal component scores and then five classes of diversity were isolated: very low, low, average, high and very high. As a result of the assessment, five commune regions of very high ecodiversity were separated. These regions are also very attractive for tourists and valuable in terms of their rich nature which include protected areas such as Slupia Valley Landscape Park. The suggested method of ecodiversity assessment with the use of principal component analysis may constitute an alternative methodological proposition to other research methods used so far. Literature Jedicke E., 2001. Biodiversität, Geodiversität, Ökodiversität. Kriterien zur Analyse der Landschaftsstruktur - ein konzeptioneller Diskussionsbeitrag. Naturschutz und Landschaftsplanung, 33(2/3), 59-68.

Building Block Approach' for Structural Analysis of Thermoplastic Composite Components for Automotive Applications

NASA Astrophysics Data System (ADS)

Carello, M.; Amirth, N.; Airale, A. G.; Monti, M.; Romeo, A.

2017-12-01

Advanced thermoplastic prepreg composite materials stand out with regard to their ability to allow complex designs with high specific strength and stiffness. This makes them an excellent choice for lightweight automotive components to reduce mass and increase fuel efficiency, while maintaining the functionality of traditional thermosetting prepreg (and mechanical characteristics) and with a production cycle time and recyclability suited to mass production manufacturing. Currently, the aerospace and automotive sectors struggle to carry out accurate Finite Elements (FE) component analyses and in some cases are unable to validate the obtained results. In this study, structural Finite Elements Analysis (FEA) has been done on a thermoplastic fiber reinforced component designed and manufactured through an integrated injection molding process, which consists in thermoforming the prepreg laminate and overmolding the other parts. This process is usually referred to as hybrid molding, and has the provision to reinforce the zones subjected to additional stresses with thermoformed themoplastic prepreg as required and overmolded with a shortfiber thermoplastic resin in single process. This paper aims to establish an accurate predictive model on a rational basis and an innovative methodology for the structural analysis of thermoplastic composite components by comparison with the experimental tests results.

3-D inelastic analysis methods for hot section components. Volume 2: Advanced special functions models

NASA Technical Reports Server (NTRS)

Wilson, R. B.; Banerjee, P. K.

1987-01-01

This Annual Status Report presents the results of work performed during the third year of the 3-D Inelastic Analysis Methods for Hot Sections Components program (NASA Contract NAS3-23697). The objective of the program is to produce a series of computer codes that permit more accurate and efficient three-dimensional analyses of selected hot section components, i.e., combustor liners, turbine blades, and turbine vanes. The computer codes embody a progression of mathematical models and are streamlined to take advantage of geometrical features, loading conditions, and forms of material response that distinguish each group of selected components.

Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits.

PubMed

Qiu, Shanshan; Wang, Jun

2015-10-01

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile. © 2015 Institute of Food Technologists®

Identification and apportionment of hazardous elements in the sediments in the Yangtze River estuary.

PubMed

Wang, Jiawei; Liu, Ruimin; Wang, Haotian; Yu, Wenwen; Xu, Fei; Shen, Zhenyao

2015-12-01

In this study, positive matrix factorization (PMF) and principal components analysis (PCA) were combined to identify and apportion pollution-based sources of hazardous elements in the surface sediments in the Yangtze River estuary (YRE). Source identification analysis indicated that PC1, including Al, Fe, Mn, Cr, Ni, As, Cu, and Zn, can be defined as a sewage component; PC2, including Pb and Sb, can be considered as an atmospheric deposition component; and PC3, containing Cd and Hg, can be considered as an agricultural nonpoint component. To better identify the sources and quantitatively apportion the concentrations to their sources, eight sources were identified with PMF: agricultural/industrial sewage mixed (18.6 %), mining wastewater (15.9 %), agricultural fertilizer (14.5 %), atmospheric deposition (12.8 %), agricultural nonpoint (10.6 %), industrial wastewater (9.8 %), marine activity (9.0 %), and nickel plating industry (8.8 %). Overall, the hazardous element content seems to be more connected to anthropogenic activity instead of natural sources. The PCA results laid the foundation for the PMF analysis by providing a general classification of sources. PMF resolves more factors with a higher explained variance than PCA; PMF provided both the internal analysis and the quantitative analysis. The combination of the two methods can provide more reasonable and reliable results.

Specialized data analysis of SSME and advanced propulsion system vibration measurements

NASA Technical Reports Server (NTRS)

Coffin, Thomas; Swanson, Wayne L.; Jong, Yen-Yi

1993-01-01

The basic objectives of this contract were to perform detailed analysis and evaluation of dynamic data obtained during Space Shuttle Main Engine (SSME) test and flight operations, including analytical/statistical assessment of component dynamic performance, and to continue the development and implementation of analytical/statistical models to effectively define nominal component dynamic characteristics, detect anomalous behavior, and assess machinery operational conditions. This study was to provide timely assessment of engine component operational status, identify probable causes of malfunction, and define feasible engineering solutions. The work was performed under three broad tasks: (1) Analysis, Evaluation, and Documentation of SSME Dynamic Test Results; (2) Data Base and Analytical Model Development and Application; and (3) Development and Application of Vibration Signature Analysis Techniques.

FIBER AND INTEGRATED OPTICS, LASER APPLICATIONS, AND OTHER PROBLEMS IN QUANTUM ELECTRONICS: Optical components for the analysis and formation of the transverse mode composition

NASA Astrophysics Data System (ADS)

Golub, M. A.; Sisakyan, I. N.; Soĭfer, V. A.; Uvarov, G. V.

1989-04-01

Theoretical and experimental investigations are reported of new mode optical components (elements) which are analogs of sinusoidal phase diffraction gratings with a variable modulation depth. Expressions are derived for nonlinear predistortion and depth of modulation, which are essential for effective operation of amplitude and phase mode optical components in devices used for analysis and formation of the transverse mode composition of coherent radiation. An estimate is obtained of the energy efficiency of phase and amplitude mode optical components, and a comparison is made with the results of an experimental investigation of a set of phase optical components matched to Gauss-Laguerre modes. It is shown that the improvement in the energy efficiency of phase mode components, compared with amplitude components, is the same as the improvement achieved using a phase diifraction grating, compared with amplitude grating with the same depth of modulation.

Simultaneous quantitative analysis of main components in linderae reflexae radix with one single marker.

PubMed

Wang, Li-Li; Zhang, Yun-Bin; Sun, Xiao-Ya; Chen, Sui-Qing

2016-05-08

Establish a quantitative analysis of multi-components by the single marker (QAMS) method for quality evaluation and validate its feasibilities by the simultaneous quantitative assay of four main components in Linderae Reflexae Radix. Four main components of pinostrobin, pinosylvin, pinocembrin, and 3,5-dihydroxy-2-(1- p -mentheneyl)- trans -stilbene were selected as analytes to evaluate the quality by RP-HPLC coupled with a UV-detector. The method was evaluated by a comparison of the quantitative results between the external standard method and QAMS with a different HPLC system. The results showed that no significant differences were found in the quantitative results of the four contents of Linderae Reflexae Radix determined by the external standard method and QAMS (RSD <3%). The contents of four analytes (pinosylvin, pinocembrin, pinostrobin, and Reflexanbene I) in Linderae Reflexae Radix were determined by the single marker of pinosylvin. This fingerprint was the spectra determined by Shimadzu LC-20AT and Waters e2695 HPLC that were equipped with three different columns.

Enantiomer-specific analysis of multi-component mixtures by correlated electron imaging-ion mass spectrometry

NASA Astrophysics Data System (ADS)

Fanood, Mohammad M. Rafiee; Ram, N. Bhargava; Lehmann, C. Stefan; Powis, Ivan; Janssen, Maurice H. M.

2015-06-01

Simultaneous, enantiomer-specific identification of chiral molecules in multi-component mixtures is extremely challenging. Many established techniques for single-component analysis fail to provide selectivity in multi-component mixtures and lack sensitivity for dilute samples. Here we show how enantiomers may be differentiated by mass-selected photoelectron circular dichroism using an electron-ion coincidence imaging spectrometer. As proof of concept, vapours containing ~1% of two chiral monoterpene molecules, limonene and camphor, are irradiated by a circularly polarized femtosecond laser, resulting in multiphoton near-threshold ionization with little molecular fragmentation. Large chiral asymmetries (2-4%) are observed in the mass-tagged photoelectron angular distributions. These asymmetries switch sign according to the handedness (R- or S-) of the enantiomer in the mixture and scale with enantiomeric excess of a component. The results demonstrate that mass spectrometric identification of mixtures of chiral molecules and quantitative determination of enantiomeric excess can be achieved in a table-top instrument.

Enantiomer-specific analysis of multi-component mixtures by correlated electron imaging–ion mass spectrometry

PubMed Central

Fanood, Mohammad M Rafiee; Ram, N. Bhargava; Lehmann, C. Stefan; Powis, Ivan; Janssen, Maurice H. M.

2015-01-01

Simultaneous, enantiomer-specific identification of chiral molecules in multi-component mixtures is extremely challenging. Many established techniques for single-component analysis fail to provide selectivity in multi-component mixtures and lack sensitivity for dilute samples. Here we show how enantiomers may be differentiated by mass-selected photoelectron circular dichroism using an electron–ion coincidence imaging spectrometer. As proof of concept, vapours containing ∼1% of two chiral monoterpene molecules, limonene and camphor, are irradiated by a circularly polarized femtosecond laser, resulting in multiphoton near-threshold ionization with little molecular fragmentation. Large chiral asymmetries (2–4%) are observed in the mass-tagged photoelectron angular distributions. These asymmetries switch sign according to the handedness (R- or S-) of the enantiomer in the mixture and scale with enantiomeric excess of a component. The results demonstrate that mass spectrometric identification of mixtures of chiral molecules and quantitative determination of enantiomeric excess can be achieved in a table-top instrument. PMID:26104140

Reliability Evaluation of Machine Center Components Based on Cascading Failure Analysis

NASA Astrophysics Data System (ADS)

Zhang, Ying-Zhi; Liu, Jin-Tong; Shen, Gui-Xiang; Long, Zhe; Sun, Shu-Guang

2017-07-01

In order to rectify the problems that the component reliability model exhibits deviation, and the evaluation result is low due to the overlook of failure propagation in traditional reliability evaluation of machine center components, a new reliability evaluation method based on cascading failure analysis and the failure influenced degree assessment is proposed. A direct graph model of cascading failure among components is established according to cascading failure mechanism analysis and graph theory. The failure influenced degrees of the system components are assessed by the adjacency matrix and its transposition, combined with the Pagerank algorithm. Based on the comprehensive failure probability function and total probability formula, the inherent failure probability function is determined to realize the reliability evaluation of the system components. Finally, the method is applied to a machine center, it shows the following: 1) The reliability evaluation values of the proposed method are at least 2.5% higher than those of the traditional method; 2) The difference between the comprehensive and inherent reliability of the system component presents a positive correlation with the failure influenced degree of the system component, which provides a theoretical basis for reliability allocation of machine center system.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

PubMed Central

2011-01-01

Background Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. Results We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA. Conclusions GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches. PMID:21914205

A symmetrical subtraction combined with interpolated values for eliminating scattering from fluorescence EEM data

NASA Astrophysics Data System (ADS)

Xu, Jing; Liu, Xiaofei; Wang, Yutian

2016-08-01

Parallel factor analysis is a widely used method to extract qualitative and quantitative information of the analyte of interest from fluorescence emission-excitation matrix containing unknown components. Big amplitude of scattering will influence the results of parallel factor analysis. Many methods of eliminating scattering have been proposed. Each of these methods has its advantages and disadvantages. The combination of symmetrical subtraction and interpolated values has been discussed. The combination refers to both the combination of results and the combination of methods. Nine methods were used for comparison. The results show the combination of results can make a better concentration prediction for all the components.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography

NASA Astrophysics Data System (ADS)

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

A hybrid symplectic principal component analysis and central tendency measure method for detection of determinism in noisy time series with application to mechanomyography.

PubMed

Xie, Hong-Bo; Dokos, Socrates

2013-06-01

We present a hybrid symplectic geometry and central tendency measure (CTM) method for detection of determinism in noisy time series. CTM is effective for detecting determinism in short time series and has been applied in many areas of nonlinear analysis. However, its performance significantly degrades in the presence of strong noise. In order to circumvent this difficulty, we propose to use symplectic principal component analysis (SPCA), a new chaotic signal de-noising method, as the first step to recover the system dynamics. CTM is then applied to determine whether the time series arises from a stochastic process or has a deterministic component. Results from numerical experiments, ranging from six benchmark deterministic models to 1/f noise, suggest that the hybrid method can significantly improve detection of determinism in noisy time series by about 20 dB when the data are contaminated by Gaussian noise. Furthermore, we apply our algorithm to study the mechanomyographic (MMG) signals arising from contraction of human skeletal muscle. Results obtained from the hybrid symplectic principal component analysis and central tendency measure demonstrate that the skeletal muscle motor unit dynamics can indeed be deterministic, in agreement with previous studies. However, the conventional CTM method was not able to definitely detect the underlying deterministic dynamics. This result on MMG signal analysis is helpful in understanding neuromuscular control mechanisms and developing MMG-based engineering control applications.

Temporal Processing of Dynamic Positron Emission Tomography via Principal Component Analysis in the Sinogram Domain

NASA Astrophysics Data System (ADS)

Chen, Zhe; Parker, B. J.; Feng, D. D.; Fulton, R.

2004-10-01

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

PubMed

Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y

1992-01-01

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Computing Reliabilities Of Ceramic Components Subject To Fracture

NASA Technical Reports Server (NTRS)

Nemeth, N. N.; Gyekenyesi, J. P.; Manderscheid, J. M.

1992-01-01

CARES calculates fast-fracture reliability or failure probability of macroscopically isotropic ceramic components. Program uses results from commercial structural-analysis program (MSC/NASTRAN or ANSYS) to evaluate reliability of component in presence of inherent surface- and/or volume-type flaws. Computes measure of reliability by use of finite-element mathematical model applicable to multiple materials in sense model made function of statistical characterizations of many ceramic materials. Reliability analysis uses element stress, temperature, area, and volume outputs, obtained from two-dimensional shell and three-dimensional solid isoparametric or axisymmetric finite elements. Written in FORTRAN 77.

Mental and physical health-related quality of life in obese patients before and after bariatric surgery: a meta-analysis.

PubMed

Magallares, Alejandro; Schomerus, Georg

2015-01-01

In this meta-analysis, we review studies that compare mental and physical health-related quality of life measured with the Short-Form 36 of obese patients before and after bariatric surgery with a follow-up measure until one year. Twenty-one studies were selected to conduct the meta-analysis about the relationship between quality of life in obesity before (2680 subjects) and after (2251 subjects) bariatric surgery. Results reveal that obese patients scored less in the mental health component of the Short-Form 36 before bariatric surgery than after (d = -9.00). The same pattern could be observed in the case of the physical health component of the Short-Form 36 (d = -22.84). The results show the strong improvement that obese patients experience in both mental and physical components of the Short-Form 36 after receiving bariatric surgery.

Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.

A New Approach for Quantitative Evaluation of Ultrasonic Wave Attenuation in Composites

NASA Astrophysics Data System (ADS)

Ni, Qing-Qing; Li, Ran; Xia, Hong

2017-02-01

When ultrasonic waves propagate in composite materials, the propagation behaviors result from the combination effects of various factors, such as material anisotropy and viscoelastic property, internal microstructure and defects, incident wave characteristics and interface condition between composite components. It is essential to make it clear how these factors affect the ultrasonic wave propagation and attenuation characteristics, and how they mutually interact on each other. In the present paper, based on a newly developed time-domain finite element analysis code, PZflex, a unique approach for clarifying the detailed influence mechanism of aforementioned factors is proposed, in which each attenuation component can be extracted from the overall attenuation and analyzed respectively. By taking into consideration the interrelation between each individual attenuation component, the variation behaviors of each component and internal dynamic stress distribution against material anisotropy and matrix viscosity are separately and quantitatively evaluated. From the detailed analysis results of each attenuation component, the energy dissipation at interface is a major component in ultrasonic wave attenuation characteristics, which can provide a maximum contribution rate of 68.2 % to the overall attenuation, and each attenuation component is closely related to the material anisotropy and viscoelasticity. The results clarify the correlation between ultrasonic wave propagation characteristics and material viscoelastic properties, which will be useful in the further development of ultrasonic technology in defect detection.

Revealing the ultrafast outflow in IRAS 13224-3809 through spectral variability

NASA Astrophysics Data System (ADS)

Parker, M. L.; Alston, W. N.; Buisson, D. J. K.; Fabian, A. C.; Jiang, J.; Kara, E.; Lohfink, A.; Pinto, C.; Reynolds, C. S.

2017-08-01

We present an analysis of the long-term X-ray variability of the extreme narrow-line Seyfert 1 galaxy IRAS 13224-3809 using principal component analysis (PCA) and fractional excess variability (Fvar) spectra to identify model-independent spectral components. We identify a series of variability peaks in both the first PCA component and Fvar spectrum which correspond to the strongest predicted absorption lines from the ultrafast outflow (UFO) discovered by Parker et al. (2017). We also find higher order PCA components, which correspond to variability of the soft excess and reflection features. The subtle differences between RMS and PCA results argue that the observed flux-dependence of the absorption is due to increased ionization of the gas, rather than changes in column density or covering fraction. This result demonstrates that we can detect outflows from variability alone and that variability studies of UFOs are an extremely promising avenue for future research.

A Fully Automated and Robust Method to Incorporate Stamping Data in Crash, NVH and Durability Analysis

NASA Astrophysics Data System (ADS)

Palaniswamy, Hariharasudhan; Kanthadai, Narayan; Roy, Subir; Beauchesne, Erwan

2011-08-01

Crash, NVH (Noise, Vibration, Harshness), and durability analysis are commonly deployed in structural CAE analysis for mechanical design of components especially in the automotive industry. Components manufactured by stamping constitute a major portion of the automotive structure. In CAE analysis they are modeled at a nominal state with uniform thickness and no residual stresses and strains. However, in reality the stamped components have non-uniformly distributed thickness and residual stresses and strains resulting from stamping. It is essential to consider the stamping information in CAE analysis to accurately model the behavior of the sheet metal structures under different loading conditions. Especially with the current emphasis on weight reduction by replacing conventional steels with aluminum and advanced high strength steels it is imperative to avoid over design. Considering this growing need in industry, a highly automated and robust method has been integrated within Altair Hyperworks® to initialize sheet metal components in CAE models with stamping data. This paper demonstrates this new feature and the influence of stamping data for a full car frontal crash analysis.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Data analysis using a combination of independent component analysis and empirical mode decomposition

NASA Astrophysics Data System (ADS)

Lin, Shih-Lin; Tung, Pi-Cheng; Huang, Norden E.

2009-06-01

A combination of independent component analysis and empirical mode decomposition (ICA-EMD) is proposed in this paper to analyze low signal-to-noise ratio data. The advantages of ICA-EMD combination are these: ICA needs few sensory clues to separate the original source from unwanted noise and EMD can effectively separate the data into its constituting parts. The case studies reported here involve original sources contaminated by white Gaussian noise. The simulation results show that the ICA-EMD combination is an effective data analysis tool.

Assessing agro-environmental performance of dairy farms in northwest Italy based on aggregated results from indicators.

PubMed

Gaudino, Stefano; Goia, Irene; Grignani, Carlo; Monaco, Stefano; Sacco, Dario

2014-07-01

Dairy farms control an important share of the agricultural area of Northern Italy. Zero grazing, large maize-cropped areas, high stocking densities, and high milk production make them intensive and prone to impact the environment. Currently, few published studies have proposed indicator sets able to describe the entire dairy farm system and their internal components. This work had four aims: i) to propose a list of agro-environmental indicators to assess dairy farms; ii) to understand which indicators classify farms best; iii) to evaluate the dairy farms based on the proposed indicator list; iv) to link farmer decisions to the consequent environmental pressures. Forty agro-environmental indicators selected for this study are described. Northern Italy dairy systems were analysed considering both farmer decision indicators (farm management) and the resulting pressure indicators that demonstrate environmental stress on the entire farming system, and its components: cropping system, livestock system, and milk production. The correlations among single indicators identified redundant indicators. Principal Components Analysis distinguished which indicators provided meaningful information about each pressure indicator group. Analysis of the communalities and the correlations among indicators identified those that best represented farm variability: Farm Gate N Balance, Greenhouse Gas Emission, and Net Energy of the farm system; Net Energy and Gross P Balance of the cropping system component; Energy Use Efficiency and Purchased Feed N Input of the livestock system component; N Eco-Efficiency of the milk production component. Farm evaluation, based on the complete list of selected indicators demonstrated organic farming resulted in uniformly high values, while farms with low milk-producing herds resulted in uniformly low values. Yet on other farms, the environmental quality varied greatly when different groups of pressure indicators were considered, which highlighted the importance of expanding environmental analysis to effects within the farm. Statistical analysis demonstrated positive correlations between all farmer decision and pressure group indicators. Consumption of mineral fertiliser and pesticide negatively influenced the cropping system. Furthermore, stocking rate was found to correlate positively with the milk production component and negatively with the farm system. This study provides baseline references for ex ante policy evaluation, and monitoring tools for analysis both in itinere and ex post environment policy implementation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

NASA Astrophysics Data System (ADS)

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-04-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

Drug target identification using network analysis: Taking active components in Sini decoction as an example

PubMed Central

Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

2016-01-01

Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound. PMID:27095146

Digital Correction of Motion Artifacts in Microscopy Image Sequences Collected from Living Animals Using Rigid and Non-Rigid Registration

PubMed Central

Lorenz, Kevin S.; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.

2013-01-01

Digital image analysis is a fundamental component of quantitative microscopy. However, intravital microscopy presents many challenges for digital image analysis. In general, microscopy volumes are inherently anisotropic, suffer from decreasing contrast with tissue depth, lack object edge detail, and characteristically have low signal levels. Intravital microscopy introduces the additional problem of motion artifacts, resulting from respiratory motion and heartbeat from specimens imaged in vivo. This paper describes an image registration technique for use with sequences of intravital microscopy images collected in time-series or in 3D volumes. Our registration method involves both rigid and non-rigid components. The rigid registration component corrects global image translations, while the non-rigid component manipulates a uniform grid of control points defined by B-splines. Each control point is optimized by minimizing a cost function consisting of two parts: a term to define image similarity, and a term to ensure deformation grid smoothness. Experimental results indicate that this approach is promising based on the analysis of several image volumes collected from the kidney, lung, and salivary gland of living rodents. PMID:22092443

A feasibility study on age-related factors of wrist pulse using principal component analysis.

PubMed

Jang-Han Bae; Young Ju Jeon; Sanghun Lee; Jaeuk U Kim

2016-08-01

Various analysis methods for examining wrist pulse characteristics are needed for accurate pulse diagnosis. In this feasibility study, principal component analysis (PCA) was performed to observe age-related factors of wrist pulse from various analysis parameters. Forty subjects in the age group of 20s and 40s were participated, and their wrist pulse signal and respiration signal were acquired with the pulse tonometric device. After pre-processing of the signals, twenty analysis parameters which have been regarded as values reflecting pulse characteristics were calculated and PCA was performed. As a results, we could reduce complex parameters to lower dimension and age-related factors of wrist pulse were observed by combining-new analysis parameter derived from PCA. These results demonstrate that PCA can be useful tool for analyzing wrist pulse signal.

EMD-WVD time-frequency distribution for analysis of multi-component signals

NASA Astrophysics Data System (ADS)

Chai, Yunzi; Zhang, Xudong

2016-10-01

Time-frequency distribution (TFD) is two-dimensional function that indicates the time-varying frequency content of one-dimensional signals. And The Wigner-Ville distribution (WVD) is an important and effective time-frequency analysis method. The WVD can efficiently show the characteristic of a mono-component signal. However, a major drawback is the extra cross-terms when multi-component signals are analyzed by WVD. In order to eliminating the cross-terms, we decompose signals into single frequency components - Intrinsic Mode Function (IMF) - by using the Empirical Mode decomposition (EMD) first, then use WVD to analyze each single IMF. In this paper, we define this new time-frequency distribution as EMD-WVD. And the experiment results show that the proposed time-frequency method can solve the cross-terms problem effectively and improve the accuracy of WVD time-frequency analysis.

On 3-D inelastic analysis methods for hot section components. Volume 1: Special finite element models

NASA Technical Reports Server (NTRS)

Nakazawa, S.

1987-01-01

This Annual Status Report presents the results of work performed during the third year of the 3-D Inelastic Analysis Methods for Hot Section Components program (NASA Contract NAS3-23697). The objective of the program is to produce a series of new computer codes that permit more accurate and efficient three-dimensional analysis of selected hot section components, i.e., combustor liners, turbine blades, and turbine vanes. The computer codes embody a progression of mathematical models and are streamlined to take advantage of geometrical features, loading conditions, and forms of material response that distinguish each group of selected components. This report is presented in two volumes. Volume 1 describes effort performed under Task 4B, Special Finite Element Special Function Models, while Volume 2 concentrates on Task 4C, Advanced Special Functions Models.

Insights into the interaction between carbamazepine and natural dissolved organic matter in the Yangtze Estuary using fluorescence excitation-emission matrix spectra coupled with parallel factor analysis.

PubMed

Wang, Ying; Zhang, Manman; Fu, Jun; Li, Tingting; Wang, Jinggang; Fu, Yingyu

2016-10-01

The interaction between carbamazepine (CBZ) and dissolved organic matter (DOM) from three zones (the nearshore, the river channel, and the coastal areas) in the Yangtze Estuary was investigated using fluorescence quenching titration combined with excitation emission matrix spectra and parallel factor analysis (PARAFAC). The complexation between CBZ and DOM was demonstrated by the increase in hydrogen bonding and the disappearance of the C=O stretch obtained from the Fourier transform infrared spectroscopy analysis. The results indicated that two protein-like substances (component 2 and component3) and two humic-like substances (component 1 and 4) were identified in the DOM from the Yangtze Estuary. The fluorescence quenching curves of each component with the addition of CBZ and the Ryan and Weber model calculation results both demonstrated that the different components exhibited different complexation activities with CBZ. The protein-like components had a stronger affinity with CBZ than did the humic-like substances. On the other hand, the autochthonous tyrosine-like C2 played an important role in the complexation with DOM from the river channel and coastal areas, while C3 influenced by anthropogenic activities showed an obvious effect in the nearshore area. DOMs from the river channel have the highest binding capacity for CBZ, which may ascribe to the relatively high phenol content group in the DOM.

Laboratory spectroscopy of meteorite samples at UV-vis-NIR wavelengths: Analysis and discrimination by principal components analysis

NASA Astrophysics Data System (ADS)

Penttilä, Antti; Martikainen, Julia; Gritsevich, Maria; Muinonen, Karri

2018-02-01

Meteorite samples are measured with the University of Helsinki integrating-sphere UV-vis-NIR spectrometer. The resulting spectra of 30 meteorites are compared with selected spectra from the NASA Planetary Data System meteorite spectra database. The spectral measurements are transformed with the principal component analysis, and it is shown that different meteorite types can be distinguished from the transformed data. The motivation is to improve the link between asteroid spectral observations and meteorite spectral measurements.

Racetrack resonator as a loss measurement platform for photonic components.

PubMed

Jones, Adam M; DeRose, Christopher T; Lentine, Anthony L; Starbuck, Andrew; Pomerene, Andrew T S; Norwood, Robert A

2015-11-02

This work represents the first complete analysis of the use of a racetrack resonator to measure the insertion loss of efficient, compact photonic components. Beginning with an in-depth analysis of potential error sources and a discussion of the calibration procedure, the technique is used to estimate the insertion loss of waveguide width tapers of varying geometry with a resulting 95% confidence interval of 0.007 dB. The work concludes with a performance comparison of the analyzed tapers with results presented for four taper profiles and three taper lengths.

Multidimensional change in psychotherapy.

PubMed

Jones, E E

1980-04-01

Assessed psychotherapy outcome for 177 patients who were seen for an average of 31 therapy hours with the Rating Scales for Outcome of Therapy and a Therapist Questionnaire. Results of a components analysis did not support Storrow's rational groupings of the Rating Scales into five dimensions and suggested that two general areas of psychological adjustment underlie the 11 scales. A second components analysis that included both outcome measures supports only in part the contention that when results from diverse outcome measures are factor analyzed, the factors necessarily are associated with method of measurement rather than substantive dimensions of change.

Racetrack resonator as a loss measurement platform for photonic components

DOE PAGES

Jones, Adam M.; Univ. of Arizona, Tucson, AZ; DeRose, Christopher T.; ...

2015-10-27

This work represents the first complete analysis of the use of a racetrack resonator to measure the insertion loss of efficient, compact photonic components. Beginning with an in-depth analysis of potential error sources and a discussion of the calibration procedure, the technique is used to estimate the insertion loss of waveguide width tapers of varying geometry with a resulting 95% confidence interval of 0.007 dB. Furthermore, the work concludes with a performance comparison of the analyzed tapers with results presented for four taper profiles and three taper lengths.

Metal-backed versus all-polyethylene tibial components in primary total knee arthroplasty

PubMed Central

2011-01-01

Background and purpose The choice of either all-polyethylene (AP) tibial components or metal-backed (MB) tibial components in total knee arthroplasty (TKA) remains controversial. We therefore performed a meta-analysis and systematic review of randomized controlled trials that have evaluated MB and AP tibial components in primary TKA. Methods The search strategy included a computerized literature search (Medline, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials) and a manual search of major orthopedic journals. A meta-analysis and systematic review of randomized or quasi-randomized trials that compared the performance of tibial components in primary TKA was performed using a fixed or random effects model. We assessed the methodological quality of studies using Detsky quality scale. Results 9 randomized controlled trials (RCTs) published between 2000 and 2009 met the inclusion quality standards for the systematic review. The mean standardized Detsky score was 14 (SD 3). We found that the frequency of radiolucent lines in the MB group was significantly higher than that in the AP group. There were no statistically significant differences between the MB and AP tibial components regarding component positioning, knee score, knee range of motion, quality of life, and postoperative complications. Interpretation Based on evidence obtained from this study, the AP tibial component was comparable with or better than the MB tibial component in TKA. However, high-quality RCTs are required to validate the results. PMID:21895503

A new approach for computing a flood vulnerability index using cluster analysis

NASA Astrophysics Data System (ADS)

Fernandez, Paulo; Mourato, Sandra; Moreira, Madalena; Pereira, Luísa

2016-08-01

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes. FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores. The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified. The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.

Principal component analysis of TOF-SIMS spectra, images and depth profiles: an industrial perspective

NASA Astrophysics Data System (ADS)

Pacholski, Michaeleen L.

2004-06-01

Principal component analysis (PCA) has been successfully applied to time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra, images and depth profiles. Although SIMS spectral data sets can be small (in comparison to datasets typically discussed in literature from other analytical techniques such as gas or liquid chromatography), each spectrum has thousands of ions resulting in what can be a difficult comparison of samples. Analysis of industrially-derived samples means the identity of most surface species are unknown a priori and samples must be analyzed rapidly to satisfy customer demands. PCA enables rapid assessment of spectral differences (or lack there of) between samples and identification of chemically different areas on sample surfaces for images. Depth profile analysis helps define interfaces and identify low-level components in the system.

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less

Strategic Analysis Overview

NASA Technical Reports Server (NTRS)

Cirillo, William M.; Earle, Kevin D.; Goodliff, Kandyce E.; Reeves, J. D.; Stromgren, Chel; Andraschko, Mark R.; Merrill, R. Gabe

2008-01-01

NASA s Constellation Program employs a strategic analysis methodology in providing an integrated analysis capability of Lunar exploration scenarios and to support strategic decision-making regarding those scenarios. The strategic analysis methodology integrates the assessment of the major contributors to strategic objective satisfaction performance, affordability, and risk and captures the linkages and feedbacks between all three components. Strategic analysis supports strategic decision making by senior management through comparable analysis of alternative strategies, provision of a consistent set of high level value metrics, and the enabling of cost-benefit analysis. The tools developed to implement the strategic analysis methodology are not element design and sizing tools. Rather, these models evaluate strategic performance using predefined elements, imported into a library from expert-driven design/sizing tools or expert analysis. Specific components of the strategic analysis tool set include scenario definition, requirements generation, mission manifesting, scenario lifecycle costing, crew time analysis, objective satisfaction benefit, risk analysis, and probabilistic evaluation. Results from all components of strategic analysis are evaluated a set of pre-defined figures of merit (FOMs). These FOMs capture the high-level strategic characteristics of all scenarios and facilitate direct comparison of options. The strategic analysis methodology that is described in this paper has previously been applied to the Space Shuttle and International Space Station Programs and is now being used to support the development of the baseline Constellation Program lunar architecture. This paper will present an overview of the strategic analysis methodology and will present sample results from the application of the strategic analysis methodology to the Constellation Program lunar architecture.

Correlation of ground tests and analyses of a dynamically scaled Space Station model configuration

NASA Technical Reports Server (NTRS)

Javeed, Mehzad; Edighoffer, Harold H.; Mcgowan, Paul E.

1993-01-01

Verification of analytical models through correlation with ground test results of a complex space truss structure is demonstrated. A multi-component, dynamically scaled space station model configuration is the focus structure for this work. Previously established test/analysis correlation procedures are used to develop improved component analytical models. Integrated system analytical models, consisting of updated component analytical models, are compared with modal test results to establish the accuracy of system-level dynamic predictions. Design sensitivity model updating methods are shown to be effective for providing improved component analytical models. Also, the effects of component model accuracy and interface modeling fidelity on the accuracy of integrated model predictions is examined.

Least-dependent-component analysis based on mutual information

NASA Astrophysics Data System (ADS)

Stögbauer, Harald; Kraskov, Alexander; Astakhov, Sergey A.; Grassberger, Peter

2004-12-01

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of “independent” component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.

PubMed

Salvatore, Stefania; Bramness, Jørgen G; Røislien, Jo

2016-07-12

Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally. We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

Comparing Independent Component Analysis with Principle Component Analysis in Detecting Alterations of Porphyry Copper Deposit (case Study: Ardestan Area, Central Iran)

NASA Astrophysics Data System (ADS)

Mahmoudishadi, S.; Malian, A.; Hosseinali, F.

2017-09-01

The image processing techniques in transform domain are employed as analysis tools for enhancing the detection of mineral deposits. The process of decomposing the image into important components increases the probability of mineral extraction. In this study, the performance of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) has been evaluated for the visible and near-infrared (VNIR) and Shortwave infrared (SWIR) subsystems of ASTER data. Ardestan is located in part of Central Iranian Volcanic Belt that hosts many well-known porphyry copper deposits. This research investigated the propylitic and argillic alteration zones and outer mineralogy zone in part of Ardestan region. The two mentioned approaches were applied to discriminate alteration zones from igneous bedrock using the major absorption of indicator minerals from alteration and mineralogy zones in spectral rang of ASTER bands. Specialized PC components (PC2, PC3 and PC6) were used to identify pyrite and argillic and propylitic zones that distinguish from igneous bedrock in RGB color composite image. Due to the eigenvalues, the components 2, 3 and 6 account for 4.26% ,0.9% and 0.09% of the total variance of the data for Ardestan scene, respectively. For the purpose of discriminating the alteration and mineralogy zones of porphyry copper deposit from bedrocks, those mentioned percentages of data in ICA independent components of IC2, IC3 and IC6 are more accurately separated than noisy bands of PCA. The results of ICA method conform to location of lithological units of Ardestan region, as well.

Component Analysis of Remanent Magnetization Curves: A Revisit with a New Model Distribution

NASA Astrophysics Data System (ADS)

Zhao, X.; Suganuma, Y.; Fujii, M.

2017-12-01

Geological samples often consist of several magnetic components that have distinct origins. As the magnetic components are often indicative of their underlying geological and environmental processes, it is therefore desirable to identify individual components to extract associated information. This component analysis can be achieved using the so-called unmixing method, which fits a mixture model of certain end-member model distribution to the measured remanent magnetization curve. In earlier studies, the lognormal, skew generalized Gaussian and skewed Gaussian distributions have been used as the end-member model distribution in previous studies, which are performed on the gradient curve of remanent magnetization curves. However, gradient curves are sensitive to measurement noise as the differentiation of the measured curve amplifies noise, which could deteriorate the component analysis. Though either smoothing or filtering can be applied to reduce the noise before differentiation, their effect on biasing component analysis is vaguely addressed. In this study, we investigated a new model function that can be directly applied to the remanent magnetization curves and therefore avoid the differentiation. The new model function can provide more flexible shape than the lognormal distribution, which is a merit for modeling the coercivity distribution of complex magnetic component. We applied the unmixing method both to model and measured data, and compared the results with those obtained using other model distributions to better understand their interchangeability, applicability and limitation. The analyses on model data suggest that unmixing methods are inherently sensitive to noise, especially when the number of component is over two. It is, therefore, recommended to verify the reliability of component analysis by running multiple analyses with synthetic noise. Marine sediments and seafloor rocks are analyzed with the new model distribution. Given the same component number, the new model distribution can provide closer fits than the lognormal distribution evidenced by reduced residuals. Moreover, the new unmixing protocol is automated so that the users are freed from the labor of providing initial guesses for the parameters, which is also helpful to improve the subjectivity of component analysis.

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map.

PubMed

Yoo, Minjae; Shin, Jimin; Kim, Hyunmin; Kim, Jihye; Kang, Jaewoo; Tan, Aik Choon

2018-04-04

Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map. We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results. We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub. Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Multi-spectrometer calibration transfer based on independent component analysis.

PubMed

Liu, Yan; Xu, Hao; Xia, Zhenzhen; Gong, Zhiyong

2018-02-26

Calibration transfer is indispensable for practical applications of near infrared (NIR) spectroscopy due to the need for precise and consistent measurements across different spectrometers. In this work, a method for multi-spectrometer calibration transfer is described based on independent component analysis (ICA). A spectral matrix is first obtained by aligning the spectra measured on different spectrometers. Then, by using independent component analysis, the aligned spectral matrix is decomposed into the mixing matrix and the independent components of different spectrometers. These differing measurements between spectrometers can then be standardized by correcting the coefficients within the independent components. Two NIR datasets of corn and edible oil samples measured with three and four spectrometers, respectively, were used to test the reliability of this method. The results of both datasets reveal that spectra measurements across different spectrometers can be transferred simultaneously and that the partial least squares (PLS) models built with the measurements on one spectrometer can predict that the spectra can be transferred correctly on another.

Computer analysis of the leaf movements of pinto beans.

PubMed

Hoshizaki, T; Hamner, K C

1969-07-01

Computer analysis was used for the detection of rhythmic components and the estimation of period length in leaf movement records. The results of this study indicated that spectral analysis can be profitably used to determine rhythmic components in leaf movements.In Pinto bean plants (Phaseolus vulgaris L.) grown for 28 days under continuous light of 750 ft-c and at a constant temperature of 28 degrees , there was only 1 highly significant rhythmic component in the leaf movements. The period of this rhythm was 27.3 hr. In plants grown at 20 degrees , there were 2 highly significant rhythmic components: 1 of 13.8 hr and a much stronger 1 of 27.3 hr. At 15 degrees , the highly significant rhythmic components were also 27.3 and 13.8 hr in length but were of equal intensity. Random movements less than 9 hr in length became very pronounced at this temperature. At 10 degrees , no significant rhythm was found in the leaf movements. At 5 degrees , the leaf movements ceased within 1 day.

Independent Orbiter Assessment (IOA): Analysis of the mechanical actuation subsystem

NASA Technical Reports Server (NTRS)

Bacher, J. L.; Montgomery, A. D.; Bradway, M. W.; Slaughter, W. T.

1987-01-01

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter Mechanical Actuation System (MAS) hardware. Specifically, the MAS hardware consists of the following components: Air Data Probe (ADP); Elevon Seal Panel (ESP); External Tank Umbilical (ETU); Ku-Band Deploy (KBD); Payload Bay Doors (PBD); Payload Bay Radiators (PBR); Personnel Hatches (PH); Vent Door Mechanism (VDM); and Startracker Door Mechanism (SDM). The IOA analysis process utilized available MAS hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode.

Artifact suppression and analysis of brain activities with electroencephalography signals.

PubMed

Rashed-Al-Mahfuz, Md; Islam, Md Rabiul; Hirose, Keikichi; Molla, Md Khademul Islam

2013-06-05

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This paper presented a data adaptive technique for artifact suppression and brain wave extraction from electroencephalography signals to detect regional brain activities. Empirical mode decomposition based adaptive thresholding approach was employed here to suppress the electro-oculogram artifact. Fractional Gaussian noise was used to determine the threshold level derived from the analysis data without any training. The purified electroencephalography signal was composed of the brain waves also called rhythmic components which represent the brain activities. The rhythmic components were extracted from each electroencephalography channel using adaptive wiener filter with the original scale. The regional brain activities were mapped on the basis of the spatial distribution of rhythmic components, and the results showed that different regions of the brain are activated in response to different stimuli. This research analyzed the activities of a single rhythmic component, alpha with respect to different motor imaginations. The experimental results showed that the proposed method is very efficient in artifact suppression and identifying individual motor imagery based on the activities of alpha component.

Analysis Of Direct Numerical Simulation Results Of Adverse Pressure Gradient Boundary Layer Through Anisotropy Invariant Mapping And Comparison With The Rans Simulations

NASA Astrophysics Data System (ADS)

Gungor, Ayse Gul; Nural, Ozan Ekin; Ertunc, Ozgur

2017-11-01

Purpose of this study is to analyze the direct numerical simulation data of a turbulent boundary layer subjected to strong adverse pressure gradient through anisotropy invariant mapping. RANS simulation using the ``Elliptic Blending Model'' of Manceau and Hanjolic (2002) is also conducted for the same flow case with commercial software Star-CCM+ and comparison of the results with DNS data is done. RANS simulation captures the general trends in the velocity field but, significant deviations are found when skin friction coefficients are compared. Anisotropy invariant map of Lumley and Newman (1977) and barycentric map of Banerjee et al. (2007) are used for the analysis. Invariant mapping of the DNS data has yielded that at locations away from the wall, flow is close to one component turbulence state. In the vicinity of the wall, turbulence is at two component limit which is one border of the barycentric map and as the flow evolves along the streamwise direction, it approaches to two component turbulence state. Additionally, at the locations away from the wall, turbulence approaches to two component limit. Furthermore, analysis of the invariants of the RANS simulations shows dissimilar results. In RANS simulations invariants do not approach to any of the limit states unlike the DNS.

Distinguishing response conflict and task conflict in the Stroop task: evidence from ex-Gaussian distribution analysis.

PubMed

Steinhauser, Marco; Hübner, Ronald

2009-10-01

It has been suggested that performance in the Stroop task is influenced by response conflict as well as task conflict. The present study investigated the idea that both conflict types can be isolated by applying ex-Gaussian distribution analysis which decomposes response time into a Gaussian and an exponential component. Two experiments were conducted in which manual versions of a standard Stroop task (Experiment 1) and a separated Stroop task (Experiment 2) were performed under task-switching conditions. Effects of response congruency and stimulus bivalency were used to measure response conflict and task conflict, respectively. Ex-Gaussian analysis revealed that response conflict was mainly observed in the Gaussian component, whereas task conflict was stronger in the exponential component. Moreover, task conflict in the exponential component was selectively enhanced under task-switching conditions. The results suggest that ex-Gaussian analysis can be used as a tool to isolate different conflict types in the Stroop task. PsycINFO Database Record (c) 2009 APA, all rights reserved.

A symmetrical subtraction combined with interpolated values for eliminating scattering from fluorescence EEM data.

PubMed

Xu, Jing; Liu, Xiaofei; Wang, Yutian

2016-08-05

Parallel factor analysis is a widely used method to extract qualitative and quantitative information of the analyte of interest from fluorescence emission-excitation matrix containing unknown components. Big amplitude of scattering will influence the results of parallel factor analysis. Many methods of eliminating scattering have been proposed. Each of these methods has its advantages and disadvantages. The combination of symmetrical subtraction and interpolated values has been discussed. The combination refers to both the combination of results and the combination of methods. Nine methods were used for comparison. The results show the combination of results can make a better concentration prediction for all the components. Copyright © 2016 Elsevier B.V. All rights reserved.

The Development and Validation of the Empathy Components Questionnaire (ECQ).

PubMed

Batchelder, Laurie; Brosnan, Mark; Ashwin, Chris

2017-01-01

Key research suggests that empathy is a multidimensional construct comprising of both cognitive and affective components. More recent theories and research suggest even further factors within these components of empathy, including the ability to empathize with others versus the drive towards empathizing with others. While numerous self-report measures have been developed to examine empathy, none of them currently index all of these wider components together. The aim of the present research was to develop and validate the Empathy Components Questionnaire (ECQ) to measure cognitive and affective components, as well as ability and drive components within each. Study one utilized items measuring cognitive and affective empathy taken from various established questionnaires to create an initial version of the ECQ. Principal component analysis (PCA) was used to examine the underlying components of empathy within the ECQ in a sample of 101 typical adults. Results revealed a five-component model consisting of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor assessing affective reactivity. This five-component structure was then validated and confirmed using confirmatory factor analysis (CFA) in an independent sample of 211 typical adults. Results also showed that females scored higher than males overall on the ECQ, and on specific components, which is consistent with previous findings of a female advantage on self-reported empathy. Findings also showed certain components predicted scores on an independent measure of social behavior, which provided good convergent validity of the ECQ. Together, these findings validate the newly developed ECQ as a multidimensional measure of empathy more in-line with current theories of empathy. The ECQ provides a useful new tool for quick and easy measurement of empathy and its components for research with both healthy and clinical populations.

The Development and Validation of the Empathy Components Questionnaire (ECQ)

PubMed Central

Batchelder, Laurie; Brosnan, Mark; Ashwin, Chris

2017-01-01

Key research suggests that empathy is a multidimensional construct comprising of both cognitive and affective components. More recent theories and research suggest even further factors within these components of empathy, including the ability to empathize with others versus the drive towards empathizing with others. While numerous self-report measures have been developed to examine empathy, none of them currently index all of these wider components together. The aim of the present research was to develop and validate the Empathy Components Questionnaire (ECQ) to measure cognitive and affective components, as well as ability and drive components within each. Study one utilized items measuring cognitive and affective empathy taken from various established questionnaires to create an initial version of the ECQ. Principal component analysis (PCA) was used to examine the underlying components of empathy within the ECQ in a sample of 101 typical adults. Results revealed a five-component model consisting of cognitive ability, cognitive drive, affective ability, affective drive, and a fifth factor assessing affective reactivity. This five-component structure was then validated and confirmed using confirmatory factor analysis (CFA) in an independent sample of 211 typical adults. Results also showed that females scored higher than males overall on the ECQ, and on specific components, which is consistent with previous findings of a female advantage on self-reported empathy. Findings also showed certain components predicted scores on an independent measure of social behavior, which provided good convergent validity of the ECQ. Together, these findings validate the newly developed ECQ as a multidimensional measure of empathy more in-line with current theories of empathy. The ECQ provides a useful new tool for quick and easy measurement of empathy and its components for research with both healthy and clinical populations. PMID:28076406

Characterization of extracellular polymeric substances in biofilms under long-term exposure to ciprofloxacin antibiotic using fluorescence excitation-emission matrix and parallel factor analysis.

PubMed

Gu, Chaochao; Gao, Pin; Yang, Fan; An, Dongxuan; Munir, Mariya; Jia, Hanzhong; Xue, Gang; Ma, Chunyan

2017-05-01

The presence of antibiotic residues in the environment has been regarded as an emerging concern due to their potential adverse environmental consequences such as antibiotic resistance. However, the interaction between antibiotics and extracellular polymeric substances (EPSs) of biofilms in wastewater treatment systems is not entirely clear. In this study, the effect of ciprofloxacin (CIP) antibiotic on biofilm EPS matrix was investigated and characterized using fluorescence excitation-emission matrix (EEM) and parallel factor (PARAFAC) analysis. Physicochemical analysis showed that the proteins were the major EPS fraction, and their contents increased gradually with an increase in CIP concentration (0-300 μg/L). Based on the characterization of biofilm tightly bound EPS (TB-EPS) by EEM, three fluorescent components were identified by PARAFAC analysis. Component C1 was associated with protein-like substances, and components C2 and C3 belonged to humic-like substances. Component C1 exhibited an increasing trend as the CIP addition increased. Pearson's correlation results showed that CIP correlated significantly with the protein contents and component C1, while strong correlations were also found among UV 254 , dissolved organic carbon, humic acids, and component C3. A combined use of EEM-PARAFAC analysis and chemical measurements was demonstrated as a favorable approach for the characterization of variations in biofilm EPS in the presence of CIP antibiotic.

Accuracy evaluation of Fourier series analysis and singular spectrum analysis for predicting the volume of motorcycle sales in Indonesia

NASA Astrophysics Data System (ADS)

Sasmita, Yoga; Darmawan, Gumgum

2017-08-01

This research aims to evaluate the performance of forecasting by Fourier Series Analysis (FSA) and Singular Spectrum Analysis (SSA) which are more explorative and not requiring parametric assumption. Those methods are applied to predicting the volume of motorcycle sales in Indonesia from January 2005 to December 2016 (monthly). Both models are suitable for seasonal and trend component data. Technically, FSA defines time domain as the result of trend and seasonal component in different frequencies which is difficult to identify in the time domain analysis. With the hidden period is 2,918 ≈ 3 and significant model order is 3, FSA model is used to predict testing data. Meanwhile, SSA has two main processes, decomposition and reconstruction. SSA decomposes the time series data into different components. The reconstruction process starts with grouping the decomposition result based on similarity period of each component in trajectory matrix. With the optimum of window length (L = 53) and grouping effect (r = 4), SSA predicting testing data. Forecasting accuracy evaluation is done based on Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The result shows that in the next 12 month, SSA has MAPE = 13.54 percent, MAE = 61,168.43 and RMSE = 75,244.92 and FSA has MAPE = 28.19 percent, MAE = 119,718.43 and RMSE = 142,511.17. Therefore, to predict volume of motorcycle sales in the next period should use SSA method which has better performance based on its accuracy.

Methods of Collection of Biological Information for Fatigue Evaluation during Visual Display Terminals (VDT) Operation

NASA Astrophysics Data System (ADS)

Hachiya, Yuriko; Ogai, Harutoshi; Okazaki, Hiroko; Fujisaki, Takeshi; Uchida, Kazuhiko; Oda, Susumu; Wada, Futoshi; Mori, Koji

A method for the analysis of fatigue parameters has been rarely researched in VDT operation. Up to now, fatigue was evaluated by changing of biological information. If signals regarding fatigue are detected, fatigue can be measured. The purpose of this study proposed experiment and analysis method to extract parameters related to fatigue from the biological information during VDT operation using the Independent Component Analysis (ICA). An experiment had 11 subjects. As for the experiment were light loaded VDT operation and heavy loaded VDT operation. A measurement item were amount of work, a mistake number, subjective symptom, surface skin temperature (forehead and apex nasi), heart rate, skin blood flow of forearm and respiratory rate. In the heavy loaded operation group, mistake number and subjective symptom score were increased to compare with the other. And Two-factor ANOVA was used for analysis. The result of mistake number was confirmed that heavy loaded. After the moving averages of waveshape were calculated, it was made to extract independent components by using the ICA. The results of the ICA suggest that the independent components increase according to accumulation of fatigue. Thus, the independent components would be a possible parameter of fatigue. However, further experiments should continue in order to obtain the conclusive finding of our research.

A Study on Components of Internal Control-Based Administrative System in Secondary Schools

ERIC Educational Resources Information Center

Montri, Paitoon; Sirisuth, Chaiyuth; Lammana, Preeda

2015-01-01

The aim of this study was to study the components of the internal control-based administrative system in secondary schools, and make a Confirmatory Factor Analysis (CFA) to confirm the goodness of fit of empirical data and component model that resulted from the CFA. The study consisted of three steps: 1) studying of principles, ideas, and theories…

Determination and fingerprint analysis of steroidal saponins in roots of Liriope muscari (Decne.) L. H. Bailey by ultra high performance liquid chromatography coupled with ion trap time-of-flight mass spectrometry.

PubMed

Li, Yong-Wei; Qi, Jin; Wen-Zhang; Zhou, Shui-Ping; Yan-Wu; Yu, Bo-Yang

2014-07-01

Liriope muscari (Decne.) L. H. Bailey is a well-known traditional Chinese medicine used for treating cough and insomnia. There are few reports on the quality evaluation of this herb partly because the major steroid saponins are not readily identified by UV detectors and are not easily isolated due to the existence of many similar isomers. In this study, a qualitative and quantitative method was developed to analyze the major components in L. muscari (Decne.) L. H. Bailey roots. Sixteen components were deduced and identified primarily by the information obtained from ultra high performance liquid chromatography with ion-trap time-of-flight mass spectrometry. The method demonstrated the desired specificity, linearity, stability, precision, and accuracy for simultaneous determination of 15 constituents (13 steroidal glycosides, 25(R)-ruscogenin, and pentylbenzoate) in 26 samples from different origins. The fingerprint was established, and the evaluation was achieved using similarity analysis and principal component analysis of 15 fingerprint peaks from 26 samples by ultra high performance liquid chromatography. The results from similarity analysis were consistent with those of principal component analysis. All results suggest that the established method could be applied effectively to the determination of multi-ingredients and fingerprint analysis of steroid saponins for quality assessment and control of L. muscari (Decne.) L. H. Bailey. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An analysis of satellite state vector observability using SST tracking data

NASA Technical Reports Server (NTRS)

Englar, T. S., Jr.; Hammond, C. L.

1976-01-01

Observability of satellite state vectors, using only SST tracking data was investigated by covariance analysis under a variety of satellite and station configurations. These results indicate very precarious observability in most short arc cases. The consequences of this are large variances on many state components, such as the downrange component of the relay satellite position. To illustrate the impact of observability problems, an example is given of two distinct satellite orbit pairs generating essentially the same data arc. The physical bases for unobservability are outlined and related to proposed TDRSS configurations. Results are relevant to any mission depending upon TDRSS to determine satellite state. The required mathematical analysis and the software used is described.

Parallel line analysis: multifunctional software for the biomedical sciences

NASA Technical Reports Server (NTRS)

Swank, P. R.; Lewis, M. L.; Damron, K. L.; Morrison, D. R.

1990-01-01

An easy to use, interactive FORTRAN program for analyzing the results of parallel line assays is described. The program is menu driven and consists of five major components: data entry, data editing, manual analysis, manual plotting, and automatic analysis and plotting. Data can be entered from the terminal or from previously created data files. The data editing portion of the program is used to inspect and modify data and to statistically identify outliers. The manual analysis component is used to test the assumptions necessary for parallel line assays using analysis of covariance techniques and to determine potency ratios with confidence limits. The manual plotting component provides a graphic display of the data on the terminal screen or on a standard line printer. The automatic portion runs through multiple analyses without operator input. Data may be saved in a special file to expedite input at a future time.

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Characterization of DOM adsorption of CNTs by using excitation-emission matrix fluorescence spectroscopy and multiway analysis.

PubMed

Peng, Mingguo; Li, Huajie; Li, Dongdong; Du, Erdeng; Li, Zhihong

2017-06-01

Carbon nanotubes (CNTs) were utilized to adsorb DOM in micro-polluted water. The characteristics of DOM adsorption on CNTs were investigated based on UV 254 , TOC, and fluorescence spectrum measurements. Based on PARAFAC (parallel factor) analysis, four fluorescent components were extracted, including one protein-like component (C4) and three humic acid-like components (C1, C2, and C3). The adsorption isotherms, kinetics, and thermodynamics of DOM adsorption on CNTs were further investigated. A Freundlich isotherm model fit the adsorption data well with high values of correlation. As a type of macro-porous and meso-porous adsorbent, CNTs preferably adsorb humic acid-like substances rather than protein-like substances. The increasing temperature will speed up the adsorption process. The self-organizing map (SOM) analysis further explains the fluorescent properties of water samples. The results provide a new insight into the adsorption behaviour of DOM fluorescent components on CNTs.

funRNA: a fungi-centered genomics platform for genes encoding key components of RNAi.

PubMed

Choi, Jaeyoung; Kim, Ki-Tae; Jeon, Jongbum; Wu, Jiayao; Song, Hyeunjeong; Asiegbu, Fred O; Lee, Yong-Hwan

2014-01-01

RNA interference (RNAi) is involved in genome defense as well as diverse cellular, developmental, and physiological processes. Key components of RNAi are Argonaute, Dicer, and RNA-dependent RNA polymerase (RdRP), which have been functionally characterized mainly in model organisms. The key components are believed to exist throughout eukaryotes; however, there is no systematic platform for archiving and dissecting these important gene families. In addition, few fungi have been studied to date, limiting our understanding of RNAi in fungi. Here we present funRNA http://funrna.riceblast.snu.ac.kr/, a fungal kingdom-wide comparative genomics platform for putative genes encoding Argonaute, Dicer, and RdRP. To identify and archive genes encoding the abovementioned key components, protein domain profiles were determined from reference sequences obtained from UniProtKB/SwissProt. The domain profiles were searched using fungal, metazoan, and plant genomes, as well as bacterial and archaeal genomes. 1,163, 442, and 678 genes encoding Argonaute, Dicer, and RdRP, respectively, were predicted. Based on the identification results, active site variation of Argonaute, diversification of Dicer, and sequence analysis of RdRP were discussed in a fungus-oriented manner. funRNA provides results from diverse bioinformatics programs and job submission forms for BLAST, BLASTMatrix, and ClustalW. Furthermore, sequence collections created in funRNA are synced with several gene family analysis portals and databases, offering further analysis opportunities. funRNA provides identification results from a broad taxonomic range and diverse analysis functions, and could be used in diverse comparative and evolutionary studies. It could serve as a versatile genomics workbench for key components of RNAi.

Long Term Precipitation Pattern Identification and Derivation of Non Linear Precipitation Trend in a Catchment using Singular Spectrum Analysis

NASA Astrophysics Data System (ADS)

Unnikrishnan, Poornima; Jothiprakash, Vinayakam

2017-04-01

Precipitation is the major component in the hydrologic cycle. Awareness of not only the total amount of rainfall pertaining to a catchment, but also the pattern of its spatial and temporal distribution are equally important in the management of water resources systems in an efficient way. Trend is the long term direction of a time series; it determines the overall pattern of a time series. Singular Spectrum Analysis (SSA) is a time series analysis technique that decomposes the time series into small components (eigen triples). This property of the method of SSA has been utilized to extract the trend component of the rainfall time series. In order to derive trend from the rainfall time series, we need to select components corresponding to trend from the eigen triples. For this purpose, periodogram analysis of the eigen triples have been proposed to be coupled with SSA, in the present study. In the study, seasonal data of England and Wales Precipitation (EWP) for a time period of 1766-2013 have been analyzed and non linear trend have been derived out of the precipitation data. In order to compare the performance of SSA in deriving trend component, Mann Kendall (MK) test is also used to detect trends in EWP seasonal series and the results have been compared. The result showed that the MK test could detect the presence of positive or negative trend for a significance level, whereas the proposed methodology of SSA could extract the non-linear trend present in the rainfall series along with its shape. We will discuss further the comparison of both the methodologies along with the results in the presentation.

Rapid Characterization of Components in Bolbostemma paniculatum by UPLC/LTQ-Orbitrap MSn Analysis and Multivariate Statistical Analysis for Herb Discrimination.

PubMed

Zeng, Yanling; Lu, Yang; Chen, Zhao; Tan, Jiawei; Bai, Jie; Li, Pengyue; Wang, Zhixin; Du, Shouying

2018-05-11

Bolbostemma paniculatum is a traditional Chinese medicine (TCM) showed various therapeutic effects. Owing to its complex chemical composition, few investigations have acquired a comprehensive cognition for the chemical profiles of this herb and explicated the differences between samples collected from different places. In this study, a strategy based on UPLC tandem LTQ-Orbitrap MS n was established for characterizing chemical components of B. paniculatum . Through a systematic identification strategy, a total of 60 components in B. paniculatum were rapidly separated in 30 min and identified. Then based on peak intensities of all the characterized components, principle component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to classify 18 batches of B. paniculatum into four groups, which were highly consistent with the four climate types of their original places. And five compounds were finally screened out as chemical markers to discriminate the internal quality of B. paniculatum . As the first study to systematically characterize the chemical components of B. paniculatum by UPLC-MS n , the above results could offer essential data for its pharmacological research. And the current strategy could provide useful reference for future investigations on discovery of important chemical constituents in TCM, as well as establishment of quality control and evaluation method.

Generation mechanisms of fundamental rogue wave spatial-temporal structure.

PubMed

Ling, Liming; Zhao, Li-Chen; Yang, Zhan-Ying; Guo, Boling

2017-08-01

We discuss the generation mechanism of fundamental rogue wave structures in N-component coupled systems, based on analytical solutions of the nonlinear Schrödinger equation and modulational instability analysis. Our analysis discloses that the pattern of a fundamental rogue wave is determined by the evolution energy and growth rate of the resonant perturbation that is responsible for forming the rogue wave. This finding allows one to predict the rogue wave pattern without the need to solve the N-component coupled nonlinear Schrödinger equation. Furthermore, our results show that N-component coupled nonlinear Schrödinger systems may possess N different fundamental rogue wave patterns at most. These results can be extended to evaluate the type and number of fundamental rogue wave structure in other coupled nonlinear systems.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Convergence of sampling in protein simulations

NASA Astrophysics Data System (ADS)

Hess, Berk

2002-03-01

With molecular dynamics protein dynamics can be simulated in atomic detail. Current computers are not fast enough to probe all available conformations, but fluctuations around one conformation can be sampled to a reasonable extent. The motions with the largest fluctuations can be filtered out of a simulation using covariance or principal component analysis. A problem with this analysis is that random diffusion can appear as correlated motion. An analysis is presented of how long a simulation should be to obtain relevant results for global motions. The analysis reveals that the cosine content of the principal components is a good indicator for bad sampling.

Comparative Analysis of the Clinical Significance of Oscillatory Components in the Rhythmic Structure of Pulse Signal in the Diagnostics of Psychosomatic Disorders in School Age Children.

PubMed

Desova, A A; Dorofeyuk, A A; Anokhin, A M

2017-01-01

We performed a comparative analysis of the types of spectral density typical of various parameters of pulse signal. The experimental material was obtained during the examination of school age children with various psychosomatic disorders. We also performed a typological analysis of the spectral density functions corresponding to the time series of different parameters of a single oscillation of pulse signals; the results of their comparative analysis are presented. We determined the most significant spectral components for two disordersin children: arterial hypertension and mitral valve prolapse.

Modulation by EEG features of BOLD responses to interictal epileptiform discharges

PubMed Central

LeVan, Pierre; Tyvaert, Louise; Gotman, Jean

2013-01-01

Introduction EEG-fMRI of interictal epileptiform discharges (IEDs) usually assumes a fixed hemodynamic response function (HRF). This study investigates HRF variability with respect to IED amplitude fluctuations using independent component analysis (ICA), with the goal of improving the specificity of EEG-fMRI analyses. Methods We selected EEG-fMRI data from 10 focal epilepsy patients with a good quality EEG. IED amplitudes were calculated in an average reference montage. The fMRI data were decomposed by ICA and a deconvolution method identified IED-related components by detecting time courses with a significant HRF time-locked to the IEDs (F-test, p<0.05). Individual HRF amplitudes were then calculated for each IED. Components with a significant HRF/IED amplitude correlation (Spearman test, p< 0.05) were compared to the presumed epileptogenic focus and to results of a general linear model (GLM) analysis. Results In 7 patients, at least one IED-related component was concordant with the focus, but many IED-related components were at distant locations. When considering only components with a significant HRF/IED amplitude correlation, distant components could be discarded, significantly increasing the relative proportion of activated voxels in the focus (p=0.02). In the 3 patients without concordant IED-related components, no HRF/IED amplitude correlations were detected inside the brain. Integrating IED-related amplitudes in the GLM significantly improved fMRI signal modeling in the epileptogenic focus in 4 patients (p< 0.05). Conclusion Activations in the epileptogenic focus appear to show significant correlations between HRF and IED amplitudes, unlike distant responses. These correlations could be integrated in the analysis to increase the specificity of EEG-fMRI studies in epilepsy. PMID:20026222

Finite Element Analysis of Film Stack Architecture for Complementary Metal-Oxide-Semiconductor Image Sensors.

PubMed

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-05-02

Image sensors are the core components of computer, communication, and consumer electronic products. Complementary metal oxide semiconductor (CMOS) image sensors have become the mainstay of image-sensing developments, but are prone to leakage current. In this study, we simulate the CMOS image sensor (CIS) film stacking process by finite element analysis. To elucidate the relationship between the leakage current and stack architecture, we compare the simulated and measured leakage currents in the elements. Based on the analysis results, we further improve the performance by optimizing the architecture of the film stacks or changing the thin-film material. The material parameters are then corrected to improve the accuracy of the simulation results. The simulated and experimental results confirm a positive correlation between measured leakage current and stress. This trend is attributed to the structural defects induced by high stress, which generate leakage. Using this relationship, we can change the structure of the thin-film stack to reduce the leakage current and thereby improve the component life and reliability of the CIS components.

Finite Element Analysis of Film Stack Architecture for Complementary Metal-Oxide–Semiconductor Image Sensors

PubMed Central

Wu, Kuo-Tsai; Hwang, Sheng-Jye; Lee, Huei-Huang

2017-01-01

Image sensors are the core components of computer, communication, and consumer electronic products. Complementary metal oxide semiconductor (CMOS) image sensors have become the mainstay of image-sensing developments, but are prone to leakage current. In this study, we simulate the CMOS image sensor (CIS) film stacking process by finite element analysis. To elucidate the relationship between the leakage current and stack architecture, we compare the simulated and measured leakage currents in the elements. Based on the analysis results, we further improve the performance by optimizing the architecture of the film stacks or changing the thin-film material. The material parameters are then corrected to improve the accuracy of the simulation results. The simulated and experimental results confirm a positive correlation between measured leakage current and stress. This trend is attributed to the structural defects induced by high stress, which generate leakage. Using this relationship, we can change the structure of the thin-film stack to reduce the leakage current and thereby improve the component life and reliability of the CIS components. PMID:28468324

[Content determination of twelve major components in Tibetan medicine Zuozhu Daxi by UPLC].

PubMed

Qu, Yan; Li, Jin-hua; Zhang, Chen; Li, Chun-xue; Dong, Hong-jiao; Wang, Chang-sheng; Zeng, Rui; Chen, Xiao-hu

2015-05-01

A quantitative analytical method of ultra-high performance liquid chromatography (UPLC) was developed for simultaneously determining twelve components in Tibetan medicine Zuozhu Daxi. SIMPCA 12.0 software was used a principal component analysis PCA) and partial small squares analysis (PLSD-DA) on the twelve components in 10 batches from four pharmaceutical factories. Acquity UPLC BEH C15 column (2.1 mm x 100 mm, 1.7 µm) was adopted at the column temperature of 35 °C and eluted with acetonitrile (A) -0.05% phosphate acid solution (B) as the mobile phase with a flow rate of 0. 3 mL · min(-1). The injection volume was 1 µL. The detection wavelengths were set at 210 nm for alantolactone, isoalantolactone and oleanolic; 260 nm for trychnine and brucine; 288 nm for protopine; 306 nm for protopine, resveratrol and piperine; 370 nm for quercetin and isorhamnetin. The results showed a good separation among index components, with a good linearity relationship (R2 = 0.999 6) within the selected concentration range. The average sample recovery rates ranged between 99.44%-101.8%, with RSD between 0.37%-1.7%, indicating the method is rapid and accurate with a good repeatability and stability. The PCA and PLSD-DA analysis on the sample determination results revealed a great difference among samples from different pharmaceutical factories. The twelve components included in this study contributed significantly to the quantitative determination of intrinsic quality of Zuozhu Daxi. The UPLC established for to the quantitative determination of the twelve components can provide scientific basis for the comprehensive quality evaluation of Zuozhu Daxi.

Real-space analysis of radiation-induced specific changes with independent component analysis

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

Borek, Dominika; Bromberg, Raquel; Hattne, Johan

A method of analysis is presented that allows for the separation of specific radiation-induced changes into distinct components in real space. The method relies on independent component analysis (ICA) and can be effectively applied to electron density maps and other types of maps, provided that they can be represented as sets of numbers on a grid. Here, for glucose isomerase crystals, ICA was used in a proof-of-concept analysis to separate temperature-dependent and temperature-independent components of specific radiation-induced changes for data sets acquired from multiple crystals across multiple temperatures. ICA identified two components, with the temperature-independent component being responsible for themore » majority of specific radiation-induced changes at temperatures below 130 K. The patterns of specific temperature-independent radiation-induced changes suggest a contribution from the tunnelling of electron holes as a possible explanation. In the second case, where a group of 22 data sets was collected on a single thaumatin crystal, ICA was used in another type of analysis to separate specific radiation-induced effects happening on different exposure-level scales. Here, ICA identified two components of specific radiation-induced changes that likely result from radiation-induced chemical reactions progressing with different rates at different locations in the structure. In addition, ICA unexpectedly identified the radiation-damage state corresponding to reduced disulfide bridges rather than the zero-dose extrapolated state as the highest contrast structure. The application of ICA to the analysis of specific radiation-induced changes in real space and the data pre-processing for ICA that relies on singular value decomposition, which was used previously in data space to validate a two-component physical model of X-ray radiation-induced changes, are discussed in detail. This work lays a foundation for a better understanding of protein-specific radiation chemistries and provides a framework for analysing effects of specific radiation damage in crystallographic and cryo-EM experiments.« less

Changes in the dissolved organic matter leaching from soil under severe temperature and N-deposition.

PubMed

Nguyen, Hang Vo-Minh; Choi, Jung Hyun

2015-06-01

In this study, we conducted growth chamber experiments using three types of soil (wetland, rice paddy, and forest) under the conditions of a severe increase in the temperature and N-deposition in order to investigate how extreme weather influences the characteristics of the dissolved organic matter (DOM) leaching from different soil types. This leachate controls the quantity and quality of DOM in surface water systems. After 5 months of incubation, the dissolved organic carbon (DOC) concentrations decreased in the range of 21.1 to 88.9 %, while the specific UV absorption (SUVA) values increased substantially in the range of 19.9 to 319.9 % for all of the samples. Higher increases in the SUVA values were observed at higher temperatures, whereas the opposite trend was observed for samples with N-addition. The parallel factor analysis (PARAFAC) results showed that four fluorescence components: terrestrial humic-like (component 1 (C1)), microbial humic-like (component 2 (C2)), protein-like (component 3 (C3)), and anthropogenic humic-like (component 4 (C4)) constituted the fluorescence matrices of soil samples. During the experiment, labile DOM from the soils was consumed and transformed into resistant aromatic carbon structures and less biodegradable components via microbial processes. The principle component analysis (PCA) results indicated that severe temperatures and N-deposition could enhance the contribution of the aromatic carbon compounds and humic-like components in the soil samples.

Handheld CZT radiation detector

DOEpatents

Murray, William S.; Butterfield, Kenneth B.; Baird, William

2004-08-24

A handheld CZT radiation detector having a CZT gamma-ray sensor, a multichannel analyzer, a fuzzy-logic component, and a display component is disclosed. The CZT gamma-ray sensor may be a coplanar grid CZT gamma-ray sensor, which provides high-quality gamma-ray analysis at a wide range of operating temperatures. The multichannel analyzer categorizes pulses produce by the CZT gamma-ray sensor into channels (discrete energy levels), resulting in pulse height data. The fuzzy-logic component analyzes the pulse height data and produces a ranked listing of radioisotopes. The fuzzy-logic component is flexible and well-suited to in-field analysis of radioisotopes. The display component may be a personal data assistant, which provides a user-friendly method of interacting with the detector. In addition, the radiation detector may be equipped with a neutron sensor to provide an enhanced mechanism of sensing radioactive materials.

Practical aspects of estimating energy components in rodents

PubMed Central

van Klinken, Jan B.; van den Berg, Sjoerd A. A.; van Dijk, Ko Willems

2013-01-01

Recently there has been an increasing interest in exploiting computational and statistical techniques for the purpose of component analysis of indirect calorimetry data. Using these methods it becomes possible to dissect daily energy expenditure into its components and to assess the dynamic response of the resting metabolic rate (RMR) to nutritional and pharmacological manipulations. To perform robust component analysis, however, is not straightforward and typically requires the tuning of parameters and the preprocessing of data. Moreover the degree of accuracy that can be attained by these methods depends on the configuration of the system, which must be properly taken into account when setting up experimental studies. Here, we review the methods of Kalman filtering, linear, and penalized spline regression, and minimal energy expenditure estimation in the context of component analysis and discuss their results on high resolution datasets from mice and rats. In addition, we investigate the effect of the sample time, the accuracy of the activity sensor, and the washout time of the chamber on the estimation accuracy. We found that on the high resolution data there was a strong correlation between the results of Kalman filtering and penalized spline (P-spline) regression, except for the activity respiratory quotient (RQ). For low resolution data the basal metabolic rate (BMR) and resting RQ could still be estimated accurately with P-spline regression, having a strong correlation with the high resolution estimate (R2 > 0.997; sample time of 9 min). In contrast, the thermic effect of food (TEF) and activity related energy expenditure (AEE) were more sensitive to a reduction in the sample rate (R2 > 0.97). In conclusion, for component analysis on data generated by single channel systems with continuous data acquisition both Kalman filtering and P-spline regression can be used, while for low resolution data from multichannel systems P-spline regression gives more robust results. PMID:23641217

Measurement Characteristics of the Quality of Life Index When Used with Adults Who Have Severe Mental Retardation. Brief Report.

ERIC Educational Resources Information Center

Campo, Stephanie F.; And Others

1996-01-01

The Quality of Life Index was completed by 120 residential staff for 60 adults with severe to profound mental retardation residing in group homes. Measurement integrity was analyzed through use of principal components analysis, confirmatory rotation of components, and Cronbach alphas. Results are compared with results obtained from a more…

Job Profiling Guide. Results of 1994 Job Profiling. Part of the Ohio Vocational Competency Assessment (OVCA) Package.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Vocational Instructional Materials Lab.

This guide explains the process of job profiling and details the results of a 1994 profiling of 34 occupations. Discussed in section 1 are the following: purpose and components of the Ohio Vocational Competency Assessment (OVCA) package; purpose, contents, and use of the Ohio Competency Analysis Profiles and Work Keys components of the OVCA…

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

Classification of breast tissue in mammograms using efficient coding.

PubMed

Costa, Daniel D; Campos, Lúcio F; Barros, Allan K

2011-06-24

Female breast cancer is the major cause of death by cancer in western countries. Efforts in Computer Vision have been made in order to improve the diagnostic accuracy by radiologists. Some methods of lesion diagnosis in mammogram images were developed based in the technique of principal component analysis which has been used in efficient coding of signals and 2D Gabor wavelets used for computer vision applications and modeling biological vision. In this work, we present a methodology that uses efficient coding along with linear discriminant analysis to distinguish between mass and non-mass from 5090 region of interest from mammograms. The results show that the best rates of success reached with Gabor wavelets and principal component analysis were 85.28% and 87.28%, respectively. In comparison, the model of efficient coding presented here reached up to 90.07%. Altogether, the results presented demonstrate that independent component analysis performed successfully the efficient coding in order to discriminate mass from non-mass tissues. In addition, we have observed that LDA with ICA bases showed high predictive performance for some datasets and thus provide significant support for a more detailed clinical investigation.

Modeling vertebrate diversity in Oregon using satellite imagery

NASA Astrophysics Data System (ADS)

Cablk, Mary Elizabeth

Vertebrate diversity was modeled for the state of Oregon using a parametric approach to regression tree analysis. This exploratory data analysis effectively modeled the non-linear relationships between vertebrate richness and phenology, terrain, and climate. Phenology was derived from time-series NOAA-AVHRR satellite imagery for the year 1992 using two methods: principal component analysis and derivation of EROS data center greenness metrics. These two measures of spatial and temporal vegetation condition incorporated the critical temporal element in this analysis. The first three principal components were shown to contain spatial and temporal information about the landscape and discriminated phenologically distinct regions in Oregon. Principal components 2 and 3, 6 greenness metrics, elevation, slope, aspect, annual precipitation, and annual seasonal temperature difference were investigated as correlates to amphibians, birds, all vertebrates, reptiles, and mammals. Variation explained for each regression tree by taxa were: amphibians (91%), birds (67%), all vertebrates (66%), reptiles (57%), and mammals (55%). Spatial statistics were used to quantify the pattern of each taxa and assess validity of resulting predictions from regression tree models. Regression tree analysis was relatively robust against spatial autocorrelation in the response data and graphical results indicated models were well fit to the data.

Analysis of Floral Volatile Components and Antioxidant Activity of Different Varieties of Chrysanthemum morifolium.

PubMed

Yang, Lu; Cheng, Ping; Wang, Jin-Hui; Li, Hong

2017-10-23

This study investigated the volatile flavor compounds and antioxidant properties of the essential oil of chrysanthemums that was extracted from the fresh flowers of 10 taxa of Chrysanthemum morifolium from three species; namely Dendranthema morifolium (Ramat.) Yellow, Dendranthema morifolium (Ramat.) Red, Dendranthema morifolium (Ramat.) Pink, Dendranthema morifolium (Ramat.) White, Pericallis hybrid Blue, Pericallis hybrid Pink, Pericallis hybrid Purple, Bellis perennis Pink, Bellis perennis Yellow, and Bellis perennis White. The antioxidant capacity of the essential oil was assayed by spectrophotometric analysis. The volatile flavor compounds from the fresh flowers were collected using dynamic headspace collection, analyzed using auto thermal desorber-gas chromatography/mass spectrometry, and identified with quantification using the external standard method. The antioxidant activities of Chrysanthemum morifolium were evaluated by DPPH and FRAP assays, and the results showed that the antioxidant activity of each sample was not the same. The different varieties of fresh Chrysanthemum morifolium flowers were distinguished and classified by fingerprint similarity evaluation, principle component analysis (PCA), and cluster analysis. The results showed that the floral volatile component profiles were significantly different among the different Chrysanthemum morifolium varieties. A total of 36 volatile flavor compounds were identified with eight functional groups: hydrocarbons, terpenoids, aromatic compounds, alcohols, ketones, ethers, aldehydes, and esters. Moreover, the variability among Chrysanthemum morifolium in basis to the data, and the first three principal components (PC1, PC2, and PC3) accounted for 96.509% of the total variance (55.802%, 30.599%, and 10.108%, respectively). PCA indicated that there were marked differences among Chrysanthemum morifolium varieties. The cluster analysis confirmed the results of the PCA analysis. In conclusion, the results of this study provide a basis for breeding Chrysanthemum cultivars with desirable floral scents, and they further support the view that some plants are promising sources of natural antioxidants.

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Orbital transfer rocket engine technology 7.5K-LB thrust rocket engine preliminary design

NASA Technical Reports Server (NTRS)

Harmon, T. J.; Roschak, E.

1993-01-01

A preliminary design of an advanced LOX/LH2 expander cycle rocket engine producing 7,500 lbf thrust for Orbital Transfer vehicle missions was completed. Engine system, component and turbomachinery analysis at both on design and off design conditions were completed. The preliminary design analysis results showed engine requirements and performance goals were met. Computer models are described and model outputs are presented. Engine system assembly layouts, component layouts and valve and control system analysis are presented. Major design technologies were identified and remaining issues and concerns were listed.

Structural dynamic analysis of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Scott, L. P.; Jamison, G. T.; Mccutcheon, W. A.; Price, J. M.

1981-01-01

This structural dynamic analysis supports development of the SSME by evaluating components subjected to critical dynamic loads, identifying significant parameters, and evaluating solution methods. Engine operating parameters at both rated and full power levels are considered. Detailed structural dynamic analyses of operationally critical and life limited components support the assessment of engine design modifications and environmental changes. Engine system test results are utilized to verify analytic model simulations. The SSME main chamber injector assembly is an assembly of 600 injector elements which are called LOX posts. The overall LOX post analysis procedure is shown.

EMC analysis of MOS-1

NASA Astrophysics Data System (ADS)

Ishizawa, Y.; Abe, K.; Shirako, G.; Takai, T.; Kato, H.

The electromagnetic compatibility (EMC) control method, system EMC analysis method, and system test method which have been applied to test the components of the MOS-1 satellite are described. The merits and demerits of the problem solving, specification, and system approaches to EMC control are summarized, and the data requirements of the SEMCAP (specification and electromagnetic compatibility analysis program) computer program for verifying the EMI safety margin of the components are sumamrized. Examples of EMC design are mentioned, and the EMC design process and selection method for EMC critical points are shown along with sample EMC test results.

Multivariate analysis of historical data (2004-2013) in assessing the possible environmental impact of the Bellolampo landfill (Palermo).

PubMed

Indelicato, Serena; Bongiorno, David; Tuzzolino, Nicola; Mannino, Maria Rosaria; Muscarella, Rosalia; Fradella, Pasquale; Gargano, Maria Elena; Nicosia, Salvatore; Ceraulo, Leopoldo

2018-03-14

Multivariate analysis was performed on a large data set of groundwater and leachate samples collected during 9 years of operation of the Bellolampo municipal solid waste landfill (located above Palermo, Italy). The aim was to obtain the most likely correlations among the data. The analysis results are presented. Groundwater samples were collected in the period 2004-2013, whereas the leachate analysis refers to the period 2006-2013. For groundwater, statistical data evaluation revealed notable differences among the samples taken from the numerous wells located around the landfill. Characteristic parameters revealed by principal component analysis (PCA) were more deeply investigated, and corresponding thematic maps were drawn. The composition of the leachate was also thoroughly investigated. Several chemical macro-descriptors were calculated, and the results are presented. A comparison of PCA results for the leachate and groundwater data clearly reveals that the groundwater's main components substantially differ from those of the leachate. This outcome strongly suggests excluding leachate permeation through the multiple landfill lining.

Statistical analysis of major ion and trace element geochemistry of water, 1986-2006, at seven wells transecting the freshwater/saline-water interface of the Edwards Aquifer, San Antonio, Texas

USGS Publications Warehouse

Mahler, Barbara J.

2008-01-01

The statistical analyses taken together indicate that the geochemistry at the freshwater-zone wells is more variable than that at the transition-zone wells. The geochemical variability at the freshwater-zone wells might result from dilution of ground water by meteoric water. This is indicated by relatively constant major ion molar ratios; a preponderance of positive correlations between SC, major ions, and trace elements; and a principal components analysis in which the major ions are strongly loaded on the first principal component. Much of the variability at three of the four transition-zone wells might result from the use of different laboratory analytical methods or reporting procedures during the period of sampling. This is reflected by a lack of correlation between SC and major ion concentrations at the transition-zone wells and by a principal components analysis in which the variability is fairly evenly distributed across several principal components. The statistical analyses further indicate that, although the transition-zone wells are less well connected to surficial hydrologic conditions than the freshwater-zone wells, there is some connection but the response time is longer. 

Hilbert-Huang transform analysis of dynamic and earthquake motion recordings

USGS Publications Warehouse

Zhang, R.R.; Ma, S.; Safak, E.; Hartzell, S.

2003-01-01

This study examines the rationale of Hilbert-Huang transform (HHT) for analyzing dynamic and earthquake motion recordings in studies of seismology and engineering. In particular, this paper first provides the fundamentals of the HHT method, which consist of the empirical mode decomposition (EMD) and the Hilbert spectral analysis. It then uses the HHT to analyze recordings of hypothetical and real wave motion, the results of which are compared with the results obtained by the Fourier data processing technique. The analysis of the two recordings indicates that the HHT method is able to extract some motion characteristics useful in studies of seismology and engineering, which might not be exposed effectively and efficiently by Fourier data processing technique. Specifically, the study indicates that the decomposed components in EMD of HHT, namely, the intrinsic mode function (IMF) components, contain observable, physical information inherent to the original data. It also shows that the grouped IMF components, namely, the EMD-based low- and high-frequency components, can faithfully capture low-frequency pulse-like as well as high-frequency wave signals. Finally, the study illustrates that the HHT-based Hilbert spectra are able to reveal the temporal-frequency energy distribution for motion recordings precisely and clearly.

Revealing the microstructure of the giant component in random graph ensembles

NASA Astrophysics Data System (ADS)

Tishby, Ido; Biham, Ofer; Katzav, Eytan; Kühn, Reimer

2018-04-01

The microstructure of the giant component of the Erdős-Rényi network and other configuration model networks is analyzed using generating function methods. While configuration model networks are uncorrelated, the giant component exhibits a degree distribution which is different from the overall degree distribution of the network and includes degree-degree correlations of all orders. We present exact analytical results for the degree distributions as well as higher-order degree-degree correlations on the giant components of configuration model networks. We show that the degree-degree correlations are essential for the integrity of the giant component, in the sense that the degree distribution alone cannot guarantee that it will consist of a single connected component. To demonstrate the importance and broad applicability of these results, we apply them to the study of the distribution of shortest path lengths on the giant component, percolation on the giant component, and spectra of sparse matrices defined on the giant component. We show that by using the degree distribution on the giant component one obtains high quality results for these properties, which can be further improved by taking the degree-degree correlations into account. This suggests that many existing methods, currently used for the analysis of the whole network, can be adapted in a straightforward fashion to yield results conditioned on the giant component.

Response of basic structural elements and B-52 structural components to simulated nuclear overpressure. Volume II-program data (basic structural elements). Final report, 1 June 1977-30 September 1979

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

Syring, R.P.; Grubb, R.L.

1979-09-30

This document reports on the following: (1) experimental determination of the response of 16 basic structural elements and 7 B-52 components to simulated nuclear overpressure environments (utilizing Sandia Corporation's Thunderpipe Shock Tube), (2) analysis of these test specimens utilizing the NOVA-2 computer program, and (3) correlation of test and analysis results.

Wind Turbine Control Design to Reduce Capital Costs: 7 January 2009 - 31 August 2009

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

Darrow, P. J.

2010-01-01

This report first discusses and identifies which wind turbine components can benefit from advanced control algorithms and also presents results from a preliminary loads case analysis using a baseline controller. Next, it describes the design, implementation, and simulation-based testing of an advanced controller to reduce loads on those components. The case-by-case loads analysis and advanced controller design will help guide future control research.

Survey to Identify Substandard and Falsified Tablets in Several Asian Countries with Pharmacopeial Quality Control Tests and Principal Component Analysis of Handheld Raman Spectroscopy.

PubMed

Kakio, Tomoko; Nagase, Hitomi; Takaoka, Takashi; Yoshida, Naoko; Hirakawa, Junichi; Macha, Susan; Hiroshima, Takashi; Ikeda, Yukihiro; Tsuboi, Hirohito; Kimura, Kazuko

2018-06-01

The World Health Organization has warned that substandard and falsified medical products (SFs) can harm patients and fail to treat the diseases for which they were intended, and they affect every region of the world, leading to loss of confidence in medicines, health-care providers, and health systems. Therefore, development of analytical procedures to detect SFs is extremely important. In this study, we investigated the quality of pharmaceutical tablets containing the antihypertensive candesartan cilexetil, collected in China, Indonesia, Japan, and Myanmar, using the Japanese pharmacopeial analytical procedures for quality control, together with principal component analysis (PCA) of Raman spectrum obtained with handheld Raman spectrometer. Some samples showed delayed dissolution and failed to meet the pharmacopeial specification, whereas others failed the assay test. These products appeared to be substandard. Principal component analysis showed that all Raman spectra could be explained in terms of two components: the amount of the active pharmaceutical ingredient and the kinds of excipients. Principal component analysis score plot indicated one substandard, and the falsified tablets have similar principal components in Raman spectra, in contrast to authentic products. The locations of samples within the PCA score plot varied according to the source country, suggesting that manufacturers in different countries use different excipients. Our results indicate that the handheld Raman device will be useful for detection of SFs in the field. Principal component analysis of that Raman data clarify the difference in chemical properties between good quality products and SFs that circulate in the Asian market.

The Factor Structure of Some Piagetian Tasks

ERIC Educational Resources Information Center

Lawson, Anton E.; Nordland, Floyd H.

1976-01-01

Investigated was the hypothesis that conservation tasks are unifactor by administering eight different conservation tasks to 96 seventh-grade science students and performing a principal component analysis on the data. Results indicated that conservation tasks may measure up to three different components of cognitive thought. (SL)

Seismic Analysis of the 2017 Oroville Dam Spillway Erosion Crisis

NASA Astrophysics Data System (ADS)

Goodling, P.; Lekic, V.; Prestegaard, K. L.

2017-12-01

The outflow channel of the northern California (USA) Oroville Dam suffered catastrophic erosion damage in February and March, 2017. High discharges released through the spillway (up to 3,000 m3/s) caused rapid spillway erosion, forming a deep chasm. A repeat LiDAR survey obtained from the California Department of Water Resources indicates that the chasm eroded to a depth of 48 meters. A three-component broadband seismometer (STS-1) operated by the Berkeley Digital Seismological Network recorded microseismic energy produced by the flowing water, providing a natural laboratory to test methods for seismically monitoring sudden catastrophic floods and erosion. In this study, we evaluate the three-component waveforms recorded during five constant-discharge periods - before, during, and after the spillway crisis - each of which had a different channel geometry. We apply frequency-dependent polarization analysis (FDPA; following Park, 1987), which characterizes particle motion at each frequency. The method is based on principal component analysis on a spectral covariance matrix in one-hour windows and it produces the horizontal azimuth, vertical tilt, horizontal phase, and vertical phase of the dominant particle motion. The results indicate a greater vertical component (perhaps roughness-induced) of power at a broad range of frequencies at a given discharge after the formation of the chasm. As the outflow crater developed, the back-azimuth of the primary source of seismic energy changed from the nearby Thermalito Diversion Pool (188 degrees) to the center of the outflow channel (170 degrees). To further analyze FDPA results, we apply the 2D spectral-element solver package SPECFEM2D (Tromp et al. 2008), and find that local topography should be considered when interpreting the surface waveforms predicted by FDPA results. This research suggests that monitoring changing channel geometry and erosion in large-scale flood events may be enhanced by seismic FDPA analysis. The results of this work are compared and contrasted with 3-component seismic observations of cobble-bed stream floods in Maryland.

New insights into comparison between synthetic and practical municipal wastewater in cake layer characteristic analysis of membrane bioreactor.

PubMed

Zhou, Lijie; Zhuang, Wei-Qin; Wang, Xin; Yu, Ke; Yang, Shufang; Xia, Siqing

2017-11-01

In previous studies, cake layer analysis in membrane bioreactor (MBR) was both carried out with synthetic and practical municipal wastewater (SMW and PMW), leading to different results. This study aimed to identify the comparison between SMW and PMW in cake layer characteristic analysis of MBR. Two laboratory-scale anoxic/oxic MBRs were operated for over 90days with SMW and PMW, respectively. Results showed that PMW led to rough cake layer surface with particles, and the aggravation of cake layer formation with thinner and denser cake layer. Additionally, inorganic components, especially Si and Al, in PMW accumulated into cake layer and strengthened the cake layer structure, inducing severer biofouling. However, SMW promoted bacterial metabolism during cake layer formation, thus aggravated the accumulation of organic components into cake layer. Therefore, SMW highlighted the organic components in cake layer, but weakened the inorganic functions in practical MBR operation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Ionospheric total electron content anomalies due to Typhoon Nakri on 29 May 2008: A nonlinear principal component analysis

NASA Astrophysics Data System (ADS)

Lin, Jyh-Woei

2012-09-01

This paper uses Nonlinear Principal Component Analysis (NLPCA) and Principal Component Analysis (PCA) to determine Total Electron Content (TEC) anomalies in the ionosphere for the Nakri Typhoon on 29 May, 2008 (UTC). NLPCA, PCA and image processing are applied to the global ionospheric map (GIM) with transforms conducted for the time period 12:00-14:00 UT on 29 May 2008 when the wind was most intense. Results show that at a height of approximately 150-200 km the TEC anomaly using NLPCA is more localized; however its intensity increases with height and becomes more widespread. The TEC anomalies are not found by PCA. Potential causes of the results are discussed with emphasis given to vertical acoustic gravity waves. The approximate position of the typhoon's eye can be detected if the GIM is divided into fine enough maps with adequate spatial-resolution at GPS-TEC receivers. This implies that the trace of the typhoon in the regional GIM is caught using NLPCA.

Artifacts and noise removal in electrocardiograms using independent component analysis.

PubMed

Chawla, M P S; Verma, H K; Kumar, Vinod

2008-09-26

Independent component analysis (ICA) is a novel technique capable of separating independent components from electrocardiogram (ECG) complex signals. The purpose of this analysis is to evaluate the effectiveness of ICA in removing artifacts and noise from ECG recordings. ICA is applied to remove artifacts and noise in ECG segments of either an individual ECG CSE data base file or all files. The reconstructed ECGs are compared with the original ECG signal. For the four special cases discussed, the R-Peak magnitudes of the CSE data base ECG waveforms before and after applying ICA are also found. In the results, it is shown that in most of the cases, the percentage error in reconstruction is very small. The results show that there is a significant improvement in signal quality, i.e. SNR. All the ECG recording cases dealt showed an improved ECG appearance after the use of ICA. This establishes the efficacy of ICA in elimination of noise and artifacts in electrocardiograms.

Model-free fMRI group analysis using FENICA.

PubMed

Schöpf, V; Windischberger, C; Robinson, S; Kasess, C H; Fischmeister, F PhS; Lanzenberger, R; Albrecht, J; Kleemann, A M; Kopietz, R; Wiesmann, M; Moser, E

2011-03-01

Exploratory analysis of functional MRI data allows activation to be detected even if the time course differs from that which is expected. Independent Component Analysis (ICA) has emerged as a powerful approach, but current extensions to the analysis of group studies suffer from a number of drawbacks: they can be computationally demanding, results are dominated by technical and motion artefacts, and some methods require that time courses be the same for all subjects or that templates be defined to identify common components. We have developed a group ICA (gICA) method which is based on single-subject ICA decompositions and the assumption that the spatial distribution of signal changes in components which reflect activation is similar between subjects. This approach, which we have called Fully Exploratory Network Independent Component Analysis (FENICA), identifies group activation in two stages. ICA is performed on the single-subject level, then consistent components are identified via spatial correlation. Group activation maps are generated in a second-level GLM analysis. FENICA is applied to data from three studies employing a wide range of stimulus and presentation designs. These are an event-related motor task, a block-design cognition task and an event-related chemosensory experiment. In all cases, the group maps identified by FENICA as being the most consistent over subjects correspond to task activation. There is good agreement between FENICA results and regions identified in prior GLM-based studies. In the chemosensory task, additional regions are identified by FENICA and temporal concatenation ICA that we show is related to the stimulus, but exhibit a delayed response. FENICA is a fully exploratory method that allows activation to be identified without assumptions about temporal evolution, and isolates activation from other sources of signal fluctuation in fMRI. It has the advantage over other gICA methods that it is computationally undemanding, spotlights components relating to activation rather than artefacts, allows the use of familiar statistical thresholding through deployment of a higher level GLM analysis and can be applied to studies where the paradigm is different for all subjects. Copyright © 2010 Elsevier Inc. All rights reserved.

Evaluation of CDOM sources and their links with water quality in the lakes of Northeast China using fluorescence spectroscopy

NASA Astrophysics Data System (ADS)

Zhao, Ying; Song, Kaishan; Wen, Zhidan; Fang, Chong; Shang, Yingxin; Lv, Lili

2017-07-01

The spatial distributions of the fluorescence intensities Fmax for chromophoric dissolved organic matter (CDOM) components, the fluorescence indices (FI370 and FI310) and their correlations with water quality of 19 lakes in the Songhua River Basin (SHRB) across semiarid regions of Northeast China were examined with the data collected in September 2012 and 2015. The 19 lakes were divided into two groups according to EC (threshold value = 800 μS cm-1): fresh water (N = 13) and brackish water lakes (N = 6). The fluorescent characteristics of CDOM in the 19 lakes were investigated using excitation-emission matrix fluorescence spectroscopy (EEM) coupled with parallel factor (PARAFAC) and multivariate analysis. Two humic-like components (C1 and C3), one tryptophan-like component (C2), and one tyrosine-like component (C4) were identified by PARAFAC. The component C4 was not included in subsequent analyses due to the strong scatter in some colloidal water samples from brackish water lakes. The correlations between Fmax for the three EEM-PARAFAC extracted CDOM components C1-C3, the fluorescence indices (FI370 and FI310) and the water quality parameters (i.e., TN, TP, Chl-a, pH, EC, turbidity (Turb) and dissolved organic carbon (DOC)) were determined by redundancy analysis (RDA). The results of RDA analysis showed that spatial variation in land cover, pollution sources, and salinity/EC gradients in water quality affected Fmax for the fluorescent components C1-C3 and the fluorescence indices (FI370 and FI310). Further examination indicated that the CDOM fluorescent components and the fluorescence indices (FI370 and FI310) did not significantly differ (t-test, p > 0.05) in fresh water (N = 13) and brackish water lakes (N = 6). There was a difference in the distribution of the average Fmax for the CDOM fluorescent components between C1 to C3 from agricultural sources and urban wastewater sources in hypereutrophic brackish water lakes. The Fmax for humic-like components C1 and C3 spatially varied with land cover among the 19 lakes. Our results indicated that the spatial distributions of Fmax for CDOM fluorescent components and their correlations with water quality can be evaluated by EEM-PARAFAC and multivariate analysis among the 19 lakes across semiarid regions of Northeast China, which has potential implication for lakes with similar genesis.

Airplane Stress Analysis

NASA Technical Reports Server (NTRS)

Zahm, A F; Crook, L H

1918-01-01

Report presents stress analysis of individual components of an airplane. Normal and abnormal loads, sudden loads, simple stresses, indirect simple stresses, resultant unit stress, repetitive and equivalent stress, maximum steady load and stress are considered.

From scenarios to domain models: processes and representations

NASA Astrophysics Data System (ADS)

Haddock, Gail; Harbison, Karan

1994-03-01

The domain specific software architectures (DSSA) community has defined a philosophy for the development of complex systems. This philosophy improves productivity and efficiency by increasing the user's role in the definition of requirements, increasing the systems engineer's role in the reuse of components, and decreasing the software engineer's role to the development of new components and component modifications only. The scenario-based engineering process (SEP), the first instantiation of the DSSA philosophy, has been adopted by the next generation controller project. It is also the chosen methodology of the trauma care information management system project, and the surrogate semi-autonomous vehicle project. SEP uses scenarios from the user to create domain models and define the system's requirements. Domain knowledge is obtained from a variety of sources including experts, documents, and videos. This knowledge is analyzed using three techniques: scenario analysis, task analysis, and object-oriented analysis. Scenario analysis results in formal representations of selected scenarios. Task analysis of the scenario representations results in descriptions of tasks necessary for object-oriented analysis and also subtasks necessary for functional system analysis. Object-oriented analysis of task descriptions produces domain models and system requirements. This paper examines the representations that support the DSSA philosophy, including reference requirements, reference architectures, and domain models. The processes used to create and use the representations are explained through use of the scenario-based engineering process. Selected examples are taken from the next generation controller project.

Novel presentational approaches were developed for reporting network meta-analysis.

PubMed

Tan, Sze Huey; Cooper, Nicola J; Bujkiewicz, Sylwia; Welton, Nicky J; Caldwell, Deborah M; Sutton, Alexander J

2014-06-01

To present graphical tools for reporting network meta-analysis (NMA) results aiming to increase the accessibility, transparency, interpretability, and acceptability of NMA analyses. The key components of NMA results were identified based on recommendations by agencies such as the National Institute for Health and Care Excellence (United Kingdom). Three novel graphs were designed to amalgamate the identified components using familiar graphical tools such as the bar, line, or pie charts and adhering to good graphical design principles. Three key components for presentation of NMA results were identified, namely relative effects and their uncertainty, probability of an intervention being best, and between-study heterogeneity. Two of the three graphs developed present results (for each pairwise comparison of interventions in the network) obtained from both NMA and standard pairwise meta-analysis for easy comparison. They also include options to display the probability best, ranking statistics, heterogeneity, and prediction intervals. The third graph presents rankings of interventions in terms of their effectiveness to enable clinicians to easily identify "top-ranking" interventions. The graphical tools presented can display results tailored to the research question of interest, and targeted at a whole spectrum of users from the technical analyst to the nontechnical clinician. Copyright © 2014 Elsevier Inc. All rights reserved.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

A further component analysis for illicit drugs mixtures with THz-TDS

NASA Astrophysics Data System (ADS)

Xiong, Wei; Shen, Jingling; He, Ting; Pan, Rui

2009-07-01

A new method for quantitative analysis of mixtures of illicit drugs with THz time domain spectroscopy was proposed and verified experimentally. In traditional method we need fingerprints of all the pure chemical components. In practical as only the objective components in a mixture and their absorption features are known, it is necessary and important to present a more practical technique for the detection and identification. Our new method of quantitatively inspect of the mixtures of illicit drugs is developed by using derivative spectrum. In this method, the ratio of objective components in a mixture can be obtained on the assumption that all objective components in the mixture and their absorption features are known but the unknown components are not needed. Then methamphetamine and flour, a illicit drug and a common adulterant, were selected for our experiment. The experimental result verified the effectiveness of the method, which suggested that it could be an effective method for quantitative identification of illicit drugs. This THz spectroscopy technique is great significant in the real-world applications of illicit drugs quantitative analysis. It could be an effective method in the field of security and pharmaceuticals inspection.

Integrative sparse principal component analysis of gene expression data.

PubMed

Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge

2017-12-01

In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate

PubMed Central

Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.

2009-01-01

Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

Comparative analysis on flexibility requirements of typical Cryogenic Transfer lines

NASA Astrophysics Data System (ADS)

Jadon, Mohit; Kumar, Uday; Choukekar, Ketan; Shah, Nitin; Sarkar, Biswanath

2017-04-01

The cryogenic systems and their applications; primarily in large Fusion devices, utilize multiple cryogen transfer lines of various sizes and complexities to transfer cryogenic fluids from plant to the various user/ applications. These transfer lines are composed of various critical sections i.e. tee section, elbows, flexible components etc. The mechanical sustainability (under failure circumstances) of these transfer lines are primary requirement for safe operation of the system and applications. The transfer lines need to be designed for multiple design constraints conditions like line layout, support locations and space restrictions. The transfer lines are subjected to single load and multiple load combinations, such as operational loads, seismic loads, leak in insulation vacuum loads etc. [1]. The analytical calculations and flexibility analysis using professional software are performed for the typical transfer lines without any flexible component, the results were analysed for functional and mechanical load conditions. The failure modes were identified along the critical sections. The same transfer line was then refurbished with the flexible components and analysed for failure modes. The flexible components provide additional flexibility to the transfer line system and make it safe. The results obtained from the analytical calculations were compared with those obtained from the flexibility analysis software calculations. The optimization of the flexible component’s size and selection was performed and components were selected to meet the design requirements as per code.

Performance Analysis of Hybrid Electric Vehicle over Different Driving Cycles

NASA Astrophysics Data System (ADS)

Panday, Aishwarya; Bansal, Hari Om

2017-02-01

Article aims to find the nature and response of a hybrid vehicle on various standard driving cycles. Road profile parameters play an important role in determining the fuel efficiency. Typical parameters of road profile can be reduced to a useful smaller set using principal component analysis and independent component analysis. Resultant data set obtained after size reduction may result in more appropriate and important parameter cluster. With reduced parameter set fuel economies over various driving cycles, are ranked using TOPSIS and VIKOR multi-criteria decision making methods. The ranking trend is then compared with the fuel economies achieved after driving the vehicle over respective roads. Control strategy responsible for power split is optimized using genetic algorithm. 1RC battery model and modified SOC estimation method are considered for the simulation and improved results compared with the default are obtained.

Pattern Analysis of Dynamic Susceptibility Contrast-enhanced MR Imaging Demonstrates Peritumoral Tissue Heterogeneity

PubMed Central

Akbari, Hamed; Macyszyn, Luke; Da, Xiao; Wolf, Ronald L.; Bilello, Michel; Verma, Ragini; O’Rourke, Donald M.

2014-01-01

Purpose To augment the analysis of dynamic susceptibility contrast material–enhanced magnetic resonance (MR) images to uncover unique tissue characteristics that could potentially facilitate treatment planning through a better understanding of the peritumoral region in patients with glioblastoma. Materials and Methods Institutional review board approval was obtained for this study, with waiver of informed consent for retrospective review of medical records. Dynamic susceptibility contrast-enhanced MR imaging data were obtained for 79 patients, and principal component analysis was applied to the perfusion signal intensity. The first six principal components were sufficient to characterize more than 99% of variance in the temporal dynamics of blood perfusion in all regions of interest. The principal components were subsequently used in conjunction with a support vector machine classifier to create a map of heterogeneity within the peritumoral region, and the variance of this map served as the heterogeneity score. Results The calculated principal components allowed near-perfect separability of tissue that was likely highly infiltrated with tumor and tissue that was unlikely infiltrated with tumor. The heterogeneity map created by using the principal components showed a clear relationship between voxels judged by the support vector machine to be highly infiltrated and subsequent recurrence. The results demonstrated a significant correlation (r = 0.46, P < .0001) between the heterogeneity score and patient survival. The hazard ratio was 2.23 (95% confidence interval: 1.4, 3.6; P < .01) between patients with high and low heterogeneity scores on the basis of the median heterogeneity score. Conclusion Analysis of dynamic susceptibility contrast-enhanced MR imaging data by using principal component analysis can help identify imaging variables that can be subsequently used to evaluate the peritumoral region in glioblastoma. These variables are potentially indicative of tumor infiltration and may become useful tools in guiding therapy, as well as individualized prognostication. © RSNA, 2014 PMID:24955928

System-Level Heat Transfer Analysis, Thermal- Mechanical Cyclic Stress Analysis, and Environmental Fatigue Modeling of a Two-Loop Pressurized Water Reactor. A Preliminary Study

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

Mohanty, Subhasish; Soppet, William; Majumdar, Saurin

This report provides an update on an assessment of environmentally assisted fatigue for light water reactor components under extended service conditions. This report is a deliverable in April 2015 under the work package for environmentally assisted fatigue under DOE's Light Water Reactor Sustainability program. In this report, updates are discussed related to a system level preliminary finite element model of a two-loop pressurized water reactor (PWR). Based on this model, system-level heat transfer analysis and subsequent thermal-mechanical stress analysis were performed for typical design-basis thermal-mechanical fatigue cycles. The in-air fatigue lives of components, such as the hot and cold legs,more » were estimated on the basis of stress analysis results, ASME in-air fatigue life estimation criteria, and fatigue design curves. Furthermore, environmental correction factors and associated PWR environment fatigue lives for the hot and cold legs were estimated by using estimated stress and strain histories and the approach described in NUREG-6909. The discussed models and results are very preliminary. Further advancement of the discussed model is required for more accurate life prediction of reactor components. This report only presents the work related to finite element modelling activities. However, in between multiple tensile and fatigue tests were conducted. The related experimental results will be presented in the year-end report.« less

Modeling of power electronic systems with EMTP

NASA Technical Reports Server (NTRS)

Tam, Kwa-Sur; Dravid, Narayan V.

1989-01-01

In view of the potential impact of power electronics on power systems, there is need for a computer modeling/analysis tool to perform simulation studies on power systems with power electronic components as well as to educate engineering students about such systems. The modeling of the major power electronic components of the NASA Space Station Freedom Electric Power System is described along with ElectroMagnetic Transients Program (EMTP) and it is demonstrated that EMTP can serve as a very useful tool for teaching, design, analysis, and research in the area of power systems with power electronic components. EMTP modeling of power electronic circuits is described and simulation results are presented.

Space Shuttle critical function audit

NASA Technical Reports Server (NTRS)

Sacks, Ivan J.; Dipol, John; Su, Paul

1990-01-01

A large fault-tolerance model of the main propulsion system of the US space shuttle has been developed. This model is being used to identify single components and pairs of components that will cause loss of shuttle critical functions. In addition, this model is the basis for risk quantification of the shuttle. The process used to develop and analyze the model is digraph matrix analysis (DMA). The DMA modeling and analysis process is accessed via a graphics-based computer user interface. This interface provides coupled display of the integrated system schematics, the digraph models, the component database, and the results of the fault tolerance and risk analyses.

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

2013-01-01

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

CARES/Life Software for Designing More Reliable Ceramic Parts

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Powers, Lynn M.; Baker, Eric H.

1997-01-01

Products made from advanced ceramics show great promise for revolutionizing aerospace and terrestrial propulsion, and power generation. However, ceramic components are difficult to design because brittle materials in general have widely varying strength values. The CAPES/Life software eases this task by providing a tool to optimize the design and manufacture of brittle material components using probabilistic reliability analysis techniques. Probabilistic component design involves predicting the probability of failure for a thermomechanically loaded component from specimen rupture data. Typically, these experiments are performed using many simple geometry flexural or tensile test specimens. A static, dynamic, or cyclic load is applied to each specimen until fracture. Statistical strength and SCG (fatigue) parameters are then determined from these data. Using these parameters and the results obtained from a finite element analysis, the time-dependent reliability for a complex component geometry and loading is then predicted. Appropriate design changes are made until an acceptable probability of failure has been reached.

Computer program uses Monte Carlo techniques for statistical system performance analysis

NASA Technical Reports Server (NTRS)

Wohl, D. P.

1967-01-01

Computer program with Monte Carlo sampling techniques determines the effect of a component part of a unit upon the overall system performance. It utilizes the full statistics of the disturbances and misalignments of each component to provide unbiased results through simulated random sampling.

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

PubMed

Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J

2003-09-01

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

Accuracy analysis and design of A3 parallel spindle head

NASA Astrophysics Data System (ADS)

Ni, Yanbing; Zhang, Biao; Sun, Yupeng; Zhang, Yuan

2016-03-01

As functional components of machine tools, parallel mechanisms are widely used in high efficiency machining of aviation components, and accuracy is one of the critical technical indexes. Lots of researchers have focused on the accuracy problem of parallel mechanisms, but in terms of controlling the errors and improving the accuracy in the stage of design and manufacturing, further efforts are required. Aiming at the accuracy design of a 3-DOF parallel spindle head(A3 head), its error model, sensitivity analysis and tolerance allocation are investigated. Based on the inverse kinematic analysis, the error model of A3 head is established by using the first-order perturbation theory and vector chain method. According to the mapping property of motion and constraint Jacobian matrix, the compensatable and uncompensatable error sources which affect the accuracy in the end-effector are separated. Furthermore, sensitivity analysis is performed on the uncompensatable error sources. The sensitivity probabilistic model is established and the global sensitivity index is proposed to analyze the influence of the uncompensatable error sources on the accuracy in the end-effector of the mechanism. The results show that orientation error sources have bigger effect on the accuracy in the end-effector. Based upon the sensitivity analysis results, the tolerance design is converted into the issue of nonlinearly constrained optimization with the manufacturing cost minimum being the optimization objective. By utilizing the genetic algorithm, the allocation of the tolerances on each component is finally determined. According to the tolerance allocation results, the tolerance ranges of ten kinds of geometric error sources are obtained. These research achievements can provide fundamental guidelines for component manufacturing and assembly of this kind of parallel mechanisms.

The Separation of Blood Components Using Standing Surface Acoustic Waves (SSAWs) Microfluidic Devices: Analysis and Simulation.

PubMed

Soliman, Ahmed M; Eldosoky, Mohamed A; Taha, Taha E

2017-03-29

The separation of blood components (WBCs, RBCs, and platelets) is important for medical applications. Recently, standing surface acoustic wave (SSAW) microfluidic devices are used for the separation of particles. In this paper, the design analysis of SSAW microfluidics is presented. Also, the analysis of SSAW force with Rayleigh angle effect and its attenuation in liquid-loaded substrate, viscous drag force, hydrodynamic force, and diffusion force are explained and analyzed. The analyses are provided for selecting the piezoelectric material, width of the main microchannel, working area of SAW, wavelength, minimum input power required for the separation process, and widths of outlet collecting microchannels. The design analysis of SSAW microfluidics is provided for determining the minimum input power required for the separation process with appropriated the displacement contrast of the particles.The analyses are applied for simulation the separation of blood components. The piezoelectric material, width of the main microchannel, working area of SAW, wavelength, and minimum input power required for the separation process are selected as LiNbO₃, 120 μm, 1.08 mm², 300 μm, 371 mW. The results are compared to other published results. The results of these simulations achieve minimum power consumption, less complicated setup, and high collecting efficiency. All simulation programs are built by MATLAB.

The Separation of Blood Components Using Standing Surface Acoustic Waves (SSAWs) Microfluidic Devices: Analysis and Simulation

PubMed Central

Soliman, Ahmed M.; Eldosoky, Mohamed A.; Taha, Taha E.

2017-01-01

The separation of blood components (WBCs, RBCs, and platelets) is important for medical applications. Recently, standing surface acoustic wave (SSAW) microfluidic devices are used for the separation of particles. In this paper, the design analysis of SSAW microfluidics is presented. Also, the analysis of SSAW force with Rayleigh angle effect and its attenuation in liquid-loaded substrate, viscous drag force, hydrodynamic force, and diffusion force are explained and analyzed. The analyses are provided for selecting the piezoelectric material, width of the main microchannel, working area of SAW, wavelength, minimum input power required for the separation process, and widths of outlet collecting microchannels. The design analysis of SSAW microfluidics is provided for determining the minimum input power required for the separation process with appropriated the displacement contrast of the particles.The analyses are applied for simulation the separation of blood components. The piezoelectric material, width of the main microchannel, working area of SAW, wavelength, and minimum input power required for the separation process are selected as LiNbO3, 120 μm, 1.08 mm2, 300 μm, 371 mW. The results are compared to other published results. The results of these simulations achieve minimum power consumption, less complicated setup, and high collecting efficiency. All simulation programs are built by MATLAB. PMID:28952506

Optimized Kernel Entropy Components.

PubMed

Izquierdo-Verdiguier, Emma; Laparra, Valero; Jenssen, Robert; Gomez-Chova, Luis; Camps-Valls, Gustau

2017-06-01

This brief addresses two main issues of the standard kernel entropy component analysis (KECA) algorithm: the optimization of the kernel decomposition and the optimization of the Gaussian kernel parameter. KECA roughly reduces to a sorting of the importance of kernel eigenvectors by entropy instead of variance, as in the kernel principal components analysis. In this brief, we propose an extension of the KECA method, named optimized KECA (OKECA), that directly extracts the optimal features retaining most of the data entropy by means of compacting the information in very few features (often in just one or two). The proposed method produces features which have higher expressive power. In particular, it is based on the independent component analysis framework, and introduces an extra rotation to the eigen decomposition, which is optimized via gradient-ascent search. This maximum entropy preservation suggests that OKECA features are more efficient than KECA features for density estimation. In addition, a critical issue in both the methods is the selection of the kernel parameter, since it critically affects the resulting performance. Here, we analyze the most common kernel length-scale selection criteria. The results of both the methods are illustrated in different synthetic and real problems. Results show that OKECA returns projections with more expressive power than KECA, the most successful rule for estimating the kernel parameter is based on maximum likelihood, and OKECA is more robust to the selection of the length-scale parameter in kernel density estimation.

Inventory of File gfs.t06z.pgrb2.0p25.anl

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 005 10 mb VGRD analysis V-Component of Wind [m/s] 006 10 mb -Component of Wind [m/s] 011 20 mb VGRD analysis V-Component of Wind [m/s] 012 20 mb ABSV analysis Absolute UGRD analysis U-Component of Wind [m/s] 018 30 mb VGRD analysis V-Component of Wind [m/s] 019 30 mb

Inventory of File gfs.t06z.pgrb2.0p50.anl

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 005 10 mb VGRD analysis V-Component of Wind [m/s] 006 10 mb -Component of Wind [m/s] 011 20 mb VGRD analysis V-Component of Wind [m/s] 012 20 mb ABSV analysis Absolute UGRD analysis U-Component of Wind [m/s] 018 30 mb VGRD analysis V-Component of Wind [m/s] 019 30 mb

Appliance of Independent Component Analysis to System Intrusion Analysis

NASA Astrophysics Data System (ADS)

Ishii, Yoshikazu; Takagi, Tarou; Nakai, Kouji

In order to analyze the output of the intrusion detection system and the firewall, we evaluated the applicability of ICA(independent component analysis). We developed a simulator for evaluation of intrusion analysis method. The simulator consists of the network model of an information system, the service model and the vulnerability model of each server, and the action model performed on client and intruder. We applied the ICA for analyzing the audit trail of simulated information system. We report the evaluation result of the ICA on intrusion analysis. In the simulated case, ICA separated two attacks correctly, and related an attack and the abnormalities of the normal application produced under the influence of the attach.

Thermal Analysis System

NASA Technical Reports Server (NTRS)

DiStefano, III, Frank James (Inventor); Wobick, Craig A. (Inventor); Chapman, Kirt Auldwin (Inventor); McCloud, Peter L. (Inventor)

2014-01-01

A thermal fluid system modeler including a plurality of individual components. A solution vector is configured and ordered as a function of one or more inlet dependencies of the plurality of individual components. A fluid flow simulator simulates thermal energy being communicated with the flowing fluid and between first and second components of the plurality of individual components. The simulation extends from an initial time to a later time step and bounds heat transfer to be substantially between the flowing fluid, walls of tubes formed in each of the individual components of the plurality, and between adjacent tubes. Component parameters of the solution vector are updated with simulation results for each of the plurality of individual components of the simulation.

Identification of bovine serum albumin as an IgE-reactive beef component in a dog with food hypersensitivity against beef.

PubMed

Ohmori, Keitaro; Masuda, Kenichi; Kawarai, Shinpei; Yasuda, Nobutaka; Sakaguchi, Masahiro; Tsujimoto, Hajime

2007-08-01

IgE-reactive beef components were examined by an immunoblot analysis using a serum from a dog with food hypersensitivity against beef. The immunoblot analysis revealed a distinct band at approximately 66 kDa and a faint band at approximately 50 kDa. The immunoblot analysis for serum IgE reactivity to bovine serum albumin (BSA) also revealed a positive band at 66 kDa. Serum IgE reactivity to the 66-kDa protein of beef was diminished by pre-incubating the serum sample with BSA. Furthermore, a positive reaction to BSA was detected in intradermal testing in the dog. These results clearly indicated that BSA was an IgE-reactive beef component in the dog with food hypersensitivity against beef.

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

PubMed Central

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D.

2009-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings. PMID:19834575

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function.

PubMed

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D

2008-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

An accurate nonlinear finite element analysis and test correlation of a stiffened composite wing panel

NASA Astrophysics Data System (ADS)

Davis, D. D., Jr.; Krishnamurthy, T.; Stroud, W. J.; McCleary, S. L.

1991-05-01

State-of-the-art nonlinear finite element analysis techniques are evaluated by applying them to a realistic aircraft structural component. A wing panel from the V-22 tiltrotor aircraft is chosen because it is a typical modern aircraft structural component for which there is experimental data for comparison of results. From blueprints and drawings, a very detailed finite element model containing 2284 9-node Assumed Natural-Coordinate Strain elements was generated. A novel solution strategy which accounts for geometric nonlinearity through the use of corotating element reference frames and nonlinear strain-displacement relations is used to analyze this detailed model. Results from linear analyses using the same finite element model are presented in order to illustrate the advantages and costs of the nonlinear analysis as compared with the more traditional linear analysis.

An accurate nonlinear finite element analysis and test correlation of a stiffened composite wing panel

NASA Technical Reports Server (NTRS)

Davis, D. D., Jr.; Krishnamurthy, T.; Stroud, W. J.; Mccleary, S. L.

1991-01-01

State-of-the-art nonlinear finite element analysis techniques are evaluated by applying them to a realistic aircraft structural component. A wing panel from the V-22 tiltrotor aircraft is chosen because it is a typical modern aircraft structural component for which there is experimental data for comparison of results. From blueprints and drawings, a very detailed finite element model containing 2284 9-node Assumed Natural-Coordinate Strain elements was generated. A novel solution strategy which accounts for geometric nonlinearity through the use of corotating element reference frames and nonlinear strain-displacement relations is used to analyze this detailed model. Results from linear analyses using the same finite element model are presented in order to illustrate the advantages and costs of the nonlinear analysis as compared with the more traditional linear analysis.

Integrated Droplet-Based Microextraction with ESI-MS for Removal of Matrix Interference in Single-Cell Analysis.

PubMed

Zhang, Xiao-Chao; Wei, Zhen-Wei; Gong, Xiao-Yun; Si, Xing-Yu; Zhao, Yao-Yao; Yang, Cheng-Dui; Zhang, Si-Chun; Zhang, Xin-Rong

2016-04-29

Integrating droplet-based microfluidics with mass spectrometry is essential to high-throughput and multiple analysis of single cells. Nevertheless, matrix effects such as the interference of culture medium and intracellular components influence the sensitivity and the accuracy of results in single-cell analysis. To resolve this problem, we developed a method that integrated droplet-based microextraction with single-cell mass spectrometry. Specific extraction solvent was used to selectively obtain intracellular components of interest and remove interference of other components. Using this method, UDP-Glc-NAc, GSH, GSSG, AMP, ADP and ATP were successfully detected in single MCF-7 cells. We also applied the method to study the change of unicellular metabolites in the biological process of dysfunctional oxidative phosphorylation. The method could not only realize matrix-free, selective and sensitive detection of metabolites in single cells, but also have the capability for reliable and high-throughput single-cell analysis.

Reliability and availability analysis of a 10 kW@20 K helium refrigerator

NASA Astrophysics Data System (ADS)

Li, J.; Xiong, L. Y.; Liu, L. Q.; Wang, H. R.; Wang, B. M.

2017-02-01

A 10 kW@20 K helium refrigerator has been established in the Technical Institute of Physics and Chemistry, Chinese Academy of Sciences. To evaluate and improve this refrigerator’s reliability and availability, a reliability and availability analysis is performed. According to the mission profile of this refrigerator, a functional analysis is performed. The failure data of the refrigerator components are collected and failure rate distributions are fitted by software Weibull++ V10.0. A Failure Modes, Effects & Criticality Analysis (FMECA) is performed and the critical components with higher risks are pointed out. Software BlockSim V9.0 is used to calculate the reliability and the availability of this refrigerator. The result indicates that compressors, turbine and vacuum pump are the critical components and the key units of this refrigerator. The mitigation actions with respect to design, testing, maintenance and operation are proposed to decrease those major and medium risks.

Analysis on unevenness of skin color using the melanin and hemoglobin components separated by independent component analysis of skin color image

NASA Astrophysics Data System (ADS)

Ojima, Nobutoshi; Fujiwara, Izumi; Inoue, Yayoi; Tsumura, Norimichi; Nakaguchi, Toshiya; Iwata, Kayoko

2011-03-01

Uneven distribution of skin color is one of the biggest concerns about facial skin appearance. Recently several techniques to analyze skin color have been introduced by separating skin color information into chromophore components, such as melanin and hemoglobin. However, there are not many reports on quantitative analysis of unevenness of skin color by considering type of chromophore, clusters of different sizes and concentration of the each chromophore. We propose a new image analysis and simulation method based on chromophore analysis and spatial frequency analysis. This method is mainly composed of three techniques: independent component analysis (ICA) to extract hemoglobin and melanin chromophores from a single skin color image, an image pyramid technique which decomposes each chromophore into multi-resolution images, which can be used for identifying different sizes of clusters or spatial frequencies, and analysis of the histogram obtained from each multi-resolution image to extract unevenness parameters. As the application of the method, we also introduce an image processing technique to change unevenness of melanin component. As the result, the method showed high capabilities to analyze unevenness of each skin chromophore: 1) Vague unevenness on skin could be discriminated from noticeable pigmentation such as freckles or acne. 2) By analyzing the unevenness parameters obtained from each multi-resolution image for Japanese ladies, agerelated changes were observed in the parameters of middle spatial frequency. 3) An image processing system modulating the parameters was proposed to change unevenness of skin images along the axis of the obtained age-related change in real time.

Time-Resolved and Spectroscopic Three-Dimensional Optical Breast Tomography

DTIC Science & Technology

2008-04-01

Appendix 1. Each raw image was then cropped to select out the information-rich region, and binned to enhance the signal-to-noise ratio. All the binned...component analysis, near infrared (NIR) imaging, optical mammography , optical imaging using independent component analysis (OPTICA). I. INTRODUCTION N EAR...merging 5 × 5 pixels into one to enhance the SNR, resulting in a total of 352 images of 54 × 55 pixels each. All the binned images corresponding to

Computer program for determination of concentrations of trace elements in components of water systems by nondestructive activation analysis (in German)

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

Slavic, I.; Draskovic, R.; Tasovac, T.

1973-03-01

A computer program for the determination of trace elements in components of the water systems bed material, suspended material, dissolved substances, plankton, algae) by nondestructive activation analysis was developed. Results of the determination of Cr, Sb, Sc, Fe, Co, Na, and La concentrations in suspended materials from the Danube river, obtained by interpretation of data with a CDC- 3600 computer (64 k words), are presented. (auth)

Decomposition-Based Failure Mode Identification Method for Risk-Free Design of Large Systems

NASA Technical Reports Server (NTRS)

Tumer, Irem Y.; Stone, Robert B.; Roberts, Rory A.; Clancy, Daniel (Technical Monitor)

2002-01-01

When designing products, it is crucial to assure failure and risk-free operation in the intended operating environment. Failures are typically studied and eliminated as much as possible during the early stages of design. The few failures that go undetected result in unacceptable damage and losses in high-risk applications where public safety is of concern. Published NASA and NTSB accident reports point to a variety of components identified as sources of failures in the reported cases. In previous work, data from these reports were processed and placed in matrix form for all the system components and failure modes encountered, and then manipulated using matrix methods to determine similarities between the different components and failure modes. In this paper, these matrices are represented in the form of a linear combination of failures modes, mathematically formed using Principal Components Analysis (PCA) decomposition. The PCA decomposition results in a low-dimensionality representation of all failure modes and components of interest, represented in a transformed coordinate system. Such a representation opens the way for efficient pattern analysis and prediction of failure modes with highest potential risks on the final product, rather than making decisions based on the large space of component and failure mode data. The mathematics of the proposed method are explained first using a simple example problem. The method is then applied to component failure data gathered from helicopter, accident reports to demonstrate its potential.

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

Moulin, D.; Chapuliot, S.; Drubay, B.

For structures like vessels or pipes containing a fluid, the Leak-Before-Break (LBB) assessment requires to demonstrate that it is possible, during the lifetime of the component, to detect a rate of leakage due to a possible defect, the growth of which would result in a leak before-break of the component. This LBB assessment could be an important contribution to the overall structural integrity argument for many components. The aim of this paper is to review some practices used for LBB assessment and to describe how some new R & D results have been used to provide a simplified approach ofmore » fracture mechanics analysis and especially the evaluation of crack shape and size during the lifetime of the component.« less

Genome-scale analysis of the high-efficient protein secretion system of Aspergillus oryzae

PubMed Central

2014-01-01

Background The koji mold, Aspergillus oryzae is widely used for the production of industrial enzymes due to its particularly high protein secretion capacity and ability to perform post-translational modifications. However, systemic analysis of its secretion system is lacking, generally due to the poorly annotated proteome. Results Here we defined a functional protein secretory component list of A. oryzae using a previously reported secretory model of S. cerevisiae as scaffold. Additional secretory components were obtained by blast search with the functional components reported in other closely related fungal species such as Aspergillus nidulans and Aspergillus niger. To evaluate the defined component list, we performed transcriptome analysis on three α-amylase over-producing strains with varying levels of secretion capacities. Specifically, secretory components involved in the ER-associated processes (including components involved in the regulation of transport between ER and Golgi) were significantly up-regulated, with many of them never been identified for A. oryzae before. Furthermore, we defined a complete list of the putative A. oryzae secretome and monitored how it was affected by overproducing amylase. Conclusion In combination with the transcriptome data, the most complete secretory component list and the putative secretome, we improved the systemic understanding of the secretory machinery of A. oryzae in response to high levels of protein secretion. The roles of many newly predicted secretory components were experimentally validated and the enriched component list provides a better platform for driving more mechanistic studies of the protein secretory pathway in this industrially important fungus. PMID:24961398

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

A comparison of independent component analysis algorithms and measures to discriminate between EEG and artifact components.

PubMed

Dharmaprani, Dhani; Nguyen, Hoang K; Lewis, Trent W; DeLosAngeles, Dylan; Willoughby, John O; Pope, Kenneth J

2016-08-01

Independent Component Analysis (ICA) is a powerful statistical tool capable of separating multivariate scalp electrical signals into their additive independent or source components, specifically EEG or electroencephalogram and artifacts. Although ICA is a widely accepted EEG signal processing technique, classification of the recovered independent components (ICs) is still flawed, as current practice still requires subjective human decisions. Here we build on the results from Fitzgibbon et al. [1] to compare three measures and three ICA algorithms. Using EEG data acquired during neuromuscular paralysis, we tested the ability of the measures (spectral slope, peripherality and spatial smoothness) and algorithms (FastICA, Infomax and JADE) to identify components containing EMG. Spatial smoothness showed differentiation between paralysis and pre-paralysis ICs comparable to spectral slope, whereas peripherality showed less differentiation. A combination of the measures showed better differentiation than any measure alone. Furthermore, FastICA provided the best discrimination between muscle-free and muscle-contaminated recordings in the shortest time, suggesting it may be the most suited to EEG applications of the considered algorithms. Spatial smoothness results suggest that a significant number of ICs are mixed, i.e. contain signals from more than one biological source, and so the development of an ICA algorithm that is optimised to produce ICs that are easily classifiable is warranted.

Evaluation of RCAS Inflow Models for Wind Turbine Analysis

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

Tangler, J.; Bir, G.

The finite element structural modeling in the Rotorcraft Comprehensive Analysis System (RCAS) provides a state-of-the-art approach to aeroelastic analysis. This, coupled with its ability to model all turbine components, results in a methodology that can simulate complex system interactions characteristic of large wind. In addition, RCAS is uniquely capable of modeling advanced control algorithms and the resulting dynamic responses.

Background Information and User’s Guide for MIL-F-9490

DTIC Science & Technology

1975-01-01

requirements, although different analysis results will apply to each requirement. Basic differences between the two realibility requirements are: MIL-F-8785B...provides the rationale for establishing such limits. The specific risk analysis comprises the same data which formed the average risk analysis , except...statistical analysis will be based on statistical data taken using limited exposure Limes of components and equipment. The exposure times and resulting

Determining the optimal number of independent components for reproducible transcriptomic data analysis.

PubMed

Kairov, Ulykbek; Cantini, Laura; Greco, Alessandro; Molkenov, Askhat; Czerwinska, Urszula; Barillot, Emmanuel; Zinovyev, Andrei

2017-09-11

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data. Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets. We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

Decoupled 1D/3D analysis of a hydraulic valve

NASA Astrophysics Data System (ADS)

Mehring, Carsten; Zopeya, Ashok; Latham, Matt; Ihde, Thomas; Massie, Dan

2014-10-01

Analysis approaches during product development of fluid valves and other aircraft fluid delivery components vary greatly depending on the development stage. Traditionally, empirical or simplistic one-dimensional tools are being deployed during preliminary design, whereas detailed analysis such as CFD (Computational Fluid Dynamics) tools are used to refine a selected design during the detailed design stage. In recent years, combined 1D/3D co-simulation has been deployed specifically for system level simulations requiring an increased level of analysis detail for one or more components. The present paper presents a decoupled 1D/3D analysis approach where 3D CFD analysis results are utilized to enhance the fidelity of a dynamic 1D modelin context of an aircraft fuel valve.

Analyzing the Impact of a Data Analysis Process to Improve Instruction Using a Collaborative Model

ERIC Educational Resources Information Center

Good, Rebecca B.

2006-01-01

The Data Collaborative Model (DCM) assembles assessment literacy, reflective practices, and professional development into a four-component process. The sub-components include assessing students, reflecting over data, professional dialogue, professional development for the teachers, interventions for students based on data results, and re-assessing…

An ICT Adoption Framework for Education: A Case Study in Public Secondary School of Indonesia

NASA Astrophysics Data System (ADS)

Nurjanah, S.; Santoso, H. B.; Hasibuan, Z. A.

2017-01-01

This paper presents preliminary research findings on the ICT adoption framework for education. Despite many studies have been conducted on ICT adoption framework in education at various countries, they are lack of analysis on the degree of component contribution to the success to the framework. In this paper a set of components that link to ICT adoption in education is observed based on literatures and explorative analysis. The components are Infrastructure, Application, User Skills, Utilization, Finance, and Policy. The components are used as a basis to develop a questionnaire to capture the current ICT adoption condition in schools. The data from questionnaire are processed using Structured Equation Model (SEM). The results show that each component contributes differently to the ICT adoption framework. Finance provides the strongest affect to Infrastructure readiness, whilst User Skills provides the strongest affect to Utilization. The study concludes that development of ICT adoption framework should consider components contribution weights among the components that can be used to guide the implementation of ICT adoption in education.

Grey Relational Analysis Coupled with Principal Component Analysis for Optimization of Stereolithography Process to Enhance Part Quality

NASA Astrophysics Data System (ADS)

Raju, B. S.; Sekhar, U. Chandra; Drakshayani, D. N.

2017-08-01

The paper investigates optimization of stereolithography process for SL5530 epoxy resin material to enhance part quality. The major characteristics indexed for performance selected to evaluate the processes are tensile strength, Flexural strength, Impact strength and Density analysis and corresponding process parameters are Layer thickness, Orientation and Hatch spacing. In this study, the process is intrinsically with multiple parameters tuning so that grey relational analysis which uses grey relational grade as performance index is specially adopted to determine the optimal combination of process parameters. Moreover, the principal component analysis is applied to evaluate the weighting values corresponding to various performance characteristics so that their relative importance can be properly and objectively desired. The results of confirmation experiments reveal that grey relational analysis coupled with principal component analysis can effectively acquire the optimal combination of process parameters. Hence, this confirm that the proposed approach in this study can be an useful tool to improve the process parameters in stereolithography process, which is very useful information for machine designers as well as RP machine users.

Metabolite profiling of soy sauce using gas chromatography with time-of-flight mass spectrometry and analysis of correlation with quantitative descriptive analysis.

PubMed

Yamamoto, Shinya; Bamba, Takeshi; Sano, Atsushi; Kodama, Yukako; Imamura, Miho; Obata, Akio; Fukusaki, Eiichiro

2012-08-01

Soy sauces, produced from different ingredients and brewing processes, have variations in components and quality. Therefore, it is extremely important to comprehend the relationship between components and the sensory attributes of soy sauces. The current study sought to perform metabolite profiling in order to devise a method of assessing the attributes of soy sauces. Quantitative descriptive analysis (QDA) data for 24 soy sauce samples were obtained from well selected sensory panelists. Metabolite profiles primarily concerning low-molecular-weight hydrophilic components were based on gas chromatography with time-of-flightmass spectrometry (GC/TOFMS). QDA data for soy sauces were accurately predicted by projection to latent structure (PLS), with metabolite profiles serving as explanatory variables and QDA data set serving as a response variable. Moreover, analysis of correlation between matrices of metabolite profiles and QDA data indicated contributing compounds that were highly correlated with QDA data. Especially, it was indicated that sugars are important components of the tastes of soy sauces. This new approach which combines metabolite profiling with QDA is applicable to analysis of sensory attributes of food as a result of the complex interaction between its components. This approach is effective to search important compounds that contribute to the attributes. Copyright © 2012 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

Evaluation on the concentration change of paeoniflorin and glycyrrhizic acid in different formulations of Shaoyao-Gancao-Tang by the tri-level infrared macro-fingerprint spectroscopy and the whole analysis method

NASA Astrophysics Data System (ADS)

Liu, Aoxue; Wang, Jingjuan; Guo, Yizhen; Xiao, Yao; Wang, Yue; Sun, Suqin; Chen, Jianbo

2018-03-01

As a kind of common prescriptions, Shaoyao-Gancao-Tang (SGT) contains two Chinese herbs with four different proportions which have different clinical efficacy because of their various components. In order to investigate the herb-herb interaction mechanisms, we used the method of tri-level infrared macro-fingerprint spectroscopy to evaluate the concentration change of active components of four SGTs in this research. Fourier transform infrared spectroscopy (FT-IR) and Second derivative infrared spectroscopy (SD-IR) can recognize the multiple prescriptions directly and simultaneously. 2D-IR spectra enhance the spectral resolution and obtain much new information for discriminating the similar complicated samples of SGT. Furthermore, the whole analysis method from the analysis of the main components to the specific components and the relative content of the components may evaluate the quality of TCM better. Then we concluded that paeoniflorin and glycyrrhizic acid were the highest proportion in active ingredients in SGT-12:1 and the lowest one in SGT-12:12, which matched the HPLC-DAD results. It is demonstrated that the method composed by the tri-level infrared macro-fingerprint spectroscopy and the whole analysis can be applicable for effective, visual and accurate analysis and identification of very complicated and similar mixture systems of traditional Chinese medicine.

Probabilistic structural analysis methods for improving Space Shuttle engine reliability

NASA Technical Reports Server (NTRS)

Boyce, L.

1989-01-01

Probabilistic structural analysis methods are particularly useful in the design and analysis of critical structural components and systems that operate in very severe and uncertain environments. These methods have recently found application in space propulsion systems to improve the structural reliability of Space Shuttle Main Engine (SSME) components. A computer program, NESSUS, based on a deterministic finite-element program and a method of probabilistic analysis (fast probability integration) provides probabilistic structural analysis for selected SSME components. While computationally efficient, it considers both correlated and nonnormal random variables as well as an implicit functional relationship between independent and dependent variables. The program is used to determine the response of a nickel-based superalloy SSME turbopump blade. Results include blade tip displacement statistics due to the variability in blade thickness, modulus of elasticity, Poisson's ratio or density. Modulus of elasticity significantly contributed to blade tip variability while Poisson's ratio did not. Thus, a rational method for choosing parameters to be modeled as random is provided.

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885

Levelized cost-benefit analysis of proposed diagnostics for the Ammunition Transfer Arm of the US Army`s Future Armored Resupply Vehicle

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

Wilkinson, V.K.; Young, J.M.

1995-07-01

The US Army`s Project Manager, Advanced Field Artillery System/Future Armored Resupply Vehicle (PM-AFAS/FARV) is sponsoring the development of technologies that can be applied to the resupply vehicle for the Advanced Field Artillery System. The Engineering Technology Division of the Oak Ridge National Laboratory has proposed adding diagnostics/prognostics systems to four components of the Ammunition Transfer Arm of this vehicle, and a cost-benefit analysis was performed on the diagnostics/prognostics to show the potential savings that may be gained by incorporating these systems onto the vehicle. Possible savings could be in the form of reduced downtime, less unexpected or unnecessary maintenance, fewermore » regular maintenance checks. and/or tower collateral damage or loss. The diagnostics/prognostics systems are used to (1) help determine component problems, (2) determine the condition of the components, and (3) estimate the remaining life of the monitored components. The four components on the arm that are targeted for diagnostics/prognostics are (1) the electromechanical brakes, (2) the linear actuators, (3) the wheel/roller bearings, and (4) the conveyor drive system. These would be monitored using electrical signature analysis, vibration analysis, or a combination of both. Annual failure rates for the four components were obtained along with specifications for vehicle costs, crews, number of missions, etc. Accident scenarios based on component failures were postulated, and event trees for these scenarios were constructed to estimate the annual loss of the resupply vehicle, crew, arm. or mission aborts. A levelized cost-benefit analysis was then performed to examine the costs of such failures, both with and without some level of failure reduction due to the diagnostics/prognostics systems. Any savings resulting from using diagnostics/prognostics were calculated.« less

HepG2 cells biospecific extraction and HPLC-ESI-MS analysis for screening potential antiatherosclerotic active components in Bupeuri radix.

PubMed

Liu, Shuqiang; Tan, Zhibin; Li, Pingting; Gao, Xiaoling; Zeng, Yuaner; Wang, Shuling

2016-03-20

HepG2 cells biospecific extraction method and high performance liquid chromatography-electrospray ionization-mass spectrometry (HPLC-ESI-MS) analysis was proposed for screening of potential antiatherosclerotic active components in Bupeuri radix, a well-known Traditional Chinese Medicine (TCM). The hypothesis suggested that when cells are incubated together with the extracts of TCM, the potential bioactive components in the TCM should selectively combine with the receptor or channel of HepG2 cells, then the eluate which contained biospecific component binding to HepG2 cells was identified using HPLC-ESI-MS analysis. The potential bioactive components of Bupeuri radix were investigated using the proposed approach. Five compounds in the saikosaponins of Bupeuri radix were detected as these components selectively combined with HepG2 cells, among these compounds, two potentially bioactive compounds namely saikosaponin b1 and saikosaponin b2 (SSb2) were identified by comparing with the chromatography of the standard sample and analysis of the structural clearance characterization of MS. Then SSb2 was used to assess the uptake of DiI-high density lipoprotein (HDL) in HepG2 cells for antiatherosclerotic activity. The results have showed that SSb2, with indicated concentrations (5, 15, 25, and 40 μM) could remarkably uptake dioctadecylindocarbocyanine labeled- (DiI) -HDL in HepG2 cells (Vs control group, *P<0.01). In conclusion, the application of HepG2 biospecific extraction coupled with HPLC-ESI-MS analysis is a rapid, convenient, and reliable method for screening potential bioactive components in TCM and SSb2 may be a valuable novel drug agent for the treatment of atherosclerosis. Copyright © 2016 Elsevier B.V. All rights reserved.

Spatially resolved spectroscopy analysis of the XMM-Newton large program on SN1006

NASA Astrophysics Data System (ADS)

Li, Jiang-Tao; Decourchelle, Anne; Miceli, Marco; Vink, Jacco; Bocchino, Fabrizio

2016-04-01

We perform analysis of the XMM-Newton large program on SN1006 based on our newly developed methods of spatially resolved spectroscopy analysis. We extract spectra from low and high resolution meshes. The former (3596 meshes) is used to roughly decompose the thermal and non-thermal components and characterize the spatial distributions of different parameters, such as temperature, abundances of different elements, ionization age, and electron density of the thermal component, as well as photon index and cutoff frequency of the non-thermal component. On the other hand, the low resolution meshes (583 meshes) focus on the interior region dominated by the thermal emission and have enough counts to well characterize the Si lines. We fit the spectra from the low resolution meshes with different models, in order to decompose the multiple plasma components at different thermal and ionization states and compare their spatial distributions. In this poster, we will present the initial results of this project.

K-Fold Crossvalidation in Canonical Analysis.

ERIC Educational Resources Information Center

Liang, Kun-Hsia; And Others

1995-01-01

A computer-assisted, K-fold cross-validation technique is discussed in the framework of canonical correlation analysis of randomly generated data sets. Analysis results suggest that this technique can effectively reduce the contamination of canonical variates and canonical correlations by sample-specific variance components. (Author/SLD)

Virtual directions in paleomagnetism: A global and rapid approach to evaluate the NRM components.

NASA Astrophysics Data System (ADS)

Ramón, Maria J.; Pueyo, Emilio L.; Oliva-Urcia, Belén; Larrasoaña, Juan C.

2017-02-01

We introduce a method and software to process demagnetization data for a rapid and integrative estimation of characteristic remanent magnetization (ChRM) components. The virtual directions (VIDI) of a paleomagnetic site are “all” possible directions that can be calculated from a given demagnetization routine of “n” steps (being m the number of specimens in the site). If the ChRM can be defined for a site, it will be represented in the VIDI set. Directions can be calculated for successive steps using principal component analysis, both anchored to the origin (resultant virtual directions RVD; m * (n2+n)/2) and not anchored (difference virtual directions DVD; m * (n2-n)/2). The number of directions per specimen (n2) is very large and will enhance all ChRM components with noisy regions where two components were fitted together (mixing their unblocking intervals). In the same way, resultant and difference virtual circles (RVC, DVC) are calculated. Virtual directions and circles are a global and objective approach to unravel different natural remanent magnetization (NRM) components for a paleomagnetic site without any assumption. To better constrain the stable components, some filters can be applied, such as establishing an upper boundary to the MAD, removing samples with anomalous intensities, or stating a minimum number of demagnetization steps (objective filters) or selecting a given unblocking interval (subjective but based on the expertise). On the other hand, the VPD program also allows the application of standard approaches (classic PCA fitting of directions a circles) and other ancillary methods (stacking routine, linearity spectrum analysis) giving an objective, global and robust idea of the demagnetization structure with minimal assumptions. Application of the VIDI method to natural cases (outcrops in the Pyrenees and u-channel data from a Roman dam infill in northern Spain) and their comparison to other approaches (classic end-point, demagnetization circle analysis, stacking routine and linearity spectrum analysis) allows validation of this technique. The VIDI is a global approach and it is especially useful for large data sets and rapid estimation of the NRM components.

Thermoelastic Stress Analysis: An NDE Tool for the Residual Stress Assessment of Metallic Alloys

NASA Technical Reports Server (NTRS)

Gyekenyesi, Andrew L.; Baaklini, George Y.

2000-01-01

During manufacturing, certain propulsion components that will be used in a cyclic fatigue environment are fabricated to contain compressive residual stresses on their surfaces because these stresses inhibit the nucleation of cracks. Overloads and elevated temperature excursions cause the induced residual stresses to dissipate while the component is still in service, lowering its resistance to crack initiation. Research at the NASA Glenn Research Center at Lewis Field has focused on employing the Thermoelastic Stress Analysis technique (TSA, also recognized as SPATE: Stress Pattern Analysis by Thermal Emission) as a tool for monitoring the residual stress state of propulsion components. TSA is based on the fact that materials experience small temperature changes when they are compressed or expanded. When a structure is cyclically loaded (i.e., cyclically compressed and expanded), the resulting surface-temperature profile correlates to the stress state of the structure s surface. The surface-temperature variations resulting from a cyclic load are measured with an infrared camera. Traditionally, the temperature amplitude of a TSA signal has been theoretically defined to be linearly dependent on the cyclic stress amplitude. As a result, the temperature amplitude resulting from an applied cyclic stress was assumed to be independent of the cyclic mean stress.

Reliability Prediction Analysis: Airborne System Results and Best Practices

NASA Astrophysics Data System (ADS)

Silva, Nuno; Lopes, Rui

2013-09-01

This article presents the results of several reliability prediction analysis for aerospace components, made by both methodologies, the 217F and the 217Plus. Supporting and complementary activities are described, as well as the differences concerning the results and the applications of both methodologies that are summarized in a set of lessons learned that are very useful for RAMS and Safety Prediction practitioners.The effort that is required for these activities is also an important point that is discussed, as is the end result and their interpretation/impact on the system design.The article concludes while positioning these activities and methodologies in an overall process for space and aeronautics equipment/components certification, and highlighting their advantages. Some good practices have also been summarized and some reuse rules have been laid down.

Application of principal component analysis to multispectral imaging data for evaluation of pigmented skin lesions

NASA Astrophysics Data System (ADS)

Jakovels, Dainis; Lihacova, Ilze; Kuzmina, Ilona; Spigulis, Janis

2013-11-01

Non-invasive and fast primary diagnostics of pigmented skin lesions is required due to frequent incidence of skin cancer - melanoma. Diagnostic potential of principal component analysis (PCA) for distant skin melanoma recognition is discussed. Processing of the measured clinical multi-spectral images (31 melanomas and 94 nonmalignant pigmented lesions) in the wavelength range of 450-950 nm by means of PCA resulted in 87 % sensitivity and 78 % specificity for separation between malignant melanomas and pigmented nevi.

Dynamic competitive probabilistic principal components analysis.

PubMed

López-Rubio, Ezequiel; Ortiz-DE-Lazcano-Lobato, Juan Miguel

2009-04-01

We present a new neural model which extends the classical competitive learning (CL) by performing a Probabilistic Principal Components Analysis (PPCA) at each neuron. The model also has the ability to learn the number of basis vectors required to represent the principal directions of each cluster, so it overcomes a drawback of most local PCA models, where the dimensionality of a cluster must be fixed a priori. Experimental results are presented to show the performance of the network with multispectral image data.

Source Analysis of the Crandall Canyon, Utah, Mine Collapse

DOE PAGES

Dreger, D. S.; Ford, S. R.; Walter, W. R.

2008-07-11

Analysis of seismograms from a magnitude 3.9 seismic event on August 6, 2007 in central Utah reveals an anomalous radiation pattern that is contrary to that expected for a tectonic earthquake, and which is dominated by an implosive component. The results show the seismic event is best modeled as a shallow underground collapse. Interestingly, large transverse surface waves require a smaller additional non-collapse source component that represents either faulting in the rocks above the mine workings or deformation of the medium surrounding the mine.

A network analysis of the Chinese medicine Lianhua-Qingwen formula to identify its main effective components.

PubMed

Wang, Chun-Hua; Zhong, Yi; Zhang, Yan; Liu, Jin-Ping; Wang, Yue-Fei; Jia, Wei-Na; Wang, Guo-Cai; Li, Zheng; Zhu, Yan; Gao, Xiu-Mei

2016-02-01

Chinese medicine is known to treat complex diseases with multiple components and multiple targets. However, the main effective components and their related key targets and functions remain to be identified. Herein, a network analysis method was developed to identify the main effective components and key targets of a Chinese medicine, Lianhua-Qingwen Formula (LQF). The LQF is commonly used for the prevention and treatment of viral influenza in China. It is composed of 11 herbs, gypsum and menthol with 61 compounds being identified in our previous work. In this paper, these 61 candidate compounds were used to find their related targets and construct the predicted-target (PT) network. An influenza-related protein-protein interaction (PPI) network was constructed and integrated with the PT network. Then the compound-effective target (CET) network and compound-ineffective target network (CIT) were extracted, respectively. A novel approach was developed to identify effective components by comparing CET and CIT networks. As a result, 15 main effective components were identified along with 61 corresponding targets. 7 of these main effective components were further experimentally validated to have antivirus efficacy in vitro. The main effective component-target (MECT) network was further constructed with main effective components and their key targets. Gene Ontology (GO) analysis of the MECT network predicted key functions such as NO production being modulated by the LQF. Interestingly, five effective components were experimentally tested and exhibited inhibitory effects on NO production in the LPS induced RAW 264.7 cell. In summary, we have developed a novel approach to identify the main effective components in a Chinese medicine LQF and experimentally validated some of the predictions.

Inventory of File sref_em.t03z.pgrb212.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_nmm.t03z.pgrb132.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_nmm.t03z.pgrb221.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_em.t03z.pgrb132.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_nmm.t03z.pgrb243.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_em.t03z.pgrb243.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_em.t03z.pgrb221.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_nmm.t03z.pgrb212.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_nmm.t03z.pgrb216.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Inventory of File sref_em.t03z.pgrb216.ctl.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 006 10 m above ground VGRD analysis V-Component of Wind [m/s of Wind [m/s] 018 250 mb VGRD analysis V-Component of Wind [m/s] 019 500 mb HGT analysis Geopotential Height [gpm] 020 500 mb UGRD analysis U-Component of Wind [m/s] 021 500 mb VGRD analysis V-Component of

Unsupervised Neural Network Quantifies the Cost of Visual Information Processing.

PubMed

Orbán, Levente L; Chartier, Sylvain

2015-01-01

Untrained, "flower-naïve" bumblebees display behavioural preferences when presented with visual properties such as colour, symmetry, spatial frequency and others. Two unsupervised neural networks were implemented to understand the extent to which these models capture elements of bumblebees' unlearned visual preferences towards flower-like visual properties. The computational models, which are variants of Independent Component Analysis and Feature-Extracting Bidirectional Associative Memory, use images of test-patterns that are identical to ones used in behavioural studies. Each model works by decomposing images of floral patterns into meaningful underlying factors. We reconstruct the original floral image using the components and compare the quality of the reconstructed image to the original image. Independent Component Analysis matches behavioural results substantially better across several visual properties. These results are interpreted to support a hypothesis that the temporal and energetic costs of information processing by pollinators served as a selective pressure on floral displays: flowers adapted to pollinators' cognitive constraints.

A Genealogical Interpretation of Principal Components Analysis

PubMed Central

McVean, Gil

2009-01-01

Principal components analysis, PCA, is a statistical method commonly used in population genetics to identify structure in the distribution of genetic variation across geographical location and ethnic background. However, while the method is often used to inform about historical demographic processes, little is known about the relationship between fundamental demographic parameters and the projection of samples onto the primary axes. Here I show that for SNP data the projection of samples onto the principal components can be obtained directly from considering the average coalescent times between pairs of haploid genomes. The result provides a framework for interpreting PCA projections in terms of underlying processes, including migration, geographical isolation, and admixture. I also demonstrate a link between PCA and Wright's fst and show that SNP ascertainment has a largely simple and predictable effect on the projection of samples. Using examples from human genetics, I discuss the application of these results to empirical data and the implications for inference. PMID:19834557

Resolving fluorophores by unmixing multispectral fluorescence tomography with independent component analysis

NASA Astrophysics Data System (ADS)

Pu, Huangsheng; Zhang, Guanglei; He, Wei; Liu, Fei; Guang, Huizhi; Zhang, Yue; Bai, Jing; Luo, Jianwen

2014-09-01

It is a challenging problem to resolve and identify drug (or non-specific fluorophore) distribution throughout the whole body of small animals in vivo. In this article, an algorithm of unmixing multispectral fluorescence tomography (MFT) images based on independent component analysis (ICA) is proposed to solve this problem. ICA is used to unmix the data matrix assembled by the reconstruction results from MFT. Then the independent components (ICs) that represent spatial structures and the corresponding spectrum courses (SCs) which are associated with spectral variations can be obtained. By combining the ICs with SCs, the recovered MFT images can be generated and fluorophore concentration can be calculated. Simulation studies, phantom experiments and animal experiments with different concentration contrasts and spectrum combinations are performed to test the performance of the proposed algorithm. Results demonstrate that the proposed algorithm can not only provide the spatial information of fluorophores, but also recover the actual reconstruction of MFT images.

Assessment of cluster yield components by image analysis.

PubMed

Diago, Maria P; Tardaguila, Javier; Aleixos, Nuria; Millan, Borja; Prats-Montalban, Jose M; Cubero, Sergio; Blasco, Jose

2015-04-01

Berry weight, berry number and cluster weight are key parameters for yield estimation for wine and tablegrape industry. Current yield prediction methods are destructive, labour-demanding and time-consuming. In this work, a new methodology, based on image analysis was developed to determine cluster yield components in a fast and inexpensive way. Clusters of seven different red varieties of grapevine (Vitis vinifera L.) were photographed under laboratory conditions and their cluster yield components manually determined after image acquisition. Two algorithms based on the Canny and the logarithmic image processing approaches were tested to find the contours of the berries in the images prior to berry detection performed by means of the Hough Transform. Results were obtained in two ways: by analysing either a single image of the cluster or using four images per cluster from different orientations. The best results (R(2) between 69% and 95% in berry detection and between 65% and 97% in cluster weight estimation) were achieved using four images and the Canny algorithm. The model's capability based on image analysis to predict berry weight was 84%. The new and low-cost methodology presented here enabled the assessment of cluster yield components, saving time and providing inexpensive information in comparison with current manual methods. © 2014 Society of Chemical Industry.

Application of Steinberg vibration fatigue model for structural verification of space instruments

NASA Astrophysics Data System (ADS)

García, Andrés; Sorribes-Palmer, Félix; Alonso, Gustavo

2018-01-01

Electronic components in spaceships are subjected to vibration loads during the ascent phase of the launcher. It is important to verify by tests and analysis that all parts can survive in the most severe load cases. The purpose of this paper is to present the methodology and results of the application of the Steinberg's fatigue model to estimate the life of electronic components of the EPT-HET instrument for the Solar Orbiter space mission. A Nastran finite element model (FEM) of the EPT-HET instrument was created and used for the structural analysis. The methodology is based on the use of the FEM of the entire instrument to calculate the relative displacement RDSD and RMS values of the PCBs from random vibration analysis. These values are used to estimate the fatigue life of the most susceptible electronic components with the Steinberg's fatigue damage equation and the Miner's cumulative fatigue index. The estimations are calculated for two different configurations of the instrument and three different inputs in order to support the redesign process. Finally, these analytical results are contrasted with the inspections and the functional tests made after the vibration tests, concluding that this methodology can adequately predict the fatigue damage or survival of the electronic components.

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

PubMed

Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

2008-03-01

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Frequency Domain Analysis of Multiwavelength Photoacoustic Signals for Differentiating Tissue Components

NASA Astrophysics Data System (ADS)

Jian, X. H.; Dong, F. L.; Xu, J.; Li, Z. J.; Jiao, Y.; Cui, Y. Y.

2018-05-01

The feasibility of differentiating tissue components by performing frequency domain analysis of photoacoustic images acquired at different wavelengths was studied in this paper. Firstly, according to the basic theory of photoacoustic imaging, a brief theoretical model for frequency domain analysis of multiwavelength photoacoustic signal was deduced. The experiment results proved that the performance of different targets in frequency domain is quite different. Especially, the acoustic spectrum characteristic peaks of different targets are unique, which are 2.93 MHz, 5.37 MHz, 6.83 MHz, and 8.78 MHz for PDMS phantom, while 13.20 MHz, 16.60 MHz, 26.86 MHz, and 29.30 MHz for pork fat. The results indicated that the acoustic spectrum of photoacoustic imaging signals is possible to be utilized for tissue composition characterization.

Automatic removal of eye-movement and blink artifacts from EEG signals.

PubMed

Gao, Jun Feng; Yang, Yong; Lin, Pan; Wang, Pei; Zheng, Chong Xun

2010-03-01

Frequent occurrence of electrooculography (EOG) artifacts leads to serious problems in interpreting and analyzing the electroencephalogram (EEG). In this paper, a robust method is presented to automatically eliminate eye-movement and eye-blink artifacts from EEG signals. Independent Component Analysis (ICA) is used to decompose EEG signals into independent components. Moreover, the features of topographies and power spectral densities of those components are extracted to identify eye-movement artifact components, and a support vector machine (SVM) classifier is adopted because it has higher performance than several other classifiers. The classification results show that feature-extraction methods are unsuitable for identifying eye-blink artifact components, and then a novel peak detection algorithm of independent component (PDAIC) is proposed to identify eye-blink artifact components. Finally, the artifact removal method proposed here is evaluated by the comparisons of EEG data before and after artifact removal. The results indicate that the method proposed could remove EOG artifacts effectively from EEG signals with little distortion of the underlying brain signals.

A Convective Vorticity Vector Associated With Tropical Convection: A 2D Cloud-Resolving Modeling Study

NASA Technical Reports Server (NTRS)

Gao, Shou-Ting; Ping, Fan; Li, Xiao-Fan; Tao, Wei-Kuo

2004-01-01

Although dry/moist potential vorticity is a useful physical quantity for meteorological analysis, it cannot be applied to the analysis of 2D simulations. A convective vorticity vector (CVV) is introduced in this study to analyze 2D cloud-resolving simulation data associated with 2D tropical convection. The cloud model is forced by the vertical velocity, zonal wind, horizontal advection, and sea surface temperature obtained from the TOGA COARE, and is integrated for a selected 10-day period. The CVV has zonal and vertical components in the 2D x-z frame. Analysis of zonally-averaged and mass-integrated quantities shows that the correlation coefficient between the vertical component of the CVV and the sum of the cloud hydrometeor mixing ratios is 0.81, whereas the correlation coefficient between the zonal component and the sum of the mixing ratios is only 0.18. This indicates that the vertical component of the CVV is closely associated with tropical convection. The tendency equation for the vertical component of the CVV is derived and the zonally-averaged and mass-integrated tendency budgets are analyzed. The tendency of the vertical component of the CVV is determined by the interaction between the vorticity and the zonal gradient of cloud heating. The results demonstrate that the vertical component of the CVV is a cloud-linked parameter and can be used to study tropical convection.

Effect of rotational alignment on outcome of total knee arthroplasty

PubMed Central

Breugem, Stefan J; van den Bekerom, Michel PJ; Tuinebreijer, Willem E; van Geenen, Rutger C I

2015-01-01

Background and purpose Poor outcomes have been linked to errors in rotational alignment of total knee arthroplasty components. The aims of this study were to determine the correlation between rotational alignment and outcome, to review the success of revision for malrotated total knee arthroplasty, and to determine whether evidence-based guidelines for malrotated total knee arthroplasty can be proposed. Patients and methods We conducted a systematic review including all studies reporting on both rotational alignment and functional outcome. Comparable studies were used in a correlation analysis and results of revision were analyzed separately. Results 846 studies were identified, 25 of which met the inclusion criteria. From this selection, 11 studies could be included in the correlation analysis. A medium positive correlation (ρ = 0.44, 95% CI: 0.27–0.59) and a large positive correlation (ρ = 0.68, 95% CI: 0.64–0.73) were found between external rotation of the tibial component and the femoral component, respectively, and the Knee Society score. Revision for malrotation gave positive results in all 6 studies in this field. Interpretation Medium and large positive correlations were found between tibial and femoral component rotational alignment on the one hand and better functional outcome on the other. Revision of malrotated total knee arthroplasty may be successful. However, a clear cutoff point for revision for malrotated total knee arthroplasty components could not be identified. PMID:25708694

Combined slope ratio analysis and linear-subtraction: An extension of the Pearce ratio method

NASA Astrophysics Data System (ADS)

De Waal, Sybrand A.

1996-07-01

A new technique, called combined slope ratio analysis, has been developed by extending the Pearce element ratio or conserved-denominator method (Pearce, 1968) to its logical conclusions. If two stoichiometric substances are mixed and certain chemical components are uniquely contained in either one of the two mixing substances, then by treating these unique components as conserved, the composition of the substance not containing the relevant component can be accurately calculated within the limits allowed by analytical and geological error. The calculated composition can then be subjected to rigorous statistical testing using the linear-subtraction method recently advanced by Woronow (1994). Application of combined slope ratio analysis to the rocks of the Uwekahuna Laccolith, Hawaii, USA, and the lavas of the 1959-summit eruption of Kilauea Volcano, Hawaii, USA, yields results that are consistent with field observations.

Identification and confirmation of chemical residues by chromatography-mass spectrometry and other techniques

USDA-ARS?s Scientific Manuscript database

A quantitative answer cannot exist in an analysis without a qualitative component to give enough confidence that the result meets the analytical needs for the analysis (i.e. the result relates to the analyte and not something else). Just as a quantitative method must typically undergo an empirical ...

Stability analysis of cylinders with circular cutouts

NASA Technical Reports Server (NTRS)

Almroth, B. O.; Brogan, F. A.; Marlowe, M. B.

1973-01-01

The stability of axially compressed cylinders with circular cutouts is analyzed numerically. An extension of the finite-difference method is used which removes the requirement that displacement components be defined in the directions of the grid lines. The results of this nonlinear analysis are found to be in good agreement with earlier experimental results.

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines.

PubMed

Irimia, Andrei; Richards, William O; Bradshaw, L Alan

2009-11-01

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

Effect of removing the common mode errors on linear regression analysis of noise amplitudes in position time series of a regional GPS network & a case study of GPS stations in Southern California

NASA Astrophysics Data System (ADS)

Jiang, Weiping; Ma, Jun; Li, Zhao; Zhou, Xiaohui; Zhou, Boye

2018-05-01

The analysis of the correlations between the noise in different components of GPS stations has positive significance to those trying to obtain more accurate uncertainty of velocity with respect to station motion. Previous research into noise in GPS position time series focused mainly on single component evaluation, which affects the acquisition of precise station positions, the velocity field, and its uncertainty. In this study, before and after removing the common-mode error (CME), we performed one-dimensional linear regression analysis of the noise amplitude vectors in different components of 126 GPS stations with a combination of white noise, flicker noise, and random walking noise in Southern California. The results show that, on the one hand, there are above-moderate degrees of correlation between the white noise amplitude vectors in all components of the stations before and after removal of the CME, while the correlations between flicker noise amplitude vectors in horizontal and vertical components are enhanced from un-correlated to moderately correlated by removing the CME. On the other hand, the significance tests show that, all of the obtained linear regression equations, which represent a unique function of the noise amplitude in any two components, are of practical value after removing the CME. According to the noise amplitude estimates in two components and the linear regression equations, more accurate noise amplitudes can be acquired in the two components.

Effects of growing location on the production of main active components and antioxidant activity of Dasiphora fruticosa (L.) Rydb. by chemometric methods.

PubMed

Liu, Wei; Wang, Dongmei; Hou, Xiaogai; Yang, Yueqin; Xue, Xian; Jia, Qishi; Zhang, Lixia; Zhao, Wei; Yin, Dongxue

2018-05-17

Traditional Chinese medicine (TCM) plays a very important role in the health system of China. The content and activity of active component are main indexes that evaluate the quality of TCM, however they may vary with environmental factors in their growing locations. In this study, effects of environmental factors on the contents of active components and antioxidant activity of Dasiphora fruticosa from the five main production areas of China were investigated. The contents of tannin, total flavonoid and rutin were determined and varied within the range of 7.65-10.69%, 2.30-5.39% and 0.18-0.81%, respectively. Antioxidant activity was determined by DPPH assay, with the DPPH IC 50 values ranged from 8.791 to 32.534μg mL -1 . In order to further explore the cause of these significant geographical variations, the chemometric methods including correlation analysis, principal component analysis, gray correlation analysis, and path analysis were conducted. The results showed environmental factors had significant effect on the active component contents and antioxidant activity. Rapidly available phosphorus (RAP) and rapidly available nitrogen (RAN) were common dominant factors, and a significant positive correlation was observed between RAP and active components and antioxidant activity (P<0.05). Contributed by their high active components and strong antioxidant activity, Bange in Tibet and Geermu in Qinghai Province was selected as a favorable growing location, respectively. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

The role of event water, a rapid shallow flow component, and catchment size in summer stormflow

USGS Publications Warehouse

Brown, V.A.; McDonnell, Jeffery J.; Burns, Douglas A.; Kendall, C.

1999-01-01

Seven nested headwater catchments (8 to 161 ha) were monitored during five summer rain events to evaluate storm runoff components and the effect of catchment size on water sources. Two-component isotopic hydrograph separation showed that event-water contributions near the time of peakflow ranged from 49% to 62% in the 7 catchments during the highest intensity event. The proportion of event water in stormflow was greater than could be accounted for by direct precipitation onto saturated areas. DOC concentrations in stormflow were strongly correlated with stream 18O composition. Bivariate mixing diagrams indicated that the large event water contributions were likely derived from flow through the soil O-horizon. Results from two-tracer, three-component hydrograph separations showed that the throughfall and O-horizon soil-water components together could account for the estimated contributions of event water to stormflow. End-member mixing analysis confirmed these results. Estimated event-water contributions were inversely related to catchment size, but the relation was significant for only the event with greatest rainfall intensity. Our results suggest that perched, shallow subsurface flow provides a substantial contribution to summer stormflow in these small catchments, but the relative contribution of this component decreases with catchment size.Seven nested headwater catchments (8 to 161 ha) were monitored during five summer rain events to evaluate storm runoff components and the effect of catchment size on water sources. Two-component isotopic hydrograph separation showed that event-water contributions near the time of peakflow ranged from 49% to 62% in the 7 catchments during the highest intensity event. The proportion of event water in stormflow was greater than could be accounted for by direct precipitation onto saturated areas. DOC concentrations in stormflow were strongly correlated with stream 18O composition. Bivariate mixing diagrams indicated that the large event water contributions were likely derived from flow through the soil O-horizon. Results from two-tracer, three-component hydrograph separations showed that the throughfall and O-horizon soil-water components together could account for the estimated contributions of event water to stormflow. End-member mixing analysis confirmed these results. Estimated event-water contributions were inversely related to catchment size, but the relation was significant for only the event with greatest rainfall intensity. Our results suggest that perched, shallow subsurface flow provides a substantial contribution to summer stormflow in these small catchments, but the relative contribution of this component decreases with catchment size.

Development of a scale to measure adherence to self-monitoring of blood glucose with latent variable measurement.

PubMed

Wagner, J A; Schnoll, R A; Gipson, M T

1998-07-01

Adherence to self-monitoring of blood glucose (SMBG) is problematic for many people with diabetes. Self-reports of adherence have been found to be unreliable, and existing paper-and-pencil measures have limitations. This study developed a brief measure of SMBG adherence with good psychometric properties and a useful factor structure that can be used in research and in practice. A total of 216 adults with diabetes responded to 30 items rated on a 9-point Likert scale that asked about blood monitoring habits. In part I of the study, items were evaluated and retained based on their psychometric properties. The sample was divided into exploratory and confirmatory halves. Using the exploratory half, items with acceptable psychometric properties were subjected to a principal components analysis. In part II of the study, structural equation modeling was used to confirm the component solution with the entire sample. Structural modeling was also used to test the relationship between these components. It was hypothesized that the scale would produce four correlated factors. Principal components analysis suggested a two-component solution, and confirmatory factor analysis confirmed this solution. The first factor measures the degree to which patients rely on others to help them test and thus was named "social influence." The second component measures the degree to which patients use physical symptoms of blood glucose levels to help them test and thus was named "physical influence." Results of the structural model show that the components are correlated and make up the higher-order latent variable adherence. The resulting 15-item scale provides a short, reliable way to assess patient adherence to SMBG. Despite the existence of several aspects of adherence, this study indicates that the construct consists of only two components. This scale is an improvement on previous measures of adherence because of its good psychometric properties, its interpretable factor structure, and its rigorous empirical development.

Object Based Numerical Zooming Between the NPSS Version 1 and a 1-Dimensional Meanline High Pressure Compressor Design Analysis Code

NASA Technical Reports Server (NTRS)

Follen, G.; Naiman, C.; auBuchon, M.

2000-01-01

Within NASA's High Performance Computing and Communication (HPCC) program, NASA Glenn Research Center is developing an environment for the analysis/design of propulsion systems for aircraft and space vehicles called the Numerical Propulsion System Simulation (NPSS). The NPSS focuses on the integration of multiple disciplines such as aerodynamics, structures, and heat transfer, along with the concept of numerical zooming between 0- Dimensional to 1-, 2-, and 3-dimensional component engine codes. The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. Current "state-of-the-art" engine simulations are 0-dimensional in that there is there is no axial, radial or circumferential resolution within a given component (e.g. a compressor or turbine has no internal station designations). In these 0-dimensional cycle simulations the individual component performance characteristics typically come from a table look-up (map) with adjustments for off-design effects such as variable geometry, Reynolds effects, and clearances. Zooming one or more of the engine components to a higher order, physics-based analysis means a higher order code is executed and the results from this analysis are used to adjust the 0-dimensional component performance characteristics within the system simulation. By drawing on the results from more predictive, physics based higher order analysis codes, "cycle" simulations are refined to closely model and predict the complex physical processes inherent to engines. As part of the overall development of the NPSS, NASA and industry began the process of defining and implementing an object class structure that enables Numerical Zooming between the NPSS Version I (0-dimension) and higher order 1-, 2- and 3-dimensional analysis codes. The NPSS Version I preserves the historical cycle engineering practices but also extends these classical practices into the area of numerical zooming for use within a companies' design system. What follows here is a description of successfully zooming I-dimensional (row-by-row) high pressure compressor results back to a NPSS engine 0-dimension simulation and a discussion of the results illustrated using an advanced data visualization tool. This type of high fidelity system-level analysis, made possible by the zooming capability of the NPSS, will greatly improve the fidelity of the engine system simulation and enable the engine system to be "pre-validated" prior to commitment to engine hardware.

Decreasing Inappropriate Vocalizations Using Classwide Group Contingencies and Color Wheel Procedures: A Component Analysis

ERIC Educational Resources Information Center

Kirk, Emily R.; Becker, Jennifer A.; Skinner, Christopher H., Fearrington, Jamie Yarbr; McCane-Bowling, Sara J.; Amburn, Christie; Luna, Elisa; Greear, Corinne

2010-01-01

Teacher referrals for consultation resulted in two independent teams collecting evidence that allowed for a treatment component evaluation of color wheel (CW) procedures and/or interdependent group-oriented reward (IGOR) procedures on inappropriate vocalizations in one third- and one first-grade classroom. Both studies involved the application of…

Adiabatic diesel engine component development: Reference engine for on-highway applications

NASA Technical Reports Server (NTRS)

Hakim, Nabil S.

1986-01-01

The main objectives were to select an advanced low heat rejection diesel reference engine (ADRE) and to carry out systems analysis and design. The ADRE concept selection consisted of: (1) rated point performance optimization; (2) study of various exhaust energy recovery scenarios; (3) components, systems and engine configuration studies; and (4) life cycle cost estimates of the ADRE economic worth. The resulting ADRE design proposed a reciprocator with many advanced features for the 1995 technology demonstration time frame. These included ceramic air gap insulated hot section structural components, high temperature tribology treatments, nonmechanical (camless) valve actuation systems, and elimination of the cylinder head gasket. ADRE system analysis and design resulted in more definition of the engine systems. These systems include: (1) electro-hydraulic valve actuation, (2) electronic common rail injection system; (3) engine electronic control; (4) power transfer for accessory drives and exhaust energy recovery systems; and (5) truck installation. Tribology and performance assessments were also carried out. Finite element and probability of survival analyses were undertaken for the ceramic low heat rejection component.

[Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

PubMed

Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

2017-06-01

The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

[Comparative analysis of the efficacy of a playful-narrative program to teach mathematics at pre-school level].

PubMed

Gil Llario, M D; Vicent Catalá, Consuelo

2009-02-01

Comparative analysis of the efficacy of a playful-narrative program to teach mathematics at pre-school level. In this paper, the effectiveness of a programme comprising several components that are meant to consolidate mathematical concepts and abilities at the pre-school level is analyzed. The instructional methodology of this programme is compared to other methodologies. One-hundred 5-6 year-old children made up the sample that was distributed in the following conditions: (1) traditional methodology; (2) methodology with perceptual and manipulative components, and (3) methodology with language and playful components. Mathematical competence was assessed with the Mathematical Criterial Pre-school Test and the subtest of quantitative-numeric concepts of BADyG. Participants were evaluated before and after the academic course during which they followed one of these methodologies. The results show that the programme with language and playful components is more effective than the traditional methodology (p<.000) and also more effective than the perceptual and manipulative methodology (p<.000). Implications of the results for instructional practices are analyzed.

Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis

PubMed Central

Fleisch, Elgar

2017-01-01

Background Existing research postulates a variety of components that show an impact on utilization of technology-mediated mental health information systems (MHIS) and treatment outcome. Although researchers assessed the effect of isolated design elements on the results of Web-based interventions and the associations between symptom reduction and use of components across computer and mobile phone platforms, there remains uncertainty with regard to which components of technology-mediated interventions for mental health exert the greatest therapeutic gain. Until now, no studies have presented results on the therapeutic benefit associated with specific service components of technology-mediated MHIS for depression. Objective This systematic review aims at identifying components of technology-mediated MHIS for patients with depression. Consequently, all randomized controlled trials comparing technology-mediated treatments for depression to either waiting-list control, treatment as usual, or any other form of treatment for depression were reviewed. Updating prior reviews, this study aims to (1) assess the effectiveness of technology-supported interventions for the treatment of depression and (2) add to the debate on what components in technology-mediated MHIS for the treatment of depression should be standard of care. Methods Systematic searches in MEDLINE, PsycINFO, and the Cochrane Library were conducted. Effect sizes for each comparison between a technology-enabled intervention and a control condition were computed using the standard mean difference (SMD). Chi-square tests were used to test for heterogeneity. Using subgroup analysis, potential sources of heterogeneity were analyzed. Publication bias was examined using visual inspection of funnel plots and Begg’s test. Qualitative data analysis was also used. In an explorative approach, a list of relevant components was extracted from the body of literature by consensus between two researchers. Results Of 6387 studies initially identified, 45 met all inclusion criteria. Programs analyzed showed a significant trend toward reduced depressive symptoms (SMD –0.58, 95% CI –0.71 to –0.45, P<.001). Heterogeneity was large (I2≥76). A total of 15 components were identified. Conclusions Technology-mediated MHIS for the treatment of depression has a consistent positive overall effect compared to controls. A total of 15 components have been identified. Further studies are needed to quantify the impact of individual components on treatment effects and to identify further components that are relevant for the design of future technology-mediated interventions for the treatment of depression and other mental disorders. PMID:28566267

Spectral decomposition of asteroid Itokawa based on principal component analysis

NASA Astrophysics Data System (ADS)

Koga, Sumire C.; Sugita, Seiji; Kamata, Shunichi; Ishiguro, Masateru; Hiroi, Takahiro; Tatsumi, Eri; Sasaki, Sho

2018-01-01

The heliocentric stratification of asteroid spectral types may hold important information on the early evolution of the Solar System. Asteroid spectral taxonomy is based largely on principal component analysis. However, how the surface properties of asteroids, such as the composition and age, are projected in the principal-component (PC) space is not understood well. We decompose multi-band disk-resolved visible spectra of the Itokawa surface with principal component analysis (PCA) in comparison with main-belt asteroids. The obtained distribution of Itokawa spectra projected in the PC space of main-belt asteroids follows a linear trend linking the Q-type and S-type regions and is consistent with the results of space-weathering experiments on ordinary chondrites and olivine, suggesting that this trend may be a space-weathering-induced spectral evolution track for S-type asteroids. Comparison with space-weathering experiments also yield a short average surface age (< a few million years) for Itokawa, consistent with the cosmic-ray-exposure time of returned samples from Itokawa. The Itokawa PC score distribution exhibits asymmetry along the evolution track, strongly suggesting that space weathering has begun saturated on this young asteroid. The freshest spectrum found on Itokawa exhibits a clear sign for space weathering, indicating again that space weathering occurs very rapidly on this body. We also conducted PCA on Itokawa spectra alone and compared the results with space-weathering experiments. The obtained results indicate that the first principal component of Itokawa surface spectra is consistent with spectral change due to space weathering and that the spatial variation in the degree of space weathering is very large (a factor of three in surface age), which would strongly suggest the presence of strong regional/local resurfacing process(es) on this small asteroid.

Multiphysics Engineering Analysis for an Integrated Design of ITER Diagnostic First Wall and Diagnostic Shield Module Design

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

Zhai, Y.; Loesser, G.; Smith, M.

ITER diagnostic first walls (DFWs) and diagnostic shield modules (DSMs) inside the port plugs (PPs) are designed to protect diagnostic instrument and components from a harsh plasma environment and provide structural support while allowing for diagnostic access to the plasma. The design of DFWs and DSMs are driven by 1) plasma radiation and nuclear heating during normal operation 2) electromagnetic loads during plasma events and associate component structural responses. A multi-physics engineering analysis protocol for the design has been established at Princeton Plasma Physics Laboratory and it was used for the design of ITER DFWs and DSMs. The analyses weremore » performed to address challenging design issues based on resultant stresses and deflections of the DFW-DSM-PP assembly for the main load cases. ITER Structural Design Criteria for In-Vessel Components (SDC-IC) required for design by analysis and three major issues driving the mechanical design of ITER DFWs are discussed. The general guidelines for the DSM design have been established as a result of design parametric studies.« less

Numerical Analysis of the Bending Properties of Cathay Poplar Glulam

PubMed Central

Gao, Ying; Wu, Yuxuan; Zhu, Xudong; Zhu, Lei; Yu, Zhiming; Wu, Yong

2015-01-01

This paper presents the formulae and finite element analysis models for predicting the Modulus of Elastic (MOE) and Modulus of Rupture (MOR) of Cathay poplar finger-jointed glulam. The formula of the MOE predicts the MOE of Cathay poplar glulam glued with one-component polyurethane precisely. Three formulae are used to predict the MOR, and Equation (12) predicts the MOR of Cathay poplar glulam precisely. The finite element analysis simulation results of both the MOE and MOR are similar to the experimental results. The predicted results of the finite element analysis are shown to be more accurate than those of the formulae, because the finite element analysis considers the glue layers, but the formulae do not. Three types of typical failure modes due to bending were summarized. The bending properties of Cathay poplar glulam were compared to those of Douglas fir glulam. The results show that Cathay poplar glulam has a lower stiffness, but a marginally higher strength. One-component polyurethane adhesive is shown to be more effective than resorcinol formaldehyde resin adhesive for Cathay poplar glulam. This study shows that Cathay poplar has the potential to be a glulam material in China. PMID:28793619

[A new approach to urinary stone analysis according to the combination of the components: experience with 7949 cases].

PubMed

Millán, F; Gracia, S; Sánchez-Martín, F M; Angerri, O; Rousaud, F; Villavicencio, H

2011-03-01

To evaluate a new approach to urinary stone analysis according to the combination of the components. A total of 7949 stones were analysed and their main components and combinations of components were classified according to gender and age. Statistical analysis was performed using the chi-square test. Calcium oxalate monohydrate (COM) was the most frequent component in both males (39%) and females (37.4%), followed by calcium oxalate dihydrate (COD) (28%) and uric acid (URI) (14.6%) in males and by phosphate (PHO) (22.2%) and COD (19.6%) in females (p=0.0001). In young people, COD and PHO were the most frequent components in males and females respectively (p=0.0001). In older patients, COM and URI (in that order) were the most frequent components in both genders (p=0.0001). COM is oxalate dependent and is related to diets with a high oxalate content and low water intake. The progressive increase in URI with age is related mainly to overweight and metabolic syndrome. Regarding the combinations of components, the most frequent were COM (26.3%), COD+Apatite (APA) (15.5%), URI (10%) and COM+COD (7.5%) (p=0.0001). This study reports not only the composition of stones but also the main combinations of components according to age and gender. The results prove that stone composition is related to the changes in dietary habits and life-style that occur over a lifetime, and the morphological structure of stones is indicative of the aetiopathogenic mechanisms. Copyright © 2010 AEU. Published by Elsevier Espana. All rights reserved.

PSHFT - COMPUTERIZED LIFE AND RELIABILITY MODELLING FOR TURBOPROP TRANSMISSIONS

NASA Technical Reports Server (NTRS)

Savage, M.

1994-01-01

The computer program PSHFT calculates the life of a variety of aircraft transmissions. A generalized life and reliability model is presented for turboprop and parallel shaft geared prop-fan aircraft transmissions. The transmission life and reliability model is a combination of the individual reliability models for all the bearings and gears in the main load paths. The bearing and gear reliability models are based on the statistical two parameter Weibull failure distribution method and classical fatigue theories. The computer program developed to calculate the transmission model is modular. In its present form, the program can analyze five different transmissions arrangements. Moreover, the program can be easily modified to include additional transmission arrangements. PSHFT uses the properties of a common block two-dimensional array to separate the component and transmission property values from the analysis subroutines. The rows correspond to specific components with the first row containing the values for the entire transmission. Columns contain the values for specific properties. Since the subroutines (which determine the transmission life and dynamic capacity) interface solely with this property array, they are separated from any specific transmission configuration. The system analysis subroutines work in an identical manner for all transmission configurations considered. Thus, other configurations can be added to the program by simply adding component property determination subroutines. PSHFT consists of a main program, a series of configuration specific subroutines, generic component property analysis subroutines, systems analysis subroutines, and a common block. The main program selects the routines to be used in the analysis and sequences their operation. The series of configuration specific subroutines input the configuration data, perform the component force and life analyses (with the help of the generic component property analysis subroutines), fill the property array, call up the system analysis routines, and finally print out the analysis results for the system and components. PSHFT is written in FORTRAN 77 and compiled on a MicroSoft FORTRAN compiler. The program will run on an IBM PC AT compatible with at least 104k bytes of memory. The program was developed in 1988.

Elementary signaling modes predict the essentiality of signal transduction network components

PubMed Central

2011-01-01

Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The source codes for the algorithms developed in this study can be found at http://www.phys.psu.edu/~ralbert/ESM. PMID:21426566

Pain sensitivity profiles in patients with advanced knee osteoarthritis

PubMed Central

Frey-Law, Laura A.; Bohr, Nicole L.; Sluka, Kathleen A.; Herr, Keela; Clark, Charles R.; Noiseux, Nicolas O.; Callaghan, John J; Zimmerman, M Bridget; Rakel, Barbara A.

2016-01-01

The development of patient profiles to subgroup individuals on a variety of variables has gained attention as a potential means to better inform clinical decision-making. Patterns of pain sensitivity response specific to quantitative sensory testing (QST) modality have been demonstrated in healthy subjects. It has not been determined if these patterns persist in a knee osteoarthritis population. In a sample of 218 participants, 19 QST measures along with pain, psychological factors, self-reported function, and quality of life were assessed prior to total knee arthroplasty. Component analysis was used to identify commonalities across the 19 QST assessments to produce standardized pain sensitivity factors. Cluster analysis then grouped individuals that exhibited similar patterns of standardized pain sensitivity component scores. The QST resulted in four pain sensitivity components: heat, punctate, temporal summation, and pressure. Cluster analysis resulted in five pain sensitivity profiles: a “low pressure pain” group, an “average pain” group, and three “high pain” sensitivity groups who were sensitive to different modalities (punctate, heat, and temporal summation). Pain and function differed between pain sensitivity profiles, along with sex distribution; however no differences in OA grade, medication use, or psychological traits were found. Residualizing QST data by age and sex resulted in similar components and pain sensitivity profiles. Further, these profiles are surprisingly similar to those reported in healthy populations suggesting that individual differences in pain sensitivity are a robust finding even in an older population with significant disease. PMID:27152688

Factor analysis of metabolic syndrome components and predicting type 2 diabetes: Results of 10-year follow-up in a Middle Eastern population.

PubMed

Ayubi, Erfan; Khalili, Davood; Delpisheh, Ali; Hadaegh, Farzad; Azizi, Fereidoun

2015-11-01

The relationship among components of metabolic syndrome (MetS) and their association with diabetes is unclear in West Asia. The aim of the present study was to conduct factor analysis of MetS components and the effect these factors have on the incidence of type 2 diabetes (T2D) in a population-based cohort study of the Tehran Lipid and Glucose Study (TLGS). The present study enrolled 1861 men and 2706 women (20-60 years of age), from Tehran (Iran) who were free of diabetes at baseline and followed them for 10 years. A principal component analysis was performed to extract standardized factors from MetS components. Logistic regression was used to detect associations between the extracted factors and the incidence of diabetes. A propensity score was used to correct differential selection bias resulting from loss to follow-up. Factor analysis identified three factors (blood pressure, lipids and glycemia). Waist circumference was shared in three all factors. Blood pressure, lipids and glycemia were related to the incidence of diabetes with odds ratios (95% confidence intervals) of 2.23 (1.31-3.78), 1.89 (1.27-3.67), and 7.54 (4.09-13.91), respectively, in men and 2.13 (1.34-3.40), 2.06 (1.35-3.15), and 13.91 (7.29-26.51), respectively, in women for the third versus the first tertile of these standardized factors. Central adiposity may have a pivotal role in MetS linking other risk factors together. Glycemia had a high impact on the incidence of diabetes, whereas blood pressure and lipid had a similar moderate effect on the incidence of diabetes. © 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd.

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed using a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify deficiencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the measured data, a sensitivity analysis of the model parameters with emphasis on the definition of the convection velocity parameter, and a least-squares fit of the predicted to the measured shock-associated noise component spectra, resulted in a new definition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

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.

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

Finite Element Model Calibration Approach for Area I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Gaspar, James L.; Lazor, Daniel R.; Parks, Russell A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of non-conventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pretest predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Finite Element Model Calibration Approach for Ares I-X

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Buehrle, Ralph D.; Templeton, Justin D.; Lazor, Daniel R.; Gaspar, James L.; Parks, Russel A.; Bartolotta, Paul A.

2010-01-01

Ares I-X is a pathfinder vehicle concept under development by NASA to demonstrate a new class of launch vehicles. Although this vehicle is essentially a shell of what the Ares I vehicle will be, efforts are underway to model and calibrate the analytical models before its maiden flight. Work reported in this document will summarize the model calibration approach used including uncertainty quantification of vehicle responses and the use of nonconventional boundary conditions during component testing. Since finite element modeling is the primary modeling tool, the calibration process uses these models, often developed by different groups, to assess model deficiencies and to update parameters to reconcile test with predictions. Data for two major component tests and the flight vehicle are presented along with the calibration results. For calibration, sensitivity analysis is conducted using Analysis of Variance (ANOVA). To reduce the computational burden associated with ANOVA calculations, response surface models are used in lieu of computationally intensive finite element solutions. From the sensitivity studies, parameter importance is assessed as a function of frequency. In addition, the work presents an approach to evaluate the probability that a parameter set exists to reconcile test with analysis. Comparisons of pre-test predictions of frequency response uncertainty bounds with measured data, results from the variance-based sensitivity analysis, and results from component test models with calibrated boundary stiffness models are all presented.

Determination of the Characteristics and Classification of Near-Infrared Spectra of Patchouli Oil (Pogostemon Cablin Benth.) from Different Origin

NASA Astrophysics Data System (ADS)

Diego, M. C. R.; Purwanto, Y. A.; Sutrisno; Budiastra, I. W.

2018-05-01

Research related to the non-destructive method of near-infrared (NIR) spectroscopy in aromatic oil is still in development in Indonesia. The objectives of the study were to determine the characteristics of the near-infrared spectra of patchouli oil and classify it based on its origin. The samples were selected from seven different places in Indonesia (Bogor and Garut from West Java, Aceh, and Jambi from Sumatra and Konawe, Masamba and Kolaka from Sulawesi Island). The spectral data of patchouli oil was obtained by FT-NIR spectrometer at the wavelength of 1000-2500 nm, and after that, the samples were subjected to composition analysis using Gas Chromatography-Mass Spectrometry. The transmittance and absorbance spectra were analyzed and then principal component analysis (PCA) was carried out. Discriminant analysis (DA) of the principal component was developed to classify patchouli oil based on its origin. The result shows that the data of both spectra (transmittance and absorbance spectra) by the PC analysis give a similar result for discriminating the seven types of patchouli oil due to their distribution and behavior. The DA of the three principal component in both data processed spectra could classify patchouli oil accurately. This result exposed that NIR spectroscopy can be successfully used as a correct method to classify patchouli oil based on its origin.

Larvicidal effect of Eucalyptus grandis essential oil and turpentine and their major components on Aedes aegypti larvae.

PubMed

Lucia, Alejandro; Gonzalez Audino, Paola; Seccacini, Emilia; Licastro, Susana; Zerba, Eduardo; Masuh, Hector

2007-09-01

In the search for new alternatives for the control of Aedes aegypti the larvicidal activity of Eucalyptus grandis essential oil and pine resin essential oil (turpentine) and their major components (alpha- and beta-pinene and 1,8-cineole) was determined. Gas chromatography-mass spectroscopy analysis of E. grandis essential oil revealed that its major components are alpha-pinene and 1,8-cineole. Similar analysis of turpentine obtained by distillation of the resin pitch of conifers showed that alpha- and beta-pinene are the only major components. Third and early 4th instars of the CIPEIN-susceptible strain of Ae. aegypti were exposed to acetonic solutions of E. grandis essential oil, turpentine, and their major components for 24 h. Turpentine, with an LC50 of 14.7 ppm, was more active than the essential oil of E. grandis (LC50: 32.4 ppm). Larvicidal activity of the essential oil components showed that alpha- and beta-pinene present low LC50 values (15.4 and 12.1 ppm, respectively), whereas pure 1,8-cineole showed an LC50 of 57.2 ppm. These results suggest that alpha-pinene in E. grandis and alpha- and beta-pinene in turpentine serve as the principal larvicidal components of both oils. Results obtained on larvicidal effects of essential oil of Eucalyptus grandis and turpentine could be considered a contribution to the search for new biodegradable larvicides of natural origin.

Using Structural Equation Modeling To Fit Models Incorporating Principal Components.

ERIC Educational Resources Information Center

Dolan, Conor; Bechger, Timo; Molenaar, Peter

1999-01-01

Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…

[Infrared spectroscopic study on the component and vigor analysis of Cistanche deserticola seeds].

PubMed

Xu, Rong; Sun, Su-Qin; Chen, Jun; Chen, Shi-Lin; Zhou, Feng

2009-01-01

Comparative study of the different parts of cistanche deserticola seeds and their changes after different processing were examined by Fourier transform infrared spectroscopy spectra (FTIR). The results of the analysis showed that components in the cistanche deserticola seeds were abundant, which contained characteristic absorption peaks of protein, fat and carbohydrate. As well, pectin and aromatic compound can be also found in the seeds. However, the components were different in different parts of cistanche deserticola seeds. The characteristic absorption peak intensities of fat at 2,926, 1,746, 1,161 and 721 cm(-1) were the strongest in the seed kernels. However, the seed coats mainly consisted of carbohydrate and pectin, which were showed at 1,054 cm(-1). The contents of protein and carbohydrate were decreased distinctly in the moldy and dead seeds after processing. The characteristic absorption peak intensity ratio of protein to fat (I1,630/I1,745 ) was all higher than 1.05 in the live seeds. The characteristic absorption peak intensity ratio of amido link I of protein to fat (11,653/I1,745) in the dead seed kernels of the cistanche deserticola was decreased from 0.31 to 0. 23, which was 25.8% less than that in vital seed kernels. The results suggest that FTIR not only can be used in fast comprehensive analysis of seed components, but also can be used in the seed vigor analysis, seed longevity determination and seed quality evaluation.

Visual target modulation of functional connectivity networks revealed by self-organizing group ICA.

PubMed

van de Ven, Vincent; Bledowski, Christoph; Prvulovic, David; Goebel, Rainer; Formisano, Elia; Di Salle, Francesco; Linden, David E J; Esposito, Fabrizio

2008-12-01

We applied a data-driven analysis based on self-organizing group independent component analysis (sogICA) to fMRI data from a three-stimulus visual oddball task. SogICA is particularly suited to the investigation of the underlying functional connectivity and does not rely on a predefined model of the experiment, which overcomes some of the limitations of hypothesis-driven analysis. Unlike most previous applications of ICA in functional imaging, our approach allows the analysis of the data at the group level, which is of particular interest in high order cognitive studies. SogICA is based on the hierarchical clustering of spatially similar independent components, derived from single subject decompositions. We identified four main clusters of components, centered on the posterior cingulate, bilateral insula, bilateral prefrontal cortex, and right posterior parietal and prefrontal cortex, consistently across all participants. Post hoc comparison of time courses revealed that insula, prefrontal cortex and right fronto-parietal components showed higher activity for targets than for distractors. Activation for distractors was higher in the posterior cingulate cortex, where deactivation was observed for targets. While our results conform to previous neuroimaging studies, they also complement conventional results by showing functional connectivity networks with unique contributions to the task that were consistent across subjects. SogICA can thus be used to probe functional networks of active cognitive tasks at the group-level and can provide additional insights to generate new hypotheses for further study. Copyright 2007 Wiley-Liss, Inc.

COMPADRE: an R and web resource for pathway activity analysis by component decompositions.

PubMed

Ramos-Rodriguez, Roberto-Rafael; Cuevas-Diaz-Duran, Raquel; Falciani, Francesco; Tamez-Peña, Jose-Gerardo; Trevino, Victor

2012-10-15

The analysis of biological networks has become essential to study functional genomic data. Compadre is a tool to estimate pathway/gene sets activity indexes using sub-matrix decompositions for biological networks analyses. The Compadre pipeline also includes one of the direct uses of activity indexes to detect altered gene sets. For this, the gene expression sub-matrix of a gene set is decomposed into components, which are used to test differences between groups of samples. This procedure is performed with and without differentially expressed genes to decrease false calls. During this process, Compadre also performs an over-representation test. Compadre already implements four decomposition methods [principal component analysis (PCA), Isomaps, independent component analysis (ICA) and non-negative matrix factorization (NMF)], six statistical tests (t- and f-test, SAM, Kruskal-Wallis, Welch and Brown-Forsythe), several gene sets (KEGG, BioCarta, Reactome, GO and MsigDB) and can be easily expanded. Our simulation results shown in Supplementary Information suggest that Compadre detects more pathways than over-representation tools like David, Babelomics and Webgestalt and less false positives than PLAGE. The output is composed of results from decomposition and over-representation analyses providing a more complete biological picture. Examples provided in Supplementary Information show the utility, versatility and simplicity of Compadre for analyses of biological networks. Compadre is freely available at http://bioinformatica.mty.itesm.mx:8080/compadre. The R package is also available at https://sourceforge.net/p/compadre.

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin using excitation-emission matrix (EEM) fluorescence and parallel factor analysis (PARAFAC).

PubMed

Singh, Shatrughan; D'Sa, Eurico J; Swenson, Erick M

2010-07-15

Chromophoric dissolved organic matter (CDOM) variability in Barataria Basin, Louisiana, USA,was examined by excitation emission matrix (EEM) fluorescence combined with parallel factor analysis (PARAFAC). CDOM optical properties of absorption and fluorescence at 355nm along an axial transect (36 stations) during March, April, and May 2008 showed an increasing trend from the marine end member to the upper basin with mean CDOM absorption of 11.06 + or - 5.01, 10.05 + or - 4.23, 11.67 + or - 6.03 (m(-)(1)) and fluorescence 0.80 + or - 0.37, 0.78 + or - 0.39, 0.75 + or - 0.51 (RU), respectively. PARAFAC analysis identified two terrestrial humic-like (component 1 and 2), one non-humic like (component 3), and one soil derived humic acid like (component 4) components. The spatial variation of the components showed an increasing trend from station 1 (near the mouth of basin) to station 36 (end member of bay; upper basin). Deviations from this increasing trend were observed at a bayou channel with very high chlorophyll-a concentrations especially for component 3 in May 2008 that suggested autochthonous production of CDOM. The variability of components with salinity indicated conservative mixing along the middle part of the transect. Component 1 and 4 were found to be relatively constant, while components 2 and 3 revealed an inverse relationship for the sampling period. Total organic carbon showed increasing trend for each of the components. An increase in humification and a decrease in fluorescence indices along the transect indicated an increase in terrestrial derived organic matter and reduced microbial activity from lower to upper basin. The use of these indices along with PARAFAC results improved dissolved organic matter characterization in the Barataria Basin. Copyright 2010 Elsevier B.V. All rights reserved.

An automated method for identifying artifact in independent component analysis of resting-state FMRI.

PubMed

Bhaganagarapu, Kaushik; Jackson, Graeme D; Abbott, David F

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.

An Automated Method for Identifying Artifact in Independent Component Analysis of Resting-State fMRI

PubMed Central

Bhaganagarapu, Kaushik; Jackson, Graeme D.; Abbott, David F.

2013-01-01

An enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available. PMID:23847511

Principal component analysis for designed experiments.

PubMed

Konishi, Tomokazu

2015-01-01

Principal component analysis is used to summarize matrix data, such as found in transcriptome, proteome or metabolome and medical examinations, into fewer dimensions by fitting the matrix to orthogonal axes. Although this methodology is frequently used in multivariate analyses, it has disadvantages when applied to experimental data. First, the identified principal components have poor generality; since the size and directions of the components are dependent on the particular data set, the components are valid only within the data set. Second, the method is sensitive to experimental noise and bias between sample groups. It cannot reflect the experimental design that is planned to manage the noise and bias; rather, it estimates the same weight and independence to all the samples in the matrix. Third, the resulting components are often difficult to interpret. To address these issues, several options were introduced to the methodology. First, the principal axes were identified using training data sets and shared across experiments. These training data reflect the design of experiments, and their preparation allows noise to be reduced and group bias to be removed. Second, the center of the rotation was determined in accordance with the experimental design. Third, the resulting components were scaled to unify their size unit. The effects of these options were observed in microarray experiments, and showed an improvement in the separation of groups and robustness to noise. The range of scaled scores was unaffected by the number of items. Additionally, unknown samples were appropriately classified using pre-arranged axes. Furthermore, these axes well reflected the characteristics of groups in the experiments. As was observed, the scaling of the components and sharing of axes enabled comparisons of the components beyond experiments. The use of training data reduced the effects of noise and bias in the data, facilitating the physical interpretation of the principal axes. Together, these introduced options result in improved generality and objectivity of the analytical results. The methodology has thus become more like a set of multiple regression analyses that find independent models that specify each of the axes.

Reliability analysis of component of affination centrifugal 1 machine by using reliability engineering

NASA Astrophysics Data System (ADS)

Sembiring, N.; Ginting, E.; Darnello, T.

2017-12-01

Problems that appear in a company that produces refined sugar, the production floor has not reached the level of critical machine availability because it often suffered damage (breakdown). This results in a sudden loss of production time and production opportunities. This problem can be solved by Reliability Engineering method where the statistical approach to historical damage data is performed to see the pattern of the distribution. The method can provide a value of reliability, rate of damage, and availability level, of an machine during the maintenance time interval schedule. The result of distribution test to time inter-damage data (MTTF) flexible hose component is lognormal distribution while component of teflon cone lifthing is weibull distribution. While from distribution test to mean time of improvement (MTTR) flexible hose component is exponential distribution while component of teflon cone lifthing is weibull distribution. The actual results of the flexible hose component on the replacement schedule per 720 hours obtained reliability of 0.2451 and availability 0.9960. While on the critical components of teflon cone lifthing actual on the replacement schedule per 1944 hours obtained reliability of 0.4083 and availability 0.9927.

Independent Orbiter Assessment (IOA): Analysis of the active thermal control subsystem

NASA Technical Reports Server (NTRS)

Sinclair, S. K.; Parkman, W. E.

1987-01-01

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical (PCIs) items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results corresponding to the Orbiter Active Thermal Control Subsystem (ATCS) are documented. The major purpose of the ATCS is to remove the heat, generated during normal Shuttle operations from the Orbiter systems and subsystems. The four major components of the ATCS contributing to the heat removal are: Freon Coolant Loops; Radiator and Flow Control Assembly; Flash Evaporator System; and Ammonia Boiler System. In order to perform the analysis, the IOA process utilized available ATCS hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 310 failure modes analyzed, 101 were determined to be PCIs.

Independent Orbiter Assessment (IOA): Analysis of the remote manipulator system

NASA Technical Reports Server (NTRS)

Tangorra, F.; Grasmeder, R. F.; Montgomery, A. D.

1987-01-01

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items (PCIs). To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. The independent analysis results for the Orbiter Remote Manipulator System (RMS) are documented. The RMS hardware and software are primarily required for deploying and/or retrieving up to five payloads during a single mission, capture and retrieve free-flying payloads, and for performing Manipulator Foot Restraint operations. Specifically, the RMS hardware consists of the following components: end effector; displays and controls; manipulator controller interface unit; arm based electronics; and the arm. The IOA analysis process utilized available RMS hardware drawings, schematics and documents for defining hardware assemblies, components and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Of the 574 failure modes analyzed, 413 were determined to be PCIs.

Independent Orbiter Assessment (IOA): Analysis of the electrical power distribution and control/electrical power generation subsystem

NASA Technical Reports Server (NTRS)

Patton, Jeff A.

1986-01-01

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis (FMEA) and Critical Items List (CIL) are presented. The IOA approach features a top-down analysis of the hardware to determine failure modes, criticality, and potential critical items. To preserve independence, this analysis was accomplished without reliance upon the results contained within the NASA FMEA/CIL documentation. This report documents the independent analysis results corresponding to the Orbiter Electrical Power Distribution and Control (EPD and C)/Electrical Power Generation (EPG) hardware. The EPD and C/EPG hardware is required for performing critical functions of cryogenic reactant storage, electrical power generation and product water distribution in the Orbiter. Specifically, the EPD and C/EPG hardware consists of the following components: Power Section Assembly (PSA); Reactant Control Subsystem (RCS); Thermal Control Subsystem (TCS); Water Removal Subsystem (WRS); and Power Reactant Storage and Distribution System (PRSDS). The IOA analysis process utilized available EPD and C/EPG hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-09-20

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors.

The rate of change in declining steroid hormones: a new parameter of healthy aging in men?

PubMed Central

Walther, Andreas; Philipp, Michel; Lozza, Niclà; Ehlert, Ulrike

2016-01-01

Research on healthy aging in men has increasingly focused on age-related hormonal changes. Testosterone (T) decline is primarily investigated, while age-related changes in other sex steroids (dehydroepiandrosterone [DHEA], estradiol [E2], progesterone [P]) are mostly neglected. An integrated hormone parameter reflecting aging processes in men has yet to be identified. 271 self-reporting healthy men between 40 and 75 provided both psychometric data and saliva samples for hormone analysis. Correlation analysis between age and sex steroids revealed negative associations for the four sex steroids (T, DHEA, E2, and P). Principal component analysis including ten salivary analytes identified a principal component mainly unifying the variance of the four sex steroid hormones. Subsequent principal component analysis including the four sex steroids extracted the principal component of declining steroid hormones (DSH). Moderation analysis of the association between age and DSH revealed significant moderation effects for psychosocial factors such as depression, chronic stress and perceived general health. In conclusion, these results provide further evidence that sex steroids decline in aging men and that the integrated hormone parameter DSH and its rate of change can be used as biomarkers for healthy aging in men. Furthermore, the negative association of age and DSH is moderated by psychosocial factors. PMID:27589836

Computational model for the analysis of cartilage and cartilage tissue constructs

PubMed Central

Smith, David W.; Gardiner, Bruce S.; Davidson, John B.; Grodzinsky, Alan J.

2013-01-01

We propose a new non-linear poroelastic model that is suited to the analysis of soft tissues. In this paper the model is tailored to the analysis of cartilage and the engineering design of cartilage constructs. The proposed continuum formulation of the governing equations enables the strain of the individual material components within the extracellular matrix (ECM) to be followed over time, as the individual material components are synthesized, assembled and incorporated within the ECM or lost through passive transport or degradation. The material component analysis developed here naturally captures the effect of time-dependent changes of ECM composition on the deformation and internal stress states of the ECM. For example, it is shown that increased synthesis of aggrecan by chondrocytes embedded within a decellularized cartilage matrix initially devoid of aggrecan results in osmotic expansion of the newly synthesized proteoglycan matrix and tension within the structural collagen network. Specifically, we predict that the collagen network experiences a tensile strain, with a maximum of ~2% at the fixed base of the cartilage. The analysis of an example problem demonstrates the temporal and spatial evolution of the stresses and strains in each component of a self-equilibrating composite tissue construct, and the role played by the flux of water through the tissue. PMID:23784936

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Principal component analysis of the Norwegian version of the quality of life in late-stage dementia scale.

PubMed

Mjørud, Marit; Kirkevold, Marit; Røsvik, Janne; Engedal, Knut

2014-01-01

To investigate which factors the Quality of Life in Late-Stage Dementia (QUALID) scale holds when used among people with dementia (pwd) in nursing homes and to find out how the symptom load varies across the different severity levels of dementia. We included 661 pwd [mean age ± SD, 85.3 ± 8.6 years; 71.4% women]. The QUALID and the Clinical Dementia Rating (CDR) scale were applied. A principal component analysis (PCA) with varimax rotation and Kaiser normalization was applied to test the factor structure. Nonparametric analyses were applied to examine differences of symptom load across the three CDR groups. The mean QUALID score was 21.5 (±7.1), and the CDR scores of the three groups were 1 in 22.5%, 2 in 33.6% and 3 in 43.9%. The results of the statistical measures employed were the following: Crohnbach's α of QUALID, 0.74; Bartlett's test of sphericity, p <0.001; the Kaiser-Meyer-Olkin measure, 0.77. The PCA analysis resulted in three components accounting for 53% of the variance. The first component was 'tension' ('facial expression of discomfort', 'appears physically uncomfortable', 'verbalization suggests discomfort', 'being irritable and aggressive', 'appears calm', Crohnbach's α = 0.69), the second was 'well-being' ('smiles', 'enjoys eating', 'enjoys touching/being touched', 'enjoys social interaction', Crohnbach's α = 0.62) and the third was 'sadness' ('appears sad', 'cries', 'facial expression of discomfort', Crohnbach's α 0.65). The mean score on the components 'tension' and 'well-being' increased significantly with increasing severity levels of dementia. Three components of quality of life (qol) were identified. Qol decreased with increasing severity of dementia. © 2013 S. Karger AG, Basel.

Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Yang, Gang; Li, Jialin; Zhang, Dianfa

2018-01-01

A randomized subspace-based robust principal component analysis (RSRPCA) method for anomaly detection in hyperspectral imagery (HSI) is proposed. The RSRPCA combines advantages of randomized column subspace and robust principal component analysis (RPCA). It assumes that the background has low-rank properties, and the anomalies are sparse and do not lie in the column subspace of the background. First, RSRPCA implements random sampling to sketch the original HSI dataset from columns and to construct a randomized column subspace of the background. Structured random projections are also adopted to sketch the HSI dataset from rows. Sketching from columns and rows could greatly reduce the computational requirements of RSRPCA. Second, the RSRPCA adopts the columnwise RPCA (CWRPCA) to eliminate negative effects of sampled anomaly pixels and that purifies the previous randomized column subspace by removing sampled anomaly columns. The CWRPCA decomposes the submatrix of the HSI data into a low-rank matrix (i.e., background component), a noisy matrix (i.e., noise component), and a sparse anomaly matrix (i.e., anomaly component) with only a small proportion of nonzero columns. The algorithm of inexact augmented Lagrange multiplier is utilized to optimize the CWRPCA problem and estimate the sparse matrix. Nonzero columns of the sparse anomaly matrix point to sampled anomaly columns in the submatrix. Third, all the pixels are projected onto the complemental subspace of the purified randomized column subspace of the background and the anomaly pixels in the original HSI data are finally exactly located. Several experiments on three real hyperspectral images are carefully designed to investigate the detection performance of RSRPCA, and the results are compared with four state-of-the-art methods. Experimental results show that the proposed RSRPCA outperforms four comparison methods both in detection performance and in computational time.

Comparative Normal/Failing Rat Myocardium Cell Membrane Chromatographic Analysis System for Screening Specific Components That Counteract Doxorubicin-Induced Heart Failure from Acontium carmichaeli

PubMed Central

2015-01-01

Cell membrane chromatography (CMC) derived from pathological tissues is ideal for screening specific components acting on specific diseases from complex medicines owing to the maximum simulation of in vivo drug-receptor interactions. However, there are no pathological tissue-derived CMC models that have ever been developed, as well as no visualized affinity comparison of potential active components between normal and pathological CMC columns. In this study, a novel comparative normal/failing rat myocardium CMC analysis system based on online column selection and comprehensive two-dimensional (2D) chromatography/monolithic column/time-of-flight mass spectrometry was developed for parallel comparison of the chromatographic behaviors on both normal and pathological CMC columns, as well as rapid screening of the specific therapeutic agents that counteract doxorubicin (DOX)-induced heart failure from Acontium carmichaeli (Fuzi). In total, 16 potential active alkaloid components with similar structures in Fuzi were retained on both normal and failing myocardium CMC models. Most of them had obvious decreases of affinities on failing myocardium CMC compared with normal CMC model except for four components, talatizamine (TALA), 14-acetyl-TALA, hetisine, and 14-benzoylneoline. One compound TALA with the highest affinity was isolated for further in vitro pharmacodynamic validation and target identification to validate the screen results. Voltage-dependent K+ channel was confirmed as a binding target of TALA and 14-acetyl-TALA with high affinities. The online high throughput comparative CMC analysis method is suitable for screening specific active components from herbal medicines by increasing the specificity of screened results and can also be applied to other biological chromatography models. PMID:24731167

The time course of individual face recognition: A pattern analysis of ERP signals.

PubMed

Nemrodov, Dan; Niemeier, Matthias; Mok, Jenkin Ngo Yin; Nestor, Adrian

2016-05-15

An extensive body of work documents the time course of neural face processing in the human visual cortex. However, the majority of this work has focused on specific temporal landmarks, such as N170 and N250 components, derived through univariate analyses of EEG data. Here, we take on a broader evaluation of ERP signals related to individual face recognition as we attempt to move beyond the leading theoretical and methodological framework through the application of pattern analysis to ERP data. Specifically, we investigate the spatiotemporal profile of identity recognition across variation in emotional expression. To this end, we apply pattern classification to ERP signals both in time, for any single electrode, and in space, across multiple electrodes. Our results confirm the significance of traditional ERP components in face processing. At the same time though, they support the idea that the temporal profile of face recognition is incompletely described by such components. First, we show that signals associated with different facial identities can be discriminated from each other outside the scope of these components, as early as 70ms following stimulus presentation. Next, electrodes associated with traditional ERP components as well as, critically, those not associated with such components are shown to contribute information to stimulus discriminability. And last, the levels of ERP-based pattern discrimination are found to correlate with recognition accuracy across subjects confirming the relevance of these methods for bridging brain and behavior data. Altogether, the current results shed new light on the fine-grained time course of neural face processing and showcase the value of novel methods for pattern analysis to investigating fundamental aspects of visual recognition. Copyright © 2016 Elsevier Inc. All rights reserved.

Three-way parallel independent component analysis for imaging genetics using multi-objective optimization.

PubMed

Ulloa, Alvaro; Jingyu Liu; Vergara, Victor; Jiayu Chen; Calhoun, Vince; Pattichis, Marios

2014-01-01

In the biomedical field, current technology allows for the collection of multiple data modalities from the same subject. In consequence, there is an increasing interest for methods to analyze multi-modal data sets. Methods based on independent component analysis have proven to be effective in jointly analyzing multiple modalities, including brain imaging and genetic data. This paper describes a new algorithm, three-way parallel independent component analysis (3pICA), for jointly identifying genomic loci associated with brain function and structure. The proposed algorithm relies on the use of multi-objective optimization methods to identify correlations among the modalities and maximally independent sources within modality. We test the robustness of the proposed approach by varying the effect size, cross-modality correlation, noise level, and dimensionality of the data. Simulation results suggest that 3p-ICA is robust to data with SNR levels from 0 to 10 dB and effect-sizes from 0 to 3, while presenting its best performance with high cross-modality correlations, and more than one subject per 1,000 variables. In an experimental study with 112 human subjects, the method identified links between a genetic component (pointing to brain function and mental disorder associated genes, including PPP3CC, KCNQ5, and CYP7B1), a functional component related to signal decreases in the default mode network during the task, and a brain structure component indicating increases of gray matter in brain regions of the default mode region. Although such findings need further replication, the simulation and in-vivo results validate the three-way parallel ICA algorithm presented here as a useful tool in biomedical data decomposition applications.

Visualization and Semiquantitative Study of the Distribution of Major Components in Wheat Straw in Mesoscopic Scale using Fourier Transform Infrared Microspectroscopic Imaging.

PubMed

Yang, Zengling; Mei, Jiaqi; Liu, Zhiqiang; Huang, Guangqun; Huang, Guan; Han, Lujia

2018-06-19

Understanding the biochemical heterogeneity of plant tissue linked to crop straw anatomy is attractive to plant researchers and researchers in the field of biomass refinery. This study provides an in situ analysis and semiquantitative visualization of major components distribution in internodal transverse sections of wheat straw based on Fourier transform infrared (FTIR) microspectroscopic imaging, with a fast non-negativity-constrained least squares (fast NNLS) fitting. This paper investigates changes in biochemical components of tissue during stages of elongation, booting, heading, flowering, grain-filling, milk-ripening, dough, and full-ripening. Visualization analysis was carried out with reference spectra for five components (microcrystalline cellulose, xylan, lignin, pectin, and starch) of wheat straw. Our result showed that (a) the cellulose and lignin distribution is consistent with that from tissue-dyeing with safranin O-fast green and (b) the distribution of cellulose, lignin, and starch is consistent with chemical images for characteristic wavelength at 1432, 1507, and 987 cm -1 , respectively, showing no interference from the other components analyzed. With the validation from biochemical images using characteristic wavelength and tissue-dyeing techniques, further semiquantitative analysis in local tissues based on fast NNLS was carried out, and the result showed that (a) the contents of cellulose in various tissues are very different, with most in parenchyma tissue and least in the epidermis and (b) during plant development, the fluctuation of each component in tissues follows nearly the same trend, especially within vascular bundles and parenchyma tissue. Thus, FTIR microspectroscopic imaging combined with suitable chemometric methods can be successfully applied to study chemical distributions within the internodes transverse sections of wheat straw, providing semiquantitative chemical information.

Rhythms of life: antecedents and outcomes of work-family balance in employed parents.

PubMed

Aryee, Samuel; Srinivas, E S; Tan, Hwee Hoon

2005-01-01

This study examined antecedents and outcomes of a fourfold taxonomy of work-family balance in terms of the direction of influence (work-family vs. family-work) and type of effect (conflict vs. facilitation). Respondents were full-time employed parents in India. Confirmatory factor analysis results provided evidence for the discriminant validity of M. R. Frone's (2003) fourfold taxonomy of work-family balance. Results of moderated regression analysis revealed that different processes underlie the conflict and facilitation components. Furthermore, gender had only a limited moderating influence on the relationships between the antecedents and the components of work-family balance. Last, work-family facilitation was related to the work outcomes of job satisfaction and organizational commitment.

Component mode synthesis and large deflection vibrations of complex structures. [beams and trusses

NASA Technical Reports Server (NTRS)

Mei, C.

1984-01-01

The accuracy of the NASTRAN modal synthesis analysis was assessed by comparing it with full structure NASTRAN and nine other modal synthesis results using a nine-bay truss. A NASTRAN component mode transient response analysis was also performed on the free-free truss structure. A finite element method was developed for nonlinear vibration of beam structures subjected to harmonic excitation. Longitudinal deformation and inertia are both included in the formula. Tables show the finite element free vibration results with and without considering the effects of longitudinal deformation and inertia as well as the frequency ratios for a simply supported and a clamped beam subjected to a uniform harmonic force.

Study of ionospheric anomalies due to impact of typhoon using Principal Component Analysis and image processing

NASA Astrophysics Data System (ADS)

LIN, JYH-WOEI

2012-08-01

Principal Component Analysis (PCA) and image processing are used to determine Total Electron Content (TEC) anomalies in the F-layer of the ionosphere relating to Typhoon Nakri for 29 May, 2008 (UTC). PCA and image processing are applied to the global ionospheric map (GIM) with transforms conducted for the time period 12:00-14:00 UT on 29 May, 2008 when the wind was most intense. Results show that at a height of approximately 150-200 km the TEC anomaly is highly localized; however, it becomes more intense and widespread with height. Potential causes of these results are discussed with emphasis given to acoustic gravity waves caused by wind force.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Rotordynamic Characteristics of the HPOTP (High Pressure Oxygen Turbopump) of the SSME (Space Shuttle Main Engine)

NASA Technical Reports Server (NTRS)

Childs, D. W.

1984-01-01

Rotational stability of turbopump components in the space shuttle main engine was studied via analysis of component and structural dynamic models. Subsynchronous vibration caused unacceptable migration of the rotor/housing unit with unequal load sharing of the synchronous bearings that resulted in the failure of the High Pressure Oxygen Turbopump. Linear analysis shows that a shrouded inducer eliminates the second critical speed and the stability problem, a stiffened rotor improves the rotordynamic characteristics of the turbopump, and installing damper boost/impeller seals reduces bearing loads. Nonlinear analysis shows that by increasing the "dead band' clearances, a marked reduction in peak bearing loads occurs.

Sublaminate analysis of interlaminar fracture in composites

NASA Technical Reports Server (NTRS)

Armanios, E. A.; Rehfield, L. W.

1986-01-01

A simple analysis method based upon a transverse shear deformation theory and a sublaminate approach is utilized to analyze a mixed-mode edge delamination specimen. The analysis provides closed form expressions for the interlaminar shear stresses ahead of the crack, the total energy release rate, and the energy release rate components. The parameters controlling the behavior are identified. The effect of specimen stacking sequence and delamination interface on the strain energy release rate components is investigated. Results are compared with a finite element simulation for reference. The simple nature of the method makes it suitable for preliminary design analyses which require a large number of configurations to be evaluated quickly and economically.

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

NASA Astrophysics Data System (ADS)

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

2012-07-01

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

Simulation of multispectral multisource for device of consumer and medicine products analysis

NASA Astrophysics Data System (ADS)

Korolev, Timofey K.; Peretyagin, Vladimir S.

2017-06-01

One of the results of intensive development of led technology was the creation of a multi-component, managed devices and illumination/irradiation used in various fields of production (e.g., food industry analysis, food quality). The use of LEDs has become possible due to their structure determining spatial, energy, electrical, thermal and other characteristics. However, the development of the devices for illumination/irradiation require closer attention in the case if you want to provide precise illumination to the area of analysis, located at a specified distance from the radiation source. The present work is devoted to the development and modelling of a specialized source of radiation intended for solving problems of analysis of food products, medicines and water for suitability in drinking. In this work, we provided a mathematical model of spatial and spectral distribution of irridation from the source of infrared radiation ring structure. When you create this kind of source, you address factors such spectral component, the power settings, the spatial and energy components of the diodes.

Inventory of File sref.t03z.pgrb216.mean_3hrly.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] wt ens-mean 002 10 m above ground VGRD analysis V-Component of Wind [m/s] wt ens-mean 003 1000 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 004 850 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 005 700 mb UGRD analysis U-Component of Wind [m/s] wt ens

Inventory of File sref.t03z.pgrb212.spread_3hrly.grib2

Science.gov Websites

ground UGRD analysis U-Component of Wind [m/s] std dev 002 10 m above ground VGRD analysis V-Component of Wind [m/s] std dev 003 1000 mb UGRD analysis U-Component of Wind [m/s] std dev 004 850 mb UGRD analysis U-Component of Wind [m/s] std dev 005 700 mb UGRD analysis U-Component of Wind [m/s] std dev 006 600

Inventory of File sref.t03z.pgrb243.mean_3hrly.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] wt ens-mean 002 10 m above ground VGRD analysis V-Component of Wind [m/s] wt ens-mean 003 1000 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 004 850 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 005 700 mb UGRD analysis U-Component of Wind [m/s] wt ens

Inventory of File sref.t03z.pgrb216.spread_3hrly.grib2

Science.gov Websites

ground UGRD analysis U-Component of Wind [m/s] std dev 002 10 m above ground VGRD analysis V-Component of Wind [m/s] std dev 003 1000 mb UGRD analysis U-Component of Wind [m/s] std dev 004 850 mb UGRD analysis U-Component of Wind [m/s] std dev 005 700 mb UGRD analysis U-Component of Wind [m/s] std dev 006 600

Inventory of File sref.t03z.pgrb243.spread_3hrly.grib2

Science.gov Websites

ground UGRD analysis U-Component of Wind [m/s] std dev 002 10 m above ground VGRD analysis V-Component of Wind [m/s] std dev 003 1000 mb UGRD analysis U-Component of Wind [m/s] std dev 004 850 mb UGRD analysis U-Component of Wind [m/s] std dev 005 700 mb UGRD analysis U-Component of Wind [m/s] std dev 006 600

Inventory of File sref.t03z.pgrb212.mean_3hrly.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] wt ens-mean 002 10 m above ground VGRD analysis V-Component of Wind [m/s] wt ens-mean 003 1000 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 004 850 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 005 700 mb UGRD analysis U-Component of Wind [m/s] wt ens

Inventory of File sref.t03z.pgrb132.spread_3hrly.grib2

Science.gov Websites

ground UGRD analysis U-Component of Wind [m/s] std dev 002 10 m above ground VGRD analysis V-Component of Wind [m/s] std dev 003 1000 mb UGRD analysis U-Component of Wind [m/s] std dev 004 850 mb UGRD analysis U-Component of Wind [m/s] std dev 005 700 mb UGRD analysis U-Component of Wind [m/s] std dev 006 600

Inventory of File sref.t03z.pgrb132.mean_3hrly.grib2

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] wt ens-mean 002 10 m above ground VGRD analysis V-Component of Wind [m/s] wt ens-mean 003 1000 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 004 850 mb UGRD analysis U-Component of Wind [m/s] wt ens-mean 005 700 mb UGRD analysis U-Component of Wind [m/s] wt ens

Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors.

PubMed

Sung, Yun Ju; Di, Yanming; Fu, Audrey Q; Rothstein, Joseph H; Sieh, Weiva; Tong, Liping; Thompson, Elizabeth A; Wijsman, Ellen M

2007-01-01

We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus.

Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors

PubMed Central

Sung, Yun Ju; Di, Yanming; Fu, Audrey Q; Rothstein, Joseph H; Sieh, Weiva; Tong, Liping; Thompson, Elizabeth A; Wijsman, Ellen M

2007-01-01

We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus. PMID:18466597

Peri-apatite coating decreases uncemented tibial component migration: long-term RSA results of a randomized controlled trial and limitations of short-term results.

PubMed

Van Hamersveld, Koen T; Marang-Van De Mheen, Perla J; Nelissen, Rob G H H; Toksvig-Larsen, Sören

2018-05-09

Background and purpose - Biological fixation of uncemented knee prostheses can be improved by applying hydroxyapatite coating around the porous surface via a solution deposition technique called Peri-Apatite (PA). The 2-year results of a randomized controlled trial, evaluating the effect of PA, revealed several components with continuous migration in the second postoperative year, particularly in the uncoated group. To evaluate whether absence of early stabilization is diagnostic of loosening, we now present long-term follow-up results. Patients and methods - 60 patients were randomized to PA-coated or uncoated (porous only) total knee arthroplasty of which 58 were evaluated with radiostereometric analysis (RSA) performed at baseline, at 3 months postoperatively and at 1, 2, 5, 7, and 10 years. A linear mixed-effects model was used to analyze the repeated measurements. Results - PA-coated components had a statistically significantly lower mean migration at 10 years of 0.94 mm (95% CI 0.72-1.2) compared with the uncoated group showing a mean migration of 1.72 mm (95% CI 1.4-2.1). Continuous migration in the second postoperative year was seen in 7 uncoated components and in 1 PA-coated component. All of these implants stabilized after 2 years except for 2 uncoated components. Interpretation - Peri-apatite enhances stabilization of uncemented components. The number of components that stabilized after 2 years emphasizes the importance of longer follow-up to determine full stabilization and risk of loosening in uncemented components with biphasic migration profiles.

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

The contribution of component variation and phytoplankton growth to the distribution variation of chromophoric dissolved organic matter content in a mid-latitude subtropical drinking water source reservoir for two different seasons.

PubMed

Sun, Qiyuan; Jiang, Juan; Zheng, Yuyi; Wang, Feifeng; Wu, Chunshan; Xie, Rong-Rong

2017-07-01

The distribution variation in chromophoric dissolved organic matter (CDOM) content in mid-latitude subtropical drinking water source reservoirs (MDWSRs) has great significance in the security of aquatic environments and human health. CDOM distribution is heavily influenced by biogeochemical processes and anthropogenic activity. However, little is known regarding the impact of component variation and phytoplankton growth on CDOM distribution variation in MDWSR. Therefore, samples were collected from a representative MDWSR (the Shanzai Reservoir) for analysis. CDOM absorption and fluorescence coupling with parallel factor analysis were measured and calculated. The results indicated that only two CDOM components were found in the surface water of Shanzai Reservoir, fulvic acid, and high-excitation tryptophan, originating from terrestrial and autochthonous sources, respectively. The types of components did not change with the season. The average molecular weight of CDOM increased in proportion to its fulvic acid content. The distribution variation in CDOM content mainly resulted from the variation in two CDOM components in summer and from high-excitation tryptophan in winter. Phytoplankton growth strongly influenced the distribution variation of CDOM content in summer; the metabolic processes of Cyanobacteria and Bacillariophyta consumed fulvic acid, while that of Cryptophyta produced high-excitation tryptophan.

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

NASA Astrophysics Data System (ADS)

Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

2017-09-01

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

High variable mixture ratio oxygen/hydrogen engine

NASA Technical Reports Server (NTRS)

Erickson, C. M.; Tu, W. H.; Weiss, A. H.

1988-01-01

The ability of an O2/H2 engine to operate over a range of high-propellant mixture ratios was previously shown to be advantageous in single stage to orbit (SSTO) vehicles. The results are presented for the analysis of high-performance engine power cycles operating over propellant mixture ratio ranges of 12 to 6 and 9 to 6. A requirement to throttle up to 60 percent of nominal thrust was superimposed as a typical throttle range to limit vehicle acceleration as propellant is expended. The object of the analysis was to determine areas of concern relative to component and engine operability or potential hazards resulting from the operating requirements and ranges of conditions that derive from the overall engine requirements. The SSTO mission necessitates a high-performance, lightweight engine. Therefore, staged combustion power cycles employing either dual fuel-rich preburners or dual mixed (fuel-rich and oxygen-rich) preburners were examined. Engine mass flow and power balances were made and major component operating ranges were defined. Component size and arrangement were determined through engine layouts for one of the configurations evaluated. Each component is being examined to determine if there are areas of concern with respect to component efficiency, operability, reliability, or hazard. The effects of reducing the maximum chamber pressure were investigated for one of the cycles.

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

NASA Astrophysics Data System (ADS)

Ueki, Kenta; Iwamori, Hikaru

2017-10-01

In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

A system-level view of optimizing high-channel-count wireless biosignal telemetry.

PubMed

Chandler, Rodney J; Gibson, Sarah; Karkare, Vaibhav; Farshchi, Shahin; Marković, Dejan; Judy, Jack W

2009-01-01

In this paper we perform a system-level analysis of a wireless biosignal telemetry system. We perform an analysis of each major system component (e.g., analog front end, analog-to-digital converter, digital signal processor, and wireless link), in which we consider physical, algorithmic, and design limitations. Since there are a wide range applications for wireless biosignal telemetry systems, each with their own unique set of requirements for key parameters (e.g., channel count, power dissipation, noise level, number of bits, etc.), our analysis is equally broad. The net result is a set of plots, in which the power dissipation for each component and as the system as a whole, are plotted as a function of the number of channels for different architectural strategies. These results are also compared to existing implementations of complete wireless biosignal telemetry systems.

Principal Components Analysis on the spectral Bidirectional Reflectance Distribution Function of ceramic colour standards.

PubMed

Ferrero, A; Campos, J; Rabal, A M; Pons, A; Hernanz, M L; Corróns, A

2011-09-26

The Bidirectional Reflectance Distribution Function (BRDF) is essential to characterize an object's reflectance properties. This function depends both on the various illumination-observation geometries as well as on the wavelength. As a result, the comprehensive interpretation of the data becomes rather complex. In this work we assess the use of the multivariable analysis technique of Principal Components Analysis (PCA) applied to the experimental BRDF data of a ceramic colour standard. It will be shown that the result may be linked to the various reflection processes occurring on the surface, assuming that the incoming spectral distribution is affected by each one of these processes in a specific manner. Moreover, this procedure facilitates the task of interpolating a series of BRDF measurements obtained for a particular sample. © 2011 Optical Society of America

Chemical and biotechnological processing of collagen-containing raw materials into functional components of feed suitable for production of high-quality meat from farm animals

NASA Astrophysics Data System (ADS)

Baburina, M. I.; Ivankin, A. N.; Stanovova, I. A.

2017-09-01

The process of chemical biotechnological processing of collagen-containing raw materials into functional components of feeds for effective pig rearing was studied. Protein components of feeds were obtained as a result of hydrolysis in the presence of lactic acid of the animal collagen from secondary raw materials, which comprised subcutaneous collagen (cuticle), skin and veined mass with tendons from cattle. For comparison, a method is described for preparing protein components of feeds by cultivating Lactobacillus plantarum. Analysis of the kinetic data of the conversion of a high-molecular collagen protein to an aminolyte polypeptide mixture showed the advantage of microbiological synthesis in obtaining a protein for feeds. Feed formulations have been developed to include the components obtained, and which result in high quality pork suitable for the production of quality meat products.

Patient-specific bone modeling and analysis: the role of integration and automation in clinical adoption.

PubMed

Zadpoor, Amir A; Weinans, Harrie

2015-03-18

Patient-specific analysis of bones is considered an important tool for diagnosis and treatment of skeletal diseases and for clinical research aimed at understanding the etiology of skeletal diseases and the effects of different types of treatment on their progress. In this article, we discuss how integration of several important components enables accurate and cost-effective patient-specific bone analysis, focusing primarily on patient-specific finite element (FE) modeling of bones. First, the different components are briefly reviewed. Then, two important aspects of patient-specific FE modeling, namely integration of modeling components and automation of modeling approaches, are discussed. We conclude with a section on validation of patient-specific modeling results, possible applications of patient-specific modeling procedures, current limitations of the modeling approaches, and possible areas for future research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Fetal source extraction from magnetocardiographic recordings by dependent component analysis

NASA Astrophysics Data System (ADS)

de Araujo, Draulio B.; Kardec Barros, Allan; Estombelo-Montesco, Carlos; Zhao, Hui; Roque da Silva Filho, A. C.; Baffa, Oswaldo; Wakai, Ronald; Ohnishi, Noboru

2005-10-01

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Failure analysis of aluminum alloy components

NASA Technical Reports Server (NTRS)

Johari, O.; Corvin, I.; Staschke, J.

1973-01-01

Analysis of six service failures in aluminum alloy components which failed in aerospace applications is reported. Identification of fracture surface features from fatigue and overload modes was straightforward, though the specimens were not always in a clean, smear-free condition most suitable for failure analysis. The presence of corrosion products and of chemically attacked or mechanically rubbed areas here hindered precise determination of the cause of crack initiation, which was then indirectly inferred from the scanning electron fractography results. In five failures the crack propagation was by fatigue, though in each case the fatigue crack initiated from a different cause. Some of these causes could be eliminated in future components by better process control. In one failure, the cause was determined to be impact during a crash; the features of impact fracture were distinguished from overload fractures by direct comparisons of the received specimens with laboratory-generated failures.

[Extraction of evoked related potentials by using the combination of independent component analysis and wavelet analysis].

PubMed

Zou, Ling; Chen, Shuyue; Sun, Yuqiang; Ma, Zhenghua

2010-08-01

In this paper we present a new method of combining Independent Component Analysis (ICA) and Wavelet de-noising algorithm to extract Evoked Related Potentials (ERPs). First, the extended Infomax-ICA algorithm is used to analyze EEG signals and obtain the independent components (Ics); Then, the Wave Shrink (WS) method is applied to the demixed Ics as an intermediate step; the EEG data were rebuilt by using the inverse ICA based on the new Ics; the ERPs were extracted by using de-noised EEG data after being averaged several trials. The experimental results showed that the combined method and ICA method could remove eye artifacts and muscle artifacts mixed in the ERPs, while the combined method could retain the brain neural activity mixed in the noise Ics and could extract the weak ERPs efficiently from strong background artifacts.

Component Analyses Using Single-Subject Experimental Designs: A Review

ERIC Educational Resources Information Center

Ward-Horner, John; Sturmey, Peter

2010-01-01

A component analysis is a systematic assessment of 2 or more independent variables or components that comprise a treatment package. Component analyses are important for the analysis of behavior; however, previous research provides only cursory descriptions of the topic. Therefore, in this review the definition of "component analysis" is discussed,…

A Meta-Analytic Review of Components Associated with Parent Training Program Effectiveness

ERIC Educational Resources Information Center

Kaminski, Jennifer Wyatt; Valle, Linda Anne; Filene, Jill H.; Boyle, Cynthia L.

2008-01-01

This component analysis used meta-analytic techniques to synthesize the results of 77 published evaluations of parent training programs (i.e., programs that included the active acquisition of parenting skills) to enhance behavior and adjustment in children aged 0-7. Characteristics of program content and delivery method were used to predict effect…

Correlation study between vibrational environmental and failure rates of civil helicopter components

NASA Technical Reports Server (NTRS)

Alaniz, O.

1979-01-01

An investigation of two selected helicopter types, namely, the Models 206A/B and 212, is reported. An analysis of the available vibration and reliability data for these two helicopter types resulted in the selection of ten components located in five different areas of the helicopter and consisting primarily of instruments, electrical components, and other noncritical flight hardware. The potential for advanced technology in suppressing vibration in helicopters was assessed. The are still several unknowns concerning both the vibration environment and the reliability of helicopter noncritical flight components. Vibration data for the selected components were either insufficient or inappropriate. The maintenance data examined for the selected components were inappropriate due to variations in failure mode identification, inconsistent reporting, or inaccurate informaton.

Principal Components Analysis of a JWST NIRSpec Detector Subsystem

NASA Technical Reports Server (NTRS)

Arendt, Richard G.; Fixsen, D. J.; Greenhouse, Matthew A.; Lander, Matthew; Lindler, Don; Loose, Markus; Moseley, S. H.; Mott, D. Brent; Rauscher, Bernard J.; Wen, Yiting;

Overview of the Systems Special Investigation Group investigation

NASA Technical Reports Server (NTRS)

Mason, James B.; Dursch, Harry; Edelman, Joel

1993-01-01

The Long Duration Exposure Facility (LDEF) carried a remarkable variety of electrical, mechanical, thermal, and optical systems, subsystems, and components. Nineteen of the fifty-seven experiments flown on LDEF contained functional systems that were active on-orbit. Almost all of the other experiments possessed at least a few specific components of interest to the Systems Special Investigation Group (Systems SIG), such as adhesives, seals, fasteners, optical components, and thermal blankets. Almost all top level functional testing of the active LDEF and experiment systems has been completed. Failure analysis of both LDEF hardware and individual experiments that failed to perform as designed has also been completed. Testing of system components and experimenter hardware of interest to the Systems SIG is ongoing. All available testing and analysis results were collected and integrated by the Systems SIG. An overview of our findings is provided. An LDEF Optical Experiment Database containing information for all 29 optical related experiments is also discussed.

Empirical validation of the triple-code model of numerical processing for complex math operations using functional MRI and group Independent Component Analysis of the mental addition and subtraction of fractions.

PubMed

Schmithorst, Vincent J; Brown, Rhonda Douglas

2004-07-01

The suitability of a previously hypothesized triple-code model of numerical processing, involving analog magnitude, auditory verbal, and visual Arabic codes of representation, was investigated for the complex mathematical task of the mental addition and subtraction of fractions. Functional magnetic resonance imaging (fMRI) data from 15 normal adult subjects were processed using exploratory group Independent Component Analysis (ICA). Separate task-related components were found with activation in bilateral inferior parietal, left perisylvian, and ventral occipitotemporal areas. These results support the hypothesized triple-code model corresponding to the activated regions found in the individual components and indicate that the triple-code model may be a suitable framework for analyzing the neuropsychological bases of the performance of complex mathematical tasks. Copyright 2004 Elsevier Inc.

Make or buy decision model with multi-stage manufacturing process and supplier imperfect quality

NASA Astrophysics Data System (ADS)

Pratama, Mega Aria; Rosyidi, Cucuk Nur

2017-11-01

This research develops an make or buy decision model considering supplier imperfect quality. This model can be used to help companies make the right decision in case of make or buy component with the best quality and the least cost in multistage manufacturing process. The imperfect quality is one of the cost component that must be minimizing in this model. Component with imperfect quality, not necessarily defective. It still can be rework and used for assembly. This research also provide a numerical example and sensitivity analysis to show how the model work. We use simulation and help by crystal ball to solve the numerical problem. The sensitivity analysis result show that percentage of imperfect generally not affect to the model significantly, and the model is not sensitive to changes in these parameters. This is because the imperfect cost are smaller than overall total cost components.

Comparison of cemented and uncemented fixation in total knee arthroplasty.

PubMed

Brown, Thomas E; Harper, Benjamin L; Bjorgul, Kristian

2013-05-01

As a result of reading this article, physicians should be able to :1. Understand the rationale behind using uncemented fixation in total knee arthroplasty.2.Discuss the current literature comparing cemented and uncemented total knee arthroplasty3. Describe the value of radiostereographic analysis in assessing implant stability.4. Appreciate the limitations in the available literature advocating 1 mode of fixation in total knee arthroplasty. Total knee arthroplasty performed worldwide uses either cemented, cementless, or hybrid (cementless femur with a cemented tibia) fixation of the components. No recent literature review concerning the outcomes of cemented vs noncemented components has been performed. Noncemented components offer the potential advantage of a biologic interface between the bone and implants, which could demonstrate the greatest advantage in long-term durable fixation in the follow-up of young patients undergoing arthroplasty. Several advances have been made in the backing of the tibial components that have not been available long enough to yield long-term comparative follow-up studies. Short-term radiostereographic analysis studies have yielded differing results. Although long-term, high-quality studies are still needed, material advances in biologic fixation surfaces, such as trabecular metal and hydroxyapatite, may offer promising results for young and active patients undergoing total knee arthroplasty when compared with traditional cemented options. Copyright 2013, SLACK Incorporated.

Effect of Punch Stroke on Deformation During Sheet Forming Through Finite Element

NASA Astrophysics Data System (ADS)

Akinlabi, Stephen; Akinlabi, Esther

2017-08-01

Forming is one of the traditional methods of making shapes, bends and curvature in metallic components during a fabrication process. Mechanical forming, in particular, employs the use of a punch, which is pressed against the sheet material to be deformed into a die by the application of an external force. This study reports on the finite element analysis of the effects of punch stroke on the resulting sheet deformation, which is directly a function of the structural integrity of the formed components for possible application in the automotive industry. The results show that punch stroke is directly proportional to the resulting bend angle of the formed components. It was further revealed that the developed plastic strain increases as the punch stroke increases.

[Detection of quadratic phase coupling between EEG signal components by nonparamatric and parametric methods of bispectral analysis].

PubMed

Schmidt, K; Witte, H

1999-11-01

Recently the assumption of the independence of individual frequency components in a signal has been rejected, for example, for the EEG during defined physiological states such as sleep or sedation [9, 10]. Thus, the use of higher-order spectral analysis capable of detecting interrelations between individual signal components has proved useful. The aim of the present study was to investigate the quality of various non-parametric and parametric estimation algorithms using simulated as well as true physiological data. We employed standard algorithms available for the MATLAB. The results clearly show that parametric bispectral estimation is superior to non-parametric estimation in terms of the quality of peak localisation and the discrimination from other peaks.

Quantitative analysis of ginger components in commercial products using liquid chromatography with electrochemical array detection

PubMed Central

Shao, Xi; Lv, Lishuang; Parks, Tiffany; Wu, Hou; Ho, Chi-Tang; Sang, Shengmin

2010-01-01

For the first time, a sensitive reversed-phase HPLC electrochemical array method has been developed for the quantitative analysis of eight major ginger components ([6]-, [8]-, and [10]-gingerol, [6]-, [8]-, and [10]-shogaol, [6]-paradol, and [1]-dehydrogingerdione) in eleven ginger-containing commercial products. This method was valid with unrivaled sensitivity as low as 7.3 – 20.2 pg of limit of detection and a range of 14.5 to 40.4 pg of limit of quantification. Using this method, we quantified the levels of eight ginger components in eleven different commercial products. Our results found that both levels and ratios among the eight compounds vary greatly in commercial products. PMID:21090746

Mission definition study for Stanford relativity satellite. Volume 3: Appendices

NASA Technical Reports Server (NTRS)

1971-01-01

An analysis is presented for the cost of the mission as a function of the following variables: amount of redundancy in the spacecraft, amount of care taken in building the spacecraft (functional and environmental tests, screening of components, quality control, etc), and the number of flights necessary to accomplish the mission. Thermal analysis and mathematical models for the experimental components are presented. The results of computer structural and stress analyses for support and cylinders are discussed. Reliability, quality control, and control system simulation by computer are also considered.

Isomorphisms between Petri nets and dataflow graphs

NASA Technical Reports Server (NTRS)

Kavi, Krishna M.; Buckles, Billy P.; Bhat, U. Narayan

1987-01-01

Dataflow graphs are a generalized model of computation. Uninterpreted dataflow graphs with nondeterminism resolved via probabilities are shown to be isomorphic to a class of Petri nets known as free choice nets. Petri net analysis methods are readily available in the literature and this result makes those methods accessible to dataflow research. Nevertheless, combinatorial explosion can render Petri net analysis inoperative. Using a previously known technique for decomposing free choice nets into smaller components, it is demonstrated that, in principle, it is possible to determine aspects of the overall behavior from the particular behavior of components.

A project to improve the capabilities of minorities in energy fields and a cost benefit analysis of an ethyl alcohol plant

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

Sara, T.S.; Jones, M. Jr.

1986-08-01

The project being reported in this document had three components: (1) a research project to carry out cost-benefit analysis of an ethyl alcohol plant at Tuskegee University, (2) seminars to improve the high-technology capabilities of minority persons, and (3) a class in energy management. The report provides a background on the three components listed above. The results from the research on the ethyl alcohol plant, are discussed, along with the seminars, and details of the energy management class.

Fast grasping of unknown objects using principal component analysis

NASA Astrophysics Data System (ADS)

Lei, Qujiang; Chen, Guangming; Wisse, Martijn

2017-09-01

Fast grasping of unknown objects has crucial impact on the efficiency of robot manipulation especially subjected to unfamiliar environments. In order to accelerate grasping speed of unknown objects, principal component analysis is utilized to direct the grasping process. In particular, a single-view partial point cloud is constructed and grasp candidates are allocated along the principal axis. Force balance optimization is employed to analyze possible graspable areas. The obtained graspable area with the minimal resultant force is the best zone for the final grasping execution. It is shown that an unknown object can be more quickly grasped provided that the component analysis principle axis is determined using single-view partial point cloud. To cope with the grasp uncertainty, robot motion is assisted to obtain a new viewpoint. Virtual exploration and experimental tests are carried out to verify this fast gasping algorithm. Both simulation and experimental tests demonstrated excellent performances based on the results of grasping a series of unknown objects. To minimize the grasping uncertainty, the merits of the robot hardware with two 3D cameras can be utilized to suffice the partial point cloud. As a result of utilizing the robot hardware, the grasping reliance is highly enhanced. Therefore, this research demonstrates practical significance for increasing grasping speed and thus increasing robot efficiency under unpredictable environments.

A Component-Centered Meta-Analysis of Family-Based Prevention Programs for Adolescent Substance Use

PubMed Central

Roseth, Cary J.; Fosco, Gregory M.; Lee, You-kyung; Chen, I-Chien

2016-01-01

Although research has documented the positive effects of family-based prevention programs, the field lacks specific information regarding why these programs are effective. The current study summarized the effects of family-based programs on adolescent substance use using a component-based approach to meta-analysis in which we decomposed programs into a set of key topics or components that were specifically addressed by program curricula (e.g., parental monitoring/behavior management, problem solving, positive family relations, etc.). Components were coded according to the amount of time spent on program services that targeted youth, parents, and the whole family; we also coded effect sizes across studies for each substance-related outcome. Given the nested nature of the data, we used hierarchical linear modeling to link program components (Level 2) with effect sizes (Level 1). The overall effect size across programs was .31, which did not differ by type of substance. Youth-focused components designed to encourage more positive family relationships and a positive orientation toward the future emerged as key factors predicting larger than average effect sizes. Our results suggest that, within the universe of family-based prevention, where components such as parental monitoring/behavior management are almost universal, adding or expanding certain youth-focused components may be able to enhance program efficacy. PMID:27064553

Remote sensing image denoising application by generalized morphological component analysis

NASA Astrophysics Data System (ADS)

Yu, Chong; Chen, Xiong

2014-12-01

In this paper, we introduced a remote sensing image denoising method based on generalized morphological component analysis (GMCA). This novel algorithm is the further extension of morphological component analysis (MCA) algorithm to the blind source separation framework. The iterative thresholding strategy adopted by GMCA algorithm firstly works on the most significant features in the image, and then progressively incorporates smaller features to finely tune the parameters of whole model. Mathematical analysis of the computational complexity of GMCA algorithm is provided. Several comparison experiments with state-of-the-art denoising algorithms are reported. In order to make quantitative assessment of algorithms in experiments, Peak Signal to Noise Ratio (PSNR) index and Structural Similarity (SSIM) index are calculated to assess the denoising effect from the gray-level fidelity aspect and the structure-level fidelity aspect, respectively. Quantitative analysis on experiment results, which is consistent with the visual effect illustrated by denoised images, has proven that the introduced GMCA algorithm possesses a marvelous remote sensing image denoising effectiveness and ability. It is even hard to distinguish the original noiseless image from the recovered image by adopting GMCA algorithm through visual effect.

Reading comprehension and immersion schooling: evidence from component skills.

PubMed

Hansen, Laura Birke; Morales, Julia; Macizo, Pedro; Duñabeitia, Jon Andoni; Saldaña, David; Carreiras, Manuel; Fuentes, Luis J; Bajo, M Teresa

2017-01-01

The present research aims to assess literacy acquisition in children becoming bilingual via second language immersion in school. We adopt a cognitive components approach, assessing text-level reading comprehension, a complex literacy skill, as well as underlying cognitive and linguistic components in 144 children aged 7 to 14 (72 immersion bilinguals, 72 controls). Using principal component analysis, a nuanced pattern of results was observed: although emergent bilinguals lag behind their monolingual counterparts on measures of linguistic processing, they showed enhanced performance on a memory and reasoning component. For reading comprehension, no between-group differences were evident, suggesting that selective benefits compensate costs at the level of underlying cognitive components. Overall, the results seem to indicate that literacy skills may be modulated by emerging bilingualism even when no between-group differences are evident at the level of complex skill, and the detection of such differences may depend on the focus and selectivity of the task battery used. © 2016 John Wiley & Sons Ltd.

Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

NASA Astrophysics Data System (ADS)

Li, Qian; Tang, Yongjiao; Yan, Zhiwei; Zhang, Pudun

2017-06-01

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

The correlation between biofilm biopolymer composition and membrane fouling in submerged membrane bioreactors.

PubMed

Luo, Jinxue; Zhang, Jinsong; Tan, Xiaohui; McDougald, Diane; Zhuang, Guoqiang; Fane, Anthony G; Kjelleberg, Staffan; Cohen, Yehuda; Rice, Scott A

2014-10-01

Biofouling, the combined effect of microorganism and biopolymer accumulation, significantly reduces the process efficiency of membrane bioreactors (MBRs). Here, four biofilm components, alpha-polysaccharides, beta-polysaccharides, proteins and microorganisms, were quantified in MBRs. The biomass of each component was positively correlated with the transmembrane pressure increase in MBRs. Proteins were the most abundant biopolymer in biofilms and showed the fastest rate of increase. The spatial distribution and co-localization analysis of the biofouling components indicated at least 60% of the extracellular polysaccharide (EPS) components were associated with the microbial cells when the transmembrane pressure (TMP) entered the jump phase, suggesting that the EPS components were either secreted by the biofilm cells or that the deposition of these components facilitated biofilm formation. It is suggested that biofilm formation and the accumulation of EPS are intrinsically coupled, resulting in biofouling and loss of system performance. Therefore, strategies that control biofilm formation on membranes may result in a significant improvement of MBR performance.

Atmospheric radiation model for water surfaces

NASA Technical Reports Server (NTRS)

Turner, R. E.; Gaskill, D. W.; Lierzer, J. R.

1982-01-01

An atmospheric correction model was extended to account for various atmospheric radiation components in remotely sensed data. Components such as the atmospheric path radiance which results from singly scattered sky radiation specularly reflected by the water surface are considered. A component which is referred to as the virtual Sun path radiance, i.e. the singly scattered path radiance which results from the solar radiation which is specularly reflected by the water surface is also considered. These atmospheric radiation components are coded into a computer program for the analysis of multispectral remote sensor data over the Great Lakes of the United States. The user must know certain parameters, such as the visibility or spectral optical thickness of the atmosphere and the geometry of the sensor with respect to the Sun and the target elements under investigation.

Analysis of transient state in HTS tapes under ripple DC load current

NASA Astrophysics Data System (ADS)

Stepien, M.; Grzesik, B.

2014-05-01

The paper concerns the analysis of transient state (quench transition) in HTS tapes loaded with the current having DC component together with a ripple component. Two shapes of the ripple were taken into account: sinusoidal and triangular. Very often HTS tape connected to a power electronic current supply (i.e. superconducting coil for SMES) that delivers DC current with ripples and it needs to be examined under such conditions. Additionally, measurements of electrical (and thermal) parameters under such ripple excitation is useful to tape characterization in broad range of load currents. The results presented in the paper were obtained using test bench which contains programmable DC supply and National Instruments data acquisition system. Voltage drops and load currents were measured vs. time. Analysis of measured parameters as a function of the current was used to tape description with quench dynamics taken into account. Results of measurements were also used to comparison with the results of numerical modelling based on FEM. Presented provisional results show possibility to use results of measurements in transient state to prepare inverse models of superconductors and their detailed numerical modelling.

Micro-Logistics Analysis for Human Space Exploration

NASA Technical Reports Server (NTRS)

Cirillo, William; Stromgren, Chel; Galan, Ricardo

2008-01-01

Traditionally, logistics analysis for space missions has focused on the delivery of elements and goods to a destination. This type of logistics analysis can be referred to as "macro-logistics". While the delivery of goods is a critical component of mission analysis, it captures only a portion of the constraints that logistics planning may impose on a mission scenario. The other component of logistics analysis concerns the local handling of goods at the destination, including storage, usage, and disposal. This type of logistics analysis, referred to as "micro-logistics", may also be a primary driver in the viability of a human lunar exploration scenario. With the rigorous constraints that will be placed upon a human lunar outpost, it is necessary to accurately evaluate micro-logistics operations in order to develop exploration scenarios that will result in an acceptable level of system performance.

Comparison and analysis of organic components of biogas slurry from eichhornia crassipes solms and corn straw biogas slurry

NASA Astrophysics Data System (ADS)

Li, Q.; Li, Y. B.; Liu, Z. H.; Min, J.; Cui, Y.; Gao, X. H.

2017-11-01

Biogas slurry is one of anaerobic fermentations, and biomass fermentation biogas slurries with different compositions are different. This paper mainly presents through the anaerobic fermentation of Eichhornia crassipes solms biogas slurry and biogas slurry of corn straw, the organic components of two kinds of biogas slurry after extraction were compared by TLC, HPLC and spectrophotometric determination of nucleic acid and protein of two kinds of biogas slurry organic components, and analyzes the result of comparison.

Machine learning of frustrated classical spin models. I. Principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Ce; Zhai, Hui

2017-10-01

This work aims at determining whether artificial intelligence can recognize a phase transition without prior human knowledge. If this were successful, it could be applied to, for instance, analyzing data from the quantum simulation of unsolved physical models. Toward this goal, we first need to apply the machine learning algorithm to well-understood models and see whether the outputs are consistent with our prior knowledge, which serves as the benchmark for this approach. In this work, we feed the computer data generated by the classical Monte Carlo simulation for the X Y model in frustrated triangular and union jack lattices, which has two order parameters and exhibits two phase transitions. We show that the outputs of the principal component analysis agree very well with our understanding of different orders in different phases, and the temperature dependences of the major components detect the nature and the locations of the phase transitions. Our work offers promise for using machine learning techniques to study sophisticated statistical models, and our results can be further improved by using principal component analysis with kernel tricks and the neural network method.

Analysis of truss, beam, frame, and membrane components. [composite structures

NASA Technical Reports Server (NTRS)

Knoell, A. C.; Robinson, E. Y.

1975-01-01

Truss components are considered, taking into account composite truss structures, truss analysis, column members, and truss joints. Beam components are discussed, giving attention to composite beams, laminated beams, and sandwich beams. Composite frame components and composite membrane components are examined. A description is given of examples of flat membrane components and examples of curved membrane elements. It is pointed out that composite structural design and analysis is a highly interactive, iterative procedure which does not lend itself readily to characterization by design or analysis function only.-

Sparse principal component analysis in medical shape modeling

NASA Astrophysics Data System (ADS)

Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus

2006-03-01

Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

Developing techniques for cause-responsibility analysis of occupational accidents.

PubMed

Jabbari, Mousa; Ghorbani, Roghayeh

2016-11-01

The aim of this study was to specify the causes of occupational accidents, determine social responsibility and the role of groups involved in work-related accidents. This study develops occupational accidents causes tree, occupational accidents responsibility tree, and occupational accidents component-responsibility analysis worksheet; based on these methods, it develops cause-responsibility analysis (CRA) techniques, and for testing them, analyzes 100 fatal/disabling occupational accidents in the construction setting that were randomly selected from all the work-related accidents in Tehran, Iran, over a 5-year period (2010-2014). The main result of this study involves two techniques for CRA: occupational accidents tree analysis (OATA) and occupational accidents components analysis (OACA), used in parallel for determination of responsible groups and responsibilities rate. From the results, we find that the management group of construction projects has 74.65% responsibility of work-related accidents. The developed techniques are purposeful for occupational accidents investigation/analysis, especially for the determination of detailed list of tasks, responsibilities, and their rates. Therefore, it is useful for preventing work-related accidents by focusing on the responsible group's duties. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Multimodal Emotion Detection System during Human-Robot Interaction

PubMed Central

Alonso-Martín, Fernando; Malfaz, María; Sequeira, João; Gorostiza, Javier F.; Salichs, Miguel A.

2013-01-01

In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately. PMID:24240598

Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

Comparative Analysis of Metabolic Syndrome Components in over 15,000 African Americans Identifies Pleiotropic Variants: Results from the PAGE Study

PubMed Central

Carty, Cara L.; Bhattacharjee, Samsiddhi; Haessler, Jeff; Cheng, Iona; Hindorff, Lucia A.; Aroda, Vanita; Carlson, Christopher S.; Hsu, Chun-Nan; Wilkens, Lynne; Liu, Simin; Selvin, Elizabeth; Jackson, Rebecca; North, Kari E.; Peters, Ulrike; Pankow, James S.; Chatterjee, Nilanjan; Kooperberg, Charles

2014-01-01

Background Metabolic syndrome (MetS) refers to the clustering of cardio-metabolic risk factors including dyslipidemia, central adiposity, hypertension and hyperglycemia in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Methods and Results Using the Metabochip array in 15,148 African Americans (AA) from the PAGE Study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models which lack power when associations for MetS components are null or have opposite effects, ASSET uses one-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With ASSET, we identify 27 SNPs in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, LPL, APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P< 2.5e-7, the Bonferroni adjusted P-value. Three loci replicate in a Hispanic population, n=5172. A novel AA-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2 and CETP variants, many with opposing effects; e.g. the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity. Conclusions We highlight a method to increase power in large-scale genomic association analyses, and report a novel variant associated with all MetS components in AA. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications. PMID:25023634

Relationships between NIR spectra and sensory attributes of Thai commercial fish sauces.

PubMed

Ritthiruangdej, Pitiporn; Suwonsichon, Thongchai

2007-07-01

Twenty Thai commercial fish sauces were characterized by sensory descriptive analysis and near-infrared (NIR) spectroscopy. The main objectives were i) to investigate the relationships between sensory attributes and NIR spectra of samples and ii) to characterize the sensory characteristics of fish sauces based on NIR data. A generic descriptive analysis with 12 trained panels was used to characterize the sensory attributes. These attributes consisted of 15 descriptors: brown color, 5 aromatics (sweet, caramelized, fermented, fishy, and musty), 4 tastes (sweet, salty, bitter, and umami), 3 aftertastes (sweet, salty and bitter) and 2 flavors (caramelized and fishy). The results showed that Thai fish sauce samples exhibited significant differences in all of sensory attribute values (p < 0.05). NIR transflectance spectra were obtained from 1100 to 2500 nm. Prior to investigation of the relationships between sensory attributes and NIR spectra, principal component analysis (PCA) was applied to reduce the dimensionality of the spectral data from 622 wavelengths to two uncorrelated components (NIR1 and NIR2) which explained 92 and 7% of the total variation, respectively. NIR1 was highly correlated with the wavelength regions of 1100 - 1544, 1774 - 2062, 2092 - 2308, and 2358 - 2440 nm, while NIR2 was highly correlated with the wavelength regions of 1742 - 1764, 2066 - 2088, and 2312 - 2354 nm. Subsequently, the relationships among these two components and all sensory attributes were also investigated by PCA. The results showed that the first three principal components (PCs) named as fishy flavor component (PC1), sweet component (PC2) and bitterness component (PC3), respectively, explained a total of 66.86% of the variation. NIR1 was mainly correlated to the sensory attributes of fishy aromatic, fishy flavor and sweet aftertaste on PC1. In addition, the PCA using only the factor loadings of NIR1 and NIR2 could be used to classify samples into three groups which showed high, medium and low degrees of fishy aromatic, fishy flavor and sweet aftertaste.

Structure and content components of self-management interventions that improve health-related quality of life in people with inflammatory bowel disease: a systematic review, meta-analysis and meta-regression.

PubMed

Tu, Wenjing; Xu, Guihua; Du, Shizheng

2015-10-01

The purpose of this review was to identify and categorise the components of the content and structure of effective self-management interventions for patients with inflammatory bowel disease. Inflammatory bowel diseases are chronic gastrointestinal disorders impacting health-related quality of life. Although the efficacy of self-management interventions has been demonstrated in previous studies, the most effective components of the content and structure of these interventions remain unknown. A systematic review, meta-analysis and meta-regression of randomised controlled trials was used. A systematic search of six electronic databases, including Pubmed, Embase, Cochrane central register of controlled trials, Web of Science, Cumulative Index of Nursing and Allied Health Literature and Chinese Biomedical Literature Database, was conducted. Content analysis was used to categorise the components of the content and structure of effective self-management interventions for inflammatory bowel disease. Clinically important and statistically significant beneficial effects on health-related quality of life were explored, by comparing the association between effect sizes and various components of self-management interventions such as the presence or absence of specific content and different delivery methods. Fifteen randomised controlled trials were included in this review. Distance or remote self-management interventions demonstrated a larger effect size. However, there is no evidence for a positive effect associated with specific content component of self-management interventions in adult patients with inflammatory bowel disease in general. The results showed that self-management interventions have positive effects on health-related quality of life in patients with inflammatory bowel disease, and distance or remote self-management programmes had better outcomes than other types of interventions. This review provides useful information to clinician and researchers when determining components of effective self-management programmes for patients with inflammatory bowel disease. More high-quality randomised controlled trials are needed to test the results. © 2015 John Wiley & Sons Ltd.

Progressive Disintegration of Brain Networking from Normal Aging to Alzheimer Disease: Analysis of Independent Components of 18F-FDG PET Data.

PubMed

Pagani, Marco; Giuliani, Alessandro; Öberg, Johanna; De Carli, Fabrizio; Morbelli, Silvia; Girtler, Nicola; Arnaldi, Dario; Accardo, Jennifer; Bauckneht, Matteo; Bongioanni, Francesca; Chincarini, Andrea; Sambuceti, Gianmario; Jonsson, Cathrine; Nobili, Flavio

2017-07-01

Brain connectivity has been assessed in several neurodegenerative disorders investigating the mutual correlations between predetermined regions or nodes. Selective breakdown of brain networks during progression from normal aging to Alzheimer disease dementia (AD) has also been observed. Methods: We implemented independent-component analysis of 18 F-FDG PET data in 5 groups of subjects with cognitive states ranging from normal aging to AD-including mild cognitive impairment (MCI) not converting or converting to AD-to disclose the spatial distribution of the independent components in each cognitive state and their accuracy in discriminating the groups. Results: We could identify spatially distinct independent components in each group, with generation of local circuits increasing proportionally to the severity of the disease. AD-specific independent components first appeared in the late-MCI stage and could discriminate converting MCI and AD from nonconverting MCI with an accuracy of 83.5%. Progressive disintegration of the intrinsic networks from normal aging to MCI to AD was inversely proportional to the conversion time. Conclusion: Independent-component analysis of 18 F-FDG PET data showed a gradual disruption of functional brain connectivity with progression of cognitive decline in AD. This information might be useful as a prognostic aid for individual patients and as a surrogate biomarker in intervention trials. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Psychometric characteristics of the Mobility Inventory in a longitudinal study of anxiety disorders: Replicating and exploring a three component solution

PubMed Central

Rodriguez, Benjamin F.; Pagano, Maria E.; Keller, Martin B.

2008-01-01

Psychometric characteristics of the Mobility Inventory (MI) were examined in 216 outpatients diagnosed with panic disorder with agoraphobia participating in a longitudinal study of anxiety disorders. An exploratory principal components analysis replicated a three-component solution for the MI reported in prior studies, with components corresponding to avoidance of public spaces, avoidance of enclosed spaces, and avoidance of open spaces. Correlational analyses suggested that the components tap unique but related areas of avoidance that were remarkably stable across periods of 1,3, and 5 years between administrations. Implications of these results for future studies of agoraphobia are discussed. PMID:17079112

Identification by GC-EAD of the two-component trail-following pheromone of Prorhinotermes simplex (Isoptera, Rhinotermitidae, Prorhinotermitinae).

PubMed

Sillam-Dussès, David; Kalinová, Blanka; Jiros, Pavel; Brezinová, Anna; Cvacka, Josef; Hanus, Robert; Sobotník, Jan; Bordereau, Christian; Valterová, Irena

2009-08-01

GC/MS analysis confirmed that neocembrene is the major component of the trail pheromone in the three species of the termite genus Prorhinotermes (P. simplex, P. canalifrons, P. inopinatus). In addition, EAG and GC-EAD experiments with P. simplex strongly suggest that dodecatrienol is a quantitatively minor component but a qualitatively important component of this trail pheromone. Trail-following bioassays confirmed the two-component nature of the trail pheromone. This is the first report of the use of the GC-EAD for the identification of trail pheromone in termites. These original results underline once again the special phylogenetic status of the Prorhinotermitinae among Rhinotermitidae.

[Relationship between four components of assertiveness and mental health among high school students].

PubMed

Watanabe, Asami

2009-04-01

This study examines the relationship between four components of assertiveness ("open expression", "control of emotion", "consideration for others" and "self-direction") and mental health. In Study 1, the analysis of interviews with thirteen high school students suggested that some components did not have a positive relationship with mental health. In Study 2, 176 high school students completed a questionnaire which included the UCLA isolation scale, the General Health Questionnaire (GHQ) and a scale to measure the four components of assertiveness. The results showed that an excessively high score for "consideration for others" was associated with mental unhealthiness. This component probably has an optimum level to maintain mental health.

COMPUTATIONAL ANALYSIS OF SWALLOWING MECHANICS UNDERLYING IMPAIRED EPIGLOTTIC INVERSION

PubMed Central

Pearson, William G.; Taylor, Brandon K; Blair, Julie; Martin-Harris, Bonnie

2015-01-01

Objective Determine swallowing mechanics associated with the first and second epiglottic movements, that is, movement to horizontal and full inversion respectively, in order to provide a clinical interpretation of impaired epiglottic function. Study Design Retrospective cohort study. Methods A heterogeneous cohort of patients with swallowing difficulties was identified (n=92). Two speech-language pathologists reviewed 5ml thin and 5ml pudding videofluoroscopic swallow studies per subject, and assigned epiglottic component scores of 0=complete inversion, 1=partial inversion, and 2=no inversion forming three groups of videos for comparison. Coordinates mapping minimum and maximum excursion of the hyoid, pharynx, larynx, and tongue base during pharyngeal swallowing were recorded using ImageJ software. A canonical variate analysis with post-hoc discriminant function analysis of coordinates was performed using MorphoJ software to evaluate mechanical differences between groups. Eigenvectors characterizing swallowing mechanics underlying impaired epiglottic movements were visualized. Results Nineteen of 184 video-swallows were rejected for poor quality (n=165). A Goodman-Kruskal index of predictive association showed no correlation between epiglottic component scores and etiologies of dysphagia (λ=.04). A two-way analysis of variance by epiglottic component scores showed no significant interaction effects between sex and age (f=1.4, p=.25). Discriminant function analysis demonstrated statistically significant mechanical differences between epiglottic component scores: 1&2, representing the first epiglottic movement (Mahalanobis distance=1.13, p=.0007); and, 0&1, representing the second epiglottic movement (Mahalanobis distance=0.83, p=.003). Eigenvectors indicate that laryngeal elevation and tongue base retraction underlie both epiglottic movements. Conclusion Results suggest that reduced tongue base retraction and laryngeal elevation underlie impaired first and second epiglottic movements. The styloglossus, hyoglossus and long pharyngeal muscles are implicated as targets for rehabilitation in dysphagic patients with impaired epiglottic inversion. PMID:27426940

Adhesive properties and adhesive joints strength of graphite/epoxy composites

NASA Astrophysics Data System (ADS)

Rudawska, Anna; Stančeková, Dana; Cubonova, Nadezda; Vitenko, Tetiana; Müller, Miroslav; Valášek, Petr

2017-05-01

The article presents the results of experimental research of the adhesive joints strength of graphite/epoxy composites and the results of the surface free energy of the composite surfaces. Two types of graphite/epoxy composites with different thickness were tested which are used to aircraft structure. The single-lap adhesive joints of epoxy composites were considered. Adhesive properties were described by surface free energy. Owens-Wendt method was used to determine surface free energy. The epoxy two-component adhesive was used to preparing the adhesive joints. Zwick/Roell 100 strength device were used to determination the shear strength of adhesive joints of epoxy composites. The strength test results showed that the highest value was obtained for adhesive joints of graphite-epoxy composite of smaller material thickness (0.48 mm). Statistical analysis of the results obtained, the study showed statistically significant differences between the values of the strength of the confidence level of 0.95. The statistical analysis of the results also showed that there are no statistical significant differences in average values of surface free energy (0.95 confidence level). It was noted that in each of the results the dispersion component of surface free energy was much greater than polar component of surface free energy.

On the Stress Analysis of Rails and Ties

DOT National Transportation Integrated Search

1976-09-01

This report covers first the methods presented in the literature for the stress analysis of railroad track components and results of a variety of validation tests. It was found that a formula can yield deflections and bending stresses in the rails of...

SSME HPOTP post-test diagnostic system enhancement project

NASA Technical Reports Server (NTRS)

Bickmore, Timothy W.

1995-01-01

An assessment of engine and component health is routinely made after each test or flight firing of a space shuttle main engine (SSME). Currently, this health assessment is done by teams of engineers who manually review sensor data, performance data, and engine and component operating histories. Based on review of information from these various sources, an evaluation is made as to the health of each component of the SSME and the preparedness of the engine for another test or flight. The objective of this project is to further develop a computer program which automates the analysis of test data from the SSME high-pressure oxidizer turbopump (HPOTP) in order to detect and diagnose anomalies. This program fits into a larger system, the SSME Post-Test Diagnostic System (PTDS), which will eventually be extended to assess the health and status of most SSME components on the basis of test data analysis. The HPOTP module is an expert system, which uses 'rules-of-thumb' obtained from interviews with experts from NASA Marshall Space Flight Center (MSFC) to detect and diagnose anomalies. Analyses of the raw test data are first performed using pattern recognition techniques which result in features such as spikes, shifts, peaks, and drifts being detected and written to a database. The HPOTP module then looks for combination of these features which are indicative of known anomalies, using the rules gathered from the turbomachinery experts. Results of this analysis are then displayed via a graphical user interface which provides ranked lists of anomalies and observations by engine component, along with supporting data plots for each.

[Correspondence analysis between traditional commercial specifications and quantitative quality indices of Notopterygii Rhizoma et Radix].

PubMed

Jiang, Shun-Yuan; Sun, Hong-Bing; Sun, Hui; Ma, Yu-Ying; Chen, Hong-Yu; Zhu, Wen-Tao; Zhou, Yi

2016-03-01

This paper aims to explore a comprehensive assessment method combined traditional Chinese medicinal material specifications with quantitative quality indicators. Seventy-six samples of Notopterygii Rhizoma et Radix were collected on market and at producing areas. Traditional commercial specifications were described and assigned, and 10 chemical components and volatile oils were determined for each sample. Cluster analysis, Fisher discriminant analysis and correspondence analysis were used to establish the relationship between the traditional qualitative commercial specifications and quantitative chemical indices for comprehensive evaluating quality of medicinal materials, and quantitative classification of commercial grade and quality grade. A herb quality index (HQI) including traditional commercial specifications and chemical components for quantitative grade classification were established, and corresponding discriminant function were figured out for precise determination of quality grade and sub-grade of Notopterygii Rhizoma et Radix. The result showed that notopterol, isoimperatorin and volatile oil were the major components for determination of chemical quality, and their dividing values were specified for every grade and sub-grade of the commercial materials of Notopterygii Rhizoma et Radix. According to the result, essential relationship between traditional medicinal indicators, qualitative commercial specifications, and quantitative chemical composition indicators can be examined by K-mean cluster, Fisher discriminant analysis and correspondence analysis, which provide a new method for comprehensive quantitative evaluation of traditional Chinese medicine quality integrated traditional commodity specifications and quantitative modern chemical index. Copyright© by the Chinese Pharmaceutical Association.

Tailored multivariate analysis for modulated enhanced diffraction

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

Caliandro, Rocco; Guccione, Pietro; Nico, Giovanni

2015-10-21

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited forin situandoperandostructural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scoresmore » and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.« less

Stimulus-related independent component and voxel-wise analysis of human brain activity during free viewing of a feature film.

PubMed

Lahnakoski, Juha M; Salmi, Juha; Jääskeläinen, Iiro P; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film

PubMed Central

Lahnakoski, Juha M.; Salmi, Juha; Jääskeläinen, Iiro P.; Lampinen, Jouko; Glerean, Enrico; Tikka, Pia; Sams, Mikko

2012-01-01

Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments. PMID:22496909

KSC 50-MHz Doppler Radar Wind Profiler (DRWP) Operational Acceptance Test (OAT) Report

NASA Technical Reports Server (NTRS)

Barbre, Robert E.

2015-01-01

This report documents analysis results of the Kennedy Space Center updated 50-MHz Doppler Radar Wind Profiler (DRWP) Operational Acceptance Test (OAT). This test was designed to demonstrate that the new DRWP operates in a similar manner to the previous DRWP for use as a situational awareness asset for mission operations at the Eastern Range to identify rapid changes in the wind environment that weather balloons cannot depict. Data examination and two analyses showed that the updated DRWP meets the specifications in the OAT test plan and performs at least as well as the previous DRWP. Data examination verified that the DRWP provides complete profiles every five minutes from 1.8-19.5 km in vertical increments of 150 m. Analysis of 5,426 wind component reports from 49 concurrent DRWP and balloon profiles presented root mean square (RMS) wind component differences around 2.0 m/s. The DRWP's effective vertical resolution (EVR) was found to be 300 m for both the westerly and southerly wind component, which the best EVR possible given the DRWP's vertical sampling interval. A third analysis quantified the sensitivity to rejecting data that do not have adequate signal by assessing the number of first-guess propagations at each altitude. This report documents the data, quality control procedures, methodology, and results of each analysis. It also shows that analysis of the updated DRWP produced results that were at least as good as the previous DRWP with proper rationale. The report recommends acceptance of the updated DRWP for situational awareness usage as per the OAT's intent.

Analysis of metabolic syndrome components in >15 000 african americans identifies pleiotropic variants: results from the population architecture using genomics and epidemiology study.

PubMed

Carty, Cara L; Bhattacharjee, Samsiddhi; Haessler, Jeff; Cheng, Iona; Hindorff, Lucia A; Aroda, Vanita; Carlson, Christopher S; Hsu, Chun-Nan; Wilkens, Lynne; Liu, Simin; Selvin, Elizabeth; Jackson, Rebecca; North, Kari E; Peters, Ulrike; Pankow, James S; Chatterjee, Nilanjan; Kooperberg, Charles

2014-08-01

Metabolic syndrome (MetS) refers to the clustering of cardiometabolic risk factors, including dyslipidemia, central adiposity, hypertension, and hyperglycemia, in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS. Using the Metabochip array in 15 148 African Americans from the Population Architecture using Genomics and Epidemiology (PAGE) study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models that lack power when associations for MetS components are null or have opposite effects, Association-analysis-based-on-subsets uses 1-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With Association-analysis-based-on-subsets, we identify 27 single nucleotide polymorphisms in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, and APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P<2.5e-7, the Bonferroni adjusted P value. Three loci replicate in a Hispanic population, n=5172. A novel African American-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2, and CETP variants, many with opposing effects (eg, the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity). We highlight a method to increase power in large-scale genomic association analyses and report a novel variant associated with all MetS components in African Americans. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications. © 2014 American Heart Association, Inc.

Analysis system for characterisation of simple, low-cost microfluidic components

NASA Astrophysics Data System (ADS)

Smith, Suzanne; Naidoo, Thegaran; Nxumalo, Zandile; Land, Kevin; Davies, Emlyn; Fourie, Louis; Marais, Philip; Roux, Pieter

2014-06-01

There is an inherent trade-off between cost and operational integrity of microfluidic components, especially when intended for use in point-of-care devices. We present an analysis system developed to characterise microfluidic components for performing blood cell counting, enabling the balance between function and cost to be established quantitatively. Microfluidic components for sample and reagent introduction, mixing and dispensing of fluids were investigated. A simple inlet port plugging mechanism is used to introduce and dispense a sample of blood, while a reagent is released into the microfluidic system through compression and bursting of a blister pack. Mixing and dispensing of the sample and reagent are facilitated via air actuation. For these microfluidic components to be implemented successfully, a number of aspects need to be characterised for development of an integrated point-of-care device design. The functional components were measured using a microfluidic component analysis system established in-house. Experiments were carried out to determine: 1. the force and speed requirements for sample inlet port plugging and blister pack compression and release using two linear actuators and load cells for plugging the inlet port, compressing the blister pack, and subsequently measuring the resulting forces exerted, 2. the accuracy and repeatability of total volumes of sample and reagent dispensed, and 3. the degree of mixing and dispensing uniformity of the sample and reagent for cell counting analysis. A programmable syringe pump was used for air actuation to facilitate mixing and dispensing of the sample and reagent. Two high speed cameras formed part of the analysis system and allowed for visualisation of the fluidic operations within the microfluidic device. Additional quantitative measures such as microscopy were also used to assess mixing and dilution accuracy, as well as uniformity of fluid dispensing - all of which are important requirements towards the successful implementation of a blood cell counting system.

Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-06-01

Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

Analysis of indoor air pollutants checklist using environmetric technique for health risk assessment of sick building complaint in nonindustrial workplace

PubMed Central

Syazwan, AI; Rafee, B Mohd; Juahir, Hafizan; Azman, AZF; Nizar, AM; Izwyn, Z; Syahidatussyakirah, K; Muhaimin, AA; Yunos, MA Syafiq; Anita, AR; Hanafiah, J Muhamad; Shaharuddin, MS; Ibthisham, A Mohd; Hasmadi, I Mohd; Azhar, MN Mohamad; Azizan, HS; Zulfadhli, I; Othman, J; Rozalini, M; Kamarul, FT

2012-01-01

Purpose To analyze and characterize a multidisciplinary, integrated indoor air quality checklist for evaluating the health risk of building occupants in a nonindustrial workplace setting. Design A cross-sectional study based on a participatory occupational health program conducted by the National Institute of Occupational Safety and Health (Malaysia) and Universiti Putra Malaysia. Method A modified version of the indoor environmental checklist published by the Department of Occupational Health and Safety, based on the literature and discussion with occupational health and safety professionals, was used in the evaluation process. Summated scores were given according to the cluster analysis and principal component analysis in the characterization of risk. Environmetric techniques was used to classify the risk of variables in the checklist. Identification of the possible source of item pollutants was also evaluated from a semiquantitative approach. Result Hierarchical agglomerative cluster analysis resulted in the grouping of factorial components into three clusters (high complaint, moderate-high complaint, moderate complaint), which were further analyzed by discriminant analysis. From this, 15 major variables that influence indoor air quality were determined. Principal component analysis of each cluster revealed that the main factors influencing the high complaint group were fungal-related problems, chemical indoor dispersion, detergent, renovation, thermal comfort, and location of fresh air intake. The moderate-high complaint group showed significant high loading on ventilation, air filters, and smoking-related activities. The moderate complaint group showed high loading on dampness, odor, and thermal comfort. Conclusion This semiquantitative assessment, which graded risk from low to high based on the intensity of the problem, shows promising and reliable results. It should be used as an important tool in the preliminary assessment of indoor air quality and as a categorizing method for further IAQ investigations and complaints procedures. PMID:23055779

A Fully Non-Metallic Gas Turbine Engine Enabled by Additive Manufacturing Part I: System Analysis, Component Identification, Additive Manufacturing, and Testing of Polymer Composites

NASA Technical Reports Server (NTRS)

Grady, Joseph E.; Haller, William J.; Poinsatte, Philip E.; Halbig, Michael C.; Schnulo, Sydney L.; Singh, Mrityunjay; Weir, Don; Wali, Natalie; Vinup, Michael; Jones, Michael G.;

Technique for Early Reliability Prediction of Software Components Using Behaviour Models

PubMed Central

Ali, Awad; N. A. Jawawi, Dayang; Adham Isa, Mohd; Imran Babar, Muhammad

2016-01-01

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction. PMID:27668748

Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

PubMed Central

Kessler, Daniel; Angstadt, Michael; Welsh, Robert C.

2014-01-01

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations. PMID:25505309

Independent Orbiter Assessment (IOA): Analysis of the DPS subsystem

NASA Technical Reports Server (NTRS)

Lowery, H. J.; Haufler, W. A.; Pietz, K. C.

1986-01-01

The results of the Independent Orbiter Assessment (IOA) of the Failure Modes and Effects Analysis/Critical Items List (FMEA/CIL) is presented. The IOA approach features a top-down analysis of the hardware to independently determine failure modes, criticality, and potential critical items. The independent analysis results corresponding to the Orbiter Data Processing System (DPS) hardware are documented. The DPS hardware is required for performing critical functions of data acquisition, data manipulation, data display, and data transfer throughout the Orbiter. Specifically, the DPS hardware consists of the following components: Multiplexer/Demultiplexer (MDM); General Purpose Computer (GPC); Multifunction CRT Display System (MCDS); Data Buses and Data Bus Couplers (DBC); Data Bus Isolation Amplifiers (DBIA); Mass Memory Unit (MMU); and Engine Interface Unit (EIU). The IOA analysis process utilized available DPS hardware drawings and schematics for defining hardware assemblies, components, and hardware items. Each level of hardware was evaluated and analyzed for possible failure modes and effects. Criticality was assigned based upon the severity of the effect for each failure mode. Due to the extensive redundancy built into the DPS the number of critical items are few. Those identified resulted from premature operation and erroneous output of the GPCs.

Cnn Based Retinal Image Upscaling Using Zero Component Analysis

NASA Astrophysics Data System (ADS)

Nasonov, A.; Chesnakov, K.; Krylov, A.

2017-05-01

The aim of the paper is to obtain high quality of image upscaling for noisy images that are typical in medical image processing. A new training scenario for convolutional neural network based image upscaling method is proposed. Its main idea is a novel dataset preparation method for deep learning. The dataset contains pairs of noisy low-resolution images and corresponding noiseless highresolution images. To achieve better results at edges and textured areas, Zero Component Analysis is applied to these images. The upscaling results are compared with other state-of-the-art methods like DCCI, SI-3 and SRCNN on noisy medical ophthalmological images. Objective evaluation of the results confirms high quality of the proposed method. Visual analysis shows that fine details and structures like blood vessels are preserved, noise level is reduced and no artifacts or non-existing details are added. These properties are essential in retinal diagnosis establishment, so the proposed algorithm is recommended to be used in real medical applications.

From measurements to metrics: PCA-based indicators of cyber anomaly

NASA Astrophysics Data System (ADS)

Ahmed, Farid; Johnson, Tommy; Tsui, Sonia

2012-06-01

We present a framework of the application of Principal Component Analysis (PCA) to automatically obtain meaningful metrics from intrusion detection measurements. In particular, we report the progress made in applying PCA to analyze the behavioral measurements of malware and provide some preliminary results in selecting dominant attributes from an arbitrary number of malware attributes. The results will be useful in formulating an optimal detection threshold in the principal component space, which can both validate and augment existing malware classifiers.

Electromagnetic field scattering by a triangular aperture.

PubMed

Harrison, R E; Hyman, E

1979-03-15

The multiple Laplace transform has been applied to analysis and computation of scattering by a double triangular aperture. Results are obtained which match far-field intensity distributions observed in experiments. Arbitrary polarization components, as well as in-phase and quadrature-phase components, may be determined, in the transform domain, as a continuous function of distance from near to far-field for any orientation, aperture, and transformable waveform. Numerical results are obtained by application of numerical multiple inversions of the fully transformed solution.

Building energy analysis tool

DOEpatents

Brackney, Larry; Parker, Andrew; Long, Nicholas; Metzger, Ian; Dean, Jesse; Lisell, Lars

2016-04-12

A building energy analysis system includes a building component library configured to store a plurality of building components, a modeling tool configured to access the building component library and create a building model of a building under analysis using building spatial data and using selected building components of the plurality of building components stored in the building component library, a building analysis engine configured to operate the building model and generate a baseline energy model of the building under analysis and further configured to apply one or more energy conservation measures to the baseline energy model in order to generate one or more corresponding optimized energy models, and a recommendation tool configured to assess the one or more optimized energy models against the baseline energy model and generate recommendations for substitute building components or modifications.

Principal Component Relaxation Mode Analysis of an All-Atom Molecular Dynamics Simulation of Human Lysozyme

NASA Astrophysics Data System (ADS)

Nagai, Toshiki; Mitsutake, Ayori; Takano, Hiroshi

2013-02-01

A new relaxation mode analysis method, which is referred to as the principal component relaxation mode analysis method, has been proposed to handle a large number of degrees of freedom of protein systems. In this method, principal component analysis is carried out first and then relaxation mode analysis is applied to a small number of principal components with large fluctuations. To reduce the contribution of fast relaxation modes in these principal components efficiently, we have also proposed a relaxation mode analysis method using multiple evolution times. The principal component relaxation mode analysis method using two evolution times has been applied to an all-atom molecular dynamics simulation of human lysozyme in aqueous solution. Slow relaxation modes and corresponding relaxation times have been appropriately estimated, demonstrating that the method is applicable to protein systems.

V380 Dra: New short-period totally eclipsing active binary

NASA Astrophysics Data System (ADS)

Özdarcan, O.

2014-02-01

In this study, first complete and standard BVR light curves and photometric analysis of the eclipsing binary system V380 Dra are presented. Photometric analysis result indicates that the system has components which are cool main sequence stars. In light and color curves, remarkable asymmetry is observed, especially after secondary minimum, which is believed to be a result of chromospheric activity in one or both components. O-C diagram of available small number of eclipse times, together with new eclipse timings in this work, exhibits no significant variation. Preliminary light curve solution shows that the secondary minimum is total eclipse. By using the advantage of total eclipse and mass-luminosity relation, it is found that the system has a possible mass ratio of q = 0.81. First estimation of masses and radii of primary and secondary components are M1 = 0.77 M⊙,M2 = 0.62 M⊙ and R1 = 0.93 R⊙,R2 = 0.77 R⊙, respectively.

Calculating system reliability with SRFYDO

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

Morzinski, Jerome; Anderson - Cook, Christine M; Klamann, Richard M

2010-01-01

SRFYDO is a process for estimating reliability of complex systems. Using information from all applicable sources, including full-system (flight) data, component test data, and expert (engineering) judgment, SRFYDO produces reliability estimates and predictions. It is appropriate for series systems with possibly several versions of the system which share some common components. It models reliability as a function of age and up to 2 other lifecycle (usage) covariates. Initial output from its Exploratory Data Analysis mode consists of plots and numerical summaries so that the user can check data entry and model assumptions, and help determine a final form for themore » system model. The System Reliability mode runs a complete reliability calculation using Bayesian methodology. This mode produces results that estimate reliability at the component, sub-system, and system level. The results include estimates of uncertainty, and can predict reliability at some not-too-distant time in the future. This paper presents an overview of the underlying statistical model for the analysis, discusses model assumptions, and demonstrates usage of SRFYDO.« less

Noise deconvolution based on the L1-metric and decomposition of discrete distributions of postsynaptic responses.

PubMed

Astrelin, A V; Sokolov, M V; Behnisch, T; Reymann, K G; Voronin, L L

1997-04-25

A statistical approach to analysis of amplitude fluctuations of postsynaptic responses is described. This includes (1) using a L1-metric in the space of distribution functions for minimisation with application of linear programming methods to decompose amplitude distributions into a convolution of Gaussian and discrete distributions; (2) deconvolution of the resulting discrete distribution with determination of the release probabilities and the quantal amplitude for cases with a small number (< 5) of discrete components. The methods were tested against simulated data over a range of sample sizes and signal-to-noise ratios which mimicked those observed in physiological experiments. In computer simulation experiments, comparisons were made with other methods of 'unconstrained' (generalized) and constrained reconstruction of discrete components from convolutions. The simulation results provided additional criteria for improving the solutions to overcome 'over-fitting phenomena' and to constrain the number of components with small probabilities. Application of the programme to recordings from hippocampal neurones demonstrated its usefulness for the analysis of amplitude distributions of postsynaptic responses.

Improving the Reliability of Technological Subsystems Equipment for Steam Turbine Unit in Operation

NASA Astrophysics Data System (ADS)

Brodov, Yu. M.; Murmansky, B. E.; Aronson, R. T.

2017-11-01

The authors’ conception is presented of an integrated approach to reliability improving of the steam turbine unit (STU) state along with its implementation examples for the various STU technological subsystems. Basing on the statistical analysis of damage to turbine individual parts and components, on the development and application of modern methods and technologies of repair and on operational monitoring techniques, the critical components and elements of equipment are identified and priorities are proposed for improving the reliability of STU equipment in operation. The research results are presented of the analysis of malfunctions for various STU technological subsystems equipment operating as part of power units and at cross-linked thermal power plants and resulting in turbine unit shutdown (failure). Proposals are formulated and justified for adjustment of maintenance and repair for turbine components and parts, for condenser unit equipment, for regeneration subsystem and oil supply system that permit to increase the operational reliability, to reduce the cost of STU maintenance and repair and to optimize the timing and amount of repairs.

Principal-component analysis of two-particle azimuthal correlations in PbPb and p Pb collisions at CMS

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

Sirunyan, A. M.; Tumasyan, A.; Adam, W.

For the first time a principle-component analysis is used to separate out different orthogonal modes of the two-particle correlation matrix from heavy ion collisions. The analysis uses data from √ sNN = 2.76TeV PbPb and √ sNN = 5.02TeV pPb collisions collected by the CMS experiment at the CERN Large Hadron Collider. Two-particle azimuthal correlations have been extensively used to study hydrodynamic flow in heavy ion collisions. Recently it was shown that the expected factorization of two-particle results into a product of the constituent single-particle anisotropies is broken. The new information provided by these modes may shed light on themore » breakdown of flow factorization in heavy ion collisions. The first two modes (“leading” and “subleading”) of two-particle correlations are presented for elliptical and triangular anisotropies in PbPb and pPb collisions as a function of p T over a wide range of event activity. The leading mode is found to be essentially equivalent to the anisotropy harmonic previously extracted from two-particle correlation methods. The subleading mode represents a new experimental observable and is shown to account for a large fraction of the factorization breaking recently observed at high transverse momentum. The principle-component analysis technique was also applied to multiplicity fluctuations. These also show a subleading mode. As a result, the connection of these new results to previous studies of factorization is discussed.« less

Principal-component analysis of two-particle azimuthal correlations in PbPb and p Pb collisions at CMS

DOE PAGES

Sirunyan, A. M.; Tumasyan, A.; Adam, W.; ...

2017-12-05

For the first time a principle-component analysis is used to separate out different orthogonal modes of the two-particle correlation matrix from heavy ion collisions. The analysis uses data from √ sNN = 2.76TeV PbPb and √ sNN = 5.02TeV pPb collisions collected by the CMS experiment at the CERN Large Hadron Collider. Two-particle azimuthal correlations have been extensively used to study hydrodynamic flow in heavy ion collisions. Recently it was shown that the expected factorization of two-particle results into a product of the constituent single-particle anisotropies is broken. The new information provided by these modes may shed light on themore » breakdown of flow factorization in heavy ion collisions. The first two modes (“leading” and “subleading”) of two-particle correlations are presented for elliptical and triangular anisotropies in PbPb and pPb collisions as a function of p T over a wide range of event activity. The leading mode is found to be essentially equivalent to the anisotropy harmonic previously extracted from two-particle correlation methods. The subleading mode represents a new experimental observable and is shown to account for a large fraction of the factorization breaking recently observed at high transverse momentum. The principle-component analysis technique was also applied to multiplicity fluctuations. These also show a subleading mode. As a result, the connection of these new results to previous studies of factorization is discussed.« less

Inventory of File gfs.t06z.pgrb2b.0p25.f000

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 005 1 mb VGRD analysis V-Component of Wind [m/s] 006 1 mb ABSV Temperature [K] 011 2 mb RH analysis Relative Humidity [%] 012 2 mb UGRD analysis U-Component of Wind [m/s ] 013 2 mb VGRD analysis V-Component of Wind [m/s] 014 2 mb ABSV analysis Absolute Vorticity [1/s] 015 2

Inventory of File gfs.t06z.pgrb2b.1p00.f000

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 005 1 mb VGRD analysis V-Component of Wind [m/s] 006 1 mb ABSV Temperature [K] 011 2 mb RH analysis Relative Humidity [%] 012 2 mb UGRD analysis U-Component of Wind [m/s ] 013 2 mb VGRD analysis V-Component of Wind [m/s] 014 2 mb ABSV analysis Absolute Vorticity [1/s] 015 2

Inventory of File gfs.t06z.pgrb2b.0p50.f000

Science.gov Websites

UGRD analysis U-Component of Wind [m/s] 005 1 mb VGRD analysis V-Component of Wind [m/s] 006 1 mb ABSV Temperature [K] 011 2 mb RH analysis Relative Humidity [%] 012 2 mb UGRD analysis U-Component of Wind [m/s ] 013 2 mb VGRD analysis V-Component of Wind [m/s] 014 2 mb ABSV analysis Absolute Vorticity [1/s] 015 2

An experimental study to investigate the effects of a motion tracking electromagnetic sensor during EEG data acquisition.

PubMed

Bashashati, Ali; Noureddin, Borna; Ward, Rabab K; Lawrence, Peter D; Birch, Gary E

2006-03-01

A power spectral analysis study was conducted to investigate the effects of using an electromagnetic motion tracking sensor on an electroencephalogram (EEG) recording system. The results showed that the sensors do not generate any consistent frequency component(s) in the power spectrum of the EEG in the frequencies of interest (0.1-55 Hz).

Developmental Patterns of the Invasive Bramble (Rubus alceifolius Poiret, Rosaceae) in Réunion Island: an Architectural and Morphometric Analysis

PubMed Central

BARET, STÉPHANE; NICOLINI, ERIC; LE BOURGEOIS, THOMAS; STRASBERG, DOMINIQUE

2003-01-01

The aim of this study was to identify the developmental stages of Rubus alceifolius and to determine one or more characteristic morphological markers for each stage. The developmental reconstitution method used involved a detailed description of many individuals throughout the different stages of growth, from germination to the development of an adult shoot capable of fruiting. Results revealed that R. alceifolius passes through five developmental stages that can be distinguished by changes in several morphological markers such as internode length and diameter, pith diameter and plant shape. This analysis indicated that R. alceifolius has a heteroblastic developmental pattern, midway between that of a bush and a liana. Moreover, results showed that this species taps environmental resources early in its development, i.e. foliarization is high (the foliar component overrides the caulinary component) and an autotrophic stage is rapidly reached, whereas it ‘explores’ the environment during the adult stage, i.e. axialization is substantial (the caulinary component overrides the foliar component) and autotrophy occurs at a later stage. The morphological markers identified could benefit land‐use managers attempting to control this species before it reaches its optimum developmental stage. PMID:12495918

Study on fast discrimination of varieties of yogurt using Vis/NIR-spectroscopy

NASA Astrophysics Data System (ADS)

He, Yong; Feng, Shuijuan; Deng, Xunfei; Li, Xiaoli

2006-09-01

A new approach for discrimination of varieties of yogurt by means of VisINTR-spectroscopy was present in this paper. Firstly, through the principal component analysis (PCA) of spectroscopy curves of 5 typical kinds of yogurt, the clustering of yogurt varieties was processed. The analysis results showed that the cumulate reliabilities of PC1 and PC2 (the first two principle components) were more than 98.956%, and the cumulate reliabilities from PC1 to PC7 (the first seven principle components) was 99.97%. Secondly, a discrimination model of Artificial Neural Network (ANN-BP) was set up. The first seven principles components of the samples were applied as ANN-BP inputs, and the value of type of yogurt were applied as outputs, then the three-layer ANN-BP model was build. In this model, every variety yogurt includes 27 samples, the total number of sample is 135, and the rest 25 samples were used as prediction set. The results showed the distinguishing rate of the five yogurt varieties was 100%. It presented that this model was reliable and practicable. So a new approach for the rapid and lossless discrimination of varieties of yogurt was put forward.

Morphological, motor and situation-motor characteristics of elite female handball players according to playing performance and position.

PubMed

Cavala, Marijana; Katić, Ratko

2010-12-01

The aim of the study was to define biomotor characteristics that determine playing performance and position in female handball. A battery of 13 variables consisting of somatotype components (3 variables), basic motor abilities (5 variables) and specific motor abilities (5 variables) were applied in a sample of 52 elite female handball players. Differences in biomotor characteristics according to playing performance and position of female handball players were determined by use of the analysis of variance (ANOVA) and discriminative analysis. Study results showed the high-quality female handball players to predominantly differ from the less successful ones in the specific factor of throw strength and basic dash factor, followed by the specific abilities of movement without and with ball, basic coordination/agility and specific ability of ball manipulation, and a more pronounced mesomorphic component. Results also revealed the wing players to be superior in the speed of movement frequency (psychomotor speed), run (explosive strength) and speed of movement with ball as compared with players at other playing positions. Also, endomorphic component was less pronounced in players at the wing and back player positions as compared with goalkeeper and pivot positions, where endomorphic component was considerably more pronounced.

Performance deterioration based on existing (historical) data; JT9D jet engine diagnostics program

NASA Technical Reports Server (NTRS)

Sallee, G. P.

1978-01-01

The results of the collection and analysis of historical data pertaining to the deterioration of JT9D engine performance are presented. The results of analyses of prerepair and postrepair engine test stand performance data from a number of airlines to establish the individual as well as average losses in engine performance with respect to service use are included. Analysis of the changes in mechanical condition of parts, obtained by inspection of used gas-path parts of varying age, allowed preliminary assessments of component performance deterioration levels and identification of the causitive factors. These component performance estimates, refined by data from special engine back-to-back testing related to module performance restoration, permitted the development of preliminary models of engine component/module performance deterioration with respect to usage. The preliminary assessment of the causes of module performance deterioration and the trends with usage are explained, along with the role each module plays in overall engine performance deterioration. Preliminary recommendations with respect to operating and maintenance practices which could be adopted to control the level of performance deterioration are presented. The needs for additional component sensitivity testing as well as outstanding issues are discussed.

A Network Flow Analysis of the Nitrogen Metabolism in Beijing, China.

PubMed

Zhang, Yan; Lu, Hanjing; Fath, Brian D; Zheng, Hongmei; Sun, Xiaoxi; Li, Yanxian

2016-08-16

Rapid urbanization results in high nitrogen flows and subsequent environmental consequences. In this study, we identified the main metabolic components (nitrogen inputs, flows, and outputs) and used ecological network analysis to track the direct and integral (direct + indirect) metabolic flows of nitrogen in Beijing, China, from 1996 to 2012 and to quantify the structure of Beijing's nitrogen metabolic processes. We found that Beijing's input of new reactive nitrogen (Q, which represents nitrogen obtained from the atmosphere or nitrogen-containing materials used in production and consumption to support human activities) increased from 431 Gg in 1996 to 507 Gg in 2012. Flows to the industry, atmosphere, and household, and components of the system were clearly largest, with total integrated inputs plus outputs from these nodes accounting for 31, 29, and 15%, respectively, of the total integral flows for all paths. The flows through the sewage treatment and transportation components showed marked growth, with total integrated inputs plus outputs increasing to 3.7 and 5.2 times their 1996 values, respectively. Our results can help policymakers to locate the key nodes and pathways in an urban nitrogen metabolic system so they can monitor and manage these components of the system.

Sensitivity analysis of key components in large-scale hydroeconomic models

NASA Astrophysics Data System (ADS)

Medellin-Azuara, J.; Connell, C. R.; Lund, J. R.; Howitt, R. E.

2008-12-01

This paper explores the likely impact of different estimation methods in key components of hydro-economic models such as hydrology and economic costs or benefits, using the CALVIN hydro-economic optimization for water supply in California. In perform our analysis using two climate scenarios: historical and warm-dry. The components compared were perturbed hydrology using six versus eighteen basins, highly-elastic urban water demands, and different valuation of agricultural water scarcity. Results indicate that large scale hydroeconomic hydro-economic models are often rather robust to a variety of estimation methods of ancillary models and components. Increasing the level of detail in the hydrologic representation of this system might not greatly affect overall estimates of climate and its effects and adaptations for California's water supply. More price responsive urban water demands will have a limited role in allocating water optimally among competing uses. Different estimation methods for the economic value of water and scarcity in agriculture may influence economically optimal water allocation; however land conversion patterns may have a stronger influence in this allocation. Overall optimization results of large-scale hydro-economic models remain useful for a wide range of assumptions in eliciting promising water management alternatives.

No difference in mechanical alignment and femoral component placement between patient-specific instrumentation and conventional instrumentation in TKA.

PubMed

Fu, Huichao; Wang, Jiaxing; Zhou, Shenyuan; Cheng, Tao; Zhang, Wen; Wang, Qi; Zhang, Xianlong

2015-11-01

There is a rising interest in the use of patient-specific instrumentation (PSI) during total knee arthroplasty (TKA). The goal of this meta-analysis was to compare PSI with conventional instrumentation (CI) in patients undergoing TKA. A literature search was performed in PubMed, Embase, Springer, Ovid, China National Knowledge Infrastructure, and the Cochrane Library. A total of 10 randomized controlled studies involving 837 knees comparing outcomes of PSI TKAs with CI TKAs were included in the present analysis. Outcomes of interest included component alignment, surgical time, blood loss, and hospital stay. The results presented no significant differences between the two instrumentations in terms of restoring a neutral mechanical axis and femoral component placement. However, their differences have been noted regarding the alignment of the tibial component in coronal and sagittal planes. Also, 3 min less surgical time was used in PSI patients. Based on these findings, PSI appeared not to be superior to CI in terms of the post-operative mechanical axis of the limb or femoral component placement. Despite a statistical difference for operative duration, the benefit of a small reduction in surgical time with PSI is clinically irrelevant. Therapeutic study (systematic review and meta-analysis), Level I.

CRAX/Cassandra Reliability Analysis Software

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

Robinson, D.

1999-02-10

Over the past few years Sandia National Laboratories has been moving toward an increased dependence on model- or physics-based analyses as a means to assess the impact of long-term storage on the nuclear weapons stockpile. These deterministic models have also been used to evaluate replacements for aging systems, often involving commercial off-the-shelf components (COTS). In addition, the models have been used to assess the performance of replacement components manufactured via unique, small-lot production runs. In either case, the limited amount of available test data dictates that the only logical course of action to characterize the reliability of these components ismore » to specifically consider the uncertainties in material properties, operating environment etc. within the physics-based (deterministic) model. This not only provides the ability to statistically characterize the expected performance of the component or system, but also provides direction regarding the benefits of additional testing on specific components within the system. An effort was therefore initiated to evaluate the capabilities of existing probabilistic methods and, if required, to develop new analysis methods to support the inclusion of uncertainty in the classical design tools used by analysts and design engineers at Sandia. The primary result of this effort is the CMX (Cassandra Exoskeleton) reliability analysis software.« less

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed us- ing a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify de ciencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the mea- sured data, a sensitivity analysis of the model parameters with emphasis on the de nition of the convection velocity parameter, and a least-squares t of the predicted to the mea- sured shock-associated noise component spectra, resulted in a new de nition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

Transient analysis mode participation for modal survey target mode selection using MSC/NASTRAN DMAP

NASA Technical Reports Server (NTRS)

Barnett, Alan R.; Ibrahim, Omar M.; Sullivan, Timothy L.; Goodnight, Thomas W.

1994-01-01

Many methods have been developed to aid analysts in identifying component modes which contribute significantly to component responses. These modes, typically targeted for dynamic model correlation via a modal survey, are known as target modes. Most methods used to identify target modes are based on component global dynamic behavior. It is sometimes unclear if these methods identify all modes contributing to responses important to the analyst. These responses are usually those in areas of hardware design concerns. One method used to check the completeness of target mode sets and identify modes contributing significantly to important component responses is mode participation. With this method, the participation of component modes in dynamic responses is quantified. Those modes which have high participation are likely modal survey target modes. Mode participation is most beneficial when it is used with responses from analyses simulating actual flight events. For spacecraft, these responses are generated via a structural dynamic coupled loads analysis. Using MSC/NASTRAN DMAP, a method has been developed for calculating mode participation based on transient coupled loads analysis results. The algorithm has been implemented to be compatible with an existing coupled loads methodology and has been used successfully to develop a set of modal survey target modes.

[Quantitative analysis of nucleotide mixtures with terahertz time domain spectroscopy].

PubMed

Zhang, Zeng-yan; Xiao, Ti-qiao; Zhao, Hong-wei; Yu, Xiao-han; Xi, Zai-jun; Xu, Hong-jie

2008-09-01

Adenosine, thymidine, guanosine, cytidine and uridine form the building blocks of ribose nucleic acid (RNA) and deoxyribose nucleic acid (DNA). Nucleosides and their derivants are all have biological activities. Some of them can be used as medicine directly or as materials to synthesize other medicines. It is meaningful to detect the component and content in nucleosides mixtures. In the present paper, components and contents of the mixtures of adenosine, thymidine, guanosine, cytidine and uridine were analyzed. THz absorption spectra of pure nucleosides were set as standard spectra. The mixture's absorption spectra were analyzed by linear regression with non-negative constraint to identify the components and their relative content in the mixtures. The experimental and analyzing results show that it is simple and effective to get the components and their relative percentage in the mixtures by terahertz time domain spectroscopy with a relative error less than 10%. Component which is absent could be excluded exactly by this method, and the error sources were also analyzed. All the experiments and analysis confirms that this method is of no damage or contamination to the sample. This means that it will be a simple, effective and new method in biochemical materials analysis, which extends the application field of THz-TDS.

Inventory of File nam.t00z.grbgrd00.tm00.grib2

Science.gov Websites

Humidity [kg/kg] 009.1 1 hybrid level UGRD analysis U-Component of Wind [m/s] 009.2 1 hybrid level VGRD analysis V-Component of Wind [m/s] 010 1 hybrid level TKE analysis Turbulent Kinetic Energy [J/kg] 011.1 2 hybrid level UGRD analysis U-Component of Wind [m/s] 011.2 2 hybrid level VGRD analysis V-Component of

Improved separability of dipole sources by tripolar versus conventional disk electrodes: a modeling study using independent component analysis.

PubMed

Cao, H; Besio, W; Jones, S; Medvedev, A

2009-01-01

Tripolar electrodes have been shown to have less mutual information and higher spatial resolution than disc electrodes. In this work, a four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes, and four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements, and talking. Finally, a fast fixed-point algorithm was used for signal independent component analysis (ICA). The results show that signals from tripolar electrodes generated better ICA separation results than from disc electrodes for EEG signals with these four types of artifacts.

Design and Optimization of Composite Gyroscope Momentum Wheel Rings

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2007-01-01

Stress analysis and preliminary design/optimization procedures are presented for gyroscope momentum wheel rings composed of metallic, metal matrix composite, and polymer matrix composite materials. The design of these components involves simultaneously minimizing both true part volume and mass, while maximizing angular momentum. The stress analysis results are combined with an anisotropic failure criterion to formulate a new sizing procedure that provides considerable insight into the design of gyroscope momentum wheel ring components. Results compare the performance of two optimized metallic designs, an optimized SiC/Ti composite design, and an optimized graphite/epoxy composite design. The graphite/epoxy design appears to be far superior to the competitors considered unless a much greater premium is placed on volume efficiency compared to mass efficiency.

Application of principal component analysis in the pollution assessment with heavy metals of vegetable food chain in the old mining areas

PubMed Central

2012-01-01

Background The aim of the paper is to assess by the principal components analysis (PCA) the heavy metal contamination of soil and vegetables widely used as food for people who live in areas contaminated by heavy metals (HMs) due to long-lasting mining activities. This chemometric technique allowed us to select the best model for determining the risk of HMs on the food chain as well as on people's health. Results Many PCA models were computed with different variables: heavy metals contents and some agro-chemical parameters which characterize the soil samples from contaminated and uncontaminated areas, HMs contents of different types of vegetables grown and consumed in these areas, and the complex parameter target hazard quotients (THQ). Results were discussed in terms of principal component analysis. Conclusion There were two major benefits in processing the data PCA: firstly, it helped in optimizing the number and type of data that are best in rendering the HMs contamination of the soil and vegetables. Secondly, it was valuable for selecting the vegetable species which present the highest/minimum risk of a negative impact on the food chain and human health. PMID:23234365

Reduction hybrid artifacts of EMG-EOG in electroencephalography evoked by prefrontal transcranial magnetic stimulation

NASA Astrophysics Data System (ADS)

Bai, Yang; Wan, Xiaohong; Zeng, Ke; Ni, Yinmei; Qiu, Lirong; Li, Xiaoli

2016-12-01

Objective. When prefrontal-transcranial magnetic stimulation (p-TMS) performed, it may evoke hybrid artifact mixed with muscle activity and blink activity in EEG recordings. Reducing this kind of hybrid artifact challenges the traditional preprocessing methods. We aim to explore method for the p-TMS evoked hybrid artifact removal. Approach. We propose a novel method used as independent component analysis (ICA) post processing to reduce the p-TMS evoked hybrid artifact. Ensemble empirical mode decomposition (EEMD) was used to decompose signal into multi-components, then the components were separated with artifact reduced by blind source separation (BSS) method. Three standard BSS methods, ICA, independent vector analysis, and canonical correlation analysis (CCA) were tested. Main results. Synthetic results showed that EEMD-CCA outperformed others as ICA post processing step in hybrid artifacts reduction. Its superiority was clearer when signal to noise ratio (SNR) was lower. In application to real experiment, SNR can be significantly increased and the p-TMS evoked potential could be recovered from hybrid artifact contaminated signal. Our proposed method can effectively reduce the p-TMS evoked hybrid artifacts. Significance. Our proposed method may facilitate future prefrontal TMS-EEG researches.

Machine fault feature extraction based on intrinsic mode functions

NASA Astrophysics Data System (ADS)

Fan, Xianfeng; Zuo, Ming J.

2008-04-01

This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Allergen extracts and recombinant proteins: comparison of efficiency of in vitro allergy diagnostics using multiplex assay on a biological microchip.

PubMed

Smoldovskaya, Olga; Feyzkhanova, Guzel; Arefieva, Alla; Voloshin, Sergei; Ivashkina, Olga; Reznikov, Yuriy; Rubina, Alla

2016-01-01

Immunological test systems for diagnostics of type I hypersensitivity involve the following types of antigens: whole allergen extracts, individual highly purified proteins and their recombinant analogues. The goal of this study was to compare the results obtained with whole allergen extracts (birch pollen, cat dander, and timothy grass pollen) and their respective recombinant proteins in biochip-based immunoassay. Multiplex fluorescent immunoassay of 139 patients' blood serum samples was carried out using biological microchips (biochips). sIgE concentrations for the chosen allergens and their recombinant components were measured. ROC analysis was used for comparison of the results and determination of diagnostic accuracy. The results for the birch pollen extract and its recombinant allergens have shown that the diagnostic accuracy of the methods utilizing the whole allergen extract, its major component Bet v 1 and the combination of major and minor components (Bet v 1 and Bet v 2) was the same. Values for diagnostic accuracy for the cat dander extract and its major recombinant component Fel d 1 were equal. In contrast with birch pollen and cat dander allergens, using of recombinant components of timothy grass pollen (Phl p 1, Phl p 5, Phl p 7 and Phl p 12) did not allow reaching the diagnostic accuracy of using natural extract. Multiplex analysis of samples obtained from patients with allergy to birch pollen and cat dander using biological microchips has shown that comparable accuracy was observed for the assay with natural extracts and recombinant allergens. In the case of timothy grass allergen, using the recombinant components may be insufficient.

BLIND EXTRACTION OF AN EXOPLANETARY SPECTRUM THROUGH INDEPENDENT COMPONENT ANALYSIS

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

Waldmann, I. P.; Tinetti, G.; Hollis, M. D. J.

2013-03-20

Blind-source separation techniques are used to extract the transmission spectrum of the hot-Jupiter HD189733b recorded by the Hubble/NICMOS instrument. Such a 'blind' analysis of the data is based on the concept of independent component analysis. The detrending of Hubble/NICMOS data using the sole assumption that nongaussian systematic noise is statistically independent from the desired light-curve signals is presented. By not assuming any prior or auxiliary information but the data themselves, it is shown that spectroscopic errors only about 10%-30% larger than parametric methods can be obtained for 11 spectral bins with bin sizes of {approx}0.09 {mu}m. This represents a reasonablemore » trade-off between a higher degree of objectivity for the non-parametric methods and smaller standard errors for the parametric de-trending. Results are discussed in light of previous analyses published in the literature. The fact that three very different analysis techniques yield comparable spectra is a strong indication of the stability of these results.« less

Level dependence of distortion product otoacoustic emission phase is attributed to component mixing

PubMed Central

Abdala, Carolina; Dhar, Sumitrajit; Kalluri, Radha

2011-01-01

Distortion product otoacoustic emissions (DPOAEs) measured in the ear canal represent the vector sum of components produced at two regions of the basilar membrane by distinct cochlear mechanisms. In this study, the effect of stimulus level on the 2f1 − f2 DPOAE phase was evaluated in 22 adult subjects across a three-octave range. Level effects were examined for the mixed DPOAE signal measured in the ear canal and after unmixing components to assess level effects individually on the distortion (generated at the f1, f2 overlap) and reflection (at fdp) sources. Results show that ear canal DPOAE phase slope becomes steeper with decreasing level; however, component analysis further explicates this result, indicating that interference between DPOAE components (rather than a shift in mechanics related to distortion generation) drives the level dependence of DPOAE phase measured in the ear canal. The relative contribution from the reflection source increased with decreasing level, producing more component interference and, at times, a reflection-dominated response at the lowest stimulus levels. These results have implications for the use of DPOAE phase to study cochlear mechanics and for the potential application of DPOAE phase for clinical purposes. PMID:21568415

Solvent Hold Tank Sample Results for MCU-15-129-130-131: January 2015 Monthly Sample

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

Fondeur, F. F.; Taylor-Pashow, K. M. L.

2015-02-19

SRNL received one set of SHT samples (MCU-15-129, MCU-15-130, and MCU-15-131), pulled on 01/25/2015 for analysis. The samples were combined and analyzed for composition. Analysis of the composite sample MCU-15-129-130-131 indicated low concentrations of the suppressor (TiDG), of the extractant (MaxCalix), and of the modifier (CS-7SB) in the solvent relative to their nominal values. This analysis confirms a downward trend of these components. No impurities were found in this solvent. The laboratory will continue to monitor the quality of the solvent in particular for any new impurity or degradation of the solvent components.

Comprehensive NMR analysis of compositional changes of black garlic during thermal processing.

PubMed

Liang, Tingfu; Wei, Feifei; Lu, Yi; Kodani, Yoshinori; Nakada, Mitsuhiko; Miyakawa, Takuya; Tanokura, Masaru

2015-01-21

Black garlic is a processed food product obtained by subjecting whole raw garlic to thermal processing that causes chemical reactions, such as the Maillard reaction, which change the composition of the garlic. In this paper, we report a nuclear magnetic resonance (NMR)-based comprehensive analysis of raw garlic and black garlic extracts to determine the compositional changes resulting from thermal processing. (1)H NMR spectra with a detailed signal assignment showed that 38 components were altered by thermal processing of raw garlic. For example, the contents of 11 l-amino acids increased during the first step of thermal processing over 5 days and then decreased. Multivariate data analysis revealed changes in the contents of fructose, glucose, acetic acid, formic acid, pyroglutamic acid, cycloalliin, and 5-(hydroxymethyl)furfural (5-HMF). Our results provide comprehensive information on changes in NMR-detectable components during thermal processing of whole garlic.

Finite element elastic-plastic-creep and cyclic life analysis of a cowl lip

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Melis, Matthew E.; Halford, Gary R.

1990-01-01

Results are presented of elastic, elastic-plastic, and elastic-plastic-creep analyses of a test-rig component of an actively cooled cowl lip. A cowl lip is part of the leading edge of an engine inlet of proposed hypersonic aircraft and is subject to severe thermal loadings and gradients during flight. Values of stresses calculated by elastic analysis are well above the yield strength of the cowl lip material. Such values are highly unrealistic, and thus elastic stress analyses are inappropriate. The inelastic (elastic-plastic and elastic-plastic-creep) analyses produce more reasonable and acceptable stress and strain distributions in the component. Finally, using the results from these analyses, predictions are made for the cyclic crack initiation life of a cowl lip. A comparison of predicted cyclic lives shows the cyclic life prediction from the elastic-plastic-creep analysis to be the lowest and, hence, most realistic.

Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry.

PubMed

Yang, Yanqin; Pan, Yuanjiang; Zhou, Guojun; Chu, Guohai; Jiang, Jian; Yuan, Kailong; Xia, Qian; Cheng, Changhe

2016-11-01

A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

PubMed

Nesakumar, Noel; Baskar, Chanthini; Kesavan, Srinivasan; Rayappan, John Bosco Balaguru; Alwarappan, Subbiah

2018-05-22

The moisture content of beetroot varies during long-term cold storage. In this work, we propose a strategy to identify the moisture content and age of beetroot using principal component analysis coupled Fourier transform infrared spectroscopy (FTIR). Frequent FTIR measurements were recorded directly from the beetroot sample surface over a period of 34 days for analysing its moisture content employing attenuated total reflectance in the spectral ranges of 2614-4000 and 1465-1853 cm -1 with a spectral resolution of 8 cm -1 . In order to estimate the transmittance peak height (T p ) and area under the transmittance curve [Formula: see text] over the spectral ranges of 2614-4000 and 1465-1853 cm -1 , Gaussian curve fitting algorithm was performed on FTIR data. Principal component and nonlinear regression analyses were utilized for FTIR data analysis. Score plot over the ranges of 2614-4000 and 1465-1853 cm -1 allowed beetroot quality discrimination. Beetroot quality predictive models were developed by employing biphasic dose response function. Validation experiment results confirmed that the accuracy of the beetroot quality predictive model reached 97.5%. This research work proves that FTIR spectroscopy in combination with principal component analysis and beetroot quality predictive models could serve as an effective tool for discriminating moisture content in fresh, half and completely spoiled stages of beetroot samples and for providing status alerts.

Content Analysis of the Concept of Addiction in High School Textbooks of Iran.

PubMed

Mirzamohammadi, Mohammad Hasan; Mousavi, Sayedeh Zainab; Massah, Omid; Farhoudian, Ali

2017-01-01

This research sought to determine how well the causes of addiction, addiction harms, and prevention of addiction have been noticed in high school textbooks. We used descriptive method to select the main related components of the addiction concept and content analysis method for analyzing the content of textbooks. The study population comprised 61 secondary school curriculum textbooks and study sample consisted of 14 secondary school textbooks selected by purposeful sampling method. The tools for collecting data were "content analysis inventory" which its validity was confirmed by educational and social sciences experts and its reliability has been found to be 91%. About 67 components were prepared for content analysis and were divided to 3 categories of causes, harms, and prevention of addiction. The analysis units in this study comprised phrases, topics, examples, course topics, words, poems, images, questions, tables, and exercises. Results of the study showed that the components of the addiction concept have presented with 212 remarks in the textbooks. Also, the degree of attention given to any of the 3 main components of the addiction concept were presented as follows: causes with 52 (24.52%) remarks, harm with 89 (41.98%) remarks, and prevention with 71 (33.49%) remarks. In high school textbooks, little attention has been paid to the concept of addiction and mostly its biological dimension were addressed while social, personal, familial, and religious dimensions of addiction have been neglected.

The first report on transcriptome analysis of the venom gland of Iranian scorpion, Hemiscorpius lepturus.

PubMed

Kazemi-Lomedasht, Fatemeh; Khalaj, Vahid; Bagheri, Kamran Pooshang; Behdani, Mahdi; Shahbazzadeh, Delavar

2017-01-01

Hemiscorpius lepturus scorpion is one of the most venomous members of the Hemiscorpiidae family. H. lepturus is distributed in Iran, Iraq and Yemen. The prevalence and severity of scorpionism is high and health services are not able to control it. Scorpionism in Iran especially in the southern regions (Khuzestan, Sistan and Baluchestan, Hormozgan, Ilam) is one of the main health challenges. Due to the medical and health importance of scorpionism, the focus of various studies has been on the identification of H. lepturus venom components. Nevertheless, until now, only a few percent of H. lepturus venom components have been identified and there is no complete information about the venom components of H. lepturus. The current study reports transcriptome analysis of the venom gland of H. lepturus scorpion. Illumina Next Generation Sequencing results identified venom components of H. lepturus. When compared with other scorpion's venom, the venom of H. lepturus consists of mixtures of peptides, proteins and enzymes such as; phospholipases, metalloproteases, hyaluronidases, potassium channel toxins, calcium channel toxins, antimicrobial peptides (AMPs), venom proteins, venom toxins, allergens, La1-like peptides, proteases and scorpine-like peptides. Comparison of identified components of H. lepturus venom was carried out with venom components of reported scorpions and various identities and similarities between them were observed. With transcriptome analysis of H. lepturus venom unique sequences, coding venom components were investigated. Moreover, our study confirmed transcript expression of previously reported peptides; Hemitoxin, Hemicalcin and Hemilipin. The gene sequences of venom components were investigated employing transcriptome analysis of venom gland of H. lepturus. In summary, new bioactive molecules identified in this study, provide basis for venomics studies of scorpions of Hemiscorpiidae family and promises development of novel biotherapeutics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tidal analysis of surface currents in the Porsanger fjord in northern Norway

NASA Astrophysics Data System (ADS)

Stramska, Malgorzata; Jankowski, Andrzej; Cieszyńska, Agata

2016-04-01

In this presentation we describe surface currents in the Porsanger fjord (Porsangerfjorden) located in the European Arctic in the vicinity of the Barents Sea. Our analysis is based on data collected in the summer of 2014 using High Frequency radar system. Our interest in this fjord comes from the fact that this is a region of high climatic sensitivity. One of our long-term goals is to develop an improved understanding of the undergoing changes and interactions between this fjord and the large-scale atmospheric and oceanic conditions. In order to derive a better understanding of the ongoing changes one must first improve the knowledge about the physical processes that create the environment of the fjord. The present study is the first step in this direction. Our main objective in this presentation is to evaluate the importance of tidal forcing. Tides in the Porsanger fjord are substantial, with tidal range on the order of about 3 meters. Tidal analysis attributes to tides about 99% of variance in sea level time series recorded in Honningsvåg. The most important tidal component based on sea level data is the M2 component (amplitude of ~90 cm). The S2 and N2 components (amplitude of ~ 20 cm) also play a significant role in the semidiurnal sea level oscillations. The most important diurnal component is K1 with amplitude of about 8 cm. Tidal analysis lead us to the conclusion that the most important tidal component in observed surface currents is also the M2 component. The second most important component is the S2 component. Our results indicate that in contrast to sea level, only about 10 - 20% of variance in surface currents can be attributed to tidal currents. This means that about 80-90% of variance can be credited to wind-induced and geostrophic currents. This work was funded by the Norway Grants (NCBR contract No. 201985, project NORDFLUX). Partial support for MS comes from the Institute of Oceanology (IO PAN).

Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

Observation of Nonthermal Emission from the Supernova Remnant IC443 with RXTE

NASA Technical Reports Server (NTRS)

Sturner, S. J.; Keohane, J. W.; Reimer, O.

2002-01-01

In this paper we present analysis of X-ray spectra from the supernova remnant IC443 obtained using the PCA on RXTE. The spectra in the 3 - 20 keV band are well fit by a two-component model consisting of thermal and nonthermal components. We compare these results with recent results of other X-ray missions and discuss the need for a cut-off in the nonthermal spectrum. Recent Chandra and XMM-Newton observations suggest that much of the nonthermal emission from IC443 can be attributed to a pulsar wind nebula. We present the results of our search for periodic emission in the RXTE PCA data. We then discuss the origin o f the nonthermal component and its possible association with the unidentified EGRET source.

Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

The Coplane Analysis Technique for Three-Dimensional Wind Retrieval Using the HIWRAP Airborne Doppler Radar

NASA Technical Reports Server (NTRS)

Didlake, Anthony C., Jr.; Heymsfield, Gerald M.; Tian, Lin; Guimond, Stephen R.

2015-01-01

The coplane analysis technique for mapping the three-dimensional wind field of precipitating systems is applied to the NASA High Altitude Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a dual-frequency Doppler radar system with two downward pointing and conically scanning beams. The coplane technique interpolates radar measurements to a natural coordinate frame, directly solves for two wind components, and integrates the mass continuity equation to retrieve the unobserved third wind component. This technique is tested using a model simulation of a hurricane and compared to a global optimization retrieval. The coplane method produced lower errors for the cross-track and vertical wind components, while the global optimization method produced lower errors for the along-track wind component. Cross-track and vertical wind errors were dependent upon the accuracy of the estimated boundary condition winds near the surface and at nadir, which were derived by making certain assumptions about the vertical velocity field. The coplane technique was then applied successfully to HIWRAP observations of Hurricane Ingrid (2013). Unlike the global optimization method, the coplane analysis allows for a transparent connection between the radar observations and specific analysis results. With this ability, small-scale features can be analyzed more adequately and erroneous radar measurements can be identified more easily.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

PubMed Central

Wagatsuma, Hiroaki

2017-01-01

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies. PMID:28194221

Reliability Centred Maintenance (RCM) Analysis of Laser Machine in Filling Lithos at PT X

NASA Astrophysics Data System (ADS)

Suryono, M. A. E.; Rosyidi, C. N.

2018-03-01

PT. X used automated machines which work for sixteen hours per day. Therefore, the machines should be maintained to keep the availability of the machines. The aim of this research is to determine maintenance tasks according to the cause of component’s failure using Reliability Centred Maintenance (RCM) and determine the amount of optimal inspection frequency which must be performed to the machine at filling lithos process. In this research, RCM is used as an analysis tool to determine the critical component and find optimal inspection frequencies to maximize machine’s reliability. From the analysis, we found that the critical machine in filling lithos process is laser machine in Line 2. Then we proceed to determine the cause of machine’s failure. Lastube component has the highest Risk Priority Number (RPN) among other components such as power supply, lens, chiller, laser siren, encoder, conveyor, and mirror galvo. Most of the components have operational consequences and the others have hidden failure consequences and safety consequences. Time-directed life-renewal task, failure finding task, and servicing task can be used to overcome these consequences. The results of data analysis show that the inspection must be performed once a month for laser machine in the form of preventive maintenance to lowering the downtime.

BioInfra.Prot: A comprehensive proteomics workflow including data standardization, protein inference, expression analysis and data publication.

PubMed

Turewicz, Michael; Kohl, Michael; Ahrens, Maike; Mayer, Gerhard; Uszkoreit, Julian; Naboulsi, Wael; Bracht, Thilo; Megger, Dominik A; Sitek, Barbara; Marcus, Katrin; Eisenacher, Martin

2017-11-10

The analysis of high-throughput mass spectrometry-based proteomics data must address the specific challenges of this technology. To this end, the comprehensive proteomics workflow offered by the de.NBI service center BioInfra.Prot provides indispensable components for the computational and statistical analysis of this kind of data. These components include tools and methods for spectrum identification and protein inference, protein quantification, expression analysis as well as data standardization and data publication. All particular methods of the workflow which address these tasks are state-of-the-art or cutting edge. As has been shown in previous publications, each of these methods is adequate to solve its specific task and gives competitive results. However, the methods included in the workflow are continuously reviewed, updated and improved to adapt to new scientific developments. All of these particular components and methods are available as stand-alone BioInfra.Prot services or as a complete workflow. Since BioInfra.Prot provides manifold fast communication channels to get access to all components of the workflow (e.g., via the BioInfra.Prot ticket system: bioinfraprot@rub.de) users can easily benefit from this service and get support by experts. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

[Association between the use of blood components and the five-year mortality after liver transplant].

PubMed

de Morais, Bruno Salomé; Sanches, Marcelo Dias; Ribeiro, Daniel Dias; Lima, Agnaldo Soares; de Abreu Ferrari, Teresa Cristina; Duarte, Malvina Maria de Freitas; Cançado, Guilherme Henrique Gomes Moreira

2011-01-01

Liver transplant (LT) surgery is associated with significant bleeding in 20% of cases, and several authors have demonstrated the risks related to blood components. The objective of the present study was to evaluate the impact of using blood components during hospitalization in five-year survival of patients undergoing LT. One hundred and thirteen patients were evaluated retrospectively. Several variables, including the use of blood components intraoperatively and throughout hospitalization, were categorized and evaluated by univariate analysis using Fisher's test. A level of significance of 5% was adopted. Results with p < 0.2 underwent multivariate analysis using multinomial logistic regression. Parenchymal diseases, preoperative renal dysfunction, and longer stay in hospital and ICU are associated with greater five-year mortality after LT (p < 0.05). Unlike the intraoperative use of blood components, the accumulated transfusion of packed red blood cell, frozen fresh plasma, and platelets during the entire hospitalization was associated with greater five-year mortality after liver transplantation (p < 0.01). This study emphasizes the relationship between the use of blood components during hospitalization and increased mortality in five years after LT. 2011 Elsevier Editora Ltda. All rights reserved.

Kinematics and hydrodynamics analysis of swimming anurans reveals striking inter-specific differences in the mechanism for producing thrust.

PubMed

Richards, Christopher T

2010-02-15

This study aimed to compare the swimming kinematics and hydrodynamics within and among aquatic and semi-aquatic/terrestrial frogs. High-speed video was used to obtain kinematics of the leg joints and feet as animals swam freely across their natural range of speeds. Blade element analysis was then used to model the hydrodynamic thrust as a function of foot kinematics. Two purely aquatic frogs, Xenopus laevis and Hymenochirus boettgeri, were compared with two semi-aquatic/terrestrial frogs, Rana pipiens and Bufo americanus. The four species performed similarly. Among swimming strokes, peak stroke velocity ranged from 3.3+/-1.1 to 20.9+/-2.5, from 6.8+/-2.1 to 28.6+/-3.7 and from 4.9+/-0.5 to 20.9+/-4.1 body lengths per second (BL s(-1)) in X. laevis, H. boettgeri and R. pipiens, respectively (means +/- s.d.; N=4 frogs for each). B. americanus swam much more slowly at 3.1+/-0.3 to 7.0+/-2.0 BL s(-1) (N=3 frogs). Time-varying joint kinematics patterns were superficially similar among species. Because foot kinematics result from the cumulative motion of joints proximal to the feet, small differences in time-varying joint kinematics among species resulted in species-specific foot kinematics (therefore hydrodynamics) patterns. To obtain a simple measure of the hydrodynamically useful motion of the foot, this study uses 'effective foot velocity' (EFV): a measure of the component of foot velocity along the axis of swimming. Resolving EFV into translational and rotational components allows predictions of species-specific propulsion strategies. Additionally, a novel kinematic analysis is presented here that enables the partitioning of translational and rotational foot velocity into velocity components contributed by extension at each individual limb joint. Data from the kinematics analysis show that R. pipiens and B. americanus translated their feet faster than their body moved forward, resulting in positive net translational EFV. Conversely, translational EFV was slower than the body velocity in H. boettgeri and X. laevis, resulting in negative net translational EFV. Consequently, the translational component of thrust (caused mostly by hip, knee and ankle extension) was twofold higher than rotational thrust in Rana pipiens. Likewise, rotational components of thrust were nearly twofold higher than translational components in H. boettgeri. X. laevis, however, was the most skewed species observed, generating nearly 100% of total thrust by foot rotation generated by hip, ankle and tmt extension. Thus, this study presents a simple kinematics analysis that is predictive of hydrodynamic differences among species. Such differences in kinematics reveal a continuum of different propulsive strategies ranging from mostly rotation-powered (X. laevis) to mostly translation-powered (R. pipiens) swimming.

High Fidelity System Simulation of Multiple Components in Support of the UEET Program

NASA Technical Reports Server (NTRS)

Plybon, Ronald C.; VanDeWall, Allan; Sampath, Rajiv; Balasubramaniam, Mahadevan; Mallina, Ramakrishna; Irani, Rohinton

2006-01-01

The High Fidelity System Simulation effort has addressed various important objectives to enable additional capability within the NPSS framework. The scope emphasized High Pressure Turbine and High Pressure Compressor components. Initial effort was directed at developing and validating intermediate fidelity NPSS model using PD geometry and extended to high-fidelity NPSS model by overlaying detailed geometry to validate CFD against rig data. Both "feedforward" and feedback" approaches of analysis zooming was employed to enable system simulation capability in NPSS. These approaches have certain benefits and applicability in terms of specific applications "feedback" zooming allows the flow-up of information from high-fidelity analysis to be used to update the NPSS model results by forcing the NPSS solver to converge to high-fidelity analysis predictions. This apporach is effective in improving the accuracy of the NPSS model; however, it can only be used in circumstances where there is a clear physics-based strategy to flow up the high-fidelity analysis results to update the NPSS system model. "Feed-forward" zooming approach is more broadly useful in terms of enabling detailed analysis at early stages of design for a specified set of critical operating points and using these analysis results to drive design decisions early in the development process.

Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions

NASA Astrophysics Data System (ADS)

BoŻek, Piotr

2018-03-01

The principal component analysis of flow correlations in heavy-ion collisions is studied. The correlation matrix of harmonic flow is generalized to correlations involving several different flow vectors. The method can be applied to study the nonlinear coupling between different harmonic modes in a double differential way in transverse momentum or pseudorapidity. The procedure is illustrated with results from the hydrodynamic model applied to Pb + Pb collisions at √{sN N}=2760 GeV. Three examples of generalized correlations matrices in transverse momentum are constructed corresponding to the coupling of v22 and v4, of v2v3 and v5, or of v23,v33 , and v6. The principal component decomposition is applied to the correlation matrices and the dominant modes are calculated.

Effect of scene illumination conditions on digital enhancement techniques of multispectral scanner LANDSAT images

NASA Technical Reports Server (NTRS)

Parada, N. D. J.; Novo, E. M. L. M.

1983-01-01

Two sets of MSS/LANDSAT data with solar elevation ranging from 22 deg to 41 deg were used at the Image-100 System to implement the Eliason et alii technique for extracting the topographic modulation component. An unsupervised cluster analysis was used to obtain an average brightness image for each channel. Analysis of the enhanced imaged shows that the technique for extracting topographic modulation component is more appropriated to MSS data obtained under high sun elevation ngles. Low sun elevation increases the variance of each cluster so that the average brightness doesn't represent its albedo proprties. The topographic modulation component applied to low sun elevation angle damages rather than enhance topographic information. Better results were produced for channels 4 and 5 than for channels 6 and 7.

Analysis and improvement measures of flight delay in China

NASA Astrophysics Data System (ADS)

Zang, Yuhang

2017-03-01

Firstly, this paper establishes the principal component regression model to analyze the data quantitatively, based on principal component analysis to get the three principal component factors of flight delays. Then the least square method is used to analyze the factors and obtained the regression equation expression by substitution, and then found that the main reason for flight delays is airlines, followed by weather and traffic. Aiming at the above problems, this paper improves the controllable aspects of traffic flow control. For reasons of traffic flow control, an adaptive genetic queuing model is established for the runway terminal area. This paper, establish optimization method that fifteen planes landed simultaneously on the three runway based on Beijing capital international airport, comparing the results with the existing FCFS algorithm, the superiority of the model is proved.

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

NASA Astrophysics Data System (ADS)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Online signature recognition using principal component analysis and artificial neural network

NASA Astrophysics Data System (ADS)

Hwang, Seung-Jun; Park, Seung-Je; Baek, Joong-Hwan

2016-12-01

In this paper, we propose an algorithm for on-line signature recognition using fingertip point in the air from the depth image acquired by Kinect. We extract 10 statistical features from X, Y, Z axis, which are invariant to changes in shifting and scaling of the signature trajectories in three-dimensional space. Artificial neural network is adopted to solve the complex signature classification problem. 30 dimensional features are converted into 10 principal components using principal component analysis, which is 99.02% of total variances. We implement the proposed algorithm and test to actual on-line signatures. In experiment, we verify the proposed method is successful to classify 15 different on-line signatures. Experimental result shows 98.47% of recognition rate when using only 10 feature vectors.

Electroencephalographic dynamics of musical emotion perception revealed by independent spectral components.

PubMed

Lin, Yuan-Pin; Duann, Jeng-Ren; Chen, Jyh-Horng; Jung, Tzyy-Ping

2010-04-21

This study explores the electroencephalographic (EEG) correlates of emotional experience during music listening. Independent component analysis and analysis of variance were used to separate statistically independent spectral changes of the EEG in response to music-induced emotional processes. An independent brain process with equivalent dipole located in the fronto-central region exhibited distinct δ-band and θ-band power changes associated with self-reported emotional states. Specifically, the emotional valence was associated with δ-power decreases and θ-power increases in the frontal-central area, whereas the emotional arousal was accompanied by increases in both δ and θ powers. The resultant emotion-related component activations that were less interfered by the activities from other brain processes complement previous EEG studies of emotion perception to music.

Lessons Learned from Application of System and Software Level RAMS Analysis to a Space Control System

NASA Astrophysics Data System (ADS)

Silva, N.; Esper, A.

2012-01-01

The work presented in this article represents the results of applying RAMS analysis to a critical space control system, both at system and software levels. The system level RAMS analysis allowed the assignment of criticalities to the high level components, which was further refined by a tailored software level RAMS analysis. The importance of the software level RAMS analysis in the identification of new failure modes and its impact on the system level RAMS analysis is discussed. Recommendations of changes in the software architecture have also been proposed in order to reduce the criticality of the SW components to an acceptable minimum. The dependability analysis was performed in accordance to ECSS-Q-ST-80, which had to be tailored and complemented in some aspects. This tailoring will also be detailed in the article and lessons learned from the application of this tailoring will be shared, stating the importance to space systems safety evaluations. The paper presents the applied techniques, the relevant results obtained, the effort required for performing the tasks and the planned strategy for ROI estimation, as well as the soft skills required and acquired during these activities.

Correspondence Analysis-Theory and Application in Management Accounting Research

NASA Astrophysics Data System (ADS)

Duller, Christine

2010-09-01

Correspondence analysis is an explanatory data analytic technique and is used to identify systematic relations between categorical variables. It is related to principal component analysis and the results provide information on the structure of categorical variables similar to the results given by a principal component analysis in case of metric variables. Classical correspondence analysis is designed two-dimensional, whereas multiple correspondence analysis is an extension to more than two variables. After an introductory overview of the idea and the implementation in standard software packages (PASW, SAS, R) an example in recent research is presented, which deals with strategic management accounting in family and non-family enterprises in Austria, where 70% to 80% of all enterprises can be classified as family firms. Although there is a growing body of literature focusing on various management issues in family firms, so far the state of the art of strategic management accounting in family firms is an empirically under-researched subject. In relevant literature only the (empirically untested) hypothesis can be found, that family firms tend to have less formalized management accounting systems than non-family enterprises. Creating a correspondence analysis will help to identify the underlying structure, which is responsible for differences in strategic management accounting.

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample.

PubMed

Wojciechowski, Karen L; Melilli, Caterina; Barbano, David M

2014-09-01

Our objectives were to determine if mixing and sampling of a raw milk sample at 4°C for determination of total bacteria count (TBC) and if incubation at 14°C for 18h and sampling for a preliminary incubation (PI) count influenced the accuracy of subsequent fat, protein, or lactose measurement by mid-infrared (IR) analysis of milk from the same sample container due to either nonrepresentative sampling or the presence of microbial metabolites produced by microbial growth in the milk from the incubation. Milks of 4 fat levels (2.2, 3, 4, and 5%) reflected the range of fat levels encountered in producer milks. If the portion of milk removed from a cold sample was not representative, then the effect on a milk component test would likely be larger as fat content increases. Within the milks at each fat level, 3 treatments were used: (1) 20 vials of the same milk sampled for testing TBC using a BactoScan FC and then used for a milk component test; (2) 20 vials for testing TBC plus PI count followed by component test; and (3) 20 vials to run for IR component test without a prior micro sampling and testing. This was repeated in 3 different weeks using a different batch of milk each week. No large effect on the accuracy of component milk testing [IR fat B (carbon hydrogen stretch) and fat A (carbonyl stretch)] due to the cold milk sample handling and mixing procedures used for TBC was detected, confirming the fact that the physical removal of milk from the vial by the BactoScan FC (Foss Electric, Hillerød, Denmark) was a representative portion of the milk. However, the representativeness of any other sampling procedure (manual or automated) of a cold milk sample before running milk component testing on the same container of milk should be demonstrated and verified periodically as a matter of routine laboratory quality assurance. Running TBC with a BactoScan FC first and then IR milk analysis after had a minimal effect on milk component tests by IR when milk bacteria counts were within pasteurized milk ordinance limits of <100,000 cfu/mL. Running raw milk PI counts (18h of incubation at 13-14°C) with the BactoScan FC before milk component testing by IR milk analysis had an effect on component tests. The effect was largest on fat test results and would decrease the accuracy of milk payment testing on individual producer milks. The effect was most likely due to the absorption of light by bacterial metabolites resulting from microbial growth or other chemical degradation processes occurring in the milk during the PI count incubation, not by the sampling procedure of the BactoScan. The direction of the effect on component test results will vary depending on the bacteria count and the type of bacteria that grew in the milk, and this could be different in every individual producer milk sample. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Survivorship comparison of all-polyethylene and metal-backed tibial components in cruciate-substituting total knee arthroplasty--Chinese experience.

PubMed

Shen, Bin; Yang, Jing; Zhou, Zongke; Kang, Pengde; Wang, Liao; Pei, Fuxing

2009-10-01

Considering its cost saving, the all-polyethylene tibial component is of potential interest in developing countries like China. But to our knowledge, a survivorship comparison of all-polyethylene and metal-backed tibial components in posterior cruciate ligament-substituting total knee arthroplasty (PS-TKA) has not been studied in China previously. Using survivorship analysis, we have studied the midterm outcome of 34 cemented PS-TKA using an all-polyethylene tibial component and of 34 cemented PS-TKA using a metal-backed tibial component which has an identical articular surface with all-polyethylene tibial components. All operations were performed by the same group of surgeons; 58 patients underwent a unilateral operation and five patients a bilateral operation. These patients had a mean follow-up of 5.9 years (range: 5-7 years); three patients were lost to follow-up and one was revised for infection. No significant difference between the two groups was reported regarding HSS scores, ROM, clinical and radiographic parameters measured and survival rates. Although the Asian lifestyle includes more squatting and bending of the knee, the results of this series of TKA using all-polyethylene tibial components in Chinese people are comparable to the satisfactory results of other reported all-polyethylene series whose patients are mainly Western people. Considering its cost saving and excellent clinical result, the all-polyethylene tibial component is of potential interest in developing countries.

A practically unconditionally gradient stable scheme for the N-component Cahn-Hilliard system

NASA Astrophysics Data System (ADS)

Lee, Hyun Geun; Choi, Jeong-Whan; Kim, Junseok

2012-02-01

We present a practically unconditionally gradient stable conservative nonlinear numerical scheme for the N-component Cahn-Hilliard system modeling the phase separation of an N-component mixture. The scheme is based on a nonlinear splitting method and is solved by an efficient and accurate nonlinear multigrid method. The scheme allows us to convert the N-component Cahn-Hilliard system into a system of N-1 binary Cahn-Hilliard equations and significantly reduces the required computer memory and CPU time. We observe that our numerical solutions are consistent with the linear stability analysis results. We also demonstrate the efficiency of the proposed scheme with various numerical experiments.

Wind modeling and lateral control for automatic landing

NASA Technical Reports Server (NTRS)

Holley, W. E.; Bryson, A. E., Jr.

1975-01-01

For the purposes of aircraft control system design and analysis, the wind can be characterized by a mean component which varies with height and by turbulent components which are described by the von Karman correlation model. The aircraft aero-dynamic forces and moments depend linearly on uniform and gradient gust components obtained by averaging over the aircraft's length and span. The correlations of the averaged components are then approximated by the outputs of linear shaping filters forced by white noise. The resulting model of the crosswind shear and turbulence effects is used in the design of a lateral control system for the automatic landing of a DC-8 aircraft.

Ad hoc Laser networks component technology for modular spacecraft

NASA Astrophysics Data System (ADS)

Huang, Xiujun; Shi, Dele; Ma, Zongfeng; Shen, Jingshi

2016-03-01

Distributed reconfigurable satellite is a new kind of spacecraft system, which is based on a flexible platform of modularization and standardization. Based on the module data flow analysis of the spacecraft, this paper proposes a network component of ad hoc Laser networks architecture. Low speed control network with high speed load network of Microwave-Laser communication mode, no mesh network mode, to improve the flexibility of the network. Ad hoc Laser networks component technology was developed, and carried out the related performance testing and experiment. The results showed that ad hoc Laser networks components can meet the demand of future networking between the module of spacecraft.