Multilevel sparse functional principal component analysis.

Science.gov (United States)

Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S

2014-01-29

We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

Urban Land Use Efficiency and Coordination in China

Directory of Open Access Journals (Sweden)

Xiaodong Yang

2017-03-01

Full Text Available Due to the focused pursuit of economic growth in the process of the large-scale urban development of China, the phenomena of low land use efficiency and discordance of land use induce unwanted economic, social, and environmental costs. This paper presents a comprehensive study of urban land use efficiency and of the degree of land use coordination of 33 cities in China, using theoretical analysis, data envelopment analysis, principal component analysis, the coordination coefficient method, and four-quadrant analysis. The findings of this study suggest a gradually increasing proportion of land use efficiency from eastern to central and western regions of China, coinciding with China’s pattern of socioeconomic development. No correlation was found between high levels of urban land use efficiency and the degree of land use coordination; however, a significant correlation was found between low land use efficiency and low degrees of land use coordination. Rational land use planning and policy design can effectively improve both urban land use efficiency and coordination.

A METHOD OF DETERMINING THE COORDINATES OF THE STIFFNESS CENTER AND THE STIFFNESS PRINCIPAL AXIS OF THE VIBRATING SYSTEM WITH DAMPING

OpenAIRE

Dang Xuan Truong; Tran Duc Chinh

2014-01-01

The report presents a methodology to determine the directions of the stiffness principal axis (in this case subject to the linear displacement and forced rotation angle) of a solid object interact with the surrounding environment by resilient bearing supports. The results also show that determining the coordinates of the stiffness center in the vibrating system with damping factors is necessary in our research.

Dimensionality reduction of collective motion by principal manifolds

Science.gov (United States)

Gajamannage, Kelum; Butail, Sachit; Porfiri, Maurizio; Bollt, Erik M.

2015-01-01

While the existence of low-dimensional embedding manifolds has been shown in patterns of collective motion, the current battery of nonlinear dimensionality reduction methods is not amenable to the analysis of such manifolds. This is mainly due to the necessary spectral decomposition step, which limits control over the mapping from the original high-dimensional space to the embedding space. Here, we propose an alternative approach that demands a two-dimensional embedding which topologically summarizes the high-dimensional data. In this sense, our approach is closely related to the construction of one-dimensional principal curves that minimize orthogonal error to data points subject to smoothness constraints. Specifically, we construct a two-dimensional principal manifold directly in the high-dimensional space using cubic smoothing splines, and define the embedding coordinates in terms of geodesic distances. Thus, the mapping from the high-dimensional data to the manifold is defined in terms of local coordinates. Through representative examples, we show that compared to existing nonlinear dimensionality reduction methods, the principal manifold retains the original structure even in noisy and sparse datasets. The principal manifold finding algorithm is applied to configurations obtained from a dynamical system of multiple agents simulating a complex maneuver called predator mobbing, and the resulting two-dimensional embedding is compared with that of a well-established nonlinear dimensionality reduction method.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

Science.gov (United States)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Sparse Principal Component Analysis in Medical Shape Modeling

DEFF Research Database (Denmark)

Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus

2006-01-01

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...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently 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...

Principal components analysis in clinical studies.

Science.gov (United States)

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.

Probabilistic Principal Component Analysis for Metabolomic Data.

LENUS (Irish Health Repository)

Nyamundanda, Gift

2010-11-23

Abstract Background Data from metabolomic studies are typically complex and high-dimensional. Principal component analysis (PCA) is currently the most widely used statistical technique for analyzing metabolomic data. However, PCA is limited by the fact that it is not based on a statistical model. Results Here, probabilistic principal component analysis (PPCA) which addresses some of the limitations of PCA, is reviewed and extended. A novel extension of PPCA, called probabilistic principal component and covariates analysis (PPCCA), is introduced which provides a flexible approach to jointly model metabolomic data and additional covariate information. The use of a mixture of PPCA models for discovering the number of inherent groups in metabolomic data is demonstrated. The jackknife technique is employed to construct confidence intervals for estimated model parameters throughout. The optimal number of principal components is determined through the use of the Bayesian Information Criterion model selection tool, which is modified to address the high dimensionality of the data. Conclusions The methods presented are illustrated through an application to metabolomic data sets. Jointly modeling metabolomic data and covariates was successfully achieved and has the potential to provide deeper insight to the underlying data structure. Examination of confidence intervals for the model parameters, such as loadings, allows for principled and clear interpretation of the underlying data structure. A software package called MetabolAnalyze, freely available through the R statistical software, has been developed to facilitate implementation of the presented methods in the metabolomics field.

A METHOD OF DETERMINING THE COORDINATES OF THE STIFFNESS CENTER AND THE STIFFNESS PRINCIPAL AXIS OF THE VIBRATING SYSTEM WITH DAMPING

Directory of Open Access Journals (Sweden)

Dang Xuan Truong

2014-12-01

Full Text Available The report presents a methodology to determine the directions of the stiffness principal axis (in this case subject to the linear displacement and forced rotation angle of a solid object interact with the surrounding environment by resilient bearing supports. The results also show that determining the coordinates of the stiffness center in the vibrating system with damping factors is necessary in our research.

COPD phenotype description using principal components analysis

DEFF Research Database (Denmark)

Roy, Kay; Smith, Jacky; Kolsum, Umme

2009-01-01

BACKGROUND: Airway inflammation in COPD can be measured using biomarkers such as induced sputum and Fe(NO). This study set out to explore the heterogeneity of COPD using biomarkers of airway and systemic inflammation and pulmonary function by principal components analysis (PCA). SUBJECTS...... AND METHODS: In 127 COPD patients (mean FEV1 61%), pulmonary function, Fe(NO), plasma CRP and TNF-alpha, sputum differential cell counts and sputum IL8 (pg/ml) were measured. Principal components analysis as well as multivariate analysis was performed. RESULTS: PCA identified four main components (% variance...... associations between the variables within components 1 and 2. CONCLUSION: COPD is a multi dimensional disease. Unrelated components of disease were identified, including neutrophilic airway inflammation which was associated with systemic inflammation, and sputum eosinophils which were related to increased Fe...

Use of Sparse Principal Component Analysis (SPCA) for Fault Detection

DEFF Research Database (Denmark)

Gajjar, Shriram; Kulahci, Murat; Palazoglu, Ahmet

2016-01-01

Principal component analysis (PCA) has been widely used for data dimension reduction and process fault detection. However, interpreting the principal components and the outcomes of PCA-based monitoring techniques is a challenging task since each principal component is a linear combination of the ...

The application of principal component analysis to quantify technique in sports.

Science.gov (United States)

Federolf, P; Reid, R; Gilgien, M; Haugen, P; Smith, G

2014-06-01

Analyzing an athlete's "technique," sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis (PCA) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three-dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78-dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements (PMk ) carried out by the athletes. The first four PMk contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore-aft movement of the trunk, and distance between skis. In summary, calculating PMk offered a data-driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement. © 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Evaluation of Staining-Dependent Colour Changes in Resin Composites Using Principal Component Analysis.

Science.gov (United States)

Manojlovic, D; Lenhardt, L; Milićević, B; Antonov, M; Miletic, V; Dramićanin, M D

2015-10-09

Colour changes in Gradia Direct™ composite after immersion in tea, coffee, red wine, Coca-Cola, Colgate mouthwash, and distilled water were evaluated using principal component analysis (PCA) and the CIELAB colour coordinates. The reflection spectra of the composites were used as input data for the PCA. The output data (scores and loadings) provided information about the magnitude and origin of the surface reflection changes after exposure to the staining solutions. The reflection spectra of the stained samples generally exhibited lower reflection in the blue spectral range, which was manifested in the lower content of the blue shade for the samples. Both analyses demonstrated the high staining abilities of tea, coffee, and red wine, which produced total colour changes of 4.31, 6.61, and 6.22, respectively, according to the CIELAB analysis. PCA revealed subtle changes in the reflection spectra of composites immersed in Coca-Cola, demonstrating Coca-Cola's ability to stain the composite to a small degree.

Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo

KAUST Repository

Martinez, Josue G.

2010-06-01

The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order to overcome this, the authors show how to apply Stochastic Approximation Monte Carlo (SAMC) to this problem, a method that has the potential to explore the entire space and does not become trapped in local extrema. The combination of reversible jump methods and SAMC in hierarchical longitudinal functional data is simplified by a polar coordinate representation of the principal components. The approach is easy to implement and does well in simulated data in determining the distribution of the number of principal components, and in terms of its frequentist estimation properties. Empirical applications are also presented.

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

Maadooliat, Mehdi; Huang, Jianhua Z.; Hu, Jianhua

2015-01-01

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

Science.gov (United States)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

ISCO and Principal Null Congruences in Extremal Kerr Spacetime

International Nuclear Information System (INIS)

Pradhan, Parthapratim

2012-01-01

The effective potential in universal like coordinates(U, V, θ, φ), which are smooth across the event horizon is derived and investigated the ISCO(Innermost Stable Circular Orbits) explicitly in these coordinates for extremal Kerr spacetime. Extremization of the effective potential for timelike circular orbit shows that the existence of a stable circular geodesics in the extremal spacetime for direct orbit, precisely on the event horizon in terms of the radial coordinate which coincides with the principal null geodesic congruences of the event horizon. These null geodesic congruences mold themselves to the spacetime curvature in such a way that Weyl conformal tensor and its dual are vanished, that is why they are in-fact doubly degenerate principal null congruences.

A coordinate descent MM algorithm for fast computation of sparse logistic PCA

KAUST Repository

Lee, Seokho

2013-06-01

Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding to be useful when the data dimension is high. We develop a computationally fast algorithm using a combination of coordinate descent and majorization-minimization (MM) auxiliary optimization. Our new algorithm decouples the joint estimation of multiple components into separate estimations and consists of closed-form elementwise updating formulas for each sparse principal component. The performance of the proposed algorithm is tested using simulation and high-dimensional real-world datasets. © 2013 Elsevier B.V. All rights reserved.

Group-wise Principal Component Analysis for Exploratory Data Analysis

NARCIS (Netherlands)

Camacho, J.; Rodriquez-Gomez, Rafael A.; Saccenti, E.

2017-01-01

In this paper, we propose a new framework for matrix factorization based on Principal Component Analysis (PCA) where sparsity is imposed. The structure to impose sparsity is defined in terms of groups of correlated variables found in correlation matrices or maps. The framework is based on three new

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

Directory of Open Access Journals (Sweden)

Stefania Salvatore

2016-07-01

Full Text Available Abstract Background 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. Methods 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. Results 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. Conclusion 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.

PEMBUATAN PERANGKAT LUNAK PENGENALAN WAJAH MENGGUNAKAN PRINCIPAL COMPONENTS ANALYSIS

Directory of Open Access Journals (Sweden)

Kartika Gunadi

2001-01-01

Full Text Available Face recognition is one of many important researches, and today, many applications have implemented it. Through development of techniques like Principal Components Analysis (PCA, computers can now outperform human in many face recognition tasks, particularly those in which large database of faces must be searched. Principal Components Analysis was used to reduce facial image dimension into fewer variables, which are easier to observe and handle. Those variables then fed into artificial neural networks using backpropagation method to recognise the given facial image. The test results show that PCA can provide high face recognition accuracy. For the training faces, a correct identification of 100% could be obtained. From some of network combinations that have been tested, a best average correct identification of 91,11% could be obtained for the test faces while the worst average result is 46,67 % correct identification Abstract in Bahasa Indonesia : Pengenalan wajah manusia merupakan salah satu bidang penelitian yang penting, dan dewasa ini banyak aplikasi yang dapat menerapkannya. Melalui pengembangan suatu teknik seperti Principal Components Analysis (PCA, komputer sekarang dapat melebihi kemampuan otak manusia dalam berbagai tugas pengenalan wajah, terutama tugas-tugas yang membutuhkan pencarian pada database wajah yang besar. Principal Components Analysis digunakan untuk mereduksi dimensi gambar wajah sehingga menghasilkan variabel yang lebih sedikit yang lebih mudah untuk diobsevasi dan ditangani. Hasil yang diperoleh kemudian akan dimasukkan ke suatu jaringan saraf tiruan dengan metode Backpropagation untuk mengenali gambar wajah yang telah diinputkan ke dalam sistem. Hasil pengujian sistem menunjukkan bahwa penggunaan PCA untuk pengenalan wajah dapat memberikan tingkat akurasi yang cukup tinggi. Untuk gambar wajah yang diikutsertakankan dalam latihan, dapat diperoleh 100% identifikasi yang benar. Dari beberapa kombinasi jaringan yang

Sparse logistic principal components analysis for binary data

KAUST Repository

Lee, Seokho

2010-09-01

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

Coordination Processes in International Organisations

DEFF Research Database (Denmark)

Nedergaard, Peter

2008-01-01

The EU is not a member of the International Labour Organisation (ILO), but relatively elaborate EU coordination takes place anyway. This paper addresses two research questions: 1) How is it possible to evaluate the coordination of the EU in its specific observable configuration in the ILO?, and 2......-à-vis their principals, the Member States. The Commission is the leading agent in the phase leading up to the Conference; the Presidency then takes over. On the one hand, due to the Treaty obligations and their interpretations by the Court of Justice, both the Presidency and the Commission are kept within tight limits...... by the principals. On the other hand, both before and during the Conference, the Member States accept the so-called discursive coordination of the Commission, which seems to be of great (but often neglected) importance. Owing to the organisational set-up in which coordination takes place, the EU is able...

Efficacy of the Principal Components Analysis Techniques Using ...

African Journals Online (AJOL)

Second, the paper reports results of principal components analysis after the artificial data were submitted to three commonly used procedures; scree plot, Kaiser rule, and modified Horn's parallel analysis, and demonstrate the pedagogical utility of using artificial data in teaching advanced quantitative concepts. The results ...

Experimental and principal component analysis of waste ...

African Journals Online (AJOL)

The present study is aimed at determining through principal component analysis the most important variables affecting bacterial degradation in ponds. Data were collected from literature. In addition, samples were also collected from the waste stabilization ponds at the University of Nigeria, Nsukka and analyzed to ...

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

Science.gov (United States)

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

Interpretable functional principal component analysis.

Science.gov (United States)

Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

2016-09-01

Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

Principal Component Analysis as an Efficient Performance ...

African Journals Online (AJOL)

This paper uses the principal component analysis (PCA) to examine the possibility of using few explanatory variables (X's) to explain the variation in Y. It applied PCA to assess the performance of students in Abia State Polytechnic, Aba, Nigeria. This was done by estimating the coefficients of eight explanatory variables in a ...

PCA: Principal Component Analysis for spectra modeling

Science.gov (United States)

Hurley, Peter D.; Oliver, Seb; Farrah, Duncan; Wang, Lingyu; Efstathiou, Andreas

2012-07-01

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. This code, written in IDL, classifies principal components of IRS spectra to define a new classification scheme using 5D Gaussian mixtures modelling. The five PCs and average spectra for the four classifications to classify objects are made available with the code.

Principal Component Analysis: Most Favourite Tool in Chemometrics

Indian Academy of Sciences (India)

Abstract. Principal component analysis (PCA) is the most commonlyused chemometric technique. It is an unsupervised patternrecognition technique. PCA has found applications in chemistry,biology, medicine and economics. The present work attemptsto understand how PCA work and how can we interpretits results.

Principal Component Analysis - A Powerful Tool in Computing Marketing Information

Directory of Open Access Journals (Sweden)

Constantin C.

2014-12-01

Full Text Available This paper is about an instrumental research regarding a powerful multivariate data analysis method which can be used by the researchers in order to obtain valuable information for decision makers that need to solve the marketing problem a company face with. The literature stresses the need to avoid the multicollinearity phenomenon in multivariate analysis and the features of Principal Component Analysis (PCA in reducing the number of variables that could be correlated with each other to a small number of principal components that are uncorrelated. In this respect, the paper presents step-by-step the process of applying the PCA in marketing research when we use a large number of variables that naturally are collinear.

ANOVA-principal component analysis and ANOVA-simultaneous component analysis: a comparison.

NARCIS (Netherlands)

Zwanenburg, G.; Hoefsloot, H.C.J.; Westerhuis, J.A.; Jansen, J.J.; Smilde, A.K.

2011-01-01

ANOVA-simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

Science.gov (United States)

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

Energy Technology Data Exchange (ETDEWEB)

Kang, Ho Yang [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of); Kim, Ki Bok [Chungnam National University, Daejeon (Korea, Republic of)

2003-06-15

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters

Analysis and Classification of Acoustic Emission Signals During Wood Drying Using the Principal Component Analysis

International Nuclear Information System (INIS)

Kang, Ho Yang; Kim, Ki Bok

2003-01-01

In this study, acoustic emission (AE) signals due to surface cracking and moisture movement in the flat-sawn boards of oak (Quercus Variablilis) during drying under the ambient conditions were analyzed and classified using the principal component analysis. The AE signals corresponding to surface cracking showed higher in peak amplitude and peak frequency, and shorter in rise time than those corresponding to moisture movement. To reduce the multicollinearity among AE features and to extract the significant AE parameters, correlation analysis was performed. Over 99% of the variance of AE parameters could be accounted for by the first to the fourth principal components. The classification feasibility and success rate were investigated in terms of two statistical classifiers having six independent variables (AE parameters) and six principal components. As a result, the statistical classifier having AE parameters showed the success rate of 70.0%. The statistical classifier having principal components showed the success rate of 87.5% which was considerably than that of the statistical classifier having AE parameters

Scalable Robust Principal Component Analysis Using Grassmann Averages

DEFF Research Database (Denmark)

Hauberg, Søren; Feragen, Aasa; Enficiaud, Raffi

2016-01-01

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortu...

Principal Component Analysis of Body Measurements In Three ...

African Journals Online (AJOL)

This study was conducted to explore the relationship among body measurements in 3 strains of broilers chicken (Arbor Acre, Marshal and Ross) using principal component analysis with the view of identifying those components that define body conformation in broilers. A total of 180 birds were used, 60 per strain.

Nonparametric inference in nonlinear principal components analysis : exploration and beyond

NARCIS (Netherlands)

Linting, Mariëlle

2007-01-01

In the social and behavioral sciences, data sets often do not meet the assumptions of traditional analysis methods. Therefore, nonlinear alternatives to traditional methods have been developed. This thesis starts with a didactic discussion of nonlinear principal components analysis (NLPCA),

Joint Coordination and Muscle Activities of Ballet Dancers During Tiptoe Standing.

Science.gov (United States)

Tanabe, Hiroko; Fujii, Keisuke; Kouzaki, Motoki

2017-01-01

We aimed to investigate joint coordination of lower limbs in dancers during tiptoe standing and the relationship between joint coordination and muscle coactivation. Seven female ballet dancers performed tiptoe standing with six leg positions (fi e classical dance positions and one modern dance position) for 10 s. The kinematic data of the metatarsophalangeal (MP), ankle, knee, and hip joints was collected, and surface electromyography (EMG) of over 13 lower limb muscles was conducted. Principal component analysis was performed to determine joint coordination. MP-ankle and ankle-knee had in-phase coordination, whereas knee-hip showed anti-phase coordination in the sagittal plane. In addition, most EMG-EMG coherence around the MP and ankle joints was significant up to 50 Hz when these two joints swayed with in-phase. This suggests that different joint coordination patterns are associated with neural processing related to different muscle coactivation patterns. In conclusion, ballet dancers showed in-phase coordination from the MP to knee joints, which was associated with muscle coactivation to a higher frequency domain (up to 50 Hz) in comparison with anti-phase coordination.

COMPARITIVE GENETIC DIVERSITY ANALYSIS OF OAT (Avena ...

African Journals Online (AJOL)

knsccf

protein and 30 to 35% dry matter. ... environment interaction, hence not much dependable but ... Principal coordinate (3D) analysis was .... Relationships among 20 genotypes of oat visualized by principal component analysis (PCoA) of rainfed ...

Assessment of the Coordination Ability of Sustainable Social-Ecological Systems Development Based on a Set Pair Analysis: A Case Study in Yanchi County, China

Directory of Open Access Journals (Sweden)

Ya Wang

2016-08-01

Full Text Available Sandy desertification is one of the most severe ecological problems in the world. Essentially, it is land degradation caused by discordance in the Social-Ecological Systems (SES. The ability to coordinate SES is a principal characteristic of regional sustainable development and a key factor in desertification control. This paper directly and comprehensively evaluates the ability to coordinate SES in the desertification reversal process. Assessment indicators and standards for SES have been established using statistical data and materials from government agencies. We applied a coordinated development model based on Identical-Discrepancy-Contrary (IDC situational ranking of a Set Pair Analysis (SPA to analyze the change in Yanchi County’s coordination ability since it implemented the grazing prohibition policy. The results indicated that Yanchi County was basically in the secondary grade of the national sustainable development level, and the subsystems’ development trend was relatively stable. Coordinate ability increased from 0.686 in 2003 to 0.957 in 2014 and experienced “weak coordination to basic coordination to high coordination” development processes. We concluded that drought, the grazing prohibition dilemma and the ecological footprint were key factors impeding the coordination of SES development in this area. These findings should provide information about desertification control and ecological policy implementation to guarantee sustainable rehabilitation.

Fast principal component analysis for stacking seismic data

Science.gov (United States)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Anatomic breast coordinate system for mammogram analysis

DEFF Research Database (Denmark)

Karemore, Gopal; Brandt, S.; Karssemeijer, N.

2011-01-01

was represented by geodesic distance (s) from nipple and parametric angle (¿) as shown in figure 1. The scoring technique called MTR (mammographic texture resemblance marker) used this breast coordinate system to extract Gaussian derivative features. The features extracted using the (x,y) and the curve......Purpose Many researchers have investigated measures also other than density in the mammogram such as measures based on texture to improve breast cancer risk assessment. However, parenchymal texture characteristics are highly dependent on the orientation of vasculature structure and fibrous tissue...... methodologies as seen from table 2 in given temporal study. Conclusion The curve-linear anatomical breast coordinate system facilitated computerized analysis of mammograms. The proposed coordinate system slightly improved the risk segregation by Mammographic Texture Resemblance and minimized the geometrical...

A Genealogical Interpretation of Principal Components Analysis

Science.gov (United States)

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

On Bayesian Principal Component Analysis

Czech Academy of Sciences Publication Activity Database

Šmídl, Václav; Quinn, A.

2007-01-01

Roč. 51, č. 9 (2007), s. 4101-4123 ISSN 0167-9473 R&D Projects: GA MŠk(CZ) 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : Principal component analysis ( PCA ) * Variational bayes (VB) * von-Mises–Fisher distribution Subject RIV: BC - Control Systems Theory Impact factor: 1.029, year: 2007 http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V8V-4MYD60N-6&_user=10&_coverDate=05%2F15%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=b8ea629d48df926fe18f9e5724c9003a

Female Traditional Principals and Co-Principals: Experiences of Role Conflict and Job Satisfaction

Science.gov (United States)

Eckman, Ellen Wexler; Kelber, Sheryl Talcott

2010-01-01

This paper presents a secondary analysis of survey data focusing on role conflict and job satisfaction of 102 female principals. Data were collected from 51 female traditional principals and 51 female co-principals. By examining the traditional and co-principal leadership models as experienced by female principals, this paper addresses the impact…

Coordinate based random effect size meta-analysis of neuroimaging studies.

Science.gov (United States)

Tench, C R; Tanasescu, Radu; Constantinescu, C S; Auer, D P; Cottam, W J

2017-06-01

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely. Copyright © 2017 Elsevier Inc. All rights reserved.

Fault Localization for Synchrophasor Data using Kernel Principal Component Analysis

Directory of Open Access Journals (Sweden)

CHEN, R.

2017-11-01

Full Text Available In this paper, based on Kernel Principal Component Analysis (KPCA of Phasor Measurement Units (PMU data, a nonlinear method is proposed for fault location in complex power systems. Resorting to the scaling factor, the derivative for a polynomial kernel is obtained. Then, the contribution of each variable to the T2 statistic is derived to determine whether a bus is the fault component. Compared to the previous Principal Component Analysis (PCA based methods, the novel version can combat the characteristic of strong nonlinearity, and provide the precise identification of fault location. Computer simulations are conducted to demonstrate the improved performance in recognizing the fault component and evaluating its propagation across the system based on the proposed method.

Characterization of the Lateral Distribution of Fluorescent Lipid in Binary-Constituent Lipid Monolayers by Principal Component Analysis

Directory of Open Access Journals (Sweden)

István P. Sugár

2010-01-01

Full Text Available Lipid lateral organization in binary-constituent monolayers consisting of fluorescent and nonfluorescent lipids has been investigated by acquiring multiple emission spectra during measurement of each force-area isotherm. The emission spectra reflect BODIPY-labeled lipid surface concentration and lateral mixing with different nonfluorescent lipid species. Using principal component analysis (PCA each spectrum could be approximated as the linear combination of only two principal vectors. One point on a plane could be associated with each spectrum, where the coordinates of the point are the coefficients of the linear combination. Points belonging to the same lipid constituents and experimental conditions form a curve on the plane, where each point belongs to a different mole fraction. The location and shape of the curve reflects the lateral organization of the fluorescent lipid mixed with a specific nonfluorescent lipid. The method provides massive data compression that preserves and emphasizes key information pertaining to lipid distribution in different lipid monolayer phases. Collectively, the capacity of PCA for handling large spectral data sets, the nanoscale resolution afforded by the fluorescence signal, and the inherent versatility of monolayers for characterization of lipid lateral interactions enable significantly enhanced resolution of lipid lateral organizational changes induced by different lipid compositions.

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2014-01-01

Full Text Available The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA. Functional data analysis (FDA deals with random variables (or process with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50. Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.

Facilitation of the PED analysis of large molecules by using global coordinates.

Science.gov (United States)

Jamróz, Michał H; Ostrowski, Sławomir; Dobrowolski, Jan Cz

2015-10-05

Global coordinates have been found to be useful in the potential energy distribution (PED) analyses of the following large molecules: [13]-acene and [33]-helicene. The global coordinate is defined based on much distanced fragments of the analysed molecule, whereas so far, the coordinates used in the analysis were based on stretchings, bendings, or torsions of the adjacent atoms. It has been shown that the PED analyses performed using the global coordinate and the classical ones can lead to exactly the same PED contributions. The global coordinates may significantly improve the facility of the analysis of the vibrational spectra of large molecules. Copyright © 2015 Elsevier B.V. All rights reserved.

APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO RELAXOGRAPHIC IMAGES

International Nuclear Information System (INIS)

STOYANOVA, R.S.; OCHS, M.F.; BROWN, T.R.; ROONEY, W.D.; LI, X.; LEE, J.H.; SPRINGER, C.S.

1999-01-01

Standard analysis methods for processing inversion recovery MR images traditionally have used single pixel techniques. In these techniques each pixel is independently fit to an exponential recovery, and spatial correlations in the data set are ignored. By analyzing the image as a complete dataset, improved error analysis and automatic segmentation can be achieved. Here, the authors apply principal component analysis (PCA) to a series of relaxographic images. This procedure decomposes the 3-dimensional data set into three separate images and corresponding recovery times. They attribute the 3 images to be spatial representations of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) content

Aeromagnetic Compensation Algorithm Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

Peilin Wu

2018-01-01

Full Text Available Aeromagnetic exploration is an important exploration method in geophysics. The data is typically measured by optically pumped magnetometer mounted on an aircraft. But any aircraft produces significant levels of magnetic interference. Therefore, aeromagnetic compensation is important in aeromagnetic exploration. However, multicollinearity of the aeromagnetic compensation model degrades the performance of the compensation. To address this issue, a novel aeromagnetic compensation method based on principal component analysis is proposed. Using the algorithm, the correlation in the feature matrix is eliminated and the principal components are using to construct the hyperplane to compensate the platform-generated magnetic fields. The algorithm was tested using a helicopter, and the obtained improvement ratio is 9.86. The compensated quality is almost the same or slightly better than the ridge regression. The validity of the proposed method was experimentally demonstrated.

An analysis of the relationship between K--5 elementary school teachers' perceptions of principal instructional leadership and their science teaching efficacy

Science.gov (United States)

Clark, Ian

The purpose of this study is to analyze the relationship between K-5 elementary school teachers' perceptions of principal instructional leadership and their science teaching efficacy. The influence of background variables on both leadership and efficacy is also analyzed. A sequential mixed methods approach was used in this study. The survey sample was comprised of teachers in the elementary divisions of schools from the nine international school regional associations. Teacher participation was obtained through an email containing an online survey link. Following the analysis of survey responses (N=356), in-depth interviews (N=17) were conducted. Reliability for the instructional leadership scale was found to be .94 (coefficient alpha) and .69 for the personal science teaching efficacy (PSTE) scale. The results show a significant correlation between elementary school teachers' perceptions of principal instructional leadership and their PSTE levels, with the most significant correlation that between the study of a science-related major or minor at college and higher PSTE scores. Strong correlations were also found between PSTE levels and having principals who discussed goals at faculty meetings, participated in science curricular review, supported recognition of student progress, encouraged new skills and concepts, discussed student progress with faculty, and used assessments to see science progress towards easily understood goals. PSTE levels were also higher in schools where principals had grade or school level science coordinators in place and where they supported the use of science kits.

Revealing the equivalence of two clonal survival models by principal component analysis

International Nuclear Information System (INIS)

Lachet, Bernard; Dufour, Jacques

1976-01-01

The principal component analysis of 21 chlorella cell survival curves, adjusted by one-hit and two-hit target models, lead to quite similar projections on the principal plan: the homologous parameters of these models are linearly correlated; the reason for the statistical equivalence of these two models, in the present state of experimental inaccuracy, is revealed [fr

A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie; Hansen, Lars Kai

2011-01-01

Small sample high-dimensional principal component analysis (PCA) suffers from variance inflation and lack of generalizability. It has earlier been pointed out that a simple leave-one-out variance renormalization scheme can cure the problem. In this paper we generalize the cure in two directions......: First, we propose a computationally less intensive approximate leave-one-out estimator, secondly, we show that variance inflation is also present in kernel principal component analysis (kPCA) and we provide a non-parametric renormalization scheme which can quite efficiently restore generalizability in kPCA....... As for PCA our analysis also suggests a simplified approximate expression. © 2011 Trine J. Abrahamsen and Lars K. Hansen....

Sparse principal component analysis in medical shape modeling

Science.gov (United States)

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.

Model Validation Using Coordinate Distance with Performance Sensitivity

Directory of Open Access Journals (Sweden)

Jiann-Shiun Lew

2008-01-01

Full Text Available This paper presents an innovative approach to model validation for a structure with significant parameter variations. Model uncertainty of the structural dynamics is quantified with the use of a singular value decomposition technique to extract the principal components of parameter change, and an interval model is generated to represent the system with parameter uncertainty. The coordinate vector, corresponding to the identified principal directions, of the validation system is computed. The coordinate distance between the validation system and the identified interval model is used as a metric for model validation. A beam structure with an attached subsystem, which has significant parameter uncertainty, is used to demonstrate the proposed approach.

Co-ordinated Classroom Lectures.

Science.gov (United States)

Harmon, Darell Boyd

From a series of lectures, a selection of eight are oriented principally toward the biologically developing child, and the physiological operations in visual process. The numbered lectures are--(1) The Coordinated Classroom, its Philosophy and Principles, (2) An Outline of a Biological Point of View, (3) The Evolution of Structure--despite man's…

Normal co-ordinate analysis of 1, 8-dibromooctane

Science.gov (United States)

Singh, Devinder; Jaggi, Neena; Singh, Nafa

2010-02-01

The organic compound 1,8-dibromooctane (1,8-DBO) exists in liquid phase at ambient temperatures and has versatile synthetic applications. In its liquid phase 1,8-DBO has been expected to exist in four most probable conformations, with all its carbon atoms in the same plane, having symmetries C 2h , C i , C 2 and C 1 . In the present study a detailed vibrational analysis in terms of assignment of Fourier transform infrared (FT-IR) and Raman bands of this molecule using normal co-ordinate calculations has been done. A systematic set of symmetry co-ordinates has been constructed for this molecule and normal co-ordinate analysis is carried out using the computer program MOLVIB. The force-field transferred from already studied lower chain bromo-alkanes is subjected to refinement so as to fit the observed infrared and Raman frequencies with those of calculated ones. The potential energy distribution (PED) has also been calculated for each mode of vibration of the molecule for the assumed conformations.

Cloud Masking for Remotely Sensed Data Using Spectral and Principal Components Analysis

Directory of Open Access Journals (Sweden)

A. Ahmad

2012-06-01

Full Text Available Two methods of cloud masking tuned to tropical conditions have been developed, based on spectral analysis and Principal Components Analysis (PCA of Moderate Resolution Imaging Spectroradiometer (MODIS data. In the spectral approach, thresholds were applied to four reflective bands (1, 2, 3, and 4, three thermal bands (29, 31 and 32, the band 2/band 1 ratio, and the difference between band 29 and 31 in order to detect clouds. The PCA approach applied a threshold to the first principal component derived from the seven quantities used for spectral analysis. Cloud detections were compared with the standard MODIS cloud mask, and their accuracy was assessed using reference images and geographical information on the study area.

Predicting Insolvency : A comparison between discriminant analysis and logistic regression using principal components

OpenAIRE

Geroukis, Asterios; Brorson, Erik

2014-01-01

In this study, we compare the two statistical techniques logistic regression and discriminant analysis to see how well they classify companies based on clusters – made from the solvency ratio ­– using principal components as independent variables. The principal components are made with different financial ratios. We use cluster analysis to find groups with low, medium and high solvency ratio of 1200 different companies found on the NASDAQ stock market and use this as an apriori definition of ...

The analysis of multivariate group differences using common principal components

NARCIS (Netherlands)

Bechger, T.M.; Blanca, M.J.; Maris, G.

2014-01-01

Although it is simple to determine whether multivariate group differences are statistically significant or not, such differences are often difficult to interpret. This article is about common principal components analysis as a tool for the exploratory investigation of multivariate group differences

C2 Network Analysis: Insights into Coordination & Understanding

National Research Council Canada - National Science Library

Hansberger, Jeffrey T; Schreiber, Craig; Spain, Randall D

2008-01-01

...) workload management. This paper will address recent efforts, tools, and approaches on measuring and analyzing two of these distributed cognitive attributes through network analysis, coordination across agents and mental models...

Coordination Analysis Using Global Structural Constraints and Alignment-based Local Features

Science.gov (United States)

Hara, Kazuo; Shimbo, Masashi; Matsumoto, Yuji

We propose a hybrid approach to coordinate structure analysis that combines a simple grammar to ensure consistent global structure of coordinations in a sentence, and features based on sequence alignment to capture local symmetry of conjuncts. The weight of the alignment-based features, which in turn determines the score of coordinate structures, is optimized by perceptron training on a given corpus. A bottom-up chart parsing algorithm efficiently finds the best scoring structure, taking both nested or non-overlapping flat coordinations into account. We demonstrate that our approach outperforms existing parsers in coordination scope detection on the Genia corpus.

Disposal criticality analysis methodology's principal isotope burnup credit

International Nuclear Information System (INIS)

Doering, T.W.; Thomas, D.A.

2001-01-01

This paper presents the burnup credit aspects of the United States Department of Energy Yucca Mountain Project's methodology for performing criticality analyses for commercial light-water-reactor fuel. The disposal burnup credit methodology uses a 'principal isotope' model, which takes credit for the reduced reactivity associated with the build-up of the primary principal actinides and fission products in irradiated fuel. Burnup credit is important to the disposal criticality analysis methodology and to the design of commercial fuel waste packages. The burnup credit methodology developed for disposal of irradiated commercial nuclear fuel can also be applied to storage and transportation of irradiated commercial nuclear fuel. For all applications a series of loading curves are developed using a best estimate methodology and depending on the application, an additional administrative safety margin may be applied. The burnup credit methodology better represents the 'true' reactivity of the irradiated fuel configuration, and hence the real safety margin, than do evaluations using the 'fresh fuel' assumption. (author)

Quantitative descriptive analysis and principal component analysis for sensory characterization of Indian milk product cham-cham.

Science.gov (United States)

Puri, Ritika; Khamrui, Kaushik; Khetra, Yogesh; Malhotra, Ravinder; Devraja, H C

2016-02-01

Promising development and expansion in the market of cham-cham, a traditional Indian dairy product is expected in the coming future with the organized production of this milk product by some large dairies. The objective of this study was to document the extent of variation in sensory properties of market samples of cham-cham collected from four different locations known for their excellence in cham-cham production and to find out the attributes that govern much of variation in sensory scores of this product using quantitative descriptive analysis (QDA) and principal component analysis (PCA). QDA revealed significant (p sensory attributes of cham-cham among the market samples. PCA identified four significant principal components that accounted for 72.4 % of the variation in the sensory data. Factor scores of each of the four principal components which primarily correspond to sweetness/shape/dryness of interior, surface appearance/surface dryness, rancid and firmness attributes specify the location of each market sample along each of the axes in 3-D graphs. These findings demonstrate the utility of quantitative descriptive analysis for identifying and measuring attributes of cham-cham that contribute most to its sensory acceptability.

Principal component analysis of image gradient orientations for face recognition

NARCIS (Netherlands)

Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

We introduce the notion of Principal Component Analysis (PCA) of image gradient orientations. As image data is typically noisy, but noise is substantially different from Gaussian, traditional PCA of pixel intensities very often fails to estimate reliably the low-dimensional subspace of a given data

The Leadership Roles of a Principal in Improving School Effectiveness

Directory of Open Access Journals (Sweden)

Burhanuddin Burhanuddin

2016-02-01

Full Text Available The purpose of this study was to investigate the leadership roles and practices of the principal in improving school effectiveness. This descriptive study involved the principal , counselor, and 11 teachers from the Tonsley Park Primary School in South Australia. The findings showed that all respondents generally regarded team leader as the most important role, while supervisor was rated as the least important. Of the 20 tasks observed, generally revealed that a principal should work cooperatively with staff to ensure more effective use of their skills. While, the task of a principal in making decisions on staff development programs was not necessarily expected by the practicing teachers. All respondents considered organization coordination as the most important area within which a principal should provide more leadership, while curriculum or instructional improvement and innovation was regarded as the least important

Radar fall detection using principal component analysis

Science.gov (United States)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

Science.gov (United States)

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

On the structure of dynamic principal component analysis used in statistical process monitoring

DEFF Research Database (Denmark)

Vanhatalo, Erik; Kulahci, Murat; Bergquist, Bjarne

2017-01-01

When principal component analysis (PCA) is used for statistical process monitoring it relies on the assumption that data are time independent. However, industrial data will often exhibit serial correlation. Dynamic PCA (DPCA) has been suggested as a remedy for high-dimensional and time...... for determining the number of principal components to retain. The number of retained principal components is determined by visual inspection of the serial correlation in the squared prediction error statistic, Q (SPE), together with the cumulative explained variance of the model. The methods are illustrated using...... driven method to determine the maximum number of lags in DPCA with a foundation in multivariate time series analysis. The method is based on the behavior of the eigenvalues of the lagged autocorrelation and partial autocorrelation matrices. Given a specific lag structure we also propose a method...

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

Science.gov (United States)

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.

Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

Directory of Open Access Journals (Sweden)

Dan Wu

2009-06-01

Full Text Available The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

Multistage principal component analysis based method for abdominal ECG decomposition

International Nuclear Information System (INIS)

Petrolis, Robertas; Krisciukaitis, Algimantas; Gintautas, Vladas

2015-01-01

Reflection of fetal heart electrical activity is present in registered abdominal ECG signals. However this signal component has noticeably less energy than concurrent signals, especially maternal ECG. Therefore traditionally recommended independent component analysis, fails to separate these two ECG signals. Multistage principal component analysis (PCA) is proposed for step-by-step extraction of abdominal ECG signal components. Truncated representation and subsequent subtraction of cardio cycles of maternal ECG are the first steps. The energy of fetal ECG component then becomes comparable or even exceeds energy of other components in the remaining signal. Second stage PCA concentrates energy of the sought signal in one principal component assuring its maximal amplitude regardless to the orientation of the fetus in multilead recordings. Third stage PCA is performed on signal excerpts representing detected fetal heart beats in aim to perform their truncated representation reconstructing their shape for further analysis. The algorithm was tested with PhysioNet Challenge 2013 signals and signals recorded in the Department of Obstetrics and Gynecology, Lithuanian University of Health Sciences. Results of our method in PhysioNet Challenge 2013 on open data set were: average score: 341.503 bpm 2 and 32.81 ms. (paper)

Judging complex movement performances for excellence: a principal components analysis-based technique applied to competitive diving.

Science.gov (United States)

Young, Cole; Reinkensmeyer, David J

2014-08-01

Athletes rely on subjective assessment of complex movements from coaches and judges to improve their motor skills. In some sports, such as diving, snowboard half pipe, gymnastics, and figure skating, subjective scoring forms the basis for competition. It is currently unclear whether this scoring process can be mathematically modeled; doing so could provide insight into what motor skill is. Principal components analysis has been proposed as a motion analysis method for identifying fundamental units of coordination. We used PCA to analyze movement quality of dives taken from USA Diving's 2009 World Team Selection Camp, first identifying eigenpostures associated with dives, and then using the eigenpostures and their temporal weighting coefficients, as well as elements commonly assumed to affect scoring - gross body path, splash area, and board tip motion - to identify eigendives. Within this eigendive space we predicted actual judges' scores using linear regression. This technique rated dives with accuracy comparable to the human judges. The temporal weighting of the eigenpostures, body center path, splash area, and board tip motion affected the score, but not the eigenpostures themselves. These results illustrate that (1) subjective scoring in a competitive diving event can be mathematically modeled; (2) the elements commonly assumed to affect dive scoring actually do affect scoring (3) skill in elite diving is more associated with the gross body path and the effect of the movement on the board and water than the units of coordination that PCA extracts, which might reflect the high level of technique these divers had achieved. We also illustrate how eigendives can be used to produce dive animations that an observer can distort continuously from poor to excellent, which is a novel approach to performance visualization. Copyright © 2014 Elsevier B.V. All rights reserved.

An in-depth analysis of theoretical frameworks for the study of care coordination

Directory of Open Access Journals (Sweden)

Sabine Van Houdt

2013-06-01

Full Text Available Introduction: Complex chronic conditions often require long-term care from various healthcare professionals. Thus, maintaining quality care requires care coordination. Concepts for the study of care coordination require clarification to develop, study and evaluate coordination strategies. In 2007, the Agency for Healthcare Research and Quality defined care coordination and proposed five theoretical frameworks for exploring care coordination. This study aimed to update current theoretical frameworks and clarify key concepts related to care coordination. Methods: We performed a literature review to update existing theoretical frameworks. An in-depth analysis of these theoretical frameworks was conducted to formulate key concepts related to care coordination.Results: Our literature review found seven previously unidentified theoretical frameworks for studying care coordination. The in-depth analysis identified fourteen key concepts that the theoretical frameworks addressed. These were ‘external factors’, ‘structure’, ‘tasks characteristics’, ‘cultural factors’, ‘knowledge and technology’, ‘need for coordination’, ‘administrative operational processes’, ‘exchange of information’, ‘goals’, ‘roles’, ‘quality of relationship’, ‘patient outcome’, ‘team outcome’, and ‘(interorganizational outcome’.Conclusion: These 14 interrelated key concepts provide a base to develop or choose a framework for studying care coordination. The relational coordination theory and the multi-level framework are interesting as these are the most comprehensive.

Process parameter optimization based on principal components analysis during machining of hardened steel

Directory of Open Access Journals (Sweden)

Suryakant B. Chandgude

2015-09-01

Full Text Available The optimum selection of process parameters has played an important role for improving the surface finish, minimizing tool wear, increasing material removal rate and reducing machining time of any machining process. In this paper, optimum parameters while machining AISI D2 hardened steel using solid carbide TiAlN coated end mill has been investigated. For optimization of process parameters along with multiple quality characteristics, principal components analysis method has been adopted in this work. The confirmation experiments have revealed that to improve performance of cutting; principal components analysis method would be a useful tool.

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

Science.gov (United States)

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.

A Note on McDonald's Generalization of Principal Components Analysis

Science.gov (United States)

Shine, Lester C., II

1972-01-01

It is shown that McDonald's generalization of Classical Principal Components Analysis to groups of variables maximally channels the totalvariance of the original variables through the groups of variables acting as groups. An equation is obtained for determining the vectors of correlations of the L2 components with the original variables.…

Principal direction of inertia for 3D trajectories from patient-specific TMJ movement.

Science.gov (United States)

Kim, Dae-Seung; Choi, Soon-Chul; Lee, Sam-Sun; Heo, Min-Suk; Huh, Kyung-Hoe; Hwang, Soon-Jung; Kim, Seong-Ha; Yi, Won-Jin

2013-03-01

Accurate simulation and evaluation of mandibular movement is fundamental for the analysis of functional changes and effects of the mandible and maxilla before and after surgical treatments. We applied principal axes of inertia to the three-dimensional (3D) trajectories generated by patient-specific simulations of TMJ movements for the functional evaluations of mandible movement. Three-dimensional movements of the mandible and the maxilla were tracked based on a patient-specific splint and an optical tracking system. The dental occlusion recorded on the sprint provided synchronization for initial movement in the tracking and the simulation phases. The translation and rotation recorded during movement tracking was applied sequentially to the mandibular model in relation to a fixed maxilla model. The sequential 3D positions of selected landmarks on the mandible were calculated based on the reference coordinate system. The landmarks selected for analysis were bilateral condyles and pogonion points. The moment of inertia tensor was calculated with respect to the 3D trajectory points. Using the unit vectors along the principal axes derived from the tensor matrix, α, β and γ rotations around z-, y- and x-axes were determined to represent the principal directions as principal rotations respectively. The γ direction showed the higher standard deviation, variation of directions, than other directions at all the landmarks. The mandible movement has larger kinematic redundancy in the γ direction than α and β during mouth opening and closing. Principal directions of inertia would be applied to analyzing the changes in angular motion of trajectories introduced by mandibular shape changes from surgical treatments and also to the analysis of the influence of skeletal deformities on mandibular movement asymmetry. Copyright © 2012 Elsevier Ltd. All rights reserved.

Principal components′ analysis of multifocal electroretinogram in retinitis pigmentosa

Directory of Open Access Journals (Sweden)

Aniruddha Maiti

2011-01-01

Full Text Available Aims : To determine waveforms of multifocal electroretinogram (mfERG in patients with retinitis pigmentosa (RP contributing significantly to the overall retinal response by using principal components′ analysis. Settings and Design: Prospective, non-randomized, single-visit, observational, case-control study from a single tertiary ophthalmic center. Materials and Methods: Patients with various forms of RP underwent mfERG testing for a period of one year. The first-order kernel responses of RP cases were compared with concurrently recruited healthy controls. Statistical Analysis Used: Parametric data was analyzed using the unpaired t test for differences between the implicit time and amplitudes of cases and controls. Principal components′ analysis was done for each implicit time and amplitude in cases with RP using the Varimax rotation method. Results: From March 2006 to March 2007, 24 cases with typical RP (56%, 47 eyes were included in the final analysis. Their mean age was 33.7 years (19-69 ± 15.5 years. Comparison of latencies and amplitudes among RP cases with log MAR acuity ≤ 0.18 and those > 0.18, revealed significant difference in the implicit time (P1 in Ring 2 only (P=0.028. Two components (predominently from Ring 1 and 2 each contributing 66.8% and 88.8% of the total variance in the data for latencies and amplitudes respectively, were seen. Conclusions : The first two rings of the mfERG contributed to the variance of waveforms in RP, irrespective of the visual acuity and poor visual field results.

Efficient training of multilayer perceptrons using principal component analysis

International Nuclear Information System (INIS)

Bunzmann, Christoph; Urbanczik, Robert; Biehl, Michael

2005-01-01

A training algorithm for multilayer perceptrons is discussed and studied in detail, which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix computed from the example inputs and their target outputs. Typical properties of the training procedure are investigated by means of a statistical physics analysis in models of learning regression and classification tasks. We demonstrate that the procedure requires by far fewer examples for good generalization than traditional online training. For networks with a large number of hidden units we derive the training prescription which achieves, within our model, the optimal generalization behavior

Exploration, Exploitation, and Organizational Coordination Mechanisms

Directory of Open Access Journals (Sweden)

Silvio Popadiuk

2016-03-01

Full Text Available This paper presents an empirical relationship among exploration, exploitation, and organizational coordination mechanisms, classified as the centralization of decision-making, formalization, and connectedness. In order to analyze the findings of this survey, we used two techniques: Principal Component Analysis (PCA and Partial Least Squares Path Modeling (PLS-PM. Our analysis was supported by 249 answers from managers of companies located in Brazil (convenience sampling. Contrary to expectations, centralization and exploitation were negatively associated. Our data supports the research hypothesis that formalization is positively associated with exploitation. Although the relationship between formalization and exploration were significant, the result is contrary to the research hypothesis that we made. The relationships among connectedness and exploitation, and connectedness and exploration were both positive and significant. This relationship means that the more connectedness increases, the higher the likelihood of exploitation and exploration.

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

Science.gov (United States)

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.

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Science.gov (United States)

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Automotive Exterior Noise Optimization Using Grey Relational Analysis Coupled with Principal Component Analysis

Science.gov (United States)

Chen, Shuming; Wang, Dengfeng; Liu, Bo

This paper investigates optimization design of the thickness of the sound package performed on a passenger automobile. The major characteristics indexes for performance selected to evaluate the processes are the SPL of the exterior noise and the weight of the sound package, and the corresponding parameters of the sound package are the thickness of the glass wool with aluminum foil for the first layer, the thickness of the glass fiber for the second layer, and the thickness of the PE foam for the third layer. In this paper, the process is fundamentally with multiple performances, thus, the grey relational analysis that utilizes grey relational grade as performance index is especially employed to determine the optimal combination of the thickness of the different layers for the designed sound package. Additionally, in order to evaluate the weighting values corresponding to various performance characteristics, the principal component analysis is used to show their relative importance properly and objectively. The results of the confirmation experiments uncover that grey relational analysis coupled with principal analysis methods can successfully be applied to find the optimal combination of the thickness for each layer of the sound package material. Therefore, the presented method can be an effective tool to improve the vehicle exterior noise and lower the weight of the sound package. In addition, it will also be helpful for other applications in the automotive industry, such as the First Automobile Works in China, Changan Automobile in China, etc.

[Content of mineral elements of Gastrodia elata by principal components analysis].

Science.gov (United States)

Li, Jin-ling; Zhao, Zhi; Liu, Hong-chang; Luo, Chun-li; Huang, Ming-jin; Luo, Fu-lai; Wang, Hua-lei

2015-03-01

To study the content of mineral elements and the principal components in Gastrodia elata. Mineral elements were determined by ICP and the data was analyzed by SPSS. K element has the highest content-and the average content was 15.31 g x kg(-1). The average content of N element was 8.99 g x kg(-1), followed by K element. The coefficient of variation of K and N was small, but the Mn was the biggest with 51.39%. The highly significant positive correlation was found among N, P and K . Three principal components were selected by principal components analysis to evaluate the quality of G. elata. P, B, N, K, Cu, Mn, Fe and Mg were the characteristic elements of G. elata. The content of K and N elements was higher and relatively stable. The variation of Mn content was biggest. The quality of G. elata in Guizhou and Yunnan was better from the perspective of mineral elements.

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

Science.gov (United States)

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.

Analysis of Moisture Content in Beetroot using Fourier Transform Infrared Spectroscopy and by Principal Component Analysis.

Science.gov (United States)

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.

Integrating Data Transformation in Principal Components Analysis

KAUST Repository

Maadooliat, Mehdi

2015-01-02

Principal component analysis (PCA) is a popular dimension reduction method to reduce the complexity and obtain the informative aspects of high-dimensional datasets. When the data distribution is skewed, data transformation is commonly used prior to applying PCA. Such transformation is usually obtained from previous studies, prior knowledge, or trial-and-error. In this work, we develop a model-based method that integrates data transformation in PCA and finds an appropriate data transformation using the maximum profile likelihood. Extensions of the method to handle functional data and missing values are also developed. Several numerical algorithms are provided for efficient computation. The proposed method is illustrated using simulated and real-world data examples.

Nonlinear principal component analysis and its applications

CERN Document Server

Mori, Yuichi; Makino, Naomichi

2016-01-01

This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data. In the part dealing with the principle, after a brief introduction of ordinary PCA, a PCA for categorical data (nominal and ordinal) is introduced as nonlinear PCA, in which an optimal scaling technique is used to quantify the categorical variables. The alternating least squares (ALS) is the main algorithm in the method. Multiple correspondence analysis (MCA), a special case of nonlinear PCA, is also introduced. All formulations in these methods are integrated in the same manner as matrix operations. Because any measurement levels data can be treated consistently as numerical data and ALS is a very powerful tool for estimations, the methods can be utilized in a variety of fields such as biometrics, econometrics, psychometrics, and sociology. In the applications part of the book, four applications are introduced: variable selection for mixed...

Quality analysis of commercial samples of Ziziphi spinosae semen (suanzaoren by means of chromatographic fingerprinting assisted by principal component analysis

Directory of Open Access Journals (Sweden)

Shuai Sun

2014-06-01

Full Text Available Due to the scarcity of resources of Ziziphi spinosae semen (ZSS, many inferior goods and even adulterants are generally found in medicine markets. To strengthen the quality control, HPLC fingerprint common pattern established in this paper showed three main bioactive compounds in one chromatogram simultaneously. Principal component analysis based on DAD signals could discriminate adulterants and inferiorities. Principal component analysis indicated that all samples could be mainly regrouped into two main clusters according to the first principal component (PC1, redefined as Vicenin II and the second principal component (PC2, redefined as zizyphusine. PC1 and PC2 could explain 91.42% of the variance. Content of zizyphusine fluctuated more greatly than that of spinosin, and this result was also confirmed by the HPTLC result. Samples with low content of jujubosides and two common adulterants could not be used equivalently with authenticated ones in clinic, while one reference standard extract could substitute the crude drug in pharmaceutical production. Giving special consideration to the well-known bioactive saponins but with low response by end absorption, a fast and cheap HPTLC method for quality control of ZSS was developed and the result obtained was commensurate well with that of HPLC analysis. Samples having similar fingerprints to HPTLC common pattern targeting at saponins could be regarded as authenticated ones. This work provided a faster and cheaper way for quality control of ZSS and laid foundation for establishing a more effective quality control method for ZSS. Keywords: Adulterant, Common pattern, Principal component analysis, Quality control, Ziziphi spinosae semen

Look Together: Analyzing Gaze Coordination with Epistemic Network Analysis

Directory of Open Access Journals (Sweden)

Sean eAndrist

2015-07-01

Full Text Available When conversing and collaborating in everyday situations, people naturally and interactively align their behaviors with each other across various communication channels, including speech, gesture, posture, and gaze. Having access to a partner's referential gaze behavior has been shown to be particularly important in achieving collaborative outcomes, but the process in which people's gaze behaviors unfold over the course of an interaction and become tightly coordinated is not well understood. In this paper, we present work to develop a deeper and more nuanced understanding of coordinated referential gaze in collaborating dyads. We recruited 13 dyads to participate in a collaborative sandwich-making task and used dual mobile eye tracking to synchronously record each participant's gaze behavior. We used a relatively new analysis technique—epistemic network analysis—to jointly model the gaze behaviors of both conversational participants. In this analysis, network nodes represent gaze targets for each participant, and edge strengths convey the likelihood of simultaneous gaze to the connected target nodes during a given time-slice. We divided collaborative task sequences into discrete phases to examine how the networks of shared gaze evolved over longer time windows. We conducted three separate analyses of the data to reveal (1 properties and patterns of how gaze coordination unfolds throughout an interaction sequence, (2 optimal time lags of gaze alignment within a dyad at different phases of the interaction, and (3 differences in gaze coordination patterns for interaction sequences that lead to breakdowns and repairs. In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in joint activities, this work has implications for the design of future technologies that engage in situated interactions with human users.

Efficient real time OD matrix estimation based on principal component analysis

NARCIS (Netherlands)

Djukic, T.; Flötteröd, G.; Van Lint, H.; Hoogendoorn, S.P.

2012-01-01

In this paper we explore the idea of dimensionality reduction and approximation of OD demand based on principal component analysis (PCA). First, we show how we can apply PCA to linearly transform the high dimensional OD matrices into the lower dimensional space without significant loss of accuracy.

Power Grid Modelling From Wind Turbine Perspective Using Principal Componenet Analysis

DEFF Research Database (Denmark)

Farajzadehbibalan, Saber; Ramezani, Mohammad Hossein; Nielsen, Peter

2015-01-01

In this study, we derive an eigenvector-based multivariate model of a power grid from the wind farm's standpoint using dynamic principal component analysis (DPCA). The main advantages of our model over previously developed models are being more realistic and having low complexity. We show that th...

Visualizing solvent mediated phase transformation behavior of carbamazepine polymorphs by principal component analysis

DEFF Research Database (Denmark)

Tian, Fang; Rades, Thomas; Sandler, Niklas

2008-01-01

The purpose of this research is to gain a greater insight into the hydrate formation processes of different carbamazepine (CBZ) anhydrate forms in aqueous suspension, where principal component analysis (PCA) was applied for data analysis. The capability of PCA to visualize and to reveal simplified...

Principal Component Analysis Based Measure of Structural Holes

Science.gov (United States)

Deng, Shiguo; Zhang, Wenqing; Yang, Huijie

2013-02-01

Based upon principal component analysis, a new measure called compressibility coefficient is proposed to evaluate structural holes in networks. This measure incorporates a new effect from identical patterns in networks. It is found that compressibility coefficient for Watts-Strogatz small-world networks increases monotonically with the rewiring probability and saturates to that for the corresponding shuffled networks. While compressibility coefficient for extended Barabasi-Albert scale-free networks decreases monotonically with the preferential effect and is significantly large compared with that for corresponding shuffled networks. This measure is helpful in diverse research fields to evaluate global efficiency of networks.

Nonlinear Principal Component Analysis Using Strong Tracking Filter

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

The paper analyzes the problem of blind source separation (BSS) based on the nonlinear principal component analysis (NPCA) criterion. An adaptive strong tracking filter (STF) based algorithm was developed, which is immune to system model mismatches. Simulations demonstrate that the algorithm converges quickly and has satisfactory steady-state accuracy. The Kalman filtering algorithm and the recursive leastsquares type algorithm are shown to be special cases of the STF algorithm. Since the forgetting factor is adaptively updated by adjustment of the Kalman gain, the STF scheme provides more powerful tracking capability than the Kalman filtering algorithm and recursive least-squares algorithm.

Principal component analysis of tomato genotypes based on some morphological and biochemical quality indicators

Directory of Open Access Journals (Sweden)

Glogovac Svetlana

2012-01-01

Full Text Available This study investigates variability of tomato genotypes based on morphological and biochemical fruit traits. Experimental material is a part of tomato genetic collection from Institute of Filed and Vegetable Crops in Novi Sad, Serbia. Genotypes were analyzed for fruit mass, locule number, index of fruit shape, fruit colour, dry matter content, total sugars, total acidity, lycopene and vitamin C. Minimum, maximum and average values and main indicators of variability (CV and σ were calculated. Principal component analysis was performed to determinate variability source structure. Four principal components, which contribute 93.75% of the total variability, were selected for analysis. The first principal component is defined by vitamin C, locule number and index of fruit shape. The second component is determined by dry matter content, and total acidity, the third by lycopene, fruit mass and fruit colour. Total sugars had the greatest part in the fourth component.

Principal Leadership for Technology-enhanced Learning in Science

Science.gov (United States)

Gerard, Libby F.; Bowyer, Jane B.; Linn, Marcia C.

2008-02-01

Reforms such as technology-enhanced instruction require principal leadership. Yet, many principals report that they need help to guide implementation of science and technology reforms. We identify strategies for helping principals provide this leadership. A two-phase design is employed. In the first phase we elicit principals' varied ideas about the Technology-enhanced Learning in Science (TELS) curriculum materials being implemented by teachers in their schools, and in the second phase we engage principals in a leadership workshop designed based on the ideas they generated. Analysis uses an emergent coding scheme to categorize principals' ideas, and a knowledge integration framework to capture the development of these ideas. The analysis suggests that principals frame their thinking about the implementation of TELS in terms of: principal leadership, curriculum, educational policy, teacher learning, student outcomes and financial resources. They seek to improve their own knowledge to support this reform. The principals organize their ideas around individual school goals and current political issues. Principals prefer professional development activities that engage them in reviewing curricula and student work with other principals. Based on the analysis, this study offers guidelines for creating learning opportunities that enhance principals' leadership abilities in technology and science reform.

Competition analysis on the operating system market using principal component analysis

Directory of Open Access Journals (Sweden)

Brătucu, G.

2011-01-01

Full Text Available Operating system market has evolved greatly. The largest software producer in the world, Microsoft, dominates the operating systems segment. With three operating systems: Windows XP, Windows Vista and Windows 7 the company held a market share of 87.54% in January 2011. Over time, open source operating systems have begun to penetrate the market very strongly affecting other manufacturers. Companies such as Apple Inc. and Google Inc. penetrated the operating system market. This paper aims to compare the best-selling operating systems on the market in terms of defining characteristics. To this purpose the principal components analysis method was used.

Quality Aware Compression of Electrocardiogram Using Principal Component Analysis.

Science.gov (United States)

Gupta, Rajarshi

2016-05-01

Electrocardiogram (ECG) compression finds wide application in various patient monitoring purposes. Quality control in ECG compression ensures reconstruction quality and its clinical acceptance for diagnostic decision making. In this paper, a quality aware compression method of single lead ECG is described using principal component analysis (PCA). After pre-processing, beat extraction and PCA decomposition, two independent quality criteria, namely, bit rate control (BRC) or error control (EC) criteria were set to select optimal principal components, eigenvectors and their quantization level to achieve desired bit rate or error measure. The selected principal components and eigenvectors were finally compressed using a modified delta and Huffman encoder. The algorithms were validated with 32 sets of MIT Arrhythmia data and 60 normal and 30 sets of diagnostic ECG data from PTB Diagnostic ECG data ptbdb, all at 1 kHz sampling. For BRC with a CR threshold of 40, an average Compression Ratio (CR), percentage root mean squared difference normalized (PRDN) and maximum absolute error (MAE) of 50.74, 16.22 and 0.243 mV respectively were obtained. For EC with an upper limit of 5 % PRDN and 0.1 mV MAE, the average CR, PRDN and MAE of 9.48, 4.13 and 0.049 mV respectively were obtained. For mitdb data 117, the reconstruction quality could be preserved up to CR of 68.96 by extending the BRC threshold. The proposed method yields better results than recently published works on quality controlled ECG compression.

Fast grasping of unknown objects using principal component analysis

Science.gov (United States)

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.

Fall detection in walking robots by multi-way principal component analysis

NARCIS (Netherlands)

Karssen, J.G.; Wisse, M.

2008-01-01

Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in

Power Transformer Differential Protection Based on Neural Network Principal Component Analysis, Harmonic Restraint and Park's Plots

OpenAIRE

Tripathy, Manoj

2012-01-01

This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to disc...

Identification of Counterfeit Alcoholic Beverages Using Cluster Analysis in Principal-Component Space

Science.gov (United States)

Khodasevich, M. A.; Sinitsyn, G. V.; Gres'ko, M. A.; Dolya, V. M.; Rogovaya, M. V.; Kazberuk, A. V.

2017-07-01

A study of 153 brands of commercial vodka products showed that counterfeit samples could be identified by introducing a unified additive at the minimum concentration acceptable for instrumental detection and multivariate analysis of UV-Vis transmission spectra. Counterfeit products were detected with 100% probability by using hierarchical cluster analysis or the C-means method in two-dimensional principal-component space.

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

Directory of Open Access Journals (Sweden)

HRISTIAN Liliana

2017-05-01

Full Text Available The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA. There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A solution to this problem can be the application of a method of factorial analysis, the so-called Principal Component Analysis, with the final goal of establishing and analyzing those variables which influence in a significant manner the internal structure of combed wool fabrics according to armire type. By applying PCA it is obtained a small number of the linear combinations (principal components from a set of variables, describing the internal structure of the fabrics, which can hold as much information as possible from the original variables. Data analysis is an important initial step in decision making, allowing identification of the causes that lead to a decision- making situations. Thus it is the action of transforming the initial data in order to extract useful information and to facilitate reaching the conclusions. The process of data analysis can be defined as a sequence of steps aimed at formulating hypotheses, collecting primary information and validation, the construction of the mathematical model describing this phenomenon and reaching these conclusions about the behavior of this model.

Principal Component Analysis: Resources for an Essential Application of Linear Algebra

Science.gov (United States)

Pankavich, Stephen; Swanson, Rebecca

2015-01-01

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and extension of the Spectral Theorem and is commonly used within a variety of fields, including statistics,…

Resonant Homoclinic Flips Bifurcation in Principal Eigendirections

Directory of Open Access Journals (Sweden)

Tiansi Zhang

2013-01-01

Full Text Available A codimension-4 homoclinic bifurcation with one orbit flip and one inclination flip at principal eigenvalue direction resonance is considered. By introducing a local active coordinate system in some small neighborhood of homoclinic orbit, we get the Poincaré return map and the bifurcation equation. A detailed investigation produces the number and the existence of 1-homoclinic orbit, 1-periodic orbit, and double 1-periodic orbits. We also locate their bifurcation surfaces in certain regions.

Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees.

Science.gov (United States)

Nye, Tom M W; Tang, Xiaoxian; Weyenberg, Grady; Yoshida, Ruriko

2017-12-01

Evolutionary relationships are represented by phylogenetic trees, and a phylogenetic analysis of gene sequences typically produces a collection of these trees, one for each gene in the analysis. Analysis of samples of trees is difficult due to the multi-dimensionality of the space of possible trees. In Euclidean spaces, principal component analysis is a popular method of reducing high-dimensional data to a low-dimensional representation that preserves much of the sample's structure. However, the space of all phylogenetic trees on a fixed set of species does not form a Euclidean vector space, and methods adapted to tree space are needed. Previous work introduced the notion of a principal geodesic in this space, analogous to the first principal component. Here we propose a geometric object for tree space similar to the [Formula: see text]th principal component in Euclidean space: the locus of the weighted Fréchet mean of [Formula: see text] vertex trees when the weights vary over the [Formula: see text]-simplex. We establish some basic properties of these objects, in particular showing that they have dimension [Formula: see text], and propose algorithms for projection onto these surfaces and for finding the principal locus associated with a sample of trees. Simulation studies demonstrate that these algorithms perform well, and analyses of two datasets, containing Apicomplexa and African coelacanth genomes respectively, reveal important structure from the second principal components.

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

Science.gov (United States)

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.

Derivation of the reduced reaction mechanisms of ozone depletion events in the Arctic spring by using concentration sensitivity analysis and principal component analysis

Directory of Open Access Journals (Sweden)

L. Cao

2016-12-01

Full Text Available The ozone depletion events (ODEs in the springtime Arctic have been investigated since the 1980s. It is found that the depletion of ozone is highly associated with an auto-catalytic reaction cycle, which involves mostly the bromine-containing compounds. Moreover, bromide stored in various substrates in the Arctic such as the underlying surface covered by ice and snow can be also activated by the absorbed HOBr. Subsequently, this leads to an explosive increase of the bromine amount in the troposphere, which is called the “bromine explosion mechanism”. In the present study, a reaction scheme representing the chemistry of ozone depletion and halogen release is processed with two different mechanism reduction approaches, namely, the concentration sensitivity analysis and the principal component analysis. In the concentration sensitivity analysis, the interdependence of the mixing ratios of ozone and principal bromine species on the rate of each reaction in the ODE mechanism is identified. Furthermore, the most influential reactions in different time periods of ODEs are also revealed. By removing 11 reactions with the maximum absolute values of sensitivities lower than 10 %, a reduced reaction mechanism of ODEs is derived. The onsets of each time period of ODEs in simulations using the original reaction mechanism and the reduced reaction mechanism are identical while the maximum deviation of the mixing ratio of principal bromine species between different mechanisms is found to be less than 1 %. By performing the principal component analysis on an array of the sensitivity matrices, the dependence of a particular species concentration on a combination of the reaction rates in the mechanism is revealed. Redundant reactions are indicated by principal components corresponding to small eigenvalues and insignificant elements in principal components with large eigenvalues. Through this investigation, aside from the 11 reactions identified as

APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

Directory of Open Access Journals (Sweden)

Khuat Thanh Tung

2016-11-01

Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.

PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

Directory of Open Access Journals (Sweden)

Hermita Bus Umar

2009-03-01

Full Text Available PCA (Principal Component Analysis are statistical techniques applied to a single set of variables when the researcher is interested in discovering which variables in the setform coherent subset that are relativity independent of one another.Variables that are correlated with one another but largely independent of other subset of variables are combined into factors. The Coals of PCA to which each variables is explained by each dimension. Step in PCA include selecting and mean measuring a set of variables, preparing the correlation matrix, extracting a set offactors from the correlation matrixs. Rotating the factor to increase interpretabilitv and interpreting the result.

Cardiorespiratory Coordination in Repeated Maximal Exercise

Directory of Open Access Journals (Sweden)

Sergi Garcia-Retortillo

2017-06-01

Full Text Available Increases in cardiorespiratory coordination (CRC after training with no differences in performance and physiological variables have recently been reported using a principal component analysis approach. However, no research has yet evaluated the short-term effects of exercise on CRC. The aim of this study was to delineate the behavior of CRC under different physiological initial conditions produced by repeated maximal exercises. Fifteen participants performed 2 consecutive graded and maximal cycling tests. Test 1 was performed without any previous exercise, and Test 2 6 min after Test 1. Both tests started at 0 W and the workload was increased by 25 W/min in males and 20 W/min in females, until they were not able to maintain the prescribed cycling frequency of 70 rpm for more than 5 consecutive seconds. A principal component (PC analysis of selected cardiovascular and cardiorespiratory variables (expired fraction of O2, expired fraction of CO2, ventilation, systolic blood pressure, diastolic blood pressure, and heart rate was performed to evaluate the CRC defined by the number of PCs in both tests. In order to quantify the degree of coordination, the information entropy was calculated and the eigenvalues of the first PC (PC1 were compared between tests. Although no significant differences were found between the tests with respect to the performed maximal workload (Wmax, maximal oxygen consumption (VO2 max, or ventilatory threshold (VT, an increase in the number of PCs and/or a decrease of eigenvalues of PC1 (t = 2.95; p = 0.01; d = 1.08 was found in Test 2 compared to Test 1. Moreover, entropy was significantly higher (Z = 2.33; p = 0.02; d = 1.43 in the last test. In conclusion, despite the fact that no significant differences were observed in the conventionally explored maximal performance and physiological variables (Wmax, VO2 max, and VT between tests, a reduction of CRC was observed in Test 2. These results emphasize the interest of CRC

Source Signals Separation and Reconstruction Following Principal Component Analysis

Directory of Open Access Journals (Sweden)

WANG Cheng

2014-02-01

Full Text Available For separation and reconstruction of source signals from observed signals problem, the physical significance of blind source separation modal and independent component analysis is not very clear, and its solution is not unique. Aiming at these disadvantages, a new linear and instantaneous mixing model and a novel source signals separation reconstruction solving method from observed signals based on principal component analysis (PCA are put forward. Assumption of this new model is statistically unrelated rather than independent of source signals, which is different from the traditional blind source separation model. A one-to-one relationship between linear and instantaneous mixing matrix of new model and linear compound matrix of PCA, and a one-to-one relationship between unrelated source signals and principal components are demonstrated using the concept of linear separation matrix and unrelated of source signals. Based on this theoretical link, source signals separation and reconstruction problem is changed into PCA of observed signals then. The theoretical derivation and numerical simulation results show that, in despite of Gauss measurement noise, wave form and amplitude information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal and normalized; only wave form information of unrelated source signal can be separated and reconstructed by PCA when linear mixing matrix is column orthogonal but not normalized, unrelated source signal cannot be separated and reconstructed by PCA when mixing matrix is not column orthogonal or linear.

Identifying the Component Structure of Satisfaction Scales by Nonlinear Principal Components Analysis

NARCIS (Netherlands)

Manisera, M.; Kooij, A.J. van der; Dusseldorp, E.

2010-01-01

The component structure of 14 Likert-type items measuring different aspects of job satisfaction was investigated using nonlinear Principal Components Analysis (NLPCA). NLPCA allows for analyzing these items at an ordinal or interval level. The participants were 2066 workers from five types of social

Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

DEFF Research Database (Denmark)

Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.

2017-01-01

Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA...

Fluoride in the Serra Geral Aquifer System: Source Evaluation Using Stable Isotopes and Principal Component Analysis

OpenAIRE

Nanni, Arthur Schmidt; Roisenberg, Ari; de Hollanda, Maria Helena Bezerra Maia; Marimon, Maria Paula Casagrande; Viero, Antonio Pedro; Scheibe, Luiz Fernando

2013-01-01

Groundwater with anomalous fluoride content and water mixture patterns were studied in the fractured Serra Geral Aquifer System, a basaltic to rhyolitic geological unit, using a principal component analysis interpretation of groundwater chemical data from 309 deep wells distributed in the Rio Grande do Sul State, Southern Brazil. A four-component model that explains 81% of the total variance in the Principal Component Analysis is suggested. Six hydrochemical groups were identified. δ18O and δ...

Integrative sparse principal component analysis of gene expression data.

Science.gov (United States)

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 Stability and the Rural Divide

Science.gov (United States)

Pendola, Andrew; Fuller, Edward J.

2018-01-01

This article examines the unique features of the rural school context and how these features are associated with the stability of principals in these schools. Given the small but growing literature on the characteristics of rural principals, this study presents an exploratory analysis of principal stability across schools located in different…

Research on Air Quality Evaluation based on Principal Component Analysis

Science.gov (United States)

Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

2018-01-01

Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis

Directory of Open Access Journals (Sweden)

Xiaoming Xu

2017-01-01

Full Text Available In traditional principle component analysis (PCA, because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

Pursit-evasion game analysis in a line of sight coordinate system

Science.gov (United States)

Shinar, J.; Davidovitz, A.

1985-01-01

The paper proposes to use line of sight coordinates for the analysis of pursuit-evasion games. The advantage of this method for two-target games is shown to be evident. As a demonstrative example the game of two identical cars is formulated and solved in such coordinate systems. A new type of singular surface, overlooked in a previous study of the same problem, is discovered as a consequence of the simplicity of the solution.

Coordination patterns of shoulder muscles during level-ground and incline wheelchair propulsion.

Science.gov (United States)

Qi, Liping; Wakeling, James; Grange, Simon; Ferguson-Pell, Martin

2013-01-01

The aim of this study was to investigate how the coordination patterns of shoulder muscles change with level-ground and incline wheelchair propulsion. Wheelchair kinetics and electromyography (EMG) activity of seven muscles were recorded with surface electrodes for 15 nondisabled subjects during wheelchair propulsion on a stationary ergometer and wooden ramp (4 degree slope). Kinetic data were measured by a SmartWheel. The kinetics variables and the onset, cessation, and duration of EMG activity from seven muscles were compared with paired t-tests for two sessions. Muscle coordination patterns across seven muscles were analyzed by principal component analysis. Push forces on the push rim and the percentage of push phase in the cycle increased significantly during incline propulsion. Propulsion condition and posture affected muscle coordination patterns. During incline propulsion, there was more intense and longer EMG activity of push muscles in the push phase and less EMG activity of the recovery muscles, which corresponded with the increased kinetic data total force output and longer push phase in the incline condition. This work establishes a framework for developing a performance feedback system for wheelchair users to better coordinate their muscle patterning activity.

Principal components

NARCIS (Netherlands)

Hallin, M.; Hörmann, S.; Piegorsch, W.; El Shaarawi, A.

2012-01-01

Principal Components are probably the best known and most widely used of all multivariate analysis techniques. The essential idea consists in performing a linear transformation of the observed k-dimensional variables in such a way that the new variables are vectors of k mutually orthogonal

Principal component analysis of FDG PET in amnestic MCI

International Nuclear Information System (INIS)

Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido; Salmaso, Dario; Morbelli, Silvia; Piccardo, Arnoldo; Larsson, Stig A.; Pagani, Marco

2008-01-01

The purpose of the study is to evaluate the combined accuracy of episodic memory performance and 18 F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). 18 F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and 18 F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

Principal component analysis of FDG PET in amnestic MCI

Energy Technology Data Exchange (ETDEWEB)

Nobili, Flavio; Girtler, Nicola; Brugnolo, Andrea; Dessi, Barbara; Rodriguez, Guido [University of Genoa, Clinical Neurophysiology, Department of Endocrinological and Medical Sciences, Genoa (Italy); S. Martino Hospital, Alzheimer Evaluation Unit, Genoa (Italy); S. Martino Hospital, Head-Neck Department, Genoa (Italy); Salmaso, Dario [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Morbelli, Silvia [University of Genoa, Nuclear Medicine Unit, Department of Internal Medicine, Genoa (Italy); Piccardo, Arnoldo [Galliera Hospital, Nuclear Medicine Unit, Department of Imaging Diagnostics, Genoa (Italy); Larsson, Stig A. [Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden); Pagani, Marco [CNR, Institute of Cognitive Sciences and Technologies, Rome (Italy); CNR, Institute of Cognitive Sciences and Technologies, Padua (Italy); Karolinska Hospital, Department of Nuclear Medicine, Stockholm (Sweden)

2008-12-15

The purpose of the study is to evaluate the combined accuracy of episodic memory performance and {sup 18}F-FDG PET in identifying patients with amnestic mild cognitive impairment (aMCI) converting to Alzheimer's disease (AD), aMCI non-converters, and controls. Thirty-three patients with aMCI and 15 controls (CTR) were followed up for a mean of 21 months. Eleven patients developed AD (MCI/AD) and 22 remained with aMCI (MCI/MCI). {sup 18}F-FDG PET volumetric regions of interest underwent principal component analysis (PCA) that identified 12 principal components (PC), expressed by coarse component scores (CCS). Discriminant analysis was performed using the significant PCs and episodic memory scores. PCA highlighted relative hypometabolism in PC5, including bilateral posterior cingulate and left temporal pole, and in PC7, including the bilateral orbitofrontal cortex, both in MCI/MCI and MCI/AD vs CTR. PC5 itself plus PC12, including the left lateral frontal cortex (LFC: BAs 44, 45, 46, 47), were significantly different between MCI/AD and MCI/MCI. By a three-group discriminant analysis, CTR were more accurately identified by PET-CCS + delayed recall score (100%), MCI/MCI by PET-CCS + either immediate or delayed recall scores (91%), while MCI/AD was identified by PET-CCS alone (82%). PET increased by 25% the correct allocations achieved by memory scores, while memory scores increased by 15% the correct allocations achieved by PET. Combining memory performance and {sup 18}F-FDG PET yielded a higher accuracy than each single tool in identifying CTR and MCI/MCI. The PC containing bilateral posterior cingulate and left temporal pole was the hallmark of MCI/MCI patients, while the PC including the left LFC was the hallmark of conversion to AD. (orig.)

Portable XRF and principal component analysis for bill characterization in forensic science

International Nuclear Information System (INIS)

Appoloni, C.R.; Melquiades, F.L.

2014-01-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. - Highlights: • The paper is about a direct method for bills discrimination by EDXRF and principal component analysis. • The bills are analyzed directly, without sample preparation and non destructively. • The results demonstrates that the methodology is feasible and could be applied in forensic science for identification of origin and false banknotes. • The novelty is that portable EDXRF is very fast and efficient for bills characterization

Iris recognition based on robust principal component analysis

Science.gov (United States)

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.

Achievements and barriers in the organ donation process: a critical analysis of donation coordinators' discourse.

Science.gov (United States)

Mercado-Martínez, Francisco J; Díaz-Medina, Blanca A; Hernández-Ibarra, Eduardo

2013-09-01

Donation coordinators play an important role in the success or failure of organ donation and transplant programs. Nevertheless, these professionals' perspectives and practices have hardly been explored, particularly in low- and middle-income countries. To examine donation coordinators' discourse on the organ donation process and the barriers they perceive. A critical qualitative study was carried out in Guadalajara, Mexico. Twelve donation coordinators from public and private hospitals participated. DATA GATHERING AND ANALYSIS: Data were gathered by using semistructured interviews and critical discourse analysis. Participants indicated that partial results have been achieved in deceased organ donation. Concomitantly, multiple obstacles have adversely affected the process and outcomes: at the structural level, the fragmentation of the health system and the scarcity of financial and material resources; at the relational level, nonegalitarian relationships between coordinators and hospital personnel; at the ideational level, the transplant domain and its specialists overshadow the donation domain and its coordinators. Negative images are associated with donation coordinators. Organ donation faces structural, relational, and ideational barriers; hence, complex interventions should be undertaken. Donation coordinators also should be recognized by the health system.

Definition of coordinate system for three-dimensional data analysis in the foot and ankle.

LENUS (Irish Health Repository)

Green, Connor

2012-02-01

BACKGROUND: Three-dimensional data is required to have advanced knowledge of foot and ankle kinematics and morphology. However, studies have been difficult to compare due to a lack of a common coordinate system. Therefore, we present a means to define a coordinate frame in the foot and ankle and its clinical application. MATERIALS AND METHODS: We carried out ten CT scans in anatomically normal feet and segmented them in a general purpose segmentation program for grey value images. 3D binary formatted stereolithography files were then create and imported to a shape analysis program for biomechanics which was used to define a coordinate frame and carry out morphological analysis of the forefoot. RESULTS: The coordinate frame had axes standard deviations of 2.36 which are comparable to axes variability of other joint coordinate systems. We showed a strong correlation between the lengths of the metatarsals within and between the columns of the foot and also among the lesser metatarsal lengths. CONCLUSION: We present a reproducible method for construction of a coordinate system for the foot and ankle with low axes variability. CLINICAL RELEVANCE: To conduct meaningful comparison between multiple subjects the coordinate system must be constant. This system enables such comparison and therefore will aid morphological data collection and improve preoperative planning accuracy.

Adaptive operational modal identification for slow linear time-varying structures based on frozen-in coefficient method and limited memory recursive principal component analysis

Science.gov (United States)

Wang, Cheng; Guan, Wei; Wang, J. Y.; Zhong, Bineng; Lai, Xiongming; Chen, Yewang; Xiang, Liang

2018-02-01

To adaptively identify the transient modal parameters for linear weakly damped structures with slow time-varying characteristics under unmeasured stationary random ambient loads, this paper proposes a novel operational modal analysis (OMA) method based on the frozen-in coefficient method and limited memory recursive principal component analysis (LMRPCA). In the modal coordinate, the random vibration response signals of mechanical weakly damped structures can be decomposed into the inner product of modal shapes and modal responses, from which the natural frequencies and damping ratios can be well acquired by single-degree-of-freedom (SDOF) identification approach such as FFT. Hence, for the OMA method based on principal component analysis (PCA), it becomes very crucial to examine the relation between the transformational matrix and the modal shapes matrix, to find the association between the principal components (PCs) matrix and the modal responses matrix, and to turn the operational modal parameter identification problem into PCA of the stationary random vibration response signals of weakly damped mechanical structures. Based on the theory of "time-freezing", the method of frozen-in coefficient, and the assumption of "short time invariant" and "quasistationary", the non-stationary random response signals of the weakly damped and slow linear time-varying structures (LTV) can approximately be seen as the stationary random response time series of weakly damped and linear time invariant structures (LTI) in a short interval. Thus, the adaptive identification of time-varying operational modal parameters is turned into decompositing the PCs of stationary random vibration response signals subsection of weakly damped mechanical structures after choosing an appropriate limited memory window. Finally, a three-degree-of-freedom (DOF) structure with weakly damped and slow time-varying mass is presented to illustrate this method of identification. Results show that the LMRPCA

Principal component analysis of solar flares in the soft X-ray flux

International Nuclear Information System (INIS)

Teuber, D.L.; Reichmann, E.J.; Wilson, R.M.; National Aeronautics and Space Administration, Huntsville, AL

1979-01-01

Principal component analysis is a technique for extracting the salient features from a mass of data. It applies, in particular, to the analysis of nonstationary ensembles. Computational schemes for this task require the evaluation of eigenvalues of matrices. We have used EISPACK Matrix Eigen System Routines on an IBM 360-75 to analyze full-disk proportional-counter data from the X-ray event analyzer (X-REA) which was part of the Skylab ATM/S-056 experiment. Empirical orthogonal functions have been derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time frames between June 1973 and January 1974. Results indicate that approximately 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve-fit through the raw data and a characterization of solar flares in the soft X-ray flux. Power spectra of the two largest eigenvectors reveal a previously reported periodicity of approximately 5 min. Similar signatures were also obtained from flares that are synchronized on maximum pulse-height when subjected to a principal component analysis. (orig.)

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4B. Paks NPP: Analysis/testing. Working material

International Nuclear Information System (INIS)

1995-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports on dynamic study of the main building of the Paks NPP; shake table investigation at Paks NPP and the Final report of the Co-ordinated Research Programme

Machine learning of frustrated classical spin models. I. Principal component analysis

Science.gov (United States)

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.

Common Data Format (CDF) and Coordinated Data Analysis Web (CDAWeb)

Science.gov (United States)

Candey, Robert M.

2010-01-01

The Coordinated Data Analysis Web (CDAWeb) data browsing system provides plotting, listing and open access v ia FTP, HTTP, and web services (REST, SOAP, OPeNDAP) for data from mo st NASA Heliophysics missions and is heavily used by the community. C ombining data from many instruments and missions enables broad resear ch analysis and correlation and coordination with other experiments a nd missions. Crucial to its effectiveness is the use of a standard se lf-describing data format, in this case, the Common Data Format (CDF) , also developed at the Space Physics Data facility , and the use of metadata standa rds (easily edited with SKTeditor ). CDAweb is based on a set of IDL routines, CDAWlib . . The CDF project also maintains soft ware and services for translating between many standard formats (CDF. netCDF, HDF, FITS, XML) .

Determining the number of components in principal components analysis: A comparison of statistical, crossvalidation and approximated methods

NARCIS (Netherlands)

Saccenti, E.; Camacho, J.

2015-01-01

Principal component analysis is one of the most commonly used multivariate tools to describe and summarize data. Determining the optimal number of components in a principal component model is a fundamental problem in many fields of application. In this paper we compare the performance of several

Patterns of shoulder muscle coordination vary between wheelchair propulsion techniques.

Science.gov (United States)

Qi, Liping; Wakeling, James; Grange, Simon; Ferguson-Pell, Martin

2014-05-01

This study investigated changes in the coordination patterns of shoulder muscles and wheelchair kinetics with different propulsion techniques by comparing wheelchair users' self-selected propulsion patterns with a semicircular pattern adopted after instruction. Wheelchair kinetics data were recorded by Smart(Wheel) on an ergometer, while EMG activity of seven muscles was recorded with surface electrodes on 15 able-bodied inexperienced participants. The performance data in two sessions, first using a self-selected and then the learned semicircular pattern, were compared with a paired t-test. Muscle coordination patterns across seven muscles were analyzed by principal component analysis. The semicircular pattern was characterized by significantly lower push frequency, significantly longer push length, push duration and push distance (p propulsion technique, synergistic muscles were recruited in distinct phases and displayed a clearer separation between activities in the push phase and recovery phase muscles. An instruction session in semicircular propulsion technique is recommended for the initial use of a wheelchair after an injury.

Principal component analysis of 1/fα noise

International Nuclear Information System (INIS)

Gao, J.B.; Cao Yinhe; Lee, J.-M.

2003-01-01

Principal component analysis (PCA) is a popular data analysis method. One of the motivations for using PCA in practice is to reduce the dimension of the original data by projecting the raw data onto a few dominant eigenvectors with large variance (energy). Due to the ubiquity of 1/f α noise in science and engineering, in this Letter we study the prototypical stochastic model for 1/f α processes--the fractional Brownian motion (fBm) processes using PCA, and find that the eigenvalues from PCA of fBm processes follow a power-law, with the exponent being the key parameter defining the fBm processes. We also study random-walk-type processes constructed from DNA sequences, and find that the eigenvalue spectrum from PCA of those random-walk processes also follow power-law relations, with the exponent characterizing the correlation structures of the DNA sequence. In fact, it is observed that PCA can automatically remove linear trends induced by patchiness in the DNA sequence, hence, PCA has a similar capability to the detrended fluctuation analysis. Implications of the power-law distributed eigenvalue spectrum are discussed

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

Science.gov (United States)

Foch, Eric; Milner, Clare E

2014-01-03

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

Modelling Monthly Mental Sickness Cases Using Principal ...

African Journals Online (AJOL)

The methodology was principal component analysis (PCA) using data obtained from the hospital to estimate regression coefficients and parameters. It was found that the principal component regression model that was derived was good predictive tool. The principal component regression model obtained was okay and this ...

Classification of Opium by UPLC-Q-TOF Analysis of Principal and Minor Alkaloids.

Science.gov (United States)

Liu, Cuimei; Hua, Zhendong; Bai, Yanping

2016-11-01

Opium is the raw material for the production of heroin, and the characterization of opium seizures through laboratory analysis is a valuable tool for law enforcement agencies to trace clandestine opium production and trafficking. In this work, a method for opium profiling based on the relative content of five principal and 14 minor opium alkaloids was developed and validated. UPLC-Q-TOF was adopted in alkaloid analysis for its high selectivity and sensitivity, which facilitated the sample preparation and testing. The authentic sample set consisted of 100 "Myanmar" and 45 "Afghanistan" opium seizures; based on the data set of the 19 alkaloid variables in them, a partial least squares discriminant analysis classification model was successfully achieved. Minor alkaloids were found to be vitally important for opium profiling, although combined use of both principal and minor alkaloids resulted in the best geographical classification result. The developed method realized a simple and accurate way to differentiate opium from Myanmar and Afghanistan, which may find wide application in forensic laboratories. © 2016 American Academy of Forensic Sciences.

A coordination class analysis of college students' judgments about animated motion

Science.gov (United States)

Thaden-Koch, Thomas Christian

The coordination class construct was invented by di5essa and Sherin to clarify what it means to learn and use scientific concepts. A coordination class is defined to consist of readout strategies, which guide observation, and the causal net, which contains knowledge necessary for making inferences from observations. A coordination class, as originally specified, reliably extracts a certain class of information from a variety of situations. The coordination class construct is relatively new. To examine its utility, transcripts of interviews with college students were analyzed in terms of the coordination class construct. In the interviews, students judged the realism of several computer animations depicting balls rolling on a pair of tracks. When shown animations with only one ball, students made judgments consistent with focusing on the ball's speed changes. Adding a second ball to each animation strongly affected judgments made by students taking introductory physics courses, but had a smaller effect on judgments made by students taking a psychology course. Reasoning was described in this analysis as the coordination of readouts about animations with causal net elements related to realistic motion. Decision-making was characterized both for individual students and for groups by the causal net elements expressed, by the types of readouts reported, and by the coordination processes involved. The coordination class construct was found useful for describing the elements and processes of student decision-making, but little evidence was found to suggest that the students studied possessed reliable coordination classes. Students' causal nets were found to include several appropriate expectations about realistic motion. Several students reached judgments that appeared contrary to their expectations and reported mutually incompatible expectations. Descriptions of students' decision-making processes often included faulty readouts, or feedback loops in which causal net

An application of principal component analysis to the clavicle and clavicle fixation devices.

Science.gov (United States)

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-03-26

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Career Paths in Educational Leadership: Examining Principals' Narratives

Science.gov (United States)

Parylo, Oksana; Zepeda, Sally J.; Bengtson, Ed

2012-01-01

This qualitative study analyzes the career path narratives of active principals. Structural narrative analysis was supplemented with sociolinguistic theory and thematic narrative analysis to discern the similarities and differences, as well as the patterns in the language used by participating principals. Thematic analysis found four major themes…

Tribological Performance Optimization of Electroless Ni-P-W Coating Using Weighted Principal Component Analysis

Directory of Open Access Journals (Sweden)

S. Roy

2013-12-01

Full Text Available The present investigation is an experimental approach to deposit electroless Ni-P-W coating on mild steel substrate and find out the optimum combination of various tribological performances on the basis of minimum friction and wear, using weighted principal component analysis (WPCA. In this study three main tribological parameters are chosen viz. load (A, speed (B and time(C. The responses are coefficient of friction and wear depth. Here Weighted Principal Component Analysis (WPCA method is adopted to convert the multi-responses into single performance index called multiple performance index (MPI and Taguchi L27 orthogonal array is used to design the experiment and to find the optimum combination of tribological parameters for minimum coefficient of friction and wear depth. ANOVA is performed to find the significance of the each tribological process parameters and their interactions. The EDX analysis, SEM and XRD are performed to study the composition and structural aspects.

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Directory of Open Access Journals (Sweden)

Yunfeng Dong

2017-01-01

Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

Vibrational spectroscopy and principal component analysis for conformational study of virus nucleic acids

Science.gov (United States)

Dovbeshko, G. I.; Repnytska, O. P.; Pererva, T.; Miruta, A.; Kosenkov, D.

2004-07-01

Conformation analysis of mutated DNA-bacteriophages (PLys-23, P23-2, P47- the numbers have been assigned by T. Pererva) induced by MS2 virus incorporated in Ecoli AB 259 Hfr 3000 has been done. Surface enhanced infrared absorption (SEIRA) spectroscopy and principal component analysis has been applied for solving this problem. The nucleic acids isolated from the mutated phages had a form of double stranded DNA with different modifications. The nucleic acid from phage P47 was undergone the structural rearrangement in the most degree. The shape and position ofthe fine structure of the Phosphate asymmetrical band at 1071cm-1 as well as the stretching OH vibration at 3370-3390 cm-1 has indicated to the appearance ofadditional OH-groups. The Z-form feature has been found in the base vibration region (1694 cm-1) and the sugar region (932 cm-1). A supposition about modification of structure of DNA by Z-fragments for P47 phage has been proposed. The P23-2 and PLys-23 phages have showed the numerous minor structural changes also. On the basis of SEIRA spectra we have determined the characteristic parameters of the marker bands of nucleic acid used for construction of principal components. Contribution of different spectral parameters of nucleic acids to principal components has been estimated.

A multi-dimensional functional principal components analysis of EEG data.

Science.gov (United States)

Hasenstab, Kyle; Scheffler, Aaron; Telesca, Donatello; Sugar, Catherine A; Jeste, Shafali; DiStefano, Charlotte; Şentürk, Damla

2017-09-01

The electroencephalography (EEG) data created in event-related potential (ERP) experiments have a complex high-dimensional structure. Each stimulus presentation, or trial, generates an ERP waveform which is an instance of functional data. The experiments are made up of sequences of multiple trials, resulting in longitudinal functional data and moreover, responses are recorded at multiple electrodes on the scalp, adding an electrode dimension. Traditional EEG analyses involve multiple simplifications of this structure to increase the signal-to-noise ratio, effectively collapsing the functional and longitudinal components by identifying key features of the ERPs and averaging them across trials. Motivated by an implicit learning paradigm used in autism research in which the functional, longitudinal, and electrode components all have critical interpretations, we propose a multidimensional functional principal components analysis (MD-FPCA) technique which does not collapse any of the dimensions of the ERP data. The proposed decomposition is based on separation of the total variation into subject and subunit level variation which are further decomposed in a two-stage functional principal components analysis. The proposed methodology is shown to be useful for modeling longitudinal trends in the ERP functions, leading to novel insights into the learning patterns of children with Autism Spectrum Disorder (ASD) and their typically developing peers as well as comparisons between the two groups. Finite sample properties of MD-FPCA are further studied via extensive simulations. © 2017, The International Biometric Society.

Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

Science.gov (United States)

Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

2018-03-01

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

Science.gov (United States)

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Characterization of reflectance variability in the industrial paint application of automotive metallic coatings by using principal component analysis

Science.gov (United States)

Medina, José M.; Díaz, José A.

2013-05-01

We have applied principal component analysis to examine trial-to-trial variability of reflectances of automotive coatings that contain effect pigments. Reflectance databases were measured from different color batch productions using a multi-angle spectrophotometer. A method to classify the principal components was used based on the eigenvalue spectra. It was found that the eigenvalue spectra follow distinct power laws and depend on the detection angle. The scaling exponent provided an estimation of the correlation between reflectances and it was higher near specular reflection, suggesting a contribution from the deposition of effect pigments. Our findings indicate that principal component analysis can be a useful tool to classify different sources of spectral variability in color engineering.

SVAMP: Sequence variation analysis, maps and phylogeny

KAUST Repository

Naeem, Raeece; Hidayah, Lailatul; Preston, Mark D.; Clark, Taane G.; Pain, Arnab

2014-01-01

Summary: SVAMP is a stand-alone desktop application to visualize genomic variants (in variant call format) in the context of geographical metadata. Users of SVAMP are able to generate phylogenetic trees and perform principal coordinate analysis

Time Series Analysis of 3D Coordinates Using Nonstochastic Observations

NARCIS (Netherlands)

Velsink, H.

2016-01-01

Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to formulate constraints on

Time Series Analysis of 3D Coordinates Using Nonstochastic Observations

NARCIS (Netherlands)

Hiddo Velsink

2016-01-01

From the article: Abstract Adjustment and testing of a combination of stochastic and nonstochastic observations is applied to the deformation analysis of a time series of 3D coordinates. Nonstochastic observations are constant values that are treated as if they were observations. They are used to

The Assessment of Hydrogen Energy Systems for Fuel Cell Vehicles Using Principal Componenet Analysis and Cluster Analysis

DEFF Research Database (Denmark)

Ren, Jingzheng; Tan, Shiyu; Dong, Lichun

2012-01-01

and analysis of the hydrogen systems is meaningful for decision makers to select the best scenario. principal component analysis (PCA) has been used to evaluate the integrated performance of different hydrogen energy systems and select the best scenario, and hierarchical cluster analysis (CA) has been used...... for transportation of hydrogen, hydrogen gas tank for the storage of hydrogen at refueling stations, and gaseous hydrogen as power energy for fuel cell vehicles has been recognized as the best scenario. Also, the clustering results calculated by CA are consistent with those determined by PCA, denoting...

Oil classification using X-ray scattering and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T., E-mail: dani.almeida84@gmail.com, E-mail: ricardo@lin.ufrj.br, E-mail: amandass@bioqmed.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ (Brazil); Oliveira, Davi F.; Anjos, Marcelino J., E-mail: davi.oliveira@uerj.br, E-mail: marcelin@uerj.br [Universidade do Estado do Rio de Janeiro (UERJ), Rio de Janeiro, RJ (Brazil). Inst. de Fisica Armando Dias Tavares

2015-07-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

Oil classification using X-ray scattering and principal component analysis

International Nuclear Information System (INIS)

Almeida, Danielle S.; Souza, Amanda S.; Lopes, Ricardo T.; Oliveira, Davi F.; Anjos, Marcelino J.

2015-01-01

X-ray scattering techniques have been considered promising for the classification and characterization of many types of samples. This study employed this technique combined with chemical analysis and multivariate analysis to characterize 54 vegetable oil samples (being 25 olive oils)with different properties obtained in commercial establishments in Rio de Janeiro city. The samples were chemically analyzed using the following indexes: iodine, acidity, saponification and peroxide. In order to obtain the X-ray scattering spectrum, an X-ray tube with a silver anode operating at 40kV and 50 μA was used. The results showed that oils cab ne divided in tow large groups: olive oils and non-olive oils. Additionally, in a multivariate analysis (Principal Component Analysis - PCA), two components were obtained and accounted for more than 80% of the variance. One component was associated with chemical parameters and the other with scattering profiles of each sample. Results showed that use of X-ray scattering spectra combined with chemical analysis and PCA can be a fast, cheap and efficient method for vegetable oil characterization. (author)

An application of principal component analysis to the clavicle and clavicle fixation devices

Directory of Open Access Journals (Sweden)

Fitzpatrick David

2010-03-01

Full Text Available Abstract Background Principal component analysis (PCA enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and methods Twenty-one high-resolution computerized tomography scans of the clavicle were reconstructed and analyzed using a specifically developed statistical software package. After performing statistical shape analysis, PCA was applied to study the factors that account for anatomical variation. Results The first principal component representing size accounted for 70.5 percent of anatomical variation. The addition of a further three principal components accounted for almost 87 percent. Using statistical shape analysis, clavicles in males have a greater lateral depth and are longer, wider and thicker than in females. However, the sternal angle in females is larger than in males. PCA confirmed these differences between genders but also noted that men exhibit greater variance and classified clavicles into five morphological groups. Discussion And Conclusions This unique approach is the first that standardizes a clavicular orientation. It provides information that is useful to both, the biomedical engineer and clinician. Other applications include implant design with regard to modifying current or designing future clavicle fixation devices. Our findings support the need for further development of clavicle fixation devices and the questioning of whether gender-specific devices are necessary.

Influencing Factors of Catering and Food Service Industry Based on Principal Component Analysis

OpenAIRE

Zi Tang

2014-01-01

Scientific analysis of influencing factors is of great importance for the healthy development of catering and food service industry. This study attempts to present a set of critical indicators for evaluating the contribution of influencing factors to catering and food service industry in the particular context of Harbin City, Northeast China. Ten indicators that correlate closely with catering and food service industry were identified and performed by the principal component analysis method u...

Coordination of Reach-to-Grasp Kinematics in Individuals With Childhood-Onset Dystonia Due to Hemiplegic Cerebral Palsy.

Science.gov (United States)

Kukke, Sahana N; Curatalo, Lindsey A; de Campos, Ana Carolina; Hallett, Mark; Alter, Katharine E; Damiano, Diane L

2016-05-01

Functional reaching is impaired in dystonia. Here, we analyze upper extremity kinematics to quantify timing and coordination abnormalities during unimanual reach-to-grasp movements in individuals with childhood-onset unilateral wrist dystonia. Kinematics were measured during movements of both upper limbs in a patient group ( n = 11, age = 17.5 ±5 years), and a typically developing control group ( n = 9, age = 16.6 ±5 years). Hand aperture was computed to study the coordination of reach and grasp. Time-varying joint synergies within one upper limb were calculated using a novel technique based on principal component analysis to study intra-limb coordination. In the non-dominant arm, results indicate reduced coordination between reach and grasp in patients who could not lift the grasped object compared to those who could lift it. Lifters exhibit incoordination in distal upper extremity joints later in the movement and non-lifters lacked coordination throughout the movement and in the whole upper limb. The amount of atypical coordination correlates with dystonia severity in patients. Reduced coordination during movement may reflect deficits in the execution of simultaneous movements, motor planning, or muscle activation. Rehabilitation efforts can focus on particular time points when kinematic patterns deviate abnormally to improve functional reaching in individuals with childhood-onset dystonia.

Location and characterisation of pollution sites by principal ...

African Journals Online (AJOL)

Location and characterisation of pollution sites by principal component analysis of trace contaminants in a slightly polluted seasonal river: a case study of the Arenales River (Salta, Argentina) ... Keywords: trace element contamination, water quality, principal component analysis, Arenales River, Salta, Argentina ...

Dynamic of consumer groups and response of commodity markets by principal component analysis

Science.gov (United States)

Nobi, Ashadun; Alam, Shafiqul; Lee, Jae Woo

2017-09-01

This study investigates financial states and group dynamics by applying principal component analysis to the cross-correlation coefficients of the daily returns of commodity futures. The eigenvalues of the cross-correlation matrix in the 6-month timeframe displays similar values during 2010-2011, but decline following 2012. A sharp drop in eigenvalue implies the significant change of the market state. Three commodity sectors, energy, metals and agriculture, are projected into two dimensional spaces consisting of two principal components (PC). We observe that they form three distinct clusters in relation to various sectors. However, commodities with distinct features have intermingled with one another and scattered during severe crises, such as the European sovereign debt crises. We observe the notable change of the position of two dimensional spaces of groups during financial crises. By considering the first principal component (PC1) within the 6-month moving timeframe, we observe that commodities of the same group change states in a similar pattern, and the change of states of one group can be used as a warning for other group.

Principal Component Analysis of Some Quantitative and Qualitative Traits in Iranian Spinach Landraces

Directory of Open Access Journals (Sweden)

Mohebodini Mehdi

2017-08-01

Full Text Available Landraces of spinach in Iran have not been sufficiently characterised for their morpho-agronomic traits. Such characterisation would be helpful in the development of new genetically improved cultivars. In this study 54 spinach accessions collected from the major spinach growing areas of Iran were evaluated to determine their phenotypic diversity profile of spinach genotypes on the basis of 10 quantitative and 9 qualitative morpho-agronomic traits. High coefficients of variation were recorded in some quantitative traits (dry yield and leaf area and all of the qualitative traits. Using principal component analysis, the first four principal components with eigen-values more than 1 contributed 87% of the variability among accessions for quantitative traits, whereas the first four principal components with eigen-values more than 0.8 contributed 79% of the variability among accessions for qualitative traits. The most important relations observed on the first two principal components were a strong positive association between leaf width and petiole length; between leaf length and leaf numbers in flowering; and among fresh yield, dry yield and petiole diameter; a near zero correlation between days to flowering with leaf width and petiole length. Prickly seeds, high percentage of female plants, smooth leaf texture, high numbers of leaves at flowering, greygreen leaves, erect petiole attitude and long petiole length are important characters for spinach breeding programmes.

Principal Component Analysis Based Two-Dimensional (PCA-2D) Correlation Spectroscopy: PCA Denoising for 2D Correlation Spectroscopy

International Nuclear Information System (INIS)

Jung, Young Mee

2003-01-01

Principal component analysis based two-dimensional (PCA-2D) correlation analysis is applied to FTIR spectra of polystyrene/methyl ethyl ketone/toluene solution mixture during the solvent evaporation. Substantial amount of artificial noise were added to the experimental data to demonstrate the practical noise-suppressing benefit of PCA-2D technique. 2D correlation analysis of the reconstructed data matrix from PCA loading vectors and scores successfully extracted only the most important features of synchronicity and asynchronicity without interference from noise or insignificant minor components. 2D correlation spectra constructed with only one principal component yield strictly synchronous response with no discernible a asynchronous features, while those involving at least two or more principal components generated meaningful asynchronous 2D correlation spectra. Deliberate manipulation of the rank of the reconstructed data matrix, by choosing the appropriate number and type of PCs, yields potentially more refined 2D correlation spectra

Fault detection of flywheel system based on clustering and principal component analysis

Directory of Open Access Journals (Sweden)

Wang Rixin

2015-12-01

Full Text Available Considering the nonlinear, multifunctional properties of double-flywheel with closed-loop control, a two-step method including clustering and principal component analysis is proposed to detect the two faults in the multifunctional flywheels. At the first step of the proposed algorithm, clustering is taken as feature recognition to check the instructions of “integrated power and attitude control” system, such as attitude control, energy storage or energy discharge. These commands will ask the flywheel system to work in different operation modes. Therefore, the relationship of parameters in different operations can define the cluster structure of training data. Ordering points to identify the clustering structure (OPTICS can automatically identify these clusters by the reachability-plot. K-means algorithm can divide the training data into the corresponding operations according to the reachability-plot. Finally, the last step of proposed model is used to define the relationship of parameters in each operation through the principal component analysis (PCA method. Compared with the PCA model, the proposed approach is capable of identifying the new clusters and learning the new behavior of incoming data. The simulation results show that it can effectively detect the faults in the multifunctional flywheels system.

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

Science.gov (United States)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Principal component analysis for neural electron/jet discrimination in highly segmented calorimeters

International Nuclear Information System (INIS)

Vassali, M.R.; Seixas, J.M.

2001-01-01

A neural electron/jet discriminator based on calorimetry is developed for the second-level trigger system of the ATLAS detector. As preprocessing of the calorimeter information, a principal component analysis is performed on each segment of the two sections (electromagnetic and hadronic) of the calorimeter system, in order to reduce significantly the dimension of the input data space and fully explore the detailed energy deposition profile, which is provided by the highly-segmented calorimeter system. It is shown that projecting calorimeter data onto 33 segmented principal components, the discrimination efficiency of the neural classifier reaches 98.9% for electrons (with only 1% of false alarm probability). Furthermore, restricting data projection onto only 9 components, an electron efficiency of 99.1% is achieved (with 3% of false alarm), which confirms that a fast triggering system may be designed using few components

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

Science.gov (United States)

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

Identification of a cis-regulatory element by transient analysis of co-ordinately regulated genes

Directory of Open Access Journals (Sweden)

Allan Andrew C

2008-07-01

Full Text Available Abstract Background Transcription factors (TFs co-ordinately regulate target genes that are dispersed throughout the genome. This co-ordinate regulation is achieved, in part, through the interaction of transcription factors with conserved cis-regulatory motifs that are in close proximity to the target genes. While much is known about the families of transcription factors that regulate gene expression in plants, there are few well characterised cis-regulatory motifs. In Arabidopsis, over-expression of the MYB transcription factor PAP1 (PRODUCTION OF ANTHOCYANIN PIGMENT 1 leads to transgenic plants with elevated anthocyanin levels due to the co-ordinated up-regulation of genes in the anthocyanin biosynthetic pathway. In addition to the anthocyanin biosynthetic genes, there are a number of un-associated genes that also change in expression level. This may be a direct or indirect consequence of the over-expression of PAP1. Results Oligo array analysis of PAP1 over-expression Arabidopsis plants identified genes co-ordinately up-regulated in response to the elevated expression of this transcription factor. Transient assays on the promoter regions of 33 of these up-regulated genes identified eight promoter fragments that were transactivated by PAP1. Bioinformatic analysis on these promoters revealed a common cis-regulatory motif that we showed is required for PAP1 dependent transactivation. Conclusion Co-ordinated gene regulation by individual transcription factors is a complex collection of both direct and indirect effects. Transient transactivation assays provide a rapid method to identify direct target genes from indirect target genes. Bioinformatic analysis of the promoters of these direct target genes is able to locate motifs that are common to this sub-set of promoters, which is impossible to identify with the larger set of direct and indirect target genes. While this type of analysis does not prove a direct interaction between protein and DNA

Using principal component analysis to understand the variability of PDS 456

Science.gov (United States)

Parker, M. L.; Reeves, J. N.; Matzeu, G. A.; Buisson, D. J. K.; Fabian, A. C.

2018-02-01

We present a spectral-variability analysis of the low-redshift quasar PDS 456 using principal component analysis. In the XMM-Newton data, we find a strong peak in the first principal component at the energy of the Fe absorption line from the highly blueshifted outflow. This indicates that the absorption feature is more variable than the continuum, and that it is responding to the continuum. We find qualitatively different behaviour in the Suzaku data, which is dominated by changes in the column density of neutral absorption. In this case, we find no evidence of the absorption produced by the highly ionized gas being correlated with this variability. Additionally, we perform simulations of the source variability, and demonstrate that PCA can trivially distinguish between outflow variability correlated, anticorrelated and un-correlated with the continuum flux. Here, the observed anticorrelation between the absorption line equivalent width and the continuum flux may be due to the ionization of the wind responding to the continuum. Finally, we compare our results with those found in the narrow-line Seyfert 1 IRAS 13224-3809. We find that the Fe K UFO feature is sharper and more prominent in PDS 456, but that it lacks the lower energy features from lighter elements found in IRAS 13224-3809, presumably due to differences in ionization.

Classification of calcium supplements through application of principal component analysis: a study by inaa and aas

International Nuclear Information System (INIS)

Waheed, S.; Rahman, S.; Siddique, N.

2013-01-01

Different types of Ca supplements are available in the local markets of Pakistan. It is sometimes difficult to classify these with respect to their composition. In the present work principal component analysis (PCA) technique was applied to classify different Ca supplements on the basis of their elemental data obtained using instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS) techniques. The graphical representation of principal component analysis (PCA) scores utilizing intricate analytical data successfully generated four different types of Ca supplements with compatible samples grouped together. These included Ca supplements with CaCO/sub 3/as Ca source along with vitamin C, the supplements with CaCO/sub 3/ as Ca source along with vitamin D, Supplements with Ca from bone meal and supplements with chelated calcium. (author)

Towards automatic analysis of dynamic radionuclide studies using principal-components factor analysis

International Nuclear Information System (INIS)

Nigran, K.S.; Barber, D.C.

1985-01-01

A method is proposed for automatic analysis of dynamic radionuclide studies using the mathematical technique of principal-components factor analysis. This method is considered as a possible alternative to the conventional manual regions-of-interest method widely used. The method emphasises the importance of introducing a priori information into the analysis about the physiology of at least one of the functional structures in a study. Information is added by using suitable mathematical models to describe the underlying physiological processes. A single physiological factor is extracted representing the particular dynamic structure of interest. Two spaces 'study space, S' and 'theory space, T' are defined in the formation of the concept of intersection of spaces. A one-dimensional intersection space is computed. An example from a dynamic 99 Tcsup(m) DTPA kidney study is used to demonstrate the principle inherent in the method proposed. The method requires no correction for the blood background activity, necessary when processing by the manual method. The careful isolation of the kidney by means of region of interest is not required. The method is therefore less prone to operator influence and can be automated. (author)

Application of principal component analysis to time series of daily air pollution and mortality

NARCIS (Netherlands)

Quant C; Fischer P; Buringh E; Ameling C; Houthuijs D; Cassee F; MGO

2004-01-01

We investigated whether cause-specific daily mortality can be attributed to specific sources of air pollution. To construct indicators of source-specific air pollution, we applied a principal component analysis (PCA) on routinely collected air pollution data in the Netherlands during the period

Reel Principals: A Descriptive Content Analysis of the Images of School Principals Depicted in Movies from 1997-2009

Science.gov (United States)

Wolfrom, Katy J.

2010-01-01

According to Glanz's early research, school principals have been depicted as autocrats, bureaucrats, buffoons, and/or villains in movies from 1950 to 1996. The purpose of this study was to determine if these stereotypical characterizations of school principals have continued in films from 1997-2009, or if more favorable images have emerged that…

Stigmergy based behavioural coordination for satellite clusters

Science.gov (United States)

Tripp, Howard; Palmer, Phil

2010-04-01

Multi-platform swarm/cluster missions are an attractive prospect for improved science return as they provide a natural capability for temporal, spatial and signal separation with further engineering and economic advantages. As spacecraft numbers increase and/or the round-trip communications delay from Earth lengthens, the traditional "remote-control" approach begins to break down. It is therefore essential to push control into space; to make spacecraft more autonomous. An autonomous group of spacecraft requires coordination, but standard terrestrial paradigms such as negotiation, require high levels of inter-spacecraft communication, which is nontrivial in space. This article therefore introduces the principals of stigmergy as a novel method for coordinating a cluster. Stigmergy is an agent-based, behavioural approach that allows for infrequent communication with decisions based on local information. Behaviours are selected dynamically using a genetic algorithm onboard. supervisors/ground stations occasionally adjust parameters and disseminate a "common environment" that is used for local decisions. After outlining the system, an analysis of some crucial parameters such as communications overhead and number of spacecraft is presented to demonstrate scalability. Further scenarios are considered to demonstrate the natural ability to deal with dynamic situations such as the failure of spacecraft, changing mission objectives and responding to sudden bursts of high priority tasks.

Comparative Analysis of Principals' Management Strategies in ...

African Journals Online (AJOL)

It was recommended among others that principals of secondary schools should adopt all the management strategies in this study as this will improve school administration and consequently students‟ academic performance. Keywords: Management Strategies; Secondary Schools; Administrative Effectiveness ...

Value-Driven Risk Analysis of Coordination Models

NARCIS (Netherlands)

Ionita, Dan; Gordijn, Jaap; Yesuf, Ahmed Seid; Wieringa, Roelf J.

2016-01-01

Coordination processes are business processes that involve independent profit-and-loss responsible business actors who collectively provide something of value to a customer. Coordination processes are meant to be profitable for the business actors that execute them. However, because business actors

Color evaluation of seventeen European unifloral honey types by means of spectrophotometrically determined CIE L*Cab*h(ab)° chromaticity coordinates.

Science.gov (United States)

Tuberoso, Carlo Ignazio Giovanni; Jerković, Igor; Sarais, Giorgia; Congiu, Francesca; Marijanović, Zvonimir; Kuś, Piotr Marek

2014-02-15

CIE (Commission Internationale de l'Eclairage) L(*)Cab(*)h(ab)° color coordinates for 305 samples of 17 unifloral honeys types (asphodel, buckwheat, black locust, sweet chestnut, citrus, eucalyptus, Garland thorn, honeydew, heather, lime, mint, rapeseed, sage, strawberry tree, sulla flower, savory and thistle) from different geographic locations in Europe were spectrophotometrically assessed and statistically evaluated. Preliminary separation of unifloral honeys was obtained by means of L(*)-C(ab)(*) color coordination correlation. Hierarchical Cluster Analysis (HCA) revealed an expected segregation of the honeys types according to their chromatic characteristics. Principal Component Analysis (PCA) allowed to obtain a more defined distinction of the 17 unifloral honey types, particularly when using 3D graphics. CIE L(*)C(ab)(*)hab(*) color coordinates were useful for the identification of several honey types. The proposed method represents a simple and efficient procedure that can be used as a basis for the authentication of unifloral honeys worldwide. Copyright © 2013 Elsevier Ltd. All rights reserved.

k-t PCA: temporally constrained k-t BLAST reconstruction using principal component analysis

DEFF Research Database (Denmark)

Pedersen, Henrik; Kozerke, Sebastian; Ringgaard, Steffen

2009-01-01

in applications exhibiting a broad range of temporal frequencies such as free-breathing myocardial perfusion imaging. We show that temporal basis functions calculated by subjecting the training data to principal component analysis (PCA) can be used to constrain the reconstruction such that the temporal resolution...... is improved. The presented method is called k-t PCA....

A Principal-Agent Analysis of the Family: Implications for the Welfare State

OpenAIRE

Munro, Lauchlan

1999-01-01

The principal-agent literature has focussed on situations where both principal and agent are assumed to be capable of defining and defending their own interests. The principal-agent literature has thus ignored an important set of cases where the principal is incapable of acting on her own behalf, and so is assigned an agent by law or custom. Such cases account for around 40% of humanity and for a similarly substantial proportion of all principal-agent interactions. This paper applies principa...

Effects of physiotherapy treatment on knee osteoarthritis gait data using principal component analysis.

Science.gov (United States)

Gaudreault, Nathaly; Mezghani, Neila; Turcot, Katia; Hagemeister, Nicola; Boivin, Karine; de Guise, Jacques A

2011-03-01

Interpreting gait data is challenging due to intersubject variability observed in the gait pattern of both normal and pathological populations. The objective of this study was to investigate the impact of using principal component analysis for grouping knee osteoarthritis (OA) patients' gait data in more homogeneous groups when studying the effect of a physiotherapy treatment. Three-dimensional (3D) knee kinematic and kinetic data were recorded during the gait of 29 participants diagnosed with knee OA before and after they received 12 weeks of physiotherapy treatment. Principal component analysis was applied to extract groups of knee flexion/extension, adduction/abduction and internal/external rotation angle and moment data. The treatment's effect on parameters of interest was assessed using paired t-tests performed before and after grouping the knee kinematic data. Increased quadriceps and hamstring strength was observed following treatment (Pphysiotherapy on gait mechanics of knee osteoarthritis patients may be masked or underestimated if kinematic data are not separated into more homogeneous groups when performing pre- and post-treatment comparisons. Copyright © 2010 Elsevier Ltd. All rights reserved.

Study of Seasonal Variation in Groundwater Quality of Sagar City (India by Principal Component Analysis

Directory of Open Access Journals (Sweden)

Hemant Pathak

2011-01-01

Full Text Available Groundwater is one of the major resources of the drinking water in Sagar city (India.. In this study 15 sampling station were selected for the investigations on 14 chemical parameters. The work was carried out during different months of the pre-monsoon, monsoon and post-monsoon seasons in June 2009 to June 2010. The multivariate statistics such as principal component and cluster analysis were applied to the datasets to investigate seasonal variations in groundwater quality. Principal axis factoring has been used to observe the mode of association of parameters and their interrelationships, for evaluating water quality. Average value of BOD, COD, ammonia and iron was high during entire study period. Elevated values of BOD and ammonia in monsoon, slightly more value of BOD in post-monsoon, BOD, ammonia and iron in pre-monsoon period reflected contribution on temporal effect on groundwater. Results of principal component analysis evinced that all the parameters equally and significantly contribute to groundwater quality variations. Factor 1 and factor 2 analysis revealed the DO value deteriorate due to organic load (BOD/Ammonia in different seasons. Hierarchical cluster analysis grouped 15 stations into four clusters in monsoon, five clusters in post-monsoon and five clusters in pre-monsoon with similar water quality features. Clustered group at monsoon, post-monsoon and pre-monsoon consisted one station exhibiting significant spatial variation in physicochemical composition. The anthropogenic nitrogenous species, as fallout from modernization activities. The study indicated that the groundwater sufficiently well oxygenated and nutrient-rich in study places.

Finger crease pattern recognition using Legendre moments and principal component analysis

Science.gov (United States)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Coordination, non-coordination and semi-coordination of perchlorates in the lanthanide adducts Ln (CLO4)3. 6dmba

International Nuclear Information System (INIS)

Tfouni, E.; Giesbrecht, E.

1983-01-01

The coordination or not of the perchlorate anions in the previously reported Ln(CLO 4 ) 3 .6 dmba is discussed. The analysis of the infrared spectral data and molar conductance data indicate that they may be formulated as [Ln(dmba) 6 (CLO 4 )n] (CLO 4 ) sub(3-n), n=0,1,2. The individual compounds may be a mixture of species with different n values and/or pure compounds with semi-coordinated and non-coordinated perchlorates. (Author) [pt

African-American Female Students and STEM: Principals' Leadership Perspectives

Science.gov (United States)

Sampson, Kristin Morgan

As the U.S. becomes more diverse, school leaders, major corporations, and areas of national defense continue to investigate science, technology, engineering and math (STEM) education issues. African-American female students have historically been underrepresented in STEM fields, yet educational leadership research, examining this population is limited. The purpose of this qualitative study was to explore how principals support African-American female students in schools with a STEM program. The Critical Race Theory (CRT)was used as a theoretical framework to highlight the inadequacies to support educational inequalities. The application of the CRT in this study is due to the embedded inequality practices within the educational system, that have resulted in the underrepresentation of African-American female students in STEM. To complement CRT, the transformative leadership model was also utilized to examine the emancipatory leadership practices principals utilized. These theories framed the context of this study by recognizing the need to address how support is actualized to African-American female students in STEM by their principals. A case study approach was an appropriate method to answer the two research questions, 1) How do principals feel they support African-American female students in their STEM programs? and 2) What practices do principals engage in that support underrepresented students in STEM? This approach intended to uncover how a principal leads a multifaceted population of underrepresented students in STEM programs. Two principals of STEM schools, where more than 50% of the population were African-American, were interviewed and observed completing daily operations at community-wide events. The STEM Coordinators and a teacher were also interviewed, and test scores were examined to provide further information about the STEM program, and public records were obtained to analyze the principals' means of communication. I found that principals supported

Development of motion image prediction method using principal component analysis

International Nuclear Information System (INIS)

Chhatkuli, Ritu Bhusal; Demachi, Kazuyuki; Kawai, Masaki; Sakakibara, Hiroshi; Kamiaka, Kazuma

2012-01-01

Respiratory motion can induce the limit in the accuracy of area irradiated during lung cancer radiation therapy. Many methods have been introduced to minimize the impact of healthy tissue irradiation due to the lung tumor motion. The purpose of this research is to develop an algorithm for the improvement of image guided radiation therapy by the prediction of motion images. We predict the motion images by using principal component analysis (PCA) and multi-channel singular spectral analysis (MSSA) method. The images/movies were successfully predicted and verified using the developed algorithm. With the proposed prediction method it is possible to forecast the tumor images over the next breathing period. The implementation of this method in real time is believed to be significant for higher level of tumor tracking including the detection of sudden abdominal changes during radiation therapy. (author)

Towards successful coordination of electronic health record based-referrals: a qualitative analysis.

Science.gov (United States)

Hysong, Sylvia J; Esquivel, Adol; Sittig, Dean F; Paul, Lindsey A; Espadas, Donna; Singh, Simran; Singh, Hardeep

2011-07-27

Successful subspecialty referrals require considerable coordination and interactive communication among the primary care provider (PCP), the subspecialist, and the patient, which may be challenging in the outpatient setting. Even when referrals are facilitated by electronic health records (EHRs) (i.e., e-referrals), lapses in patient follow-up might occur. Although compelling reasons exist why referral coordination should be improved, little is known about which elements of the complex referral coordination process should be targeted for improvement. Using Okhuysen & Bechky's coordination framework, this paper aims to understand the barriers, facilitators, and suggestions for improving communication and coordination of EHR-based referrals in an integrated healthcare system. We conducted a qualitative study to understand coordination breakdowns related to e-referrals in an integrated healthcare system and examined work-system factors that affect the timely receipt of subspecialty care. We conducted interviews with seven subject matter experts and six focus groups with a total of 30 PCPs and subspecialists at two tertiary care Department of Veterans Affairs (VA) medical centers. Using techniques from grounded theory and content analysis, we identified organizational themes that affected the referral process. Four themes emerged: lack of an institutional referral policy, lack of standardization in certain referral procedures, ambiguity in roles and responsibilities, and inadequate resources to adapt and respond to referral requests effectively. Marked differences in PCPs' and subspecialists' communication styles and individual mental models of the referral processes likely precluded the development of a shared mental model to facilitate coordination and successful referral completion. Notably, very few barriers related to the EHR were reported. Despite facilitating information transfer between PCPs and subspecialists, e-referrals remain prone to coordination

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

Science.gov (United States)

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.

Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis

Science.gov (United States)

Shah, Syed Muhammad Saqlain; Batool, Safeera; Khan, Imran; Ashraf, Muhammad Usman; Abbas, Syed Hussnain; Hussain, Syed Adnan

2017-09-01

Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzerland and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.

Neural Network for Principal Component Analysis with Applications in Image Compression

Directory of Open Access Journals (Sweden)

Luminita State

2007-04-01

Full Text Available Classical feature extraction and data projection methods have been extensively investigated in the pattern recognition and exploratory data analysis literature. Feature extraction and multivariate data projection allow avoiding the "curse of dimensionality", improve the generalization ability of classifiers and significantly reduce the computational requirements of pattern classifiers. During the past decade a large number of artificial neural networks and learning algorithms have been proposed for solving feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required. Principal Component Analysis, also called Karhunen-Loeve transform is a well-known statistical method for feature extraction, data compression and multivariate data projection and so far it has been broadly used in a large series of signal and image processing, pattern recognition and data analysis applications.

Registration of dynamic dopamine D2receptor images using principal component analysis

International Nuclear Information System (INIS)

Acton, P.D.; Ell, P.J.; Pilowsky, L.S.; Brammer, M.J.; Suckling, J.

1997-01-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D 2 receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D 2 receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P 123 I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the χ 2 of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.)

Boundary layer noise subtraction in hydrodynamic tunnel using robust principal component analysis.

Science.gov (United States)

Amailland, Sylvain; Thomas, Jean-Hugh; Pézerat, Charles; Boucheron, Romuald

2018-04-01

The acoustic study of propellers in a hydrodynamic tunnel is of paramount importance during the design process, but can involve significant difficulties due to the boundary layer noise (BLN). Indeed, advanced denoising methods are needed to recover the acoustic signal in case of poor signal-to-noise ratio. The technique proposed in this paper is based on the decomposition of the wall-pressure cross-spectral matrix (CSM) by taking advantage of both the low-rank property of the acoustic CSM and the sparse property of the BLN CSM. Thus, the algorithm belongs to the class of robust principal component analysis (RPCA), which derives from the widely used principal component analysis. If the BLN is spatially decorrelated, the proposed RPCA algorithm can blindly recover the acoustical signals even for negative signal-to-noise ratio. Unfortunately, in a realistic case, acoustic signals recorded in a hydrodynamic tunnel show that the noise may be partially correlated. A prewhitening strategy is then considered in order to take into account the spatially coherent background noise. Numerical simulations and experimental results show an improvement in terms of BLN reduction in the large hydrodynamic tunnel. The effectiveness of the denoising method is also investigated in the context of acoustic source localization.

An Introductory Application of Principal Components to Cricket Data

Science.gov (United States)

Manage, Ananda B. W.; Scariano, Stephen M.

2013-01-01

Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

Diversity analysis of Ethiopian mustard breeding lines using RAPD ...

African Journals Online (AJOL)

Using cluster analysis based on unweighted pair-group method with arithmetic average (UPGMA) and principal coordinate analysis (PCoA), the 21 Ethiopian inbred lines were grouped into three subgroups and the single genotype introduced from Sweden formed a separate group. The clustering pattern failed to show a ...

Assessment of oil weathering by gas chromatography-mass spectrometry, time warping and principal component analysis

DEFF Research Database (Denmark)

Malmquist, Linus M.V.; Olsen, Rasmus R.; Hansen, Asger B.

2007-01-01

weathering state and to distinguish between various weathering processes is investigated and discussed. The method is based on comprehensive and objective chromatographic data processing followed by principal component analysis (PCA) of concatenated sections of gas chromatography–mass spectrometry...

Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

Directory of Open Access Journals (Sweden)

Pengyu Gao

2016-03-01

Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

Validation of the academic management evaluation instru-ment based on principal component analysis for engineering and technological courses

Directory of Open Access Journals (Sweden)

Albano Oliveira Nunes

2015-05-01

Full Text Available In recent years, the expansion of higher education in Brazil has led to a series of demands related to aspects concerning training at the college level. These processes relate to: academics, professionals, entering in the labor market, among others. In this context, an important aspect is the quality of the courses. Thus, the evaluation becomes a critical diagnostic process of reality and starting point for possible interventions to be put in practice by the coordinators of the programs. This article presents the results of a questionnaire administered at the Federal University of Ceará (UFC, especially to  Systems & Digital Media and Engineering Programs professors. This research aims to identify how the professors from each department see the administrative procedures developed by the departments and also investigate the possibility of using Principal Components Analysis (PCA as a support for management of the higher education training. The methodology included the implementation of Likert scale questionnaire and subsequent mathematical treatment with PCA. The results indicate the potential application of PCA to support the management of higher education; it was possible to extract preliminaries inferences related to management methods and their characteristics. This suggests the possibility of developing the Educametrics field.

Application of empirical orthogonal functions or principal component analysis to environmental variability data

International Nuclear Information System (INIS)

Carvajal Escobar, Yesid; Marco Segura, Juan B

2005-01-01

An EOF analysis or principal component analysis (PC) was made for monthly precipitation (1972-1998) using 50 stations, and for monthly rate of flow (1951-2000) at 8 stations in the Valle del Cauca state, Colombia. Previously, we had applied 5 measures in order to verify the convenience of the analysis. These measures were: i) evaluation of significance level of correlation between variables; II) the kaiser-Meyer-Oikin (KMO) test; III) the Bartlett sphericity test; (IV) the measurement of sample adequacy (MSA), and v) the percentage of non-redundant residues with absolute values>0.05. For the selection of the significant PCS in every set of variables we applied seven criteria: the graphical method, the explained variance percentage, the mean root, the tests of Velicer, Bartlett, Broken Stich and the cross validation test. We chose the latter as the best one. It is robust and quantitative. Precipitation stations were divided in three homogeneous groups, applying a hierarchical cluster analysis, which was verified through the geographic method and the discriminate analysis for the first four EOFs of precipitation. There are many advantages to the EOF method: reduction of the dimensionality of multivariate data, calculation of missing data, evaluation and reduction of multi-co linearity, building of homogeneous groups, and detection of outliers. With the first four principal components we can explain 60.34% of the total variance of monthly precipitation for the Valle del Cauca state, and 94% of the total variance for the selected records of rates of flow

Modeling the variability of solar radiation data among weather stations by means of principal components analysis

International Nuclear Information System (INIS)

Zarzo, Manuel; Marti, Pau

2011-01-01

Research highlights: →Principal components analysis was applied to R s data recorded at 30 stations. → Four principal components explain 97% of the data variability. → The latent variables can be fitted according to latitude, longitude and altitude. → The PCA approach is more effective for gap infilling than conventional approaches. → The proposed method allows daily R s estimations at locations in the area of study. - Abstract: Measurements of global terrestrial solar radiation (R s ) are commonly recorded in meteorological stations. Daily variability of R s has to be taken into account for the design of photovoltaic systems and energy efficient buildings. Principal components analysis (PCA) was applied to R s data recorded at 30 stations in the Mediterranean coast of Spain. Due to equipment failures and site operation problems, time series of R s often present data gaps or discontinuities. The PCA approach copes with this problem and allows estimation of present and past values by taking advantage of R s records from nearby stations. The gap infilling performance of this methodology is compared with neural networks and alternative conventional approaches. Four principal components explain 66% of the data variability with respect to the average trajectory (97% if non-centered values are considered). A new method based on principal components regression was also developed for R s estimation if previous measurements are not available. By means of multiple linear regression, it was found that the latent variables associated to the four relevant principal components can be fitted according to the latitude, longitude and altitude of the station where data were recorded from. Additional geographical or climatic variables did not increase the predictive goodness-of-fit. The resulting models allow the estimation of daily R s values at any location in the area under study and present higher accuracy than artificial neural networks and some conventional approaches

A Principal Component Analysis of 39 Scientific Impact Measures

Science.gov (United States)

Bollen, Johan; Van de Sompel, Herbert

2009-01-01

Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution. PMID:19562078

A principal component analysis of 39 scientific impact measures.

Directory of Open Access Journals (Sweden)

Johan Bollen

Full Text Available BACKGROUND: The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. METHODOLOGY: We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. CONCLUSIONS: Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4C. Paks NPP: Analysis and testing. Working material

Energy Technology Data Exchange (ETDEWEB)

NONE

1996-07-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material involves comparative analysis of the seismic analysis results of the reactor building for soft soil conditions, derivation of design response spectra for components and systems; and upper range design response spectra for soft soil site conditions at Paks NPP.

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4C. Paks NPP: Analysis and testing. Working material

International Nuclear Information System (INIS)

1996-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material involves comparative analysis of the seismic analysis results of the reactor building for soft soil conditions, derivation of design response spectra for components and systems; and upper range design response spectra for soft soil site conditions at Paks NPP

Visible Leading: Principal Academy Connects and Empowers Principals

Science.gov (United States)

Hindman, Jennifer; Rozzelle, Jan; Ball, Rachel; Fahey, John

2015-01-01

The School-University Research Network (SURN) Principal Academy at the College of William & Mary in Williamsburg, Virginia, has a mission to build a leadership development program that increases principals' instructional knowledge and develops mentor principals to sustain the program. The academy is designed to connect and empower principals…

Nuclear reaction analysis of hydrogen in materials: Principals and applications

International Nuclear Information System (INIS)

Lanford, W.A.

1991-01-01

Analysis for hydrogen in materials is difficult by most traditional analytic methods. Because hydrogen has no Auger transitions, no X-ray transitions, does not neutron activate, and does not backscatter ions, it is invisible in analytical methods based on these effects. In addition, since hydrogen is a universal contaminant in vacuum systems, techniques based on mass spectrometry are difficult unless extreme measures are taken to reduce hydrogen backgrounds. Because of this situation, methods have been developed for analyzing for hydrogen in solid materials based on nuclear reactions between bombarding ions and hydrogen atoms (protons) in the samples. The nuclear reaction methods are now practiced at laboratories around the world. The basic principals of nuclear reaction analysis will be briefly presented. This method will be illustrated by applications to problems ranging from basic physics, to geology, to materials science, and to art history and archeology

Recursive Principal Components Analysis Using Eigenvector Matrix Perturbation

Directory of Open Access Journals (Sweden)

Deniz Erdogmus

2004-10-01

Full Text Available Principal components analysis is an important and well-studied subject in statistics and signal processing. The literature has an abundance of algorithms for solving this problem, where most of these algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second-order statistical criterion (like reconstruction error or output variance, and fixed point update rules with deflation. In this paper, we take a completely different approach that avoids deflation and the optimization of a cost function using gradients. The proposed method updates the eigenvector and eigenvalue matrices simultaneously with every new sample such that the estimates approximately track their true values as would be calculated from the current sample estimate of the data covariance matrix. The performance of this algorithm is compared with that of traditional methods like Sanger's rule and APEX, as well as a structurally similar matrix perturbation-based method.

Demixed principal component analysis of neural population data.

Science.gov (United States)

Kobak, Dmitry; Brendel, Wieland; Constantinidis, Christos; Feierstein, Claudia E; Kepecs, Adam; Mainen, Zachary F; Qi, Xue-Lian; Romo, Ranulfo; Uchida, Naoshige; Machens, Christian K

2016-04-12

Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure.

Correlation analysis of respiratory signals by using parallel coordinate plots.

Science.gov (United States)

Saatci, Esra

2018-01-01

The understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. In order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson's correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. The number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. It is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data. Copyright © 2017 Elsevier B.V. All rights reserved.

Principal components analysis of protein structure ensembles calculated using NMR data

International Nuclear Information System (INIS)

Howe, Peter W.A.

2001-01-01

One important problem when calculating structures of biomolecules from NMR data is distinguishing converged structures from outlier structures. This paper describes how Principal Components Analysis (PCA) has the potential to classify calculated structures automatically, according to correlated structural variation across the population. PCA analysis has the additional advantage that it highlights regions of proteins which are varying across the population. To apply PCA, protein structures have to be reduced in complexity and this paper describes two different representations of protein structures which achieve this. The calculated structures of a 28 amino acid peptide are used to demonstrate the methods. The two different representations of protein structure are shown to give equivalent results, and correct results are obtained even though the ensemble of structures used as an example contains two different protein conformations. The PCA analysis also correctly identifies the structural differences between the two conformations

Principal Curves on Riemannian Manifolds.

Science.gov (United States)

Hauberg, Soren

2016-09-01

Euclidean statistics are often generalized to Riemannian manifolds by replacing straight-line interpolations with geodesic ones. While these Riemannian models are familiar-looking, they are restricted by the inflexibility of geodesics, and they rely on constructions which are optimal only in Euclidean domains. We consider extensions of Principal Component Analysis (PCA) to Riemannian manifolds. Classic Riemannian approaches seek a geodesic curve passing through the mean that optimizes a criteria of interest. The requirements that the solution both is geodesic and must pass through the mean tend to imply that the methods only work well when the manifold is mostly flat within the support of the generating distribution. We argue that instead of generalizing linear Euclidean models, it is more fruitful to generalize non-linear Euclidean models. Specifically, we extend the classic Principal Curves from Hastie & Stuetzle to data residing on a complete Riemannian manifold. We show that for elliptical distributions in the tangent of spaces of constant curvature, the standard principal geodesic is a principal curve. The proposed model is simple to compute and avoids many of the pitfalls of traditional geodesic approaches. We empirically demonstrate the effectiveness of the Riemannian principal curves on several manifolds and datasets.

Personality disorders in substance abusers: Validation of the DIP-Q through principal components factor analysis and canonical correlation analysis

Directory of Open Access Journals (Sweden)

Hesse Morten

2005-05-01

Full Text Available Abstract Background Personality disorders are common in substance abusers. Self-report questionnaires that can aid in the assessment of personality disorders are commonly used in assessment, but are rarely validated. Methods The Danish DIP-Q as a measure of co-morbid personality disorders in substance abusers was validated through principal components factor analysis and canonical correlation analysis. A 4 components structure was constructed based on 238 protocols, representing antagonism, neuroticism, introversion and conscientiousness. The structure was compared with (a a 4-factor solution from the DIP-Q in a sample of Swedish drug and alcohol abusers (N = 133, and (b a consensus 4-components solution based on a meta-analysis of published correlation matrices of dimensional personality disorder scales. Results It was found that the 4-factor model of personality was congruent across the Danish and Swedish samples, and showed good congruence with the consensus model. A canonical correlation analysis was conducted on a subset of the Danish sample with staff ratings of pathology. Three factors that correlated highly between the two variable sets were found. These variables were highly similar to the three first factors from the principal components analysis, antagonism, neuroticism and introversion. Conclusion The findings support the validity of the DIP-Q as a measure of DSM-IV personality disorders in substance abusers.

Nonlinear Denoising and Analysis of Neuroimages With Kernel Principal Component Analysis and Pre-Image Estimation

DEFF Research Database (Denmark)

Rasmussen, Peter Mondrup; Abrahamsen, Trine Julie; Madsen, Kristoffer Hougaard

2012-01-01

We investigate the use of kernel principal component analysis (PCA) and the inverse problem known as pre-image estimation in neuroimaging: i) We explore kernel PCA and pre-image estimation as a means for image denoising as part of the image preprocessing pipeline. Evaluation of the denoising...... procedure is performed within a data-driven split-half evaluation framework. ii) We introduce manifold navigation for exploration of a nonlinear data manifold, and illustrate how pre-image estimation can be used to generate brain maps in the continuum between experimentally defined brain states/classes. We...

BANK CAPITAL AND MACROECONOMIC SHOCKS: A PRINCIPAL COMPONENTS ANALYSIS AND VECTOR ERROR CORRECTION MODEL

Directory of Open Access Journals (Sweden)

Christian NZENGUE PEGNET

2011-07-01

Full Text Available The recent financial turmoil has clearly highlighted the potential role of financial factors on amplification of macroeconomic developments and stressed the importance of analyzing the relationship between banks’ balance sheets and economic activity. This paper assesses the impact of the bank capital channel in the transmission of schocks in Europe on the basis of bank's balance sheet data. The empirical analysis is carried out through a Principal Component Analysis and in a Vector Error Correction Model.

Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

Science.gov (United States)

Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Wang, Deli; Song, Youtao; Zhang, Su

2016-03-01

This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm-1 assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved.

Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer

International Nuclear Information System (INIS)

Li, Xiaozhou; Yang, Tianyue; Wang, Deli; Li, Siqi; Song, Youtao; Zhang, Su

2016-01-01

This paper attempts to investigate the feasibility of using Raman spectroscopy for the diagnosis of colon cancer. Serum taken from 75 healthy volunteers, 65 colon cancer patients and 60 post-operation colon cancer patients was measured in this experiment. In the Raman spectra of all three groups, the Raman peaks at 750, 1083, 1165, 1321, 1629 and 1779 cm −1 assigned to nucleic acids, amino acids and chromophores were consistently observed. All of these six Raman peaks were observed to have statistically significant differences between groups. For quantitative analysis, the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbour analysis (KNN) were utilized to develop diagnostic algorithms for classification. In PCA, several peaks in the principal component (PC) loadings spectra were identified as the major contributors to the PC scores. Some of the peaks in the PC loadings spectra were also reported as characteristic peaks for colon tissues, which implies correlation between peaks in PC loadings spectra and those in the original Raman spectra. KNN was also performed on the obtained PCs, and a diagnostic accuracy of 91.0% and a specificity of 92.6% were achieved. (paper)

Principal component analysis of psoriasis lesions images

DEFF Research Database (Denmark)

Maletti, Gabriela Mariel; Ersbøll, Bjarne Kjær

2003-01-01

A set of RGB images of psoriasis lesions is used. By visual examination of these images, there seem to be no common pattern that could be used to find and align the lesions within and between sessions. It is expected that the principal components of the original images could be useful during future...

Principal Component Analysis: Most Favourite Tool in Chemometrics

Indian Academy of Sciences (India)

GENERAL ARTICLE. Principal ... Chemometrics is a discipline that combines mathematics, statis- ... workers have used PCA for air quality monitoring [8]. ..... J S Verbeke, Handbook of Chemometrics and Qualimetrics, Elsevier, New York,.

Towards successful coordination of electronic health record based-referrals: a qualitative analysis

Directory of Open Access Journals (Sweden)

Paul Lindsey A

2011-07-01

Full Text Available Abstract Background Successful subspecialty referrals require considerable coordination and interactive communication among the primary care provider (PCP, the subspecialist, and the patient, which may be challenging in the outpatient setting. Even when referrals are facilitated by electronic health records (EHRs (i.e., e-referrals, lapses in patient follow-up might occur. Although compelling reasons exist why referral coordination should be improved, little is known about which elements of the complex referral coordination process should be targeted for improvement. Using Okhuysen & Bechky's coordination framework, this paper aims to understand the barriers, facilitators, and suggestions for improving communication and coordination of EHR-based referrals in an integrated healthcare system. Methods We conducted a qualitative study to understand coordination breakdowns related to e-referrals in an integrated healthcare system and examined work-system factors that affect the timely receipt of subspecialty care. We conducted interviews with seven subject matter experts and six focus groups with a total of 30 PCPs and subspecialists at two tertiary care Department of Veterans Affairs (VA medical centers. Using techniques from grounded theory and content analysis, we identified organizational themes that affected the referral process. Results Four themes emerged: lack of an institutional referral policy, lack of standardization in certain referral procedures, ambiguity in roles and responsibilities, and inadequate resources to adapt and respond to referral requests effectively. Marked differences in PCPs' and subspecialists' communication styles and individual mental models of the referral processes likely precluded the development of a shared mental model to facilitate coordination and successful referral completion. Notably, very few barriers related to the EHR were reported. Conclusions Despite facilitating information transfer between PCPs and

Assessment of genetic divergence in tomato through agglomerative hierarchical clustering and principal component analysis

International Nuclear Information System (INIS)

Iqbal, Q.; Saleem, M.Y.; Hameed, A.; Asghar, M.

2014-01-01

For the improvement of qualitative and quantitative traits, existence of variability has prime importance in plant breeding. Data on different morphological and reproductive traits of 47 tomato genotypes were analyzed for correlation,agglomerative hierarchical clustering and principal component analysis (PCA) to select genotypes and traits for future breeding program. Correlation analysis revealed significant positive association between yield and yield components like fruit diameter, single fruit weight and number of fruits plant-1. Principal component (PC) analysis depicted first three PCs with Eigen-value higher than 1 contributing 81.72% of total variability for different traits. The PC-I showed positive factor loadings for all the traits except number of fruits plant-1. The contribution of single fruit weight and fruit diameter was highest in PC-1. Cluster analysis grouped all genotypes into five divergent clusters. The genotypes in cluster-II and cluster-V exhibited uniform maturity and higher yield. The D2 statistics confirmed highest distance between cluster- III and cluster-V while maximum similarity was observed in cluster-II and cluster-III. It is therefore suggested that crosses between genotypes of cluster-II and cluster-V with those of cluster-I and cluster-III may exhibit heterosis in F1 for hybrid breeding and for selection of superior genotypes in succeeding generations for cross breeding programme. (author)

Principal Component Analysis-Based Pattern Analysis of Dose-Volume Histograms and Influence on Rectal Toxicity

International Nuclear Information System (INIS)

Soehn, Matthias; Alber, Markus; Yan Di

2007-01-01

Purpose: The variability of dose-volume histogram (DVH) shapes in a patient population can be quantified using principal component analysis (PCA). We applied this to rectal DVHs of prostate cancer patients and investigated the correlation of the PCA parameters with late bleeding. Methods and Materials: PCA was applied to the rectal wall DVHs of 262 patients, who had been treated with a four-field box, conformal adaptive radiotherapy technique. The correlated changes in the DVH pattern were revealed as 'eigenmodes,' which were ordered by their importance to represent data set variability. Each DVH is uniquely characterized by its principal components (PCs). The correlation of the first three PCs and chronic rectal bleeding of Grade 2 or greater was investigated with uni- and multivariate logistic regression analyses. Results: Rectal wall DVHs in four-field conformal RT can primarily be represented by the first two or three PCs, which describe ∼94% or 96% of the DVH shape variability, respectively. The first eigenmode models the total irradiated rectal volume; thus, PC1 correlates to the mean dose. Mode 2 describes the interpatient differences of the relative rectal volume in the two- or four-field overlap region. Mode 3 reveals correlations of volumes with intermediate doses (∼40-45 Gy) and volumes with doses >70 Gy; thus, PC3 is associated with the maximal dose. According to univariate logistic regression analysis, only PC2 correlated significantly with toxicity. However, multivariate logistic regression analysis with the first two or three PCs revealed an increased probability of bleeding for DVHs with more than one large PC. Conclusions: PCA can reveal the correlation structure of DVHs for a patient population as imposed by the treatment technique and provide information about its relationship to toxicity. It proves useful for augmenting normal tissue complication probability modeling approaches

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

Science.gov (United States)

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.

Urban School Principals and Their Role as Multicultural Leaders

Science.gov (United States)

Gardiner, Mary E.; Enomoto, Ernestine K.

2006-01-01

This study focuses on the role of urban school principals as multicultural leaders. Using cross-case analysis, the authors describe what 6 practicing principals do in regard to multicultural leadership. The findings suggest that although multicultural preparation was lacking for these principals, some did engage in work that promoted diversity in…

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

Science.gov (United States)

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

Science.gov (United States)

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Directory of Open Access Journals (Sweden)

Lijun Wang

2013-01-01

Full Text Available Brain decoding with functional magnetic resonance imaging (fMRI requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Multivariate analysis of remote LIBS spectra using partial least squares, principal component analysis, and related techniques

Energy Technology Data Exchange (ETDEWEB)

Clegg, Samuel M [Los Alamos National Laboratory; Barefield, James E [Los Alamos National Laboratory; Wiens, Roger C [Los Alamos National Laboratory; Sklute, Elizabeth [MT HOLYOKE COLLEGE; Dyare, Melinda D [MT HOLYOKE COLLEGE

2008-01-01

Quantitative analysis with LIBS traditionally employs calibration curves that are complicated by the chemical matrix effects. These chemical matrix effects influence the LIBS plasma and the ratio of elemental composition to elemental emission line intensity. Consequently, LIBS calibration typically requires a priori knowledge of the unknown, in order for a series of calibration standards similar to the unknown to be employed. In this paper, three new Multivariate Analysis (MV A) techniques are employed to analyze the LIBS spectra of 18 disparate igneous and highly-metamorphosed rock samples. Partial Least Squares (PLS) analysis is used to generate a calibration model from which unknown samples can be analyzed. Principal Components Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) are employed to generate a model and predict the rock type of the samples. These MV A techniques appear to exploit the matrix effects associated with the chemistries of these 18 samples.

Quantifying linguistic coordination

DEFF Research Database (Denmark)

Fusaroli, Riccardo; Tylén, Kristian

task (Bahrami et al 2010, Fusaroli et al. 2012) we extend to linguistic coordination dynamical measures of recurrence employed in the analysis of sensorimotor coordination (such as heart-rate (Konvalinka et al 2011), postural sway (Shockley 2005) and eye-movements (Dale, Richardson and Kirkham 2012......). We employ nominal recurrence analysis (Orsucci et al 2005, Dale et al 2011) on the decision-making conversations between the participants. We report strong correlations between various indexes of recurrence and collective performance. We argue this method allows us to quantify the qualities...

Principal Component Analysis for Normal-Distribution-Valued Symbolic Data.

Science.gov (United States)

Wang, Huiwen; Chen, Meiling; Shi, Xiaojun; Li, Nan

2016-02-01

This paper puts forward a new approach to principal component analysis (PCA) for normal-distribution-valued symbolic data, which has a vast potential of applications in the economic and management field. We derive a full set of numerical characteristics and variance-covariance structure for such data, which forms the foundation for our analytical PCA approach. Our approach is able to use all of the variance information in the original data than the prevailing representative-type approach in the literature which only uses centers, vertices, etc. The paper also provides an accurate approach to constructing the observations in a PC space based on the linear additivity property of normal distribution. The effectiveness of the proposed method is illustrated by simulated numerical experiments. At last, our method is applied to explain the puzzle of risk-return tradeoff in China's stock market.

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

Science.gov (United States)

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.

Principal component analysis of the nonlinear coupling of harmonic modes in heavy-ion collisions

Science.gov (United States)

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.

Reshaping of Gait Coordination by Robotic Intervention in Myelopathy Patients After Surgery

Science.gov (United States)

Puentes, Sandra; Kadone, Hideki; Kubota, Shigeki; Abe, Tetsuya; Shimizu, Yukiyo; Marushima, Aiki; Sankai, Yoshiyuki; Yamazaki, Masashi; Suzuki, Kenji

2018-01-01

The Ossification of the Posterior Longitudinal Ligament (OPLL) is an idiopathic degenerative spinal disease which may cause motor deficit. For patients presenting myelopathy or severe stenosis, surgical decompression is the treatment of choice; however, despite adequate decompression residual motor impairment is found in some cases. After surgery, there is no therapeutic approach available for this population. The Hybrid Assistive Limb® (HAL) robot suit is a unique powered exoskeleton designed to predict, support, and enhance the lower extremities performance of patients using their own bioelectric signals. This approach has been used for spinal cord injury and stroke patients where the walking performance improved. However, there is no available data about gait kinematics evaluation after HAL therapy. Here we analyze the effect of HAL therapy in OPLL patients in acute and chronic stages after decompression surgery. We found that HAL therapy improved the walking performance for both groups. Interestingly, kinematics evaluation by the analysis of the elevation angles of the thigh, shank, and foot by using a principal component analysis showed that planar covariation, plane orientation, and movement range evaluation improved for acute patients suggesting an improvement in gait coordination. Being the first study performing kinematics analysis after HAL therapy, our results suggest that HAL improved the gait coordination of acute patients by supporting the relearning process and therefore reshaping their gait pattern. PMID:29551960

Two Charter School Principals' Engagement in Instructional Leadership

Science.gov (United States)

Bickmore, Dana L.; Sulentic Dowell, Margaret-Mary

2014-01-01

This comparative case (Merriam, 2009) study explored two charter school principals' engagement in instructional leadership. Analysis of three data sources--interviews, observations, and documents--revealed that principals were almost exclusively focused on state accountability and possessed limited knowledge of pedagogical practices. In…

Multiobjective Optimization of ELID Grinding Process Using Grey Relational Analysis Coupled with Principal Component Analysis

Directory of Open Access Journals (Sweden)

S. Prabhu

2014-06-01

Full Text Available Carbon nanotube (CNT mixed grinding wheel has been used in the electrolytic in-process dressing (ELID grinding process to analyze the surface characteristics of AISI D2 Tool steel material. CNT grinding wheel is having an excellent thermal conductivity and good mechanical property which is used to improve the surface finish of the work piece. The multiobjective optimization of grey relational analysis coupled with principal component analysis has been used to optimize the process parameters of ELID grinding process. Based on the Taguchi design of experiments, an L9 orthogonal array table was chosen for the experiments. The confirmation experiment verifies the proposed that grey-based Taguchi method has the ability to find out the optimal process parameters with multiple quality characteristics of surface roughness and metal removal rate. Analysis of variance (ANOVA has been used to verify and validate the model. Empirical model for the prediction of output parameters has been developed using regression analysis and the results were compared for with and without using CNT grinding wheel in ELID grinding process.

Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

Science.gov (United States)

Hendrix, Dean

2010-01-01

This study analyzed 2005-2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between…

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Understanding deformation mechanisms during powder compaction using principal component analysis of compression data.

Science.gov (United States)

Roopwani, Rahul; Buckner, Ira S

2011-10-14

Principal component analysis (PCA) was applied to pharmaceutical powder compaction. A solid fraction parameter (SF(c/d)) and a mechanical work parameter (W(c/d)) representing irreversible compression behavior were determined as functions of applied load. Multivariate analysis of the compression data was carried out using PCA. The first principal component (PC1) showed loadings for the solid fraction and work values that agreed with changes in the relative significance of plastic deformation to consolidation at different pressures. The PC1 scores showed the same rank order as the relative plasticity ranking derived from the literature for common pharmaceutical materials. The utility of PC1 in understanding deformation was extended to binary mixtures using a subset of the original materials. Combinations of brittle and plastic materials were characterized using the PCA method. The relationships between PC1 scores and the weight fractions of the mixtures were typically linear showing ideal mixing in their deformation behaviors. The mixture consisting of two plastic materials was the only combination to show a consistent positive deviation from ideality. The application of PCA to solid fraction and mechanical work data appears to be an effective means of predicting deformation behavior during compaction of simple powder mixtures. Copyright © 2011 Elsevier B.V. All rights reserved.

A Principal Component Analysis of Project Management Construction Industry Competencies for the Ghanaian

Directory of Open Access Journals (Sweden)

Rockson Dobgegah

2011-03-01

Full Text Available The study adopts a data reduction technique to examine the presence of any complex structure among a set of project management competency variables. A structured survey questionnaire was administered to 100 project managers to elicit relevant data, and this achieved a relatively high response rate of 54%. After satisfying all the necessary tests of reliability of the survey instrument, sample size adequacy and population matrix, the data was subjected to principal component analysis, resulting in the identification of six new thematic project management competency areas ; and were explained in terms of human resource management and project control; construction innovation and communication; project financial resources management; project risk and quality management; business ethics and; physical resources and procurement management. These knowledge areas now form the basis for lateral project management training requirements in the context of the Ghanaian construction industry. Key contribution of the paper is manifested in the use of the principal component analysis, which has rigorously provided understanding into the complex structure and the relationship between the various knowledge areas. The originality and value of the paper is embedded in the use of contextual-task conceptual knowledge to expound the six uncorrelated empirical utility of the project management competencies.

Metal–organic coordinated multilayer film formation: Quantitative analysis of composition and structure

Energy Technology Data Exchange (ETDEWEB)

Benson, Alexandra S.; Elinski, Meagan B.; Ohnsorg, Monica L.; Beaudoin, Christopher K.; Alexander, Kyle A.; Peaslee, Graham F.; DeYoung, Paul A.; Anderson, Mary E., E-mail: meanderson@hope.edu

2015-09-01

Metal–organic coordinated multilayers are self-assembled thin films fabricated by alternating solution–phase deposition of bifunctional organic molecules and metal ions. The multilayer film composed of α,ω-mercaptoalkanoic acid and Cu (II) has been the focus of fundamental and applied research with its robust reproducibility and seemingly simple hierarchical architecture. However, internal structure and composition have not been unambiguously established. The composition of films up to thirty layers thick was investigated using Rutherford backscattering spectrometry and particle induced X-ray emission. Findings show these films are copper enriched, elucidating a 2:1 ratio for the ion to molecule complexation at the metal–organic interface. Results also reveal that these films have an average layer density similar to literature values established for a self-assembled monolayer, indicating a robust and stable structure. The surface structures of multilayer films have been characterized by contact angle goniometry, ellipsometry, and scanning probe microscopy. A morphological transition is observed as film thickness increases from the first few foundational layers to films containing five or more layers. Surface roughness analysis quantifies this evolution as the film initially increases in roughness before obtaining a lower roughness comparable to the underlying gold substrate. Quantitative analysis of topographical structure and internal composition for metal–organic coordinated multilayers as a function of number of deposited layers has implications for their incorporation in the fields of photonics and nanolithography. - Highlights: • Layer-by-layer deposition is examined by scanning probe microscopy and ion beam analysis. • Film growth undergoes morphological evolution during foundational layer deposition. • Image analysis quantified surface features such as roughness, grain size, and coverage. • Molecular density of each film layer is found to

An application of principal component analysis to the clavicle and clavicle fixation devices.

LENUS (Irish Health Repository)

Daruwalla, Zubin J

2010-01-01

Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories.

Principal-vector-directed fringe-tracking technique.

Science.gov (United States)

Zhang, Zhihui; Guo, Hongwei

2014-11-01

Fringe tracking is one of the most straightforward techniques for analyzing a single fringe pattern. This work presents a principal-vector-directed fringe-tracking technique. It uses Gaussian derivatives for estimating fringe gradients and uses hysteresis thresholding for segmenting singular points, thus improving the principal component analysis method. Using it allows us to estimate the principal vectors of fringes from a pattern with high noise. The fringe-tracking procedure is directed by these principal vectors, so that erroneous results induced by noise and other error-inducing factors are avoided. At the same time, the singular point regions of the fringe pattern are identified automatically. Using them allows us to determine paths through which the "seed" point for each fringe skeleton is easy to find, thus alleviating the computational burden in processing the fringe pattern. The results of a numerical simulation and experiment demonstrate this method to be valid.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy

International Nuclear Information System (INIS)

Jesse, Stephen; Kalinin, Sergei V

2009-01-01

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Analysis of the impact of domestication of Warburgia ugandensis ...

African Journals Online (AJOL)

Principal co-ordinates analysis and dendrogram separated the ten populations into two major groups corresponding to Kenyan and Tanzanian populations, respectively. Ugandan populations were shared between the two major groups; this is probably because Uganda is believed to be the centre of diversity for W.

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach.

Science.gov (United States)

Panazzolo, Diogo G; Sicuro, Fernando L; Clapauch, Ruth; Maranhão, Priscila A; Bouskela, Eliete; Kraemer-Aguiar, Luiz G

2012-11-13

We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Data from 189 female subjects (34.0 ± 15.5 years, 30.5 ± 7.1 kg/m2), who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA). PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI), waist circumference, systolic and diastolic blood pressure (BP), fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides (TG), insulin, C-reactive protein (CRP), and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV) at rest and peak after 1 min of arterial occlusion (RBCV(max)), and the time taken to reach RBCV(max) (TRBCV(max)). A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCV(max) varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCV(max), but in the opposite way. Principal component 3 was associated only with microvascular

Obesity, metabolic syndrome, impaired fasting glucose, and microvascular dysfunction: a principal component analysis approach

Directory of Open Access Journals (Sweden)

Panazzolo Diogo G

2012-11-01

Full Text Available Abstract Background We aimed to evaluate the multivariate association between functional microvascular variables and clinical-laboratorial-anthropometrical measurements. Methods Data from 189 female subjects (34.0±15.5 years, 30.5±7.1 kg/m2, who were non-smokers, non-regular drug users, without a history of diabetes and/or hypertension, were analyzed by principal component analysis (PCA. PCA is a classical multivariate exploratory tool because it highlights common variation between variables allowing inferences about possible biological meaning of associations between them, without pre-establishing cause-effect relationships. In total, 15 variables were used for PCA: body mass index (BMI, waist circumference, systolic and diastolic blood pressure (BP, fasting plasma glucose, levels of total cholesterol, high-density lipoprotein cholesterol (HDL-c, low-density lipoprotein cholesterol (LDL-c, triglycerides (TG, insulin, C-reactive protein (CRP, and functional microvascular variables measured by nailfold videocapillaroscopy. Nailfold videocapillaroscopy was used for direct visualization of nutritive capillaries, assessing functional capillary density, red blood cell velocity (RBCV at rest and peak after 1 min of arterial occlusion (RBCVmax, and the time taken to reach RBCVmax (TRBCVmax. Results A total of 35% of subjects had metabolic syndrome, 77% were overweight/obese, and 9.5% had impaired fasting glucose. PCA was able to recognize that functional microvascular variables and clinical-laboratorial-anthropometrical measurements had a similar variation. The first five principal components explained most of the intrinsic variation of the data. For example, principal component 1 was associated with BMI, waist circumference, systolic BP, diastolic BP, insulin, TG, CRP, and TRBCVmax varying in the same way. Principal component 1 also showed a strong association among HDL-c, RBCV, and RBCVmax, but in the opposite way. Principal component 3 was

Static analysis of large-scale multibody system using joint coordinates and spatial algebra operator.

Science.gov (United States)

Omar, Mohamed A

2014-01-01

Initial transient oscillations inhibited in the dynamic simulations responses of multibody systems can lead to inaccurate results, unrealistic load prediction, or simulation failure. These transients could result from incompatible initial conditions, initial constraints violation, and inadequate kinematic assembly. Performing static equilibrium analysis before the dynamic simulation can eliminate these transients and lead to stable simulation. Most exiting multibody formulations determine the static equilibrium position by minimizing the system potential energy. This paper presents a new general purpose approach for solving the static equilibrium in large-scale articulated multibody. The proposed approach introduces an energy drainage mechanism based on Baumgarte constraint stabilization approach to determine the static equilibrium position. The spatial algebra operator is used to express the kinematic and dynamic equations of the closed-loop multibody system. The proposed multibody system formulation utilizes the joint coordinates and modal elastic coordinates as the system generalized coordinates. The recursive nonlinear equations of motion are formulated using the Cartesian coordinates and the joint coordinates to form an augmented set of differential algebraic equations. Then system connectivity matrix is derived from the system topological relations and used to project the Cartesian quantities into the joint subspace leading to minimum set of differential equations.

Detecting 3-D rotational motion and extracting target information from the principal component analysis of scatterer range histories

CSIR Research Space (South Africa)

Nel, W

2009-10-01

Full Text Available to estimate the 3-D position of scatterers as a by-product of the analysis. The technique is based on principal component analysis of accurate scatterer range histories and is shown only in simulation. Future research should focus on practical application....

Principal semantic components of language and the measurement of meaning.

Science.gov (United States)

Samsonovich, Alexei V; Samsonovic, Alexei V; Ascoli, Giorgio A

2010-06-11

Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence), "calm/excited" (arousal), and "open/closed" (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a

Principal semantic components of language and the measurement of meaning.

Directory of Open Access Journals (Sweden)

Alexei V Samsonovich

Full Text Available Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence, "calm/excited" (arousal, and "open/closed" (freedom, respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4 of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet, among Western languages (English, French, German, and Spanish, and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step

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

Science.gov (United States)

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

An application of principal component analysis to the clavicle and clavicle fixation devices

OpenAIRE

Daruwalla, Zubin J; Courtis, Patrick; Fitzpatrick, Clare; Fitzpatrick, David; Mullett, Hannan

2010-01-01

Abstract Background Principal component analysis (PCA) enables the building of statistical shape models of bones and joints. This has been used in conjunction with computer assisted surgery in the past. However, PCA of the clavicle has not been performed. Using PCA, we present a novel method that examines the major modes of size and three-dimensional shape variation in male and female clavicles and suggests a method of grouping the clavicle into size and shape categories. Materials and method...

An analytics of electricity consumption characteristics based on principal component analysis

Science.gov (United States)

Feng, Junshu

2018-02-01

Abstract . More detailed analysis of the electricity consumption characteristics can make demand side management (DSM) much more targeted. In this paper, an analytics of electricity consumption characteristics based on principal component analysis (PCA) is given, which the PCA method can be used in to extract the main typical characteristics of electricity consumers. Then, electricity consumption characteristics matrix is designed, which can make a comparison of different typical electricity consumption characteristics between different types of consumers, such as industrial consumers, commercial consumers and residents. In our case study, the electricity consumption has been mainly divided into four characteristics: extreme peak using, peak using, peak-shifting using and others. Moreover, it has been found that industrial consumers shift their peak load often, meanwhile commercial and residential consumers have more peak-time consumption. The conclusions can provide decision support of DSM for the government and power providers.

Portable XRF and principal component analysis for bill characterization in forensic science.

Science.gov (United States)

Appoloni, C R; Melquiades, F L

2014-02-01

Several modern techniques have been applied to prevent counterfeiting of money bills. The objective of this study was to demonstrate the potential of Portable X-ray Fluorescence (PXRF) technique and the multivariate analysis method of Principal Component Analysis (PCA) for classification of bills in order to use it in forensic science. Bills of Dollar, Euro and Real (Brazilian currency) were measured directly at different colored regions, without any previous preparation. Spectra interpretation allowed the identification of Ca, Ti, Fe, Cu, Sr, Y, Zr and Pb. PCA analysis separated the bills in three groups and subgroups among Brazilian currency. In conclusion, the samples were classified according to its origin identifying the elements responsible for differentiation and basic pigment composition. PXRF allied to multivariate discriminate methods is a promising technique for rapid and no destructive identification of false bills in forensic science. Copyright © 2013 Elsevier Ltd. All rights reserved.

PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

International Nuclear Information System (INIS)

Correia, C.; Medeiros, J. R. De; Lazarian, A.; Burkhart, B.; Pogosyan, D.

2016-01-01

In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information

PRINCIPAL COMPONENT ANALYSIS STUDIES OF TURBULENCE IN OPTICALLY THICK GAS

Energy Technology Data Exchange (ETDEWEB)

Correia, C.; Medeiros, J. R. De [Departamento de Física Teórica e Experimental, Universidade Federal do Rio Grande do Norte, 59072-970, Natal (Brazil); Lazarian, A. [Astronomy Department, University of Wisconsin, Madison, 475 N. Charter St., WI 53711 (United States); Burkhart, B. [Harvard-Smithsonian Center for Astrophysics, 60 Garden St, MS-20, Cambridge, MA 02138 (United States); Pogosyan, D., E-mail: caioftc@dfte.ufrn.br [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, ON (Canada)

2016-02-20

In this work we investigate the sensitivity of principal component analysis (PCA) to the velocity power spectrum in high-opacity regimes of the interstellar medium (ISM). For our analysis we use synthetic position–position–velocity (PPV) cubes of fractional Brownian motion and magnetohydrodynamics (MHD) simulations, post-processed to include radiative transfer effects from CO. We find that PCA analysis is very different from the tools based on the traditional power spectrum of PPV data cubes. Our major finding is that PCA is also sensitive to the phase information of PPV cubes and this allows PCA to detect the changes of the underlying velocity and density spectra at high opacities, where the spectral analysis of the maps provides the universal −3 spectrum in accordance with the predictions of the Lazarian and Pogosyan theory. This makes PCA a potentially valuable tool for studies of turbulence at high opacities, provided that proper gauging of the PCA index is made. However, we found the latter to not be easy, as the PCA results change in an irregular way for data with high sonic Mach numbers. This is in contrast to synthetic Brownian noise data used for velocity and density fields that show monotonic PCA behavior. We attribute this difference to the PCA's sensitivity to Fourier phase information.

Characterization of Land Transitions Patterns from Multivariate Time Series Using Seasonal Trend Analysis and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Benoit Parmentier

2014-12-01

Full Text Available Characterizing biophysical changes in land change areas over large regions with short and noisy multivariate time series and multiple temporal parameters remains a challenging task. Most studies focus on detection rather than the characterization, i.e., the manner by which surface state variables are altered by the process of changes. In this study, a procedure is presented to extract and characterize simultaneous temporal changes in MODIS multivariate times series from three surface state variables the Normalized Difference Vegetation Index (NDVI, land surface temperature (LST and albedo (ALB. The analysis involves conducting a seasonal trend analysis (STA to extract three seasonal shape parameters (Amplitude 0, Amplitude 1 and Amplitude 2 and using principal component analysis (PCA to contrast trends in change and no-change areas. We illustrate the method by characterizing trends in burned and unburned pixels in Alaska over the 2001–2009 time period. Findings show consistent and meaningful extraction of temporal patterns related to fire disturbances. The first principal component (PC1 is characterized by a decrease in mean NDVI (Amplitude 0 with a concurrent increase in albedo (the mean and the annual amplitude and an increase in LST annual variability (Amplitude 1. These results provide systematic empirical evidence of surface changes associated with one type of land change, fire disturbances, and suggest that STA with PCA may be used to characterize many other types of land transitions over large landscape areas using multivariate Earth observation time series.

Mapping brain activity in gradient-echo functional MRI using principal component analysis

Science.gov (United States)

Khosla, Deepak; Singh, Manbir; Don, Manuel

1997-05-01

The detection of sites of brain activation in functional MRI has been a topic of immense research interest and many technique shave been proposed to this end. Recently, principal component analysis (PCA) has been applied to extract the activated regions and their time course of activation. This method is based on the assumption that the activation is orthogonal to other signal variations such as brain motion, physiological oscillations and other uncorrelated noises. A distinct advantage of this method is that it does not require any knowledge of the time course of the true stimulus paradigm. This technique is well suited to EPI image sequences where the sampling rate is high enough to capture the effects of physiological oscillations. In this work, we propose and apply tow methods that are based on PCA to conventional gradient-echo images and investigate their usefulness as tools to extract reliable information on brain activation. The first method is a conventional technique where a single image sequence with alternating on and off stages is subject to a principal component analysis. The second method is a PCA-based approach called the common spatial factor analysis technique (CSF). As the name suggests, this method relies on common spatial factors between the above fMRI image sequence and a background fMRI. We have applied these methods to identify active brain ares during visual stimulation and motor tasks. The results from these methods are compared to those obtained by using the standard cross-correlation technique. We found good agreement in the areas identified as active across all three techniques. The results suggest that PCA and CSF methods have good potential in detecting the true stimulus correlated changes in the presence of other interfering signals.

Development of a reference database for ion beam analysis. Summary report of the first research coordination meeting

International Nuclear Information System (INIS)

Vickridge, I.; Schwerer, O.

2006-01-01

A summary is given of the First Research Coordination Meeting on the Development of a Reference Database for Ion Beam Analysis, including background information, objectives, recommendations for measurements, and a list of tasks assigned to participants. The next research co-ordination meeting will be held in May 2007. (author)

Identifikasi Wajah Manusia untuk Sistem Monitoring Kehadiran Perkuliahan menggunakan Ekstraksi Fitur Principal Component Analysis (PCA

Directory of Open Access Journals (Sweden)

Cucu Suhery

2017-04-01

Full Text Available Berbagai sistem monitoring presensi yang ada memiliki kekurangan dan kelebihan masing-masing, dan perlu  untuk terus dikembangkan sehingga memudahkan dalam proses pengolahan datanya. Pada penelitian ini dikembangkan suatu sistem monitoring presensi menggunakan deteksi wajah manusia yang diintegrasikan dengan basis data menggunakan bahasa pemrograman Python dan library opencv. Akuisisi data citra dilakukan dengan ponsel android, kemudian citra tersebut dideteksi dan dipotong sehingga hanya didapat bagian wajah saja.  Deteksi wajah menggunakan metode Haar-Cascade Classifier, kemudian ekstraksi fitur dilakukan menggunakan metode Principal Component Analysis (PCA. Hasil dari PCA diberi label sesuai dengan data manusia yang ada pada basis data. Semua citra yang telah memiliki nilai PCA dan tersimpan di basis data akan dicari kemiripannya dengan citra wajah pada proses pengujian menggunakan metoda Euclidian Distance. Pada penelitian ini basis data yang digunakan yaitu MySQL. Hasil deteksi citra wajah pada proses pelatihan memiliki tingkat keberhasilan 100% dan hasil identifikasi wajah pada proses pengujian memiliki tingkat keberhasilan 90%..   Kata kunci— android, haar-cascade classifier, principal component analysis, euclidian distance, MySQL, sistem monitoring presensi, deteksi wajah

Measuring farm sustainability using data envelope analysis with principal components: the case of Wisconsin cranberry.

Science.gov (United States)

Dong, Fengxia; Mitchell, Paul D; Colquhoun, Jed

2015-01-01

Measuring farm sustainability performance is a crucial component for improving agricultural sustainability. While extensive assessments and indicators exist that reflect the different facets of agricultural sustainability, because of the relatively large number of measures and interactions among them, a composite indicator that integrates and aggregates over all variables is particularly useful. This paper describes and empirically evaluates a method for constructing a composite sustainability indicator that individually scores and ranks farm sustainability performance. The method first uses non-negative polychoric principal component analysis to reduce the number of variables, to remove correlation among variables and to transform categorical variables to continuous variables. Next the method applies common-weight data envelope analysis to these principal components to individually score each farm. The method solves weights endogenously and allows identifying important practices in sustainability evaluation. An empirical application to Wisconsin cranberry farms finds heterogeneity in sustainability practice adoption, implying that some farms could adopt relevant practices to improve the overall sustainability performance of the industry. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

Principal Component Analysis of Working Memory Variables during Child and Adolescent Development.

Science.gov (United States)

Barriga-Paulino, Catarina I; Rodríguez-Martínez, Elena I; Rojas-Benjumea, María Ángeles; Gómez, Carlos M

2016-10-03

Correlation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6-18 years old. The correlation analysis (p 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

Variability search in M 31 using principal component analysis and the Hubble Source Catalogue

Science.gov (United States)

Moretti, M. I.; Hatzidimitriou, D.; Karampelas, A.; Sokolovsky, K. V.; Bonanos, A. Z.; Gavras, P.; Yang, M.

2018-06-01

Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18 152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, long-period variables (LPVs) and non-variables. This projection recovered more than 90 per cent of the known variables and revealed 38 previously unknown variable stars (about 30 per cent more), all LPVs except for one object of uncertain variability type. We conclude that this methodology can indeed successfully identify candidate variable stars.

Attitudes towards Addressing Medical Absenteeism of Students: A Qualitative Study among Principals and Special Education Needs Coordinators in Dutch Secondary Schools.

Science.gov (United States)

Vanneste, Yvonne; van de Loo, Marlou; Feron, Frans; Rots-de Vries, Carin; van de Goor, Ien

2016-01-01

Reducing school absenteeism benefits the health and educational opportunities of young people. The Dutch intervention Medical Advice for Sick-reported Students (abbreviated as MASS) was developed to address school absenteeism due to sickness reporting, also called medical absenteeism. This study is part of a research project on the effectiveness of MASS and explores factors that influence the implementation and dissemination of the intervention, from schools' perspectives. The research questions include reasons schools have to implement MASS, their experiences in the implementation of MASS and their views on what is needed to ensure sustainable implementation. A qualitative research method was used. Semi-structured interviews were held with nine principals and eight special education needs coordinators, working in nine secondary schools that apply MASS. Inductive content analysis was carried out. The main reasons for schools to address medical absenteeism were their concerns about students' well-being and future prospects and their wish to share these concerns with students' parents. Participants also mentioned the wish to raise the threshold for reporting sick. According to the participants, MASS makes it easier for teachers to enter into conversation with students and their parents about medical absence. MASS prevents damage to the relationship with parents and medical problems being missed. In implementing MASS the main obstacles are teachers' dialogue about medical absence with students and their parents, teachers' follow-up of the feedback of the youth health care physicians (YHCPs), and correct registration. The participants were convinced that MASS also improves collaboration with parents regarding the optimization of care for students. MASS allows schools to identify students at risk of dropout at an early stage and to optimise guidance of these students. The intervention matches schools' need to address medical absenteeism by providing a clear framework

THE STUDY OF THE CHARACTERIZATION INDICES OF FABRICS BY PRINCIPAL COMPONENT ANALYSIS METHOD

OpenAIRE

HRISTIAN Liliana; OSTAFE Maria Magdalena; BORDEIANU Demetra Lacramioara; APOSTOL Laura Liliana

2017-01-01

The paper was pursued to prioritize the worsted fabrics type, for the manufacture of outerwear products by characterization indeces of fabrics, using the mathematical model of Principal Component Analysis (PCA). There are a number of variables with a certain influence on the quality of fabrics, but some of these variables are more important than others, so it is useful to identify those variables to a better understanding the factors which can lead the improving of the fabrics quality. A s...

Characterization of soil chemical properties of strawberry fields using principal component analysis

Directory of Open Access Journals (Sweden)

Gláucia Oliveira Islabão

2013-02-01

Full Text Available One of the largest strawberry-producing municipalities of Rio Grande do Sul (RS is Turuçu, in the South of the State. The strawberry production system adopted by farmers is similar to that used in other regions in Brazil and in the world. The main difference is related to the soil management, which can change the soil chemical properties during the strawberry cycle. This study had the objective of assessing the spatial and temporal distribution of soil fertility parameters using principal component analysis (PCA. Soil sampling was based on topography, dividing the field in three thirds: upper, middle and lower. From each of these thirds, five soil samples were randomly collected in the 0-0.20 m layer, to form a composite sample for each third. Four samples were taken during the strawberry cycle and the following properties were determined: soil organic matter (OM, soil total nitrogen (N, available phosphorus (P and potassium (K, exchangeable calcium (Ca and magnesium (Mg, soil pH (pH, cation exchange capacity (CEC at pH 7.0, soil base (V% and soil aluminum saturation(m%. No spatial variation was observed for any of the studied soil fertility parameters in the strawberry fields and temporal variation was only detected for available K. Phosphorus and K contents were always high or very high from the beginning of the strawberry cycle, while pH values ranged from very low to very high. Principal component analysis allowed the clustering of all strawberry fields based on variables related to soil acidity and organic matter content.

Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.

Science.gov (United States)

Duforet-Frebourg, Nicolas; Luu, Keurcien; Laval, Guillaume; Bazin, Eric; Blum, Michael G B

2016-04-01

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt fast open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

A new methodology based on functional principal component analysis to study postural stability post-stroke.

Science.gov (United States)

Sánchez-Sánchez, M Luz; Belda-Lois, Juan-Manuel; Mena-Del Horno, Silvia; Viosca-Herrero, Enrique; Igual-Camacho, Celedonia; Gisbert-Morant, Beatriz

2018-05-05

A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke. The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol. Copyright © 2018 Elsevier Ltd. All rights reserved.

INCREMENTAL PRINCIPAL COMPONENT ANALYSIS BASED OUTLIER DETECTION METHODS FOR SPATIOTEMPORAL DATA STREAMS

Directory of Open Access Journals (Sweden)

A. Bhushan

2015-07-01

Full Text Available In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 3B. Kozloduy NPP units 5/6: Analysis/testing. Working material

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. This volume of Working material contains reports related analyses and testing of Kozloduy nuclear power plant, units 5 and 6.

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 3A. Kozloduy NPP units 5/6: Analysis/testing. Working material

International Nuclear Information System (INIS)

1995-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. This volume of Working material contains reports related analyses and testing of Kozloduy nuclear power plant, units 5 and 6

Control coordination abilities in shock combat sports

Directory of Open Access Journals (Sweden)

Natalya Boychenko

2014-12-01

Full Text Available Purpose: optimize the process control level of coordination abilities in martial arts. Material and Methods: analysis and compilation of scientific and methodological literature, interviews with coaches of drum martial arts, video analysis techniques, teacher observations. Results: identified specific types of coordination abilities in shock combat sports. Pod branny and offered specific and nonspecific tests to monitor the level of species athletes coordination abilities. Conclusion: it is determined that in order to achieve victory in the fight martial artists to navigate the space to be able to assess and manage dynamic and spatio-temporal parameters of movements, maintain balance, have a high coordination of movements. The proposed tests to monitor species coordination abilities athletes allow an objective assessment of not only the overall level of coordination, and the level of specific types of manifestations of this ability.

Morphological evaluation of common bean diversity in Bosnia and Herzegovina using the discriminant analysis of principal components (DAPC multivariate method

Directory of Open Access Journals (Sweden)

Grahić Jasmin

2013-01-01

Full Text Available In order to analyze morphological characteristics of locally cultivated common bean landraces from Bosnia and Herzegovina (B&H, thirteen quantitative and qualitative traits of 40 P. vulgaris accessions, collected from four geographical regions (Northwest B&H, Northeast B&H, Central B&H and Sarajevo and maintained at the Gene bank of the Faculty of Agriculture and Food Sciences in Sarajevo, were examined. Principal component analysis (PCA showed that the proportion of variance retained in the first two principal components was 54.35%. The first principal component had high contributing factor loadings from seed width, seed height and seed weight, whilst the second principal component had high contributing factor loadings from the analyzed traits seed per pod and pod length. PCA plot, based on the first two principal components, displayed a high level of variability among the analyzed material. The discriminant analysis of principal components (DAPC created 3 discriminant functions (DF, whereby the first two discriminant functions accounted for 90.4% of the variance retained. Based on the retained DFs, DAPC provided group membership probabilities which showed that 70% of the accessions examined were correctly classified between the geographically defined groups. Based on the taxonomic distance, 40 common bean accessions analyzed in this study formed two major clusters, whereas two accessions Acc304 and Acc307 didn’t group in any of those. Acc360 and Acc362, as well as Acc324 and Acc371 displayed a high level of similarity and are probably the same landrace. The present diversity of Bosnia and Herzegovina’s common been landraces could be useful in future breeding programs.

Neuromotor Deficits in Developmental Coordination Disorder: Evidence from a Reach-to-Grasp Task

Science.gov (United States)

Biancotto, Marina; Skabar, Aldo; Bulgheroni, Maria; Carrozzi, Marco; Zoia, Stefania

2011-01-01

Developmental coordination disorder (DCD) has been classified as a specific learning disability, nonetheless the underlying cognitive mechanisms are still a matter of discussion. After a summary of the main hypotheses on the principal neuromotor causes of DCD, this study applies a causal model framework to describe the possible coexistence of more…

A Principal Component Analysis of Skills and Competencies Required of Quantity Surveyors: Nigerian Perspective

OpenAIRE

Oluwasuji Dada, Joshua

2014-01-01

The purpose of this paper is to examine the intrinsic relationships among sets of quantity surveyors’ skill and competence variables with a view to reducing them into principal components. The research adopts a data reduction technique using factor analysis statistical technique. A structured questionnaire was administered among major stakeholders in the Nigerian construction industry. The respondents were asked to give rating, on a 5 point Likert scale, on skills and competencies re...

Molecular characterization and similarity relationships among ...

African Journals Online (AJOL)

However, some genotypes have the same specific characters that influence their clustering, and as a result, the results of the principal coordinate analysis (PCoA) largely corresponded to those obtained through cluster analysis. Key words: Cluster analysis, genetic diversity, principal coordinate analysis, sunflower, simple ...

Identifying sources of emerging organic contaminants in a mixed use watershed using principal components analysis.

Science.gov (United States)

Karpuzcu, M Ekrem; Fairbairn, David; Arnold, William A; Barber, Brian L; Kaufenberg, Elizabeth; Koskinen, William C; Novak, Paige J; Rice, Pamela J; Swackhamer, Deborah L

2014-01-01

Principal components analysis (PCA) was used to identify sources of emerging organic contaminants in the Zumbro River watershed in Southeastern Minnesota. Two main principal components (PCs) were identified, which together explained more than 50% of the variance in the data. Principal Component 1 (PC1) was attributed to urban wastewater-derived sources, including municipal wastewater and residential septic tank effluents, while Principal Component 2 (PC2) was attributed to agricultural sources. The variances of the concentrations of cotinine, DEET and the prescription drugs carbamazepine, erythromycin and sulfamethoxazole were best explained by PC1, while the variances of the concentrations of the agricultural pesticides atrazine, metolachlor and acetochlor were best explained by PC2. Mixed use compounds carbaryl, iprodione and daidzein did not specifically group with either PC1 or PC2. Furthermore, despite the fact that caffeine and acetaminophen have been historically associated with human use, they could not be attributed to a single dominant land use category (e.g., urban/residential or agricultural). Contributions from septic systems did not clarify the source for these two compounds, suggesting that additional sources, such as runoff from biosolid-amended soils, may exist. Based on these results, PCA may be a useful way to broadly categorize the sources of new and previously uncharacterized emerging contaminants or may help to clarify transport pathways in a given area. Acetaminophen and caffeine were not ideal markers for urban/residential contamination sources in the study area and may need to be reconsidered as such in other areas as well.

Evaluation of Coordination of Emergency Response Team through the Social Network Analysis. Case Study: Oil and Gas Refinery.

Science.gov (United States)

Mohammadfam, Iraj; Bastani, Susan; Esaghi, Mahbobeh; Golmohamadi, Rostam; Saee, Ali

2015-03-01

The purpose of this study was to examine the cohesions status of the coordination within response teams in the emergency response team (ERT) in a refinery. For this study, cohesion indicators of social network analysis (SNA; density, degree centrality, reciprocity, and transitivity) were utilized to examine the coordination of the response teams as a whole network. The ERT of this research, which was a case study, included seven teams consisting of 152 members. The required data were collected through structured interviews and were analyzed using the UCINET 6.0 Social Network Analysis Program. The results reported a relatively low number of triple connections, poor coordination with key members, and a high level of mutual relations in the network with low density, all implying that there were low cohesions of coordination in the ERT. The results showed that SNA provided a quantitative and logical approach for the examination of the coordination status among response teams and it also provided a main opportunity for managers and planners to have a clear understanding of the presented status. The research concluded that fundamental efforts were needed to improve the presented situations.

Micro practices of coordination based on complex adaptive systems: user needs and strategies for coordinating public health in Denmark.

Science.gov (United States)

Terkildsen, Morten Deleuran; Wittrup, Inge; Burau, Viola

2015-01-01

Many highly formalised approaches to coordination poorly fit public health and recent studies call for coordination based on complex adaptive systems. Our contribution is two-fold. Empirically, we focus on public health, and theoretically we build on the patient perspective and treat coordination as a process of contingent, two-level negotiations of user needs. The paper draws on the concept of user needs-based coordination and sees coordination as a process, whereby needs emerging from the life world of the user are made amenable to the health system through negotiations. The analysis is based on an explorative case study of a health promotion initiative in Denmark. It adopts an anthropological qualitative approach and uses a range of qualitative data. The analysis identifies four strategies of coordination: the coordinator focusing on the individual user or on relations with other professionals; and the manager coaching the coordinator or providing structural support. Crucially, the coordination strategies by management remain weak as they do not directly relate to specific user needs. In process of bottom-up negotiations user needs become blurred and this is especially a challenge for management. The study therefore calls for an increased focus on the level nature of negotiations to bridge the gap that currently weakens coordination strategies by management.

Unpacking Coordination Benefits in Supply Networks

DEFF Research Database (Denmark)

Petrick, Irene J.; Maitland, Carleen; Pogrebnyakov, Nicolai

2016-01-01

This paper examines how coordination among firms in supply networks generates benefits in the short and long terms for firms. It focuses on information technology (IT) and process improvement coordination. Analysis was performed on quantitative and qualitative data from a sample of SMEs in plastics...... manufacturing in Pennsylvania. Results indicate that coordination on both IT and process improvement leads to short- and long-term benefits. These relationships were mediated by the adoption of innovations (when coordinating on IT) and access to new capabilities (in process improvement coordination......). These results extend the understanding of how participation in supply networks benefits individual firms....

Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual

International Nuclear Information System (INIS)

Koch, C.D.; Pirkle, F.L.; Schmidt, J.S.

1981-01-01

A Principal Components Analysis (PCA) has been written to aid in the interpretation of multivariate aerial radiometric data collected by the US Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) program. The variations exhibited by these data have been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

International Nuclear Information System (INIS)

Casini, R.; Lites, B. W.; Ramos, A. Asensio; Ariste, A. López

2013-01-01

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2 4n bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2 4n as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing

Identifying apple surface defects using principal components analysis and artifical neural networks

Science.gov (United States)

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4D. Paks NPP: Analysis and testing. Working material

International Nuclear Information System (INIS)

1996-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports on seismic margin assessment and earthquake experience based methods for WWER-440/213 type NPPs; structural analysis and site inspection for site requalification; structural response of Paks NPP reactor building; analysis and testing of model worm type tanks on shaking table; vibration test of a worm tank model; evaluation of potential hazard for operating WWER control rods under seismic excitation

Enhancement of Jahani (Firouzabad salt dome lithological units, using principal components analysis

Directory of Open Access Journals (Sweden)

Houshang Pourcaseb

2016-04-01

Full Text Available In this study, principal components analysis was used to investigate lithological characteristics of Jahani salt dome, Firouzabad. The spectral curves of rocks in the study area show that the evaporate rocks have the highest reflectance at specified wavelengths. The highest reflection has been seen in gypsum and white salt, while minimal reflection can be observed in the igneous rocks from the region. The results show that PCs have significantly low information. It is clear that PC1 shows more information in the highest variance while PC2 has less information. Regional geological map and field controls show compatibility between the enhanced zones and outcrops in the field.

Principal Component Analysis of Process Datasets with Missing Values

Directory of Open Access Journals (Sweden)

Kristen A. Severson

2017-07-01

Full Text Available Datasets with missing values arising from causes such as sensor failure, inconsistent sampling rates, and merging data from different systems are common in the process industry. Methods for handling missing data typically operate during data pre-processing, but can also occur during model building. This article considers missing data within the context of principal component analysis (PCA, which is a method originally developed for complete data that has widespread industrial application in multivariate statistical process control. Due to the prevalence of missing data and the success of PCA for handling complete data, several PCA algorithms that can act on incomplete data have been proposed. Here, algorithms for applying PCA to datasets with missing values are reviewed. A case study is presented to demonstrate the performance of the algorithms and suggestions are made with respect to choosing which algorithm is most appropriate for particular settings. An alternating algorithm based on the singular value decomposition achieved the best results in the majority of test cases involving process datasets.

Coordinated Research Projects of the IAEA Atomic and Molecular Data Unit

Science.gov (United States)

Braams, B. J.; Chung, H.-K.

2011-05-01

The IAEA Atomic and Molecular Data Unit is dedicated to the provision of databases for atomic, molecular and plasma-material interaction (AM/PMI) data that are relevant for nuclear fusion research. IAEA Coordinated Research Projects (CRPs) are the principal mechanism by which the Unit encourages data evaluation and the production of new data. Ongoing and planned CRPs on AM/PMI data are briefly described here.

Coordinated Research Projects of the IAEA Atomic and Molecular Data Unit

International Nuclear Information System (INIS)

Braams, B. J.; Chung, H.-K.

2011-01-01

The IAEA Atomic and Molecular Data Unit is dedicated to the provision of databases for atomic, molecular and plasma-material interaction (AM/PMI) data that are relevant for nuclear fusion research. IAEA Coordinated Research Projects (CRPs) are the principal mechanism by which the Unit encourages data evaluation and the production of new data. Ongoing and planned CRPs on AM/PMI data are briefly described here.

Detecting coordinated regulation of multi-protein complexes using logic analysis of gene expression

Directory of Open Access Journals (Sweden)

Yeates Todd O

2009-12-01

Full Text Available Abstract Background Many of the functional units in cells are multi-protein complexes such as RNA polymerase, the ribosome, and the proteasome. For such units to work together, one might expect a high level of regulation to enable co-appearance or repression of sets of complexes at the required time. However, this type of coordinated regulation between whole complexes is difficult to detect by existing methods for analyzing mRNA co-expression. We propose a new methodology that is able to detect such higher order relationships. Results We detect coordinated regulation of multiple protein complexes using logic analysis of gene expression data. Specifically, we identify gene triplets composed of genes whose expression profiles are found to be related by various types of logic functions. In order to focus on complexes, we associate the members of a gene triplet with the distinct protein complexes to which they belong. In this way, we identify complexes related by specific kinds of regulatory relationships. For example, we may find that the transcription of complex C is increased only if the transcription of both complex A AND complex B is repressed. We identify hundreds of examples of coordinated regulation among complexes under various stress conditions. Many of these examples involve the ribosome. Some of our examples have been previously identified in the literature, while others are novel. One notable example is the relationship between the transcription of the ribosome, RNA polymerase and mannosyltransferase II, which is involved in N-linked glycan processing in the Golgi. Conclusions The analysis proposed here focuses on relationships among triplets of genes that are not evident when genes are examined in a pairwise fashion as in typical clustering methods. By grouping gene triplets, we are able to decipher coordinated regulation among sets of three complexes. Moreover, using all triplets that involve coordinated regulation with the ribosome

Honouring Roles: The Story of a Principal and a Student

Directory of Open Access Journals (Sweden)

Jerome Cranston

2012-11-01

Full Text Available The importance of the teacher-student relationship in educational practice is well established, as is the idea of principal leadership in relationship to staff. Even though principal leadership is regarded as a factor in student success, the principal’s effect is usually assumed to take place via the teaching staff. There is an absence of research about the “lived experience” of direct principal-student relationships that shed lights on the ways in which these relationships play a role in student success and principal transformation. This paper presents two narratives written about a particular set of principal-student interactions experienced by the researcher (principal and participant (student.  The analysis uses a narrative inquiry approach to explore both the individual and collective meanings of this principal-student relationship. The stories and their derived meanings have the potential to enliven and  influence educational practice as they explore the subtleties of the principal-student relationship.

Learning representative features for facial images based on a modified principal component analysis

Science.gov (United States)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

Using the Cluster Analysis and the Principal Component Analysis in Evaluating the Quality of a Destination

Directory of Open Access Journals (Sweden)

Ida Vajčnerová

2016-01-01

Full Text Available The objective of the paper is to explore possibilities of evaluating the quality of a tourist destination by means of the principal components analysis (PCA and the cluster analysis. In the paper both types of analysis are compared on the basis of the results they provide. The aim is to identify advantage and limits of both methods and provide methodological suggestion for their further use in the tourism research. The analyses is based on the primary data from the customers’ satisfaction survey with the key quality factors of a destination. As output of the two statistical methods is creation of groups or cluster of quality factors that are similar in terms of respondents’ evaluations, in order to facilitate the evaluation of the quality of tourist destinations. Results shows the possibility to use both tested methods. The paper is elaborated in the frame of wider research project aimed to develop a methodology for the quality evaluation of tourist destinations, especially in the context of customer satisfaction and loyalty.

Coordination in continuously repeated games

NARCIS (Netherlands)

Weeren, A.J.T.M.; Schumacher, J.M.; Engwerda, J.C.

1995-01-01

In this paper we propose a model to describe the effectiveness of coordination in a continuously repeated two-player game. We study how the choice of a decision rule by a coordinator affects the strategic behavior of the players, resulting in more or less cooperation. Our model requires the analysis

Bare coordination: the semantic shift

NARCIS (Netherlands)

de Swart, Henriette; Le Bruyn, Bert

2014-01-01

This paper develops an analysis of the syntax-semantics interface of two types of split coordination structures. In the first type, two bare singular count nouns appear as arguments in a coordinated structure, as in bride and groom were happy. We call this the N&N construction. In the second type,

Micro practices of coordination based on complex adaptive systems: user needs and strategies for coordinating public health in Denmark

Directory of Open Access Journals (Sweden)

Morten Deleuran Terkildsen

2015-09-01

Full Text Available Introduction: Many highly formalised approaches to coordination poorly fit public health and recent studies call for coordination based on complex adaptive systems. Our contribution is two-fold. Empirically, we focus on public health, and theoretically we build on the patient perspective and treat coordination as a process of contingent, two-level negotiations of user needs.Theory and Methods: The paper draws on the concept of user needs-based coordination and sees coordination as a process, whereby needs emerging from the life world of the user are made amenable to the health system through negotiations. The analysis is based on an explorative case study of a health promotion initiative in Denmark. It adopts an anthropological qualitative approach and uses a range of qualitative data.Results: The analysis identifies four strategies of coordination: the coordinator focusing on the individual user or on relations with other professionals; and the manager coaching the coordinator or providing structural support. Crucially, the coordination strategies by management remain weak as they do not directly relate to specific user needs.Discussion: In process of bottom-up negotiations user needs become blurred and this is especially a challenge for management. The study therefore calls for an increased focus on the level nature of negotiations to bridge the gap that currently weakens coordination strategies by management.

Subjective Performance Evaluations, Self-esteem, and Ego-threats in Principal-agent Relations

DEFF Research Database (Denmark)

Sebald, Alexander Christopher; Walzl, Markus

find that agents sanction whenever the feedback of principals is below their subjective self-evaluations even if the agents' payoff is independent of the principals' feedback. Based on our experimental analysis we propose a principal-agent model with subjective performance evaluations that accommodates...

Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

International Nuclear Information System (INIS)

Christensen, J.H.; Hansen, A.B.; Andersen, O.

2005-01-01

Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs

Chemical fingerprinting of terpanes and steranes by chromatographic alignment and principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Christensen, J.H. [Royal Veterinary and Agricultural Univ., Thorvaldsensvej (Denmark). Dept. of Natural Sciences; Hansen, A.B. [National Environmental Research Inst., Roskilde (Denmark). Dept. of Environmental Chemistry and Microbiology; Andersen, O. [Roskilde Univ., Roskilde (Denmark). Dept. of Life Sciences and Chemistry

2005-07-01

Biomarkers such as steranes and terpanes are abundant in crude oils, particularly in heavy distillate petroleum products. They are useful for matching highly weathered oil samples when other groups of petroleum hydrocarbons fail to distinguish oil samples. In this study, time warping and principal component analysis (PCA) were applied for oil hydrocarbon fingerprinting based on relative amounts of terpane and sterane isomers analyzed by gas chromatography and mass spectrometry. The 4 principal components were boiling point range, clay content, marine or organic terrestrial matter, and maturity based on differences in the terpane and sterane isomer patterns. This study is an extension of a previous fingerprinting study for identifying the sources of oil spill samples based only on the profiles of sterane isomers. Spill samples from the Baltic Carrier oil spill were correctly identified by inspection of score plots. The interpretation of the loading and score plots offered further chemical information about correlations between changes in the amounts of sterane and terpane isomers. It was concluded that this method is an objective procedure for analyzing chromatograms with more comprehensive data usage compared to other fingerprinting methods. 20 refs., 4 figs.

Clinical feasibility and validation of 3D principal strain analysis from cine MRI: comparison to 2D strain by MRI and 3D speckle tracking echocardiography.

Science.gov (United States)

Satriano, Alessandro; Heydari, Bobak; Narous, Mariam; Exner, Derek V; Mikami, Yoko; Attwood, Monica M; Tyberg, John V; Lydell, Carmen P; Howarth, Andrew G; Fine, Nowell M; White, James A

2017-12-01

Two-dimensional (2D) strain analysis is constrained by geometry-dependent reference directions of deformation (i.e. radial, circumferential, and longitudinal) following the assumption of cylindrical chamber architecture. Three-dimensional (3D) principal strain analysis may overcome such limitations by referencing intrinsic (i.e. principal) directions of deformation. This study aimed to demonstrate clinical feasibility of 3D principal strain analysis from routine 2D cine MRI with validation to strain from 2D tagged cine analysis and 3D speckle tracking echocardiography. Thirty-one patients undergoing cardiac MRI were studied. 3D strain was measured from routine, multi-planar 2D cine SSFP images using custom software designed to apply 4D deformation fields to 3D cardiac models to derive principal strain. Comparisons of strain estimates versus those by 2D tagged cine, 2D non-tagged cine (feature tracking), and 3D speckle tracking echocardiography (STE) were performed. Mean age was 51 ± 14 (36% female). Mean LV ejection fraction was 66 ± 10% (range 37-80%). 3D principal strain analysis was feasible in all subjects and showed high inter- and intra-observer reproducibility (ICC range 0.83-0.97 and 0.83-0.98, respectively-p analysis is feasible using routine, multi-planar 2D cine MRI and shows high reproducibility with strong correlations to 2D conventional strain analysis and 3D STE-based analysis. Given its independence from geometry-related directions of deformation this technique may offer unique benefit for the detection and prognostication of myocardial disease, and warrants expanded investigation.

Trajectory modeling of gestational weight: A functional principal component analysis approach.

Directory of Open Access Journals (Sweden)

Menglu Che

Full Text Available Suboptimal gestational weight gain (GWG, which is linked to increased risk of adverse outcomes for a pregnant woman and her infant, is prevalent. In the study of a large cohort of Canadian pregnant women, our goals are to estimate the individual weight growth trajectory using sparsely collected bodyweight data, and to identify the factors affecting the weight change during pregnancy, such as prepregnancy body mass index (BMI, dietary intakes and physical activity. The first goal was achieved through functional principal component analysis (FPCA by conditional expectation. For the second goal, we used linear regression with the total weight gain as the response variable. The trajectory modeling through FPCA had a significantly smaller root mean square error (RMSE and improved adaptability than the classic nonlinear mixed-effect models, demonstrating a novel tool that can be used to facilitate real time monitoring and interventions of GWG. Our regression analysis showed that prepregnancy BMI had a high predictive value for the weight changes during pregnancy, which agrees with the published weight gain guideline.

Assessment of drinking water quality using principal component ...

African Journals Online (AJOL)

Assessment of drinking water quality using principal component analysis and partial least square discriminant analysis: a case study at water treatment plants, ... water and to detect the source of pollution for the most revealing parameters.

Kernel Principal Component Analysis and its Applications in Face Recognition and Active Shape Models

OpenAIRE

Wang, Quan

2012-01-01

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which better exploits the complicated spatial structure of high-dimensional features. In this paper, we first review the basic ideas of PCA and kernel PCA. Then we focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied ...

Registration of dynamic dopamine D{sub 2}receptor images using principal component analysis

Energy Technology Data Exchange (ETDEWEB)

Acton, P.D.; Ell, P.J. [Institute of Nuclear Medicine, University College London Medical School, London (United Kingdom); Pilowsky, L.S.; Brammer, M.J. [Institute of Psychiatry, De Crespigny Park, London (United Kingdom); Suckling, J. [Clinical Age Research Unit, Kings College School of Medicine and Dentistry, London (United Kingdom)

1997-11-01

This paper describes a novel technique for registering a dynamic sequence of single-photon emission tomography (SPET) dopamine D{sub 2}receptor images, using principal component analysis (PCA). Conventional methods for registering images, such as count difference and correlation coefficient algorithms, fail to take into account the dynamic nature of the data, resulting in large systematic errors when registering time-varying images. However, by using principal component analysis to extract the temporal structure of the image sequence, misregistration can be quantified by examining the distribution of eigenvalues. The registration procedures were tested using a computer-generated dynamic phantom derived from a high-resolution magnetic resonance image of a realistic brain phantom. Each method was also applied to clinical SPET images of dopamine D {sub 2}receptors, using the ligands iodine-123 iodobenzamide and iodine-123 epidepride, to investigate the influence of misregistration on kinetic modelling parameters and the binding potential. The PCA technique gave highly significant (P <0.001) improvements in image registration, leading to alignment errors in x and y of about 25% of the alternative methods, with reductions in autocorrelations over time. It could also be applied to align image sequences which the other methods failed completely to register, particularly {sup 123}I-epidepride scans. The PCA method produced data of much greater quality for subsequent kinetic modelling, with an improvement of nearly 50% in the {chi}{sup 2}of the fit to the compartmental model, and provided superior quality registration of particularly difficult dynamic sequences. (orig.) With 4 figs., 2 tabs., 26 refs.

An Investigation of Potential Fraud in Commercial Orange Juice Products in Malaysian Market by Cluster Analysis and Principal Component Analysis

International Nuclear Information System (INIS)

Keng, S.E.; Abbas Fadhl Mubarek Al-Karkhi; Mohd Khairuddin Mohd Talib; Azhar Mat Easa; Hoong, C.L.

2015-01-01

This study was triggered by Malaysia Ministry of Health to monitor quality of commercial orange juice products sold in Malaysia market. A total of 19 orange juice samples from 14 different brands of packed orange juice products and 5 different brands of fresh orange fruit juices were analyzed for total soluble solids content, total titratable acidity, sugar composition and amino acid profiles. Hierarchical Cluster analysis (HCA) and Principal component analysis (PCA) on amino acid composition alone allowed visual discrimination between fresh squeezed orange juices and commercial packed orange juices. Suspicion of mislabel was raised in cases of miss-classification. (author)

Spatial control of groundwater contamination, using principal

Indian Academy of Sciences (India)

Spatial control of groundwater contamination, using principal component analysis ... anthropogenic (agricultural activities and domestic wastewaters), and marine ... The PC scores reflect the change of groundwater quality of geogenic origin ...

Preliminary study of soil permeability properties using principal component analysis

Science.gov (United States)

Yulianti, M.; Sudriani, Y.; Rustini, H. A.

2018-02-01

Soil permeability measurement is undoubtedly important in carrying out soil-water research such as rainfall-runoff modelling, irrigation water distribution systems, etc. It is also known that acquiring reliable soil permeability data is rather laborious, time-consuming, and costly. Therefore, it is desirable to develop the prediction model. Several studies of empirical equations for predicting permeability have been undertaken by many researchers. These studies derived the models from areas which soil characteristics are different from Indonesian soil, which suggest a possibility that these permeability models are site-specific. The purpose of this study is to identify which soil parameters correspond strongly to soil permeability and propose a preliminary model for permeability prediction. Principal component analysis (PCA) was applied to 16 parameters analysed from 37 sites consist of 91 samples obtained from Batanghari Watershed. Findings indicated five variables that have strong correlation with soil permeability, and we recommend a preliminary permeability model, which is potential for further development.

Application of Principal Component Analysis in Prompt Gamma Spectra for Material Sorting

Energy Technology Data Exchange (ETDEWEB)

Im, Hee Jung; Lee, Yun Hee; Song, Byoung Chul; Park, Yong Joon; Kim, Won Ho

2006-11-15

For the detection of illicit materials in a very short time by comparing unknown samples' gamma spectra to pre-programmed material signatures, we at first, selected a method to reduce the noise of the obtained gamma spectra. After a noise reduction, a pattern recognition technique was applied to discriminate the illicit materials from the innocuous materials in the noise reduced data. Principal component analysis was applied for a noise reduction and pattern recognition in prompt gamma spectra. A computer program for the detection of illicit materials based on PCA method was developed in our lab and can be applied to the PGNAA system for the baggage checking at all ports of entry at a very short time.

Empirical research on financial capability evaluation of A-share listed companies in the securities industry based on principal component analysis

Directory of Open Access Journals (Sweden)

Xiuping Wang

2017-11-01

Full Text Available Based on the relevant financial data indicators of A-share markets of Shanghai and Shenzhen in 2009, with all of 29 listed companies in the securities industry as the research objects, this paper selects 10variables that can fully reflect the financial capability indicators and uses the principal component analysis to carry out the empirical research on the financial capability. The research results show that the comprehensive financial capability of listed companies in A-share securities industry must be focused on the following four capabilities, investment and income, profit, capital composition and debt repayment and cash flow indicators. In addition, the principal component analysis can effectively evaluate the financial capability of listed companies in A-share securities industry, and solve the problems in the previous analysis methods, such as excessive indicators, information overlapping and so on.

Assessing the effect of oil price on world food prices: Application of principal component analysis

International Nuclear Information System (INIS)

Esmaeili, Abdoulkarim; Shokoohi, Zainab

2011-01-01

The objective of this paper is to investigate the co-movement of food prices and the macroeconomic index, especially the oil price, by principal component analysis to further understand the influence of the macroeconomic index on food prices. We examined the food prices of seven major products: eggs, meat, milk, oilseeds, rice, sugar and wheat. The macroeconomic variables studied were crude oil prices, consumer price indexes, food production indexes and GDP around the world between 1961 and 2005. We use the Scree test and the proportion of variance method for determining the optimal number of common factors. The correlation coefficient between the extracted principal component and the macroeconomic index varies between 0.87 for the world GDP and 0.36 for the consumer price index. We find the food production index has the greatest influence on the macroeconomic index and that the oil price index has an influence on the food production index. Consequently, crude oil prices have an indirect effect on food prices. - Research Highlights: →We investigate the co-movement of food prices and the macroeconomic index. →The crude oil price has indirect effect on the world GDP via its impacts on food production index. →The food production index is the source of causation for CPI and GDP is affected by CPI. →The results confirm an indirect effect among oil price, food price principal component.

Visual Analysis of North Atlantic Hurricane Trends Using Parallel Coordinates and Statistical Techniques

National Research Council Canada - National Science Library

Steed, Chad A; Fitzpatrick, Patrick J; Jankun-Kelly, T. J; Swan II, J. E

2008-01-01

... for a particular dependent variable. These capabilities are combined into a unique visualization system that is demonstrated via a North Atlantic hurricane climate study using a systematic workflow. This research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets.

Kernel principal component analysis residual diagnosis (KPCARD): An automated method for cosmic ray artifact removal in Raman spectra

International Nuclear Information System (INIS)

Li, Boyan; Calvet, Amandine; Casamayou-Boucau, Yannick; Ryder, Alan G.

2016-01-01

A new, fully automated, rapid method, referred to as kernel principal component analysis residual diagnosis (KPCARD), is proposed for removing cosmic ray artifacts (CRAs) in Raman spectra, and in particular for large Raman imaging datasets. KPCARD identifies CRAs via a statistical analysis of the residuals obtained at each wavenumber in the spectra. The method utilizes the stochastic nature of CRAs; therefore, the most significant components in principal component analysis (PCA) of large numbers of Raman spectra should not contain any CRAs. The process worked by first implementing kernel PCA (kPCA) on all the Raman mapping data and second accurately estimating the inter- and intra-spectrum noise to generate two threshold values. CRA identification was then achieved by using the threshold values to evaluate the residuals for each spectrum and assess if a CRA was present. CRA correction was achieved by spectral replacement where, the nearest neighbor (NN) spectrum, most spectroscopically similar to the CRA contaminated spectrum and principal components (PCs) obtained by kPCA were both used to generate a robust, best curve fit to the CRA contaminated spectrum. This best fit spectrum then replaced the CRA contaminated spectrum in the dataset. KPCARD efficacy was demonstrated by using simulated data and real Raman spectra collected from solid-state materials. The results showed that KPCARD was fast ( 1 million) Raman datasets. - Highlights: • New rapid, automatable method for cosmic ray artifact correction of Raman spectra. • Uses combination of kernel PCA and noise estimation for artifact identification. • Implements a best fit spectrum replacement correction approach.

The Relationship Among Principal Preparation Programs, Professional Development, and Instructional Leadership Efficacy  

OpenAIRE

Thomas III, Harry R.

2015-01-01

This study presents a qualitative analysis of principals' perceptions of the relationship among principal preparation programs, professional development and instructional leadership confidence in one urban school division in Virginia. Levine (2005) argued that the principal has a salient effect on the instructional programs within schools, and the preparation and professional development of the principal affects the degree to which they maintain and improve instruction. To examine principal p...

Examining Parents' Ratings of Middle-School Students' Academic Self-Regulation Using Principal Axis Factoring Analysis

Science.gov (United States)

Chen, Peggy P.; Cleary, Timothy J.; Lui, Angela M.

2015-01-01

This study examined the reliability and validity of a parent rating scale, the "Self-Regulation Strategy Inventory: Parent Rating Scale" ("SRSI-PRS"), using a sample of 451 parents of sixth- and seventh-grade middle-school students. Principal axis factoring (PAF) analysis revealed a 3-factor structure for the 23-item SRSI-PRS:…

Practices of Elementary Principals in Influencing New Teachers to Remain in Education

OpenAIRE

Palermo, Thelma D.

2002-01-01

The grounded theory presented in this study describes practices elementary principals utilize in influencing new teachers to remain in education. Eleven teachers and three elementary principals from one school division in Virginia participated in this study. Interview data were collected, elementary principals were shadowed, and documents were analyzed. Thematic categories and sub categories were formed through data analysis. The grounded theory that resulted from this study is: principals wh...

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China

Directory of Open Access Journals (Sweden)

Zhang YJ

2018-05-01

Full Text Available Yuji Zhang,* Xiaoju Li,* Lu Mao, Mei Zhang, Ke Li, Yinxia Zheng, Wangfei Cui, Hongpo Yin, Yanli He, Mingxia Jing Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang, China *These authors contributed equally to this work Purpose: The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis.Patients and methods: A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ2-test and a binary logistic regression model.Results: This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications.Conclusion: Community management plays an important role in improving the patients’ medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers. Keywords: hypertension, medication adherence, factors, principal component analysis, community management, China

Diagnose Test-Taker's Profile in Terms of Core Profile Patterns: Principal Component (PC) vs. Profile Analysis via MDS (PAMS) Approaches.

Science.gov (United States)

Kim, Se-Kang; Davison, Mark L.

A study was conducted to examine how principal components analysis (PCA) and Profile Analysis via Multidimensional Scaling (PAMS) can be used to diagnose individuals observed score profiles in terms of core profile patterns identified by each method. The standardization sample from the Wechsler Intelligence Scale for Children, Third Edition…

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

Science.gov (United States)

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.

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4A. Paks NPP: Analysis/testing. Working material

International Nuclear Information System (INIS)

1995-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports on data related to seismic analyses of structures of Paks and Kozloduy reactor buildings and WWER-440/213 primary coolant loops with different antiseismic devices

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 4A. Paks NPP: Analysis/testing. Working material

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports on data related to seismic analyses of structures of Paks and Kozloduy reactor buildings and WWER-440/213 primary coolant loops with different antiseismic devices.

Automatic scatter detection in fluorescence landscapes by means of spherical principal component analysis

DEFF Research Database (Denmark)

Kotwa, Ewelina Katarzyna; Jørgensen, Bo Munk; Brockhoff, Per B.

2013-01-01

In this paper, we introduce a new method, based on spherical principal component analysis (S‐PCA), for the identification of Rayleigh and Raman scatters in fluorescence excitation–emission data. These scatters should be found and eliminated as a prestep before fitting parallel factor analysis...... models to the data, in order to avoid model degeneracies. The work is inspired and based on a previous research, where scatter removal was automatic (based on a robust version of PCA called ROBPCA) and required no visual data inspection but appeared to be computationally intensive. To overcome...... this drawback, we implement the fast S‐PCA in the scatter identification routine. Moreover, an additional pattern interpolation step that complements the method, based on robust regression, will be applied. In this way, substantial time savings are gained, and the user's engagement is restricted to a minimum...

Linearization of the Principal Component Analysis method for radiative transfer acceleration: Application to retrieval algorithms and sensitivity studies

International Nuclear Information System (INIS)

Spurr, R.; Natraj, V.; Lerot, C.; Van Roozendael, M.; Loyola, D.

2013-01-01

Principal Component Analysis (PCA) is a promising tool for enhancing radiative transfer (RT) performance. When applied to binned optical property data sets, PCA exploits redundancy in the optical data, and restricts the number of full multiple-scatter calculations to those optical states corresponding to the most important principal components, yet still maintaining high accuracy in the radiance approximations. We show that the entire PCA RT enhancement process is analytically differentiable with respect to any atmospheric or surface parameter, thus allowing for accurate and fast approximations of Jacobian matrices, in addition to radiances. This linearization greatly extends the power and scope of the PCA method to many remote sensing retrieval applications and sensitivity studies. In the first example, we examine accuracy for PCA-derived UV-backscatter radiance and Jacobian fields over a 290–340 nm window. In a second application, we show that performance for UV-based total ozone column retrieval is considerably improved without compromising the accuracy. -- Highlights: •Principal Component Analysis (PCA) of spectrally-binned atmospheric optical properties. •PCA-based accelerated radiative transfer with 2-stream model for fast multiple-scatter. •Atmospheric and surface property linearization of this PCA performance enhancement. •Accuracy of PCA enhancement for radiances and bulk-property Jacobians, 290–340 nm. •Application of PCA speed enhancement to UV backscatter total ozone retrievals

Principal component analysis reveals gender-specific predictors of cardiometabolic risk in 6th graders

Directory of Open Access Journals (Sweden)

Peterson Mark D

2012-11-01

Full Text Available Abstract Background The purpose of this study was to determine the sex-specific pattern of pediatric cardiometabolic risk with principal component analysis, using several biological, behavioral and parental variables in a large cohort (n = 2866 of 6th grade students. Methods Cardiometabolic risk components included waist circumference, fasting glucose, blood pressure, plasma triglycerides levels and HDL-cholesterol. Principal components analysis was used to determine the pattern of risk clustering and to derive a continuous aggregate score (MetScore. Stratified risk components and MetScore were analyzed for association with age, body mass index (BMI, cardiorespiratory fitness (CRF, physical activity (PA, and parental factors. Results In both boys and girls, BMI and CRF were associated with multiple risk components, and overall MetScore. Maternal smoking was associated with multiple risk components in girls and boys, as well as MetScore in boys, even after controlling for children’s BMI. Paternal family history of early cardiovascular disease (CVD and parental age were associated with increased blood pressure and MetScore for girls. Children’s PA levels, maternal history of early CVD, and paternal BMI were also indicative for various risk components, but not MetScore. Conclusions Several biological and behavioral factors were independently associated with children’s cardiometabolic disease risk, and thus represent a unique gender-specific risk profile. These data serve to bolster the independent contribution of CRF, PA, and family-oriented healthy lifestyles for improving children’s health.

Coordinating a Service Supply Chain under Arms Offset Program’s Intervention by Performance-Based Contracting

Directory of Open Access Journals (Sweden)

Yi-Kuei Lin

2016-01-01

Full Text Available This paper investigates a support service supply chain for coordinating with a local third-party logistics provider by arms offset program’s intervention and develops a performance-based contracting framework for the coordinating problem, which remains scarce in the literatures. The performance-based contracting framework evaluates payments and profits for the support service by a game-theoretical approach with principal-agent model. We prove that the proposed framework is an effective tool in acquiring the balance between maximum profit and minimum payment for both parties in the coordinating problem without moral hazard issue. A numerical study consolidates the formulated schemes as contracting preference for both parties’ decision with a higher profit margin at a lower customer’s payment.

The Purification Method of Matching Points Based on Principal Component Analysis

Directory of Open Access Journals (Sweden)

DONG Yang

2017-02-01

Full Text Available The traditional purification method of matching points usually uses a small number of the points as initial input. Though it can meet most of the requirements of point constraints, the iterative purification solution is easy to fall into local extreme, which results in the missing of correct matching points. To solve this problem, we introduce the principal component analysis method to use the whole point set as initial input. And thorough mismatching points step eliminating and robust solving, more accurate global optimal solution, which intends to reduce the omission rate of correct matching points and thus reaches better purification effect, can be obtained. Experimental results show that this method can obtain the global optimal solution under a certain original false matching rate, and can decrease or avoid the omission of correct matching points.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yihang Yin

2015-08-01

Full Text Available Wireless sensor networks (WSNs have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA. First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

Science.gov (United States)

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

Spatial control of groundwater contamination, using principal ...

Indian Academy of Sciences (India)

probe into the spatial controlling processes of groundwater contamination, using principal component analysis (PCA). ... topography, soil type, depth of water levels, and water usage. Thus, the ... of effective sites for infiltration of recharge water.

Principal component analysis of air particulate data from the industrial area of islamabad, pakistan

International Nuclear Information System (INIS)

Waheed, S.; Siddique, N.; Daud, M.

2008-01-01

A Gent air sampler was used to collect 72 pairs of size fractionated coarse and fine (PM/sub 10/ and PM/sub 2.5/) particulate mass samples from the industrial zone (sector I-9) of Islamabad. These samples were analyzed for their elemental composition using Instrumental Neutron Activation Analysis (INAA). Principal component analysis (PCA), which can be used for source apportionment of quantified elemental data, was used to interpret the data. Graphical representations of loadings were used to explain the data through grouping of the elements from same source. The present work shows well defined elemental fingerprints of suspended soil and road dust, industry, motor vehicle exhaust and tyres, and coal and refuses combustions for the studied locality of Islamabad. (author)

Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

Science.gov (United States)

Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

2013-06-01

This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

Illustration and analysis of a coordinated approach to an effective forensic trace evidence capability.

Science.gov (United States)

Stoney, David A; Stoney, Paul L

2015-08-01

An effective trace evidence capability is defined as one that exploits all useful particle types, chooses appropriate technologies to do so, and directly integrates the findings with case-specific problems. Limitations of current approaches inhibit the attainment of an effective capability and it has been strongly argued that a new approach to trace evidence analysis is essential. A hypothetical case example is presented to illustrate and analyze how forensic particle analysis can be used as a powerful practical tool in forensic investigations. The specifics in this example, including the casework investigation, laboratory analyses, and close professional interactions, provide focal points for subsequent analysis of how this outcome can be achieved. This leads to the specification of five key elements that are deemed necessary and sufficient for effective forensic particle analysis: (1) a dynamic forensic analytical approach, (2) concise and efficient protocols addressing particle combinations, (3) multidisciplinary capabilities of analysis and interpretation, (4) readily accessible external specialist resources, and (5) information integration and communication. A coordinating role, absent in current approaches to trace evidence analysis, is essential to achieving these elements. However, the level of expertise required for the coordinating role is readily attainable. Some additional laboratory protocols are also essential. However, none of these has greater staffing requirements than those routinely met by existing forensic trace evidence practitioners. The major challenges that remain are organizational acceptance, planning and implementation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Optimal pattern synthesis for speech recognition based on principal component analysis

Science.gov (United States)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

International Nuclear Information System (INIS)

Lu, Wei-Zhen; He, Hong-Di; Dong, Li-yun

2011-01-01

This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO 2 ), respirable suspended particulates (RSP) and nitrogen dioxide (NO 2 ), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region. (author)

Combined approach based on principal component analysis and canonical discriminant analysis for investigating hyperspectral plant response

Directory of Open Access Journals (Sweden)

Anna Maria Stellacci

2012-07-01

Full Text Available Hyperspectral (HS data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA and Canonical Discriminant Analysis (CDA, to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR: 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined

Social control of public expenditures in a multilevel principal-agent approach

Directory of Open Access Journals (Sweden)

VALDEMIR PIRES

2015-12-01

Full Text Available ABSTRACTThis study enhances the principal-agent model by incorporating a multilevel perspective and differences among agency situations. A theoretical discussion is developed using a proposed intersection of methodological focuses and a descriptive-exemplificative hypothetical analysis. The analysis is applied to public expenditure social control in representative democracies, and as a result, a principal-agent model unfolds that incorporates a decision-making perspective and focuses on formulation, negotiation, articulation, and implementation competencies. Thus, it is possible to incorporate elements into the principal-agent model to make it more permeable to individual, group, and societal idiosyncrasies with respect to public expenditure social control.

Zero drift and solid Earth tide extracted from relative gravimetric data with principal component analysis

OpenAIRE

Hongjuan Yu; Jinyun Guo; Jiulong Li; Dapeng Mu; Qiaoli Kong

2015-01-01

Zero drift and solid Earth tide corrections to static relative gravimetric data cannot be ignored. In this paper, a new principal component analysis (PCA) algorithm is presented to extract the zero drift and the solid Earth tide, as signals, from static relative gravimetric data assuming that the components contained in the relative gravimetric data are uncorrelated. Static relative gravity observations from Aug. 15 to Aug. 23, 2014 are used as statistical variables to separate the signal and...

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

Science.gov (United States)

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.

Support vector machine and principal component analysis for microarray data classification

Science.gov (United States)

Astuti, Widi; Adiwijaya

2018-03-01

Cancer is a leading cause of death worldwide although a significant proportion of it can be cured if it is detected early. In recent decades, technology called microarray takes an important role in the diagnosis of cancer. By using data mining technique, microarray data classification can be performed to improve the accuracy of cancer diagnosis compared to traditional techniques. The characteristic of microarray data is small sample but it has huge dimension. Since that, there is a challenge for researcher to provide solutions for microarray data classification with high performance in both accuracy and running time. This research proposed the usage of Principal Component Analysis (PCA) as a dimension reduction method along with Support Vector Method (SVM) optimized by kernel functions as a classifier for microarray data classification. The proposed scheme was applied on seven data sets using 5-fold cross validation and then evaluation and analysis conducted on term of both accuracy and running time. The result showed that the scheme can obtained 100% accuracy for Ovarian and Lung Cancer data when Linear and Cubic kernel functions are used. In term of running time, PCA greatly reduced the running time for every data sets.

Driven Factors Analysis of China’s Irrigation Water Use Efficiency by Stepwise Regression and Principal Component Analysis

Directory of Open Access Journals (Sweden)

Renfu Jia

2016-01-01

Full Text Available This paper introduces an integrated approach to find out the major factors influencing efficiency of irrigation water use in China. It combines multiple stepwise regression (MSR and principal component analysis (PCA to obtain more realistic results. In real world case studies, classical linear regression model often involves too many explanatory variables and the linear correlation issue among variables cannot be eliminated. Linearly correlated variables will cause the invalidity of the factor analysis results. To overcome this issue and reduce the number of the variables, PCA technique has been used combining with MSR. As such, the irrigation water use status in China was analyzed to find out the five major factors that have significant impacts on irrigation water use efficiency. To illustrate the performance of the proposed approach, the calculation based on real data was conducted and the results were shown in this paper.

Critical Factors Explaining the Leadership Performance of High-Performing Principals

Science.gov (United States)

Hutton, Disraeli M.

2018-01-01

The study explored critical factors that explain leadership performance of high-performing principals and examined the relationship between these factors based on the ratings of school constituents in the public school system. The principal component analysis with the use of Varimax Rotation revealed that four components explain 51.1% of the…

School Principals' Job Satisfaction: The Effects of Work Intensification

Science.gov (United States)

Wang, Fei; Pollock, Katina; Hauseman, Cameron

2018-01-01

This study examines principals' job satisfaction in relation to their work intensification. Frederick Herzberg's two-factor theory was used to shed light on how motivating and maintenance factors affect principals' job satisfaction. Logistic multiple regressions were used in the analysis of survey data that were collected from 2,701 elementary and…

Optimal Coordinated Strategy Analysis for the Procurement Logistics of a Steel Group

Directory of Open Access Journals (Sweden)

Lianbo Deng

2014-01-01

Full Text Available This paper focuses on the optimization of an internal coordinated procurement logistics system in a steel group and the decision on the coordinated procurement strategy by minimizing the logistics costs. Considering the coordinated procurement strategy and the procurement logistics costs, the aim of the optimization model was to maximize the degree of quality satisfaction and to minimize the procurement logistics costs. The model was transformed into a single-objective model and solved using a simulated annealing algorithm. In the algorithm, the supplier of each subsidiary was selected according to the evaluation result for independent procurement. Finally, the effect of different parameters on the coordinated procurement strategy was analysed. The results showed that the coordinated strategy can clearly save procurement costs; that the strategy appears to be more cooperative when the quality requirement is not stricter; and that the coordinated costs have a strong effect on the coordinated procurement strategy.

Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

Directory of Open Access Journals (Sweden)

Alaba Boluwade

2016-09-01

Full Text Available Accurate characterization of soil properties such as soil water content (SWC and bulk density (BD is vital for hydrologic processes and thus, it is importance to estimate θ (water content and ρ (soil bulk density among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA and independent component analysis (ICA can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11 km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture.

Path Analysis of Work Family Conflict, Job Salary and Promotion Satisfaction, Work Engagement to Subjective Well-Being of the Primary and Middle School Principals

Science.gov (United States)

Hu, Chun-mei; Cui, Shu-jing; Wang, Lei

2016-01-01

Objective: To investigate the path analysis of work family conflict, job salary and promotion satisfaction, work engagement to subjective well-being of the primary and middle school principals, and provide advice for enhancing their well-being. Methods: Using convenient sampling, totally 300 primary and middle school principals completed the WFC,…

An argument for VP coordination: scene-setting coordination and ...

African Journals Online (AJOL)

This article demonstrates the properties of this curious construction type and proposes the first analysis to date. It is argued that this is an instance of VP coordination and that this configuration allows the possibility of high merger of direct objects in a constrained fashion. Southern African Linguistics and Applied Language ...

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 2. Generic material: Codes, standards, criteria. Working material

International Nuclear Information System (INIS)

1995-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports related to generic material, namely codes, standards and criteria for benchmark analysis

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 2. Generic material: Codes, standards, criteria. Working material

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-07-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports related to generic material, namely codes, standards and criteria for benchmark analysis.

Latitude-Time Total Electron Content Anomalies as Precursors to Japan's Large Earthquakes Associated with Principal Component Analysis

Directory of Open Access Journals (Sweden)

Jyh-Woei Lin

2011-01-01

Full Text Available The goal of this study is to determine whether principal component analysis (PCA can be used to process latitude-time ionospheric TEC data on a monthly basis to identify earthquake associated TEC anomalies. PCA is applied to latitude-time (mean-of-a-month ionospheric total electron content (TEC records collected from the Japan GEONET network to detect TEC anomalies associated with 18 earthquakes in Japan (M≥6.0 from 2000 to 2005. According to the results, PCA was able to discriminate clear TEC anomalies in the months when all 18 earthquakes occurred. After reviewing months when no M≥6.0 earthquakes occurred but geomagnetic storm activity was present, it is possible that the maximal principal eigenvalues PCA returned for these 18 earthquakes indicate earthquake associated TEC anomalies. Previously PCA has been used to discriminate earthquake-associated TEC anomalies recognized by other researchers, who found that statistical association between large earthquakes and TEC anomalies could be established in the 5 days before earthquake nucleation; however, since PCA uses the characteristics of principal eigenvalues to determine earthquake related TEC anomalies, it is possible to show that such anomalies existed earlier than this 5-day statistical window.

Non-commutativity in polar coordinates

Energy Technology Data Exchange (ETDEWEB)

Edwards, James P. [Universidad Michoacana de San Nicolas de Hidalgo, Ciudad Universitaria, Instituto de Fisica y Matematicas, Morelia, Michoacan (Mexico)

2017-05-15

We reconsider the fundamental commutation relations for non-commutative R{sup 2} described in polar coordinates with non-commutativity parameter θ. Previous analysis found that the natural transition from Cartesian coordinates to the traditional polar system led to a representation of [r, φ] as an everywhere diverging series. In this article we compute the Borel resummation of this series, showing that it can subsequently be extended throughout parameter space and hence provide an interpretation of this commutator. Our analysis provides a complete solution for arbitrary r and θ that reproduces the earlier calculations at lowest order and benefits from being generally applicable to problems in a two-dimensional non-commutative space. We compare our results to previous literature in the (pseudo-)commuting limit, finding a surprising spatial dependence for the coordinate commutator when θ >> r{sup 2}. Finally, we raise some questions for future study in light of this progress. (orig.)

Investing in Leadership: The District's Role in Managing Principal Turnover

Science.gov (United States)

Mascall, Blair; Leithwood, Kenneth

2010-01-01

This article presents the results of research into the impact of principal turnover on schools, and the ability of schools to mitigate the negative effects of frequent turnover by distributing leadership in the schools. The findings from this qualitative and quantitative analysis show that rapid principal turnover does indeed have a negative…

Vibrational spectra and normal co-ordinate analysis of 2-aminopyridine and 2-amino picoline.

Science.gov (United States)

Jose, Sujin P; Mohan, S

2006-05-01

The Fourier transform infrared (FT-IR) and Raman (FT-R) spectra of 2-aminopyridine and 2-amino picoline were recorded and the observed frequencies were assigned to various modes of vibration in terms of fundamentals by assuming Cs point group symmetry. A normal co-ordinate analysis was also carried out for the proper assignment of the vibrational frequencies using simple valence force field. A complete vibrational analysis is presented here for the molecules and the results are briefly discussed.

Development of a 3D immersive videogame to improve arm-postural coordination in patients with TBI

Directory of Open Access Journals (Sweden)

Cassavaugh Nicholas D

2011-10-01

Full Text Available Abstract Background Traumatic brain injury (TBI disrupts the central and executive mechanisms of arm(s and postural (trunk and legs coordination. To address these issues, we developed a 3D immersive videogame-- Octopus. The game was developed using the basic principles of videogame design and previous experience of using videogames for rehabilitation of patients with acquired brain injuries. Unlike many other custom-designed virtual environments, Octopus included an actual gaming component with a system of multiple rewards, making the game challenging, competitive, motivating and fun. Effect of a short-term practice with the Octopus game on arm-postural coordination in patients with TBI was tested. Methods The game was developed using WorldViz Vizard software, integrated with the Qualysis system for motion analysis. Avatars of the participant's hands precisely reproducing the real-time kinematic patterns were synchronized with the simulated environment, presented in the first person 3D view on an 82-inch DLP screen. 13 individuals with mild-to-moderate manifestations of TBI participated in the study. While standing in front of the screen, the participants interacted with a computer-generated environment by popping bubbles blown by the Octopus. The bubbles followed a specific trajectory. Interception of the bubbles with the left or right hand avatar allowed flexible use of the postural segments for balance maintenance and arm transport. All participants practiced ten 90-s gaming trials during a single session, followed by a retention test. Arm-postural coordination was analysed using principal component analysis. Results As a result of the short-term practice, the participants improved in game performance, arm movement time, and precision. Improvements were achieved mostly by adapting efficient arm-postural coordination strategies. Of the 13 participants, 10 showed an immediate increase in arm forward reach and single-leg stance time. Conclusion

Development of a 3D immersive videogame to improve arm-postural coordination in patients with TBI.

Science.gov (United States)

Ustinova, Ksenia I; Leonard, Wesley A; Cassavaugh, Nicholas D; Ingersoll, Christopher D

2011-10-31

Traumatic brain injury (TBI) disrupts the central and executive mechanisms of arm(s) and postural (trunk and legs) coordination. To address these issues, we developed a 3D immersive videogame--Octopus. The game was developed using the basic principles of videogame design and previous experience of using videogames for rehabilitation of patients with acquired brain injuries. Unlike many other custom-designed virtual environments, Octopus included an actual gaming component with a system of multiple rewards, making the game challenging, competitive, motivating and fun. Effect of a short-term practice with the Octopus game on arm-postural coordination in patients with TBI was tested. The game was developed using WorldViz Vizard software, integrated with the Qualysis system for motion analysis. Avatars of the participant's hands precisely reproducing the real-time kinematic patterns were synchronized with the simulated environment, presented in the first person 3D view on an 82-inch DLP screen. 13 individuals with mild-to-moderate manifestations of TBI participated in the study. While standing in front of the screen, the participants interacted with a computer-generated environment by popping bubbles blown by the Octopus. The bubbles followed a specific trajectory. Interception of the bubbles with the left or right hand avatar allowed flexible use of the postural segments for balance maintenance and arm transport. All participants practiced ten 90-s gaming trials during a single session, followed by a retention test. Arm-postural coordination was analysed using principal component analysis. As a result of the short-term practice, the participants improved in game performance, arm movement time, and precision. Improvements were achieved mostly by adapting efficient arm-postural coordination strategies. Of the 13 participants, 10 showed an immediate increase in arm forward reach and single-leg stance time. These results support the feasibility of using the custom-made 3D

Decision-Making by Handball Referees: Design of an ad hoc Observation Instrument and Polar Coordinate Analysis.

Science.gov (United States)

Morillo, Juan P; Reigal, Rafael E; Hernández-Mendo, Antonio; Montaña, Alejandro; Morales-Sánchez, Verónica

2017-01-01

Referees are essential for sports such as handball. However, there are few tools available to analyze the activity of handball referees. The aim of this study was to design an instrument for observing the behavior of referees in handball competitions and to analyze the resulting data by polar coordinate analysis. The instrument contained 6 criteria and 18 categories and can be used to monitor and describe the actions of handball referees according to their role/position on the playing court. For the data quality control analysis, we calculated Pearson's (0.99), Spearman's (0.99), and Tau Kendall's (1.00) correlation coefficients and Cohen's kappa (entre 0.72 y 0.75) and Phi (entre 0.83 y 0.87) coefficients. In the generalizability analysis, the absolute and relative generalizability coefficients were 0.99 in both cases. Polar coordinate analysis of referee decisions showed that correct calls were more common for central court and 7-meter throw calls. Likewise, calls were more likely to be incorrect (in terms of both errors of omission and commission) when taken from the goal-line position.

Decision-Making by Handball Referees: Design of an ad hoc Observation Instrument and Polar Coordinate Analysis

Directory of Open Access Journals (Sweden)

Juan P. Morillo

2017-10-01

Full Text Available Referees are essential for sports such as handball. However, there are few tools available to analyze the activity of handball referees. The aim of this study was to design an instrument for observing the behavior of referees in handball competitions and to analyze the resulting data by polar coordinate analysis. The instrument contained 6 criteria and 18 categories and can be used to monitor and describe the actions of handball referees according to their role/position on the playing court. For the data quality control analysis, we calculated Pearson's (0.99, Spearman's (0.99, and Tau Kendall's (1.00 correlation coefficients and Cohen's kappa (entre 0.72 y 0.75 and Phi (entre 0.83 y 0.87 coefficients. In the generalizability analysis, the absolute and relative generalizability coefficients were 0.99 in both cases. Polar coordinate analysis of referee decisions showed that correct calls were more common for central court and 7-meter throw calls. Likewise, calls were more likely to be incorrect (in terms of both errors of omission and commission when taken from the goal-line position.

Analysis of the Reliability and Validity of a Mentor's Assessment for Principal Internships

Science.gov (United States)

Koonce, Glenn L.; Kelly, Michael D.

2014-01-01

In this study, researchers analyzed the reliability and validity of the mentor's assessment for principal internships at a university in the Southeast region of the United States. The results of the study yielded how trustworthy and dependable the instrument is and the effectiveness of the instrument in the current principal preparation program.…

Geomorphometric analysis of selected Martian craters using polar coordinate transformation

Science.gov (United States)

Magyar, Zoltán; Koma, Zsófia; Székely, Balázs

2016-04-01

Centrally symmetric landform elements are very common features on the surface of the planet Mars. The most conspicuous ones of them are the impact craters of various size. However, a closer look on these features reveals that they show often asymmetric patterns as well. These are partially related to the geometry of the trajectory of the impacting body, but sometimes it is a result of surface processes (e.g., freeze/thaw cycles, mass movements). Geomorphometric studies have already been carried out to reveal these pecularities. Our approach, the application of polar coordinate transformation (PCT) very sensitively enhances the non-radial and non-circular shapes. We used digital terrain models (DTMs) derived from the ESA Mars Express HRSC imagery. The original DTM or its derivatives (e.g. slope angle or aspect) are PCT transformed. We analyzed the craters inter alia with scattergrams in polar coordinates. The resulting point cloud can be used directly for the analysis, but in some cases an interpolation should be applied to enhance certain non-circular features (especially in case of smaller craters). Visual inspection of the crater slopes, coloured by the aspect, reveals smaller features. Some of them are processing artefacts, but many of them are related to local undulations in the topography or indications of mass movements. In many cases the undulations of the crater rim are due to erosional processes. The drawbacks of the technology are related to the uneven resolution of the projected image: features in the crater centre should be left out from the analysis because PCT has a low resolution around the projection center. Furthermore, the success of the PCT depends on the correct definition of the projection centre: erroneously centered images are not suitable for analysis. The PCT transformed images are also suitable for radial averaging and calculation of standard deviations, resulting in typical, comparable craters shapes. These studies may lead to a deeper

Single shot fringe pattern phase demodulation using Hilbert-Huang transform aided by the principal component analysis.

Science.gov (United States)

Trusiak, Maciej; Służewski, Łukasz; Patorski, Krzysztof

2016-02-22

Hybrid single shot algorithm for accurate phase demodulation of complex fringe patterns is proposed. It employs empirical mode decomposition based adaptive fringe pattern enhancement (i.e., denoising, background removal and amplitude normalization) and subsequent boosted phase demodulation using 2D Hilbert spiral transform aided by the Principal Component Analysis method for novel, correct and accurate local fringe direction map calculation. Robustness to fringe pattern significant noise, uneven background and amplitude modulation as well as local fringe period and shape variations is corroborated by numerical simulations and experiments. Proposed automatic, adaptive, fast and comprehensive fringe analysis solution compares favorably with other previously reported techniques.

Comment 2 on workshop in political institutions - principal-agent relationships

International Nuclear Information System (INIS)

Feeny, D.

1992-01-01

In recent decades, economic analysis has been extended to situations in which there are important asymmetries of information between actors. One important class of these situations are principal-agent relationships, in which one party, the agent, acts on behalf of the other, the principal. In such situations of incomplete and asymmetric information (in which the agent may be better informed than the principal), is it possible to devise mechanisms to ensure that the agent acts in the best interests of the principal? How can we construct relationships so that physicians act in the best interests of patients, lawyer in the best interests of their clients, or public officials in the best interests of their constituents?

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

Science.gov (United States)

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.

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

Science.gov (United States)

Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

2017-05-01

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Principal succession: The socialisation of a primary school principal in South Africa

Directory of Open Access Journals (Sweden)

Gertruida M. Steyn

2013-04-01

Full Text Available This study focussed on the socialisation of a new principal in a South African primary school with a strong Christian culture. He was appointed when the predecessor retired after more than two decades. The conceptual framework focuses on the three phases of socialisation: professional socialisation, organisational socialisation and occupational identity, which are used to interpret the study. A qualitative study, which occurred during two phases, investigated the phenomenon, principal succession, in the particular school. The data collection methods included a number of interviews with the principal, a focus group interview with staff members who experienced the previous principal’s leadership practice, and individual interviews with staff members. The following categories emerged from the data analysis: Recalling the previous principal: ‘One sees Mr X [the predecessor] everywhere’; Entry and orientation: ‘I found it intimidating initially’; and Immersion and reshaping: ‘Reins that previously were a bit slack, he is now pulling tight’.Die sosialisering van ’n primêre skoolhoof in Suid-Afrika. Hierdie studie het gefokus op die sosialisering van ’n nuwe skoolhoof in ’n Suid-Afrikaanse primêre skool met ’n sterk Christelike kultuur. Hy is aangestel toe sy voorganger ná meer as twee dekades afgetree het. Die konseptuele raamwerk, wat gebruik is om die bevindinge te interpreteer, het op die drie fases van sosialisering gefokus, naamlik professionele sosialisering, organisatoriese sosialisering en beroepsidentiteit. ’n Kwalitatiewe ondersoek na die skoolhoofopvolgingverskynsel in die bepaalde skool is in twee fases gedoen. Die data-insamelingsmetodes het ’n aantal onderhoude met die skoolhoof, ’n fokusgroeponderhoud met personeellede wat ook onder leierskap van die vorige skoolhoof gewerk het en individuele onderhoude met personeellede ingesluit. Tydens die data-analise het die volgende kategorieë na vore gekom

Sensor Failure Detection of FASSIP System using Principal Component Analysis

Science.gov (United States)

Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

2018-02-01

In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

An Analysis of Instructional Facilitators' Relationships with Teachers and Principals

Science.gov (United States)

Range, Bret G.; Pijanowski, John C.; Duncan, Heather; Scherz, Susan; Hvidston, David

2014-01-01

This study examines the perspectives of Wyoming instructional facilitators, concerning three coaching constructs--namely, their instructional leadership roles, teachers' instructional practices, and the support that they receive from principals and teachers. Findings suggest that instructional facilitators were positive about their instructional…

Principal Components Analysis on the spectral Bidirectional Reflectance Distribution Function of ceramic colour standards.

Science.gov (United States)

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

Principal component regression for crop yield estimation

CERN Document Server

Suryanarayana, T M V

2016-01-01

This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

Fault diagnosis of main coolant pump in the nuclear power station based on the principal component analysis

International Nuclear Information System (INIS)

Feng Junting; Xu Mi; Wang Guizeng

2003-01-01

The fault diagnosis method based on principal component analysis is studied. The fault character direction storeroom of fifteen parameters abnormity is built in the simulation for the main coolant pump of nuclear power station. The measuring data are analyzed, and the results show that it is feasible for the fault diagnosis system of main coolant pump in the nuclear power station

29 CFR 1471.995 - Principal.

Science.gov (United States)

2010-07-01

... SUSPENSION (NONPROCUREMENT) Definitions § 1471.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or... 29 Labor 4 2010-07-01 2010-07-01 false Principal. 1471.995 Section 1471.995 Labor Regulations...

The role of programmed and emergent mechanisms of coordination

DEFF Research Database (Denmark)

Prætorius, Thim

Hospitals face substantial coordination challenges. To meet this hospitals more and more use standardized work processes such as care pathways. By drawing on recent coordination theory that increasingly emphasizes the role of lateral and emergent interactions alongside traditional, programmed...... mechanisms of coordination, this paper finds that standardized work processes such as care pathways should be considered as a bundle of coordination mechanisms—plans and rules, objects, routines, roles and proximity—rather than a mechanism of its own. The bundle builds the accountability, predictability...... and common understanding needed to coordinate standardized care tasks. The analysis lends theoretical insights to the traditional view that see standardized work processes as programmed processes. For health care workers who design, implement and use care pathways to solve care tasks, the analysis calls...

Portraits of Principal Practice: Time Allocation and School Principal Work

Science.gov (United States)

Sebastian, James; Camburn, Eric M.; Spillane, James P.

2018-01-01

Purpose: The purpose of this study was to examine how school principals in urban settings distributed their time working on critical school functions. We also examined who principals worked with and how their time allocation patterns varied by school contextual characteristics. Research Method/Approach: The study was conducted in an urban school…

Ecological Safety Evaluation of Land Use in Ji’an City Based on the Principal Component Analysis

Institute of Scientific and Technical Information of China (English)

2010-01-01

According to the ecological safety evaluation index data of land-use change in Ji’an City from 1999 to 2008,positive treatment on selected reverse indices is conducted by Reciprocal Method.Meanwhile,Index Method is used to standardize the selected indices,and Principal Component Analysis is applied by using year as a unit.FB is obtained,which is related with the ecological safety of land-use change from 1999 to 2008.According to the scientific,integrative,hierarchical,practical and dynamic principles,ecological safety evaluation index system of land-use change in Ji’an City is established.Principal Component Analysis and evaluation model are used to calculate four parameters,including the natural resources safety index of land use,the socio-economic safety indicators of land use,the eco-environmental safety index of land use,and the ecological safety degree of land use in Ji’an City.Result indicates that the ecological safety degree of land use in Ji’an City shows a slow upward trend as a whole.At the same time,ecological safety degree of land-use change is relatively low in Ji’an City with the safety value of 0.645,which is at a weak safety zone and needs further monitoring and maintenance.

31 CFR 19.995 - Principal.

Science.gov (United States)

2010-07-01

... SUSPENSION (NONPROCUREMENT) Definitions § 19.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory... 31 Money and Finance: Treasury 1 2010-07-01 2010-07-01 false Principal. 19.995 Section 19.995...

22 CFR 208.995 - Principal.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Principal. 208.995 Section 208.995 Foreign...) Definitions § 208.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory responsibilities related to a...

22 CFR 1006.995 - Principal.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Principal. 1006.995 Section 1006.995 Foreign... § 1006.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory responsibilities related to a...

2 CFR 180.995 - Principal.

Science.gov (United States)

2010-01-01

... 2 Grants and Agreements 1 2010-01-01 2010-01-01 false Principal. 180.995 Section 180.995 Grants and Agreements OFFICE OF MANAGEMENT AND BUDGET GOVERNMENTWIDE GUIDANCE FOR GRANTS AND AGREEMENTS... § 180.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator...

22 CFR 1508.995 - Principal.

Science.gov (United States)

2010-04-01

... 22 Foreign Relations 2 2010-04-01 2010-04-01 true Principal. 1508.995 Section 1508.995 Foreign...) Definitions § 1508.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory responsibilities related to a...

Principal stratification in causal inference.

Science.gov (United States)

Frangakis, Constantine E; Rubin, Donald B

2002-03-01

Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal stratification. Principal stratification with respect to a posttreatment variable is a cross-classification of subjects defined by the joint potential values of that posttreatment variable tinder each of the treatments being compared. Principal effects are causal effects within a principal stratum. The key property of principal strata is that they are not affected by treatment assignment and therefore can be used just as any pretreatment covariate. such as age category. As a result, the central property of our principal effects is that they are always causal effects and do not suffer from the complications of standard posttreatment-adjusted estimands. We discuss briefly that such principal causal effects are the link between three recent applications with adjustment for posttreatment variables: (i) treatment noncompliance, (ii) missing outcomes (dropout) following treatment noncompliance. and (iii) censoring by death. We then attack the problem of surrogate or biomarker endpoints, where we show, using principal causal effects, that all current definitions of surrogacy, even when perfectly true, do not generally have the desired interpretation as causal effects of treatment on outcome. We go on to forrmulate estimands based on principal stratification and principal causal effects and show their superiority.

Use of a Principal Components Analysis for the Generation of Daily Time Series.

Science.gov (United States)

Dreveton, Christine; Guillou, Yann

2004-07-01

A new approach for generating daily time series is considered in response to the weather-derivatives market. This approach consists of performing a principal components analysis to create independent variables, the values of which are then generated separately with a random process. Weather derivatives are financial or insurance products that give companies the opportunity to cover themselves against adverse climate conditions. The aim of a generator is to provide a wider range of feasible situations to be used in an assessment of risk. Generation of a temperature time series is required by insurers or bankers for pricing weather options. The provision of conditional probabilities and a good representation of the interannual variance are the main challenges of a generator when used for weather derivatives. The generator was developed according to this new approach using a principal components analysis and was applied to the daily average temperature time series of the Paris-Montsouris station in France. The observed dataset was homogenized and the trend was removed to represent correctly the present climate. The results obtained with the generator show that it represents correctly the interannual variance of the observed climate; this is the main result of the work, because one of the main discrepancies of other generators is their inability to represent accurately the observed interannual climate variance—this discrepancy is not acceptable for an application to weather derivatives. The generator was also tested to calculate conditional probabilities: for example, the knowledge of the aggregated value of heating degree-days in the middle of the heating season allows one to estimate the probability if reaching a threshold at the end of the heating season. This represents the main application of a climate generator for use with weather derivatives.

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Science.gov (United States)

Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz

2018-04-01

For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from - 0.98 to - 0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

Principals' Salaries, 2007-2008

Science.gov (United States)

Cooke, Willa D.; Licciardi, Chris

2008-01-01

How do salaries of elementary and middle school principals compare with those of other administrators and classroom teachers? Are increases in salaries of principals keeping pace with increases in salaries of classroom teachers? And how have principals' salaries fared over the years when the cost of living is taken into account? There are reliable…

21 CFR 1404.995 - Principal.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 9 2010-04-01 2010-04-01 false Principal. 1404.995 Section 1404.995 Food and...) Definitions § 1404.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory responsibilities related to a...

34 CFR 85.995 - Principal.

Science.gov (United States)

2010-07-01

... 34 Education 1 2010-07-01 2010-07-01 false Principal. 85.995 Section 85.995 Education Office of...) Definitions § 85.995 Principal. Principal means— (a) An officer, director, owner, partner, principal investigator, or other person within a participant with management or supervisory responsibilities related to a...

InterFace: A software package for face image warping, averaging, and principal components analysis.

Science.gov (United States)

Kramer, Robin S S; Jenkins, Rob; Burton, A Mike

2017-12-01

We describe InterFace, a software package for research in face recognition. The package supports image warping, reshaping, averaging of multiple face images, and morphing between faces. It also supports principal components analysis (PCA) of face images, along with tools for exploring the "face space" produced by PCA. The package uses a simple graphical user interface, allowing users to perform these sophisticated image manipulations without any need for programming knowledge. The program is available for download in the form of an app, which requires that users also have access to the (freely available) MATLAB Runtime environment.

A novel normalization method based on principal component analysis to reduce the effect of peak overlaps in two-dimensional correlation spectroscopy

Science.gov (United States)

Wang, Yanwei; Gao, Wenying; Wang, Xiaogong; Yu, Zhiwu

2008-07-01

Two-dimensional correlation spectroscopy (2D-COS) has been widely used to separate overlapped spectroscopic bands. However, band overlap may sometimes cause misleading results in the 2D-COS spectra, especially if one peak is embedded within another peak by the overlap. In this work, we propose a new normalization method, based on principal component analysis (PCA). For each spectrum under discussion, the first principal component of PCA is simply taken as the normalization factor of the spectrum. It is demonstrated that the method works well with simulated dynamic spectra. Successful result has also been obtained from the analysis of an overlapped band in the wavenumber range 1440-1486 cm -1 for the evaporation process of a solution containing behenic acid, methanol, and chloroform.

Interpolation Error Estimates for Mean Value Coordinates over Convex Polygons.

Science.gov (United States)

Rand, Alexander; Gillette, Andrew; Bajaj, Chandrajit

2013-08-01

In a similar fashion to estimates shown for Harmonic, Wachspress, and Sibson coordinates in [Gillette et al., AiCM, to appear], we prove interpolation error estimates for the mean value coordinates on convex polygons suitable for standard finite element analysis. Our analysis is based on providing a uniform bound on the gradient of the mean value functions for all convex polygons of diameter one satisfying certain simple geometric restrictions. This work makes rigorous an observed practical advantage of the mean value coordinates: unlike Wachspress coordinates, the gradient of the mean value coordinates does not become large as interior angles of the polygon approach π.

Simulation of an industrial wastewater treatment plant using artificial neural networks and principal components analysis

Directory of Open Access Journals (Sweden)

Oliveira-Esquerre K.P.

2002-01-01

Full Text Available This work presents a way to predict the biochemical oxygen demand (BOD of the output stream of the biological wastewater treatment plant at RIPASA S/A Celulose e Papel, one of the major pulp and paper plants in Brazil. The best prediction performance is achieved when the data are preprocessed using principal components analysis (PCA before they are fed to a backpropagated neural network. The influence of input variables is analyzed and satisfactory prediction results are obtained for an optimized situation.

Principal Self-Efficacy and Work Engagement: Assessing a Norwegian Principal Self-Efficacy Scale

Science.gov (United States)

Federici, Roger A.; Skaalvik, Einar M.

2011-01-01

One purpose of the present study was to develop and test the factor structure of a multidimensional and hierarchical Norwegian Principal Self-Efficacy Scale (NPSES). Another purpose of the study was to investigate the relationship between principal self-efficacy and work engagement. Principal self-efficacy was measured by the 22-item NPSES. Work…

PRINCIPAL STRESSES IN NON-LINEAR ANALYSIS OF BAKUN CONCRETE FACED ROCKFILL DAM

Directory of Open Access Journals (Sweden)

Mohd Hilton Ahmad

2017-11-01

Full Text Available With rapid population growth and accelerating economic development, much of the world’s WATER which requires urgent attention to ensure sustainable use. Nowadays, Concrete Faced Rockfill Dam (CFRD is preferred among dam consultant due to its advantages. They are designed to withstand all applied loads; namely gravity load due to its massive weight and hydrostatic load due to water thrust from the reservoir. Bakun CFRD, which ranks as the second highest CFRD in the world when completed, is analyzed to its safety due to both loads mentioned earlier by using Finite Element Method. 2-D plane strain finite element analysis of non-linear Duncan-Chang hyperbolic Model which formulated by Duncan and Chang is used to study the structural response of the dam in respect to the deformation and stresses of Main dam of Bakun’s CFRD project. Dead-Birth-Ghost element technique was used to simulate sequences of construction of the dam as well as during reservoir fillings. The comparison of rigid and flexible foundation on the behaviour of the dam was discussed. The maximum and minimum principal stresses are the maximum and minimum possible values of the normal stresses. The maximum principal stress controls brittle fracture. In the finite element modeling the concrete slab on the upstream was represented through six-noded element, while the interface characteristic between dam body and concrete slab was modeled using interface element. The maximum settlement and stresses of the cross section was founded and the distribution of them were discussed and tabulated in form of contours.

Principal Component Analysis to Explore Climatic Variability and Dengue Outbreak in Lahore

Directory of Open Access Journals (Sweden)

Syed Afrozuddin Ahmed

2014-08-01

Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan.  In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. Principal component analysis is performed for the purpose of finding if there is/are any general environmental factor/structure which could be affected in the emergence of dengue fever cases in Pakistani climate. Principal component is applied to find structure in data for all four periods i.e. 1980 to 2012, 1980 to 1995 and 1996 to 2012.  The first three PCs for the period (1980-2012, 1980-1994, 1995-2012 are almost the same and it represent hot and windy weather. The PC1s of all dengue periods are different to each other. PC2 for all period are same and it is wetness in weather. PC3s are different and it is the combination of wetness and windy weather. PC4s for all period show humid but no rain in weather. For climatic variable only minimum temperature and maximum temperature are significantly correlated with daily dengue cases.  PC1, PC3 and PC4 are highly significantly correlated with daily dengue cases 

Third research coordination meeting on reference database for neutron activation analysis. Summary report

International Nuclear Information System (INIS)

Kellett, M.A.

2009-12-01

The third meeting of the Co-ordinated Research Project on 'Reference Database for Neutron Activation Analysis' was held at the IAEA, Vienna from 17-19 November 2008. A summary of presentations made by participants is given, reports on specific tasks and subsequent discussions. With the aim of finalising the work of this CRP and in order to meet initial objectives, outputs were discussed and detailed task assignments agreed upon. (author)

Characterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data

Science.gov (United States)

He, Shiyuan; Wang, Lifan; Huang, Jianhua Z.

2018-04-01

With growing data from ongoing and future supernova surveys, it is possible to empirically quantify the shapes of SNIa light curves in more detail, and to quantitatively relate the shape parameters with the intrinsic properties of SNIa. Building such relationships is critical in controlling systematic errors associated with supernova cosmology. Based on a collection of well-observed SNIa samples accumulated in the past years, we construct an empirical SNIa light curve model using a statistical method called the functional principal component analysis (FPCA) for sparse and irregularly sampled functional data. Using this method, the entire light curve of an SNIa is represented by a linear combination of principal component functions, and the SNIa is represented by a few numbers called “principal component scores.” These scores are used to establish relations between light curve shapes and physical quantities such as intrinsic color, interstellar dust reddening, spectral line strength, and spectral classes. These relations allow for descriptions of some critical physical quantities based purely on light curve shape parameters. Our study shows that some important spectral feature information is being encoded in the broad band light curves; for instance, we find that the light curve shapes are correlated with the velocity and velocity gradient of the Si II λ6355 line. This is important for supernova surveys (e.g., LSST and WFIRST). Moreover, the FPCA light curve model is used to construct the entire light curve shape, which in turn is used in a functional linear form to adjust intrinsic luminosity when fitting distance models.

Principals' instructional management skills and middle school science teacher job satisfaction

Science.gov (United States)

Gibbs-Harper, Nzinga A.

The purpose of this research study was to determine if a relationship exists between teachers' perceptions of principals' instructional leadership behaviors and middle school teacher job satisfaction. Additionally, this study sought to assess whether principal's instructional leadership skills were predictors of middle school teachers' satisfaction with work itself. This study drew from 13 middle schools in an urban Mississippi school district. Participants included teachers who taught science. Each teacher was given the Principal Instructional Management Rating Scale (PIMRS; Hallinger, 2011) and the Teacher Job Satisfaction Questionnaire (TJSQ; Lester, 1987) to answer the research questions. The study was guided by two research questions: (a) Is there a relationship between the independent variables Defining the School's Mission, Managing the Instructional Program, and Developing the School Learning Climate Program and the dependent variable Work Itself?; (b) Are Defining the School's Mission, Managing the Instructional Program, and Developing the School Learning Climate Program predictors of Work Itself? The Pearson's correlation and multiple regression analysis were utilized to examine the relationship between the three dimensions of principals' instructional leadership and teacher satisfaction with work itself. The data revealed that there was a strong, positive correlation between all three dimensions of principals' instructional leadership and teacher satisfaction with work itself. However, the multiple regression analysis determined that teachers' perceptions of principals' instructional management skills is a slight predictor of Defining the School's Mission only.

Principal oscillation patterns

International Nuclear Information System (INIS)

Storch, H. von; Buerger, G.; Storch, J.S. von

1993-01-01

The Principal Oscillation Pattern (POP) analysis is a technique which is used to simultaneously infer the characteristic patterns and time scales of a vector time series. The POPs may be seen as the normal modes of a linearized system whose system matrix is estimated from data. The concept of POP analysis is reviewed. Examples are used to illustrate the potential of the POP technique. The best defined POPs of tropospheric day-to-day variability coincide with the most unstable modes derived from linearized theory. POPs can be derived even from a space-time subset of data. POPs are successful in identifying two independent modes with similar time scales in the same data set. The POP method can also produce forecasts which may potentially be used as a reference for other forecast models. The conventional POP analysis technique has been generalized in various ways. In the cyclostationary POP analysis, the estimated system matrix is allowed to vary deterministically with an externally forced cycle. In the complex POP analysis not only the state of the system but also its ''momentum'' is modeled. Associated correlation patterns are a useful tool to describe the appearance of a signal previously identified by a POP analysis in other parameters. (orig.)

Bimanual motor coordination controlled by cooperative interactions in intrinsic and extrinsic coordinates.

Science.gov (United States)

Sakurada, Takeshi; Ito, Koji; Gomi, Hiroaki

2016-01-01

Although strong motor coordination in intrinsic muscle coordinates has frequently been reported for bimanual movements, coordination in extrinsic visual coordinates is also crucial in various bimanual tasks. To explore the bimanual coordination mechanisms in terms of the frame of reference, here we characterized implicit bilateral interactions in visuomotor tasks. Visual perturbations (finger-cursor gain change) were applied while participants performed a rhythmic tracking task with both index fingers under an in-phase or anti-phase relationship in extrinsic coordinates. When they corrected the right finger's amplitude, the left finger's amplitude unintentionally also changed [motor interference (MI)], despite the instruction to keep its amplitude constant. Notably, we observed two specificities: one was large MI and low relative-phase variability (PV) under the intrinsic in-phase condition, and the other was large MI and high PV under the extrinsic in-phase condition. Additionally, using a multiple-interaction model, we successfully decomposed MI into intrinsic components caused by motor correction and extrinsic components caused by visual-cursor mismatch of the right finger's movements. This analysis revealed that the central nervous system facilitates MI by combining intrinsic and extrinsic components in the condition with in-phases in both intrinsic and extrinsic coordinates, and that under-additivity of the effects is explained by the brain's preference for the intrinsic interaction over extrinsic interaction. In contrast, the PV was significantly correlated with the intrinsic component, suggesting that the intrinsic interaction dominantly contributed to bimanual movement stabilization. The inconsistent features of MI and PV suggest that the central nervous system regulates multiple levels of bilateral interactions for various bimanual tasks. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and

Geometry of Quantum Principal Bundles. Pt. 1

International Nuclear Information System (INIS)

Durdevic, M.

1996-01-01

A theory of principal bundles possessing quantum structure groups and classical base manifolds is presented. Structural analysis of such quantum principal bundles is performed. A differential calculus is constructed, combining differential forms on the base manifold with an appropriate differential calculus on the structure quantum group. Relations between the calculus on the group and the calculus on the bundle are investigated. A concept of (pseudo)tensoriality is formulated. The formalism of connections is developed. In particular, operators of horizontal projection, covariant derivative and curvature are constructed and analyzed. Generalizations of the first Structure Equation and of the Bianchi identity are found. Illustrative examples are presented. (orig.)

Vaidya spacetime in the diagonal coordinates

Energy Technology Data Exchange (ETDEWEB)

Berezin, V. A., E-mail: berezin@inr.ac.ru; Dokuchaev, V. I., E-mail: dokuchaev@inr.ac.ru; Eroshenko, Yu. N., E-mail: eroshenko@inr.ac.ru [Russian Academy of Sciences, Institute for Nuclear Research (Russian Federation)

2017-03-15

We have analyzed the transformation from initial coordinates (v, r) of the Vaidya metric with light coordinate v to the most physical diagonal coordinates (t, r). An exact solution has been obtained for the corresponding metric tensor in the case of a linear dependence of the mass function of the Vaidya metric on light coordinate v. In the diagonal coordinates, a narrow region (with a width proportional to the mass growth rate of a black hole) has been detected near the visibility horizon of the Vaidya accreting black hole, in which the metric differs qualitatively from the Schwarzschild metric and cannot be represented as a small perturbation. It has been shown that, in this case, a single set of diagonal coordinates (t, r) is insufficient to cover the entire range of initial coordinates (v, r) outside the visibility horizon; at least three sets of diagonal coordinates are required, the domains of which are separated by singular surfaces on which the metric components have singularities (either g{sub 00} = 0 or g{sub 00} = ∞). The energy–momentum tensor diverges on these surfaces; however, the tidal forces turn out to be finite, which follows from an analysis of the deviation equations for geodesics. Therefore, these singular surfaces are exclusively coordinate singularities that can be referred to as false fire-walls because there are no physical singularities on them. We have also considered the transformation from the initial coordinates to other diagonal coordinates (η, y), in which the solution is obtained in explicit form, and there is no energy–momentum tensor divergence.

Instructional Leadership Challenges and Practices of Novice Principals in Rural Schools

Science.gov (United States)

Wiezorek, Douglas; Manard, Carolyn

2018-01-01

We report on a phenomenological study of the leadership experiences of six novice, rural public school principals in a midwestern U.S. state. We situated our analysis within existing research on leadership for learning, particularly how novice principals interpreted instructional leadership challenges in the context of rural school leadership. Our…

Co-ordinated research programme on benchmark study for the seismic analysis and testing of WWER-type nuclear power plants. V. 3E. Kozloduy NPP units 5/6: Analysis/testing. Working material

International Nuclear Information System (INIS)

1996-01-01

The Co-ordinated research programme on the benchmark study for the seismic analysis and testing of WWER-type nuclear power plants was initiated subsequent to the request from representatives of Member States. The conclusions adopted at the Technical Committee Meeting on Seismic Issues related to existing nuclear power plants held in Tokyo in 1991 called for the harmonization of methods and criteria used in Member States in issues related to seismic safety. The Consulltants' Meeting which followed resulted in producing a working document for CRP. It was decided that a benchmark study is the most effective way to achieve the principal objective. Two types of WWER reactors (WWER-440/213 and WWER-1000) were selected as prototypes for the benchmark exercise to be tested on a full scale using explosions and/or vibration generators. The two prototypes are Kozloduy Units 5/6 for WWER-1000 and Paks for WWER-440/213 nuclear power plants. This volume of Working material contains reports on data related to floor response spectra of Kozloduy NPP; calculational-experimental examination and ensuring of equipment and pipelines seismic resistance at starting and operating WWER-type NPPs; analysis of design floor response spectra and testing of the electrical systems; experimental investigations and seismic analysis Kozloduy NPP; testing of components on the shaking table facilities and contribution to full scale dynamic testing of Kozloduy NPP; seismic evaluation of the main steam line, piping systems, containment pre-stressing and steel ventilation chimney of Kozloduy NPP

An Investigation of Teacher, Principal, and Superintendent Perceptions on the Ability of the National Framework for Principal Evaluations to Measure Principals' Leadership Competencies

Science.gov (United States)

Lamb, Lori D.

2014-01-01

The purpose of this qualitative study was to investigate the perceptions of effective principals' leadership competencies; determine if the perceptions of teachers, principals, and superintendents aligned with the proposed National Framework for Principal Evaluations initiative. This study examined the six domains of leadership outlined by the…

Performance analysis of a Principal Component Analysis ensemble classifier for Emotiv headset P300 spellers.

Science.gov (United States)

Elsawy, Amr S; Eldawlatly, Seif; Taher, Mohamed; Aly, Gamal M

2014-01-01

The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller paradigm. We compare the performance of the proposed ensemble classifier to the performance of traditional feature extraction and classifier methods. Our results demonstrate the capability of the PCA ensemble classifier to classify P300 data recorded using the Emotiv neuroheadset with an average accuracy of 86.29% on cross-validation data. In addition, offline testing of the recorded data reveals an average classification accuracy of 73.3% that is significantly higher than that achieved using traditional methods. Finally, we demonstrate the effect of the parameters of the P300 speller paradigm on the performance of the method.

First order sensitivity analysis of flexible multibody systems using absolute nodal coordinate formulation

International Nuclear Information System (INIS)

Pi Ting; Zhang Yunqing; Chen Liping

2012-01-01

Design sensitivity analysis of flexible multibody systems is important in optimizing the performance of mechanical systems. The choice of coordinates to describe the motion of multibody systems has a great influence on the efficiency and accuracy of both the dynamic and sensitivity analysis. In the flexible multibody system dynamics, both the floating frame of reference formulation (FFRF) and absolute nodal coordinate formulation (ANCF) are frequently utilized to describe flexibility, however, only the former has been used in design sensitivity analysis. In this article, ANCF, which has been recently developed and focuses on modeling of beams and plates in large deformation problems, is extended into design sensitivity analysis of flexible multibody systems. The Motion equations of a constrained flexible multibody system are expressed as a set of index-3 differential algebraic equations (DAEs), in which the element elastic forces are defined using nonlinear strain-displacement relations. Both the direct differentiation method and adjoint variable method are performed to do sensitivity analysis and the related dynamic and sensitivity equations are integrated with HHT-I3 algorithm. In this paper, a new method to deduce system sensitivity equations is proposed. With this approach, the system sensitivity equations are constructed by assembling the element sensitivity equations with the help of invariant matrices, which results in the advantage that the complex symbolic differentiation of the dynamic equations is avoided when the flexible multibody system model is changed. Besides that, the dynamic and sensitivity equations formed with the proposed method can be efficiently integrated using HHT-I3 method, which makes the efficiency of the direct differentiation method comparable to that of the adjoint variable method when the number of design variables is not extremely large. All these improvements greatly enhance the application value of the direct differentiation

Speciation analysis of arsenic, chromium and selenium in aquatic media. Proceedings of a final research coordination meeting

International Nuclear Information System (INIS)

2007-03-01

The initiation of the Coordinated Research Project (CRP) on Development and Validation of Speciation Analysis using Nuclear Techniques resulted from the recognition that knowledge of total element concentration does not provide adequate information to understand the effects of trace and heavy metals observed in the environment and in living systems. Their toxicity, bioavailability, physiological and metabolic processes, mobility and distribution are greatly dependent on the specific chemical form of the element. Speciation analysis has yet to be developed to its full potential for biochemical, clinical and environmental investigations and still more work is needed in the near future. Seven participants from seven countries participated in this CRP covering a range of analytical techniques including GC, HPLC, AAS, and ICP-MS. The first Research Coordination Meeting (RCM) of the Coordinated Research Project on the Development and Validation of Speciation Analysis using Nuclear Techniques was held at the Reactor Centre of the Jozef Stefan Institute, Podgorica (near Ljubljana), Slovenia, 20-23 June 2001. The second RCM was held at the Technical University of Vienna, 18-22 November 2002, where, in addition to the participants, two external researchers could present their views and experience in the field. The last RCM was held in Vienna, 26-29 April 2004, and this publication is a summary of the results achieved and presented at this last RCM. The participants have developed several new procedures for the reliable analysis of As, Cr, and Se species, mainly in aquatic media. A new instrument was designed and several recommendations for speciation analysis were issued. It is hoped that this publication will make a contribution to enhancing the awareness of the importance of speciation analysis and add to the reliability of speciation results in Member State laboratories

Geometric morphometric footprint analysis of young women

OpenAIRE

Domjanic, Jacqueline; Fieder, Martin; Seidler, Horst; Mitteroecker, Philipp

2013-01-01

Background Most published attempts to quantify footprint shape are based on a small number of measurements. We applied geometric morphometric methods to study shape variation of the complete footprint outline in a sample of 83 adult women. Methods The outline of the footprint, including the toes, was represented by a comprehensive set of 85 landmarks and semilandmarks. Shape coordinates were computed by Generalized Procrustes Analysis. Results The first four principal components represented t...

The open method of coordination in vocational education and training

DEFF Research Database (Denmark)

Cort, Pia

2009-01-01

Analysis of EU modes of governance within the Copenhagen Process with a specific focus on the Open Method of Coordination.......Analysis of EU modes of governance within the Copenhagen Process with a specific focus on the Open Method of Coordination....

Mapping ash properties using principal components analysis

Science.gov (United States)

Pereira, Paulo; Brevik, Eric; Cerda, Artemi; Ubeda, Xavier; Novara, Agata; Francos, Marcos; Rodrigo-Comino, Jesus; Bogunovic, Igor; Khaledian, Yones

2017-04-01

In post-fire environments ash has important benefits for soils, such as protection and source of nutrients, crucial for vegetation recuperation (Jordan et al., 2016; Pereira et al., 2015a; 2016a,b). The thickness and distribution of ash are fundamental aspects for soil protection (Cerdà and Doerr, 2008; Pereira et al., 2015b) and the severity at which was produced is important for the type and amount of elements that is released in soil solution (Bodi et al., 2014). Ash is very mobile material, and it is important were it will be deposited. Until the first rainfalls are is very mobile. After it, bind in the soil surface and is harder to erode. Mapping ash properties in the immediate period after fire is complex, since it is constantly moving (Pereira et al., 2015b). However, is an important task, since according the amount and type of ash produced we can identify the degree of soil protection and the nutrients that will be dissolved. The objective of this work is to apply to map ash properties (CaCO3, pH, and select extractable elements) using a principal component analysis (PCA) in the immediate period after the fire. Four days after the fire we established a grid in a 9x27 m area and took ash samples every 3 meters for a total of 40 sampling points (Pereira et al., 2017). The PCA identified 5 different factors. Factor 1 identified high loadings in electrical conductivity, calcium, and magnesium and negative with aluminum and iron, while Factor 3 had high positive loadings in total phosphorous and silica. Factor 3 showed high positive loadings in sodium and potassium, factor 4 high negative loadings in CaCO3 and pH, and factor 5 high loadings in sodium and potassium. The experimental variograms of the extracted factors showed that the Gaussian model was the most precise to model factor 1, the linear to model factor 2 and the wave hole effect to model factor 3, 4 and 5. The maps produced confirm the patternd observed in the experimental variograms. Factor 1 and 2

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří

2016-02-12

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Convergence of barycentric coordinates to barycentric kernels

KAUST Repository

Kosinka, Jiří ; Barton, Michael

2016-01-01

We investigate the close correspondence between barycentric coordinates and barycentric kernels from the point of view of the limit process when finer and finer polygons converge to a smooth convex domain. We show that any barycentric kernel is the limit of a set of barycentric coordinates and prove that the convergence rate is quadratic. Our convergence analysis extends naturally to barycentric interpolants and mappings induced by barycentric coordinates and kernels. We verify our theoretical convergence results numerically on several examples.

Principal components and iterative regression analysis of geophysical series: Application to Sunspot number (1750 2004)

Science.gov (United States)

Nordemann, D. J. R.; Rigozo, N. R.; de Souza Echer, M. P.; Echer, E.

2008-11-01

We present here an implementation of a least squares iterative regression method applied to the sine functions embedded in the principal components extracted from geophysical time series. This method seems to represent a useful improvement for the non-stationary time series periodicity quantitative analysis. The principal components determination followed by the least squares iterative regression method was implemented in an algorithm written in the Scilab (2006) language. The main result of the method is to obtain the set of sine functions embedded in the series analyzed in decreasing order of significance, from the most important ones, likely to represent the physical processes involved in the generation of the series, to the less important ones that represent noise components. Taking into account the need of a deeper knowledge of the Sun's past history and its implication to global climate change, the method was applied to the Sunspot Number series (1750-2004). With the threshold and parameter values used here, the application of the method leads to a total of 441 explicit sine functions, among which 65 were considered as being significant and were used for a reconstruction that gave a normalized mean squared error of 0.146.

Future in actinoids coordination chemistry

International Nuclear Information System (INIS)

Kitazawa, Takafumi

2006-01-01

Actinoids coordination chemistry is concerned with spent nuclear fuel reprocessing, specifically with solid-state chemistry of nuclear fuels, separation process with radioactive substances, and geological disposal of high-level radioactive substances. In the 21st century, accumulation of minor actinides, Np, Am, Cm, and others will be realized according with the present program of nuclear energy development. The present article briefly introduces general properties of actinide elements, followed by their coordination chemistry compared with rare earths coordination chemistry. Special facility needed to treat actinoids as well as their chemistry is briefly explained, together with the specific experimental apparatus such as X-ray Absorption Fine Structure (XAFS) and time-resolved laser-induced fluorescence spectrometry (TRLFS) with synchrotron radiation facilities. The effect of coordination with actinoids in the environment chemistry is important in underground disposal of high-level radioactive wastes. For theoretical analysis of the results with actinoids chemistry, relativistic calculation is needed. (S. Ohno)

Principal components analysis based control of a multi-dof underactuated prosthetic hand

Directory of Open Access Journals (Sweden)

Magenes Giovanni

2010-04-01

Full Text Available Abstract Background Functionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG. Driving a multi degrees of freedom (DoF hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user. Methods A Principal Components Analysis (PCA based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs. Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control. Results Trials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture may be achieved. Conclusions This work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

Constructing principals' professional identities through life stories: an exploration

Directory of Open Access Journals (Sweden)

Jabulani Mpungose

2010-01-01

Full Text Available Adopting a humanistic perspective to the study of leadership, I discuss and describe how school principals adapt to their new roles, owing to the new education policies and educational restructuring within the South African Department of Education. The Life History approach was used to collect data from six selected school principals in KwaZulu-Natal. On the basis of the analysis of data, I conclude that leaders create their provisional selves and construct their professional identities from their personal and professional knowledge. Provisional selves, in this context, are temporary solutions principals use to close the gap between their current capacities and self-concepts, and the ideas they hold about what attitudes and behaviours are expected in their new roles.

Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

Science.gov (United States)

Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

2016-09-01

In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

Use of Principal Components Analysis to Explain Controls on Nutrient Fluxes to the Chesapeake Bay

Science.gov (United States)

Rice, K. C.; Mills, A. L.

2017-12-01

The Chesapeake Bay watershed, on the east coast of the United States, encompasses about 166,000-square kilometers (km2) of diverse land use, which includes a mixture of forested, agricultural, and developed land. The watershed is now managed under a Total Daily Maximum Load (TMDL), which requires implementation of management actions by 2025 that are sufficient to reduce nitrogen, phosphorus, and suspended-sediment fluxes to the Chesapeake Bay and restore the bay's water quality. We analyzed nutrient and sediment data along with land-use and climatic variables in nine sub watersheds to better understand the drivers of flux within the watershed and to provide relevant management implications. The nine sub watersheds range in area from 300 to 30,000 km2, and the analysis period was 1985-2014. The 31 variables specific to each sub watershed were highly statistically significantly correlated, so Principal Components Analysis was used to reduce the dimensionality of the dataset. The analysis revealed that about 80% of the variability in the whole dataset can be explained by discharge, flux, and concentration of nutrients and sediment. The first two principal components (PCs) explained about 68% of the total variance. PC1 loaded strongly on discharge and flux, and PC2 loaded on concentration. The PC scores of both PC1 and PC2 varied by season. Subsequent analysis of PC1 scores versus PC2 scores, broken out by sub watershed, revealed management implications. Some of the largest sub watersheds are largely driven by discharge, and consequently large fluxes. In contrast, some of the smaller sub watersheds are more variable in nutrient concentrations than discharge and flux. Our results suggest that, given no change in discharge, a reduction in nutrient flux to the streams in the smaller watersheds could result in a proportionately larger decrease in fluxes of nutrients down the river to the bay, than in the larger watersheds.

Examining the relationships between attention deficit/hyperactivity disorder and developmental coordination disorder symptoms, and writing performance in Japanese second grade students.

Science.gov (United States)

Noda, Wataru; Ito, Hiroyuki; Fujita, Chikako; Ohnishi, Masafumi; Takayanagi, Nobuya; Someki, Fumio; Nakajima, Syunji; Ohtake, Satoko; Mochizuki, Naoto; Tsujii, Masatsugu

2013-09-01

The purpose of this study was to explore the relationships between attention deficit/hyperactivity disorder and developmental coordination disorder symptoms and writing performance in Japanese second grade students from regular classrooms. The second grade students (N=873) in Japanese public elementary schools participated in this study. We examined a variety of writing tasks, such as tracing, copying, handwriting (Hiragana and Katakana), and spelling (Hiragana, Katakana, and Kanji). We employed the Japanese version of the home form ADHD-rating scale (ADHD-RS) and the Japanese version of the Developmental Coordination Disorder Questionnaire (DCDQ-J) to assess the developmental characteristics of the participating children. Seven writing performance scores were submitted to a principal component analysis with a promax rotation, which yielded three composite scores (Spelling Accuracy, Tracing and Copying Accuracy, and Handwriting Fluency). A multiple regression analysis found that inattention predicted Spelling Accuracy and Handwriting Fluency and that hyperactive-impulsive predicted Handwriting Fluency. In addition, fine motor ability predicted Tracing and Copying Accuracy. The current study offered empirical evidence suggesting that developmental characteristics such as inattention and fine motor skill are related to writing difficulties in Japanese typical developing children. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analysis of an ultrasonically rotating droplet by moving particle semi-implicit and distributed point source method in a rotational coordinate

Science.gov (United States)

Wada, Yuji; Yuge, Kohei; Tanaka, Hiroki; Nakamura, Kentaro

2017-07-01

Numerical analysis on the rotation of an ultrasonically levitated droplet in centrifugal coordinate is discussed. A droplet levitated in an acoustic chamber is simulated using the distributed point source method and the moving particle semi-implicit method. Centrifugal coordinate is adopted to avoid the Laplacian differential error, which causes numerical divergence or inaccuracy in the global coordinate calculation. Consequently, the duration of calculation stability has increased 30 times longer than that in a the previous paper. Moreover, the droplet radius versus rotational acceleration characteristics show a similar trend to the theoretical and experimental values in the literature.

Progress Towards Improved Analysis of TES X-ray Data Using Principal Component Analysis

Science.gov (United States)

Busch, S. E.; Adams, J. S.; Bandler, S. R.; Chervenak, J. A.; Eckart, M. E.; Finkbeiner, F. M.; Fixsen, D. J.; Kelley, R. L.; Kilbourne, C. A.; Lee, S.-J.;

Principal Components as a Data Reduction and Noise Reduction Technique

Science.gov (United States)

Imhoff, M. L.; Campbell, W. J.

1982-01-01

The potential of principal components as a pipeline data reduction technique for thematic mapper data was assessed and principal components analysis and its transformation as a noise reduction technique was examined. Two primary factors were considered: (1) how might data reduction and noise reduction using the principal components transformation affect the extraction of accurate spectral classifications; and (2) what are the real savings in terms of computer processing and storage costs of using reduced data over the full 7-band TM complement. An area in central Pennsylvania was chosen for a study area. The image data for the project were collected using the Earth Resources Laboratory's thematic mapper simulator (TMS) instrument.

Job satisfaction and importance for intensive care unit research coordinators: results from binational survey.

Science.gov (United States)

Rickard, Claire M; Roberts, Brigit L; Foote, Jonathon; McGrail, Matthew R

2007-09-01

To measure Intensive Care Unit Research coordinator job satisfaction and importance and to identify priorities for role development. Research coordinator numbers are growing internationally in response to increasing clinical research activity. In Australia, 1% of registered nurses work principally in research, many as Research coordinators. Internationally, the Association of Clinical Research Professionals currently has 6536 certified Research coordinators in 13 countries, with likely additional large numbers practicing without the voluntary certification. Research coordinators are almost always nurses, but little is know about this emerging specialty. Design. Cross-sectional study using anonymous self-report questionnaire. After ethics approval, the McCloskey-Mueller Satisfaction Scale and McCloskey-Mueller Importance Scale were administered via the Internet. The sample was 49 (response rate 71%) Research coordinators from the Australia and New Zealand Intensive Care Unit Research coordinators' Interest Group. Research coordinators were satisfied with structural aspects of the position working business hours; flexibility of working hours; high levels of responsibility and control over their work. Dissatisfaction was expressed regarding: remuneration and recognition; compensation for weekend work; salary package; career advancement opportunities; and childcare facilities. High priorities for role development are those rated highly important but with much lower satisfaction. These are: compensation for weekend call-out work; salary and remuneration package; recognition by management and clinicians; career advancement opportunities; departmental research processes; encouragement and feedback; and number of working hours. Increasing numbers of nurses have been attracted to this clinically based research position. These data contribute to the understanding and development of the role.

Analyzing Principal Professional Development Practices through the Lens of Adult Learning Theory

Science.gov (United States)

Zepeda, Sally J.; Parylo, Oksana; Bengtson, Ed

2014-01-01

This qualitative study sought to identify current principal professional development practices in four school systems in Georgia and to examine them by applying the principles of adult learning theory. The cross-case analysis of principal professional development initiatives in four school districts revealed nine common practices: connecting…

Performance analysis of coordination strategies in two-tier Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram; Kammoun, Abla; Alouini, Mohamed-Slim

2016-01-01

Large scale multi-tier Heterogeneous Networks (HetNets) are expected to ensure a consistent quality of service (QoS) in 5G systems. Such networks consist of a macro base station (BS) equipped with a large number of antennas and a dense overlay of small cells. The small cells could be deployed within the same coverage of the macro-cell BS, thereby causing high levels of inter-cell interference. In this regard, coordinated beamforming techniques are considered as a viable solution to counteract the arising interference. The goal of this work is to analyze the efficiency of coordinated beamforming techniques in mitigating both intra-cell and inter-cell interference. In particular, we consider the downlink of a Time-division duplexing (TDD) massive multiple-input-multiple-output (MIMO) tier-HetNet and analyze different beamforming schemes together with different degrees of coordination between the BSs. We exploit random matrix theory tools in order to provide, in explicit form, deterministic equivalents for the average achievable rates in the macro-cell and the micro-cells. We prove that our theoretical derivations allow us to draw some conclusions regarding the role played by coordination strategies in reducing the inter-cell interference. These findings are finally validated by a selection of some numerical results. © 2016 IEEE.

Performance analysis of coordination strategies in two-tier Heterogeneous Networks

KAUST Repository

Boukhedimi, Ikram

2016-08-11

Large scale multi-tier Heterogeneous Networks (HetNets) are expected to ensure a consistent quality of service (QoS) in 5G systems. Such networks consist of a macro base station (BS) equipped with a large number of antennas and a dense overlay of small cells. The small cells could be deployed within the same coverage of the macro-cell BS, thereby causing high levels of inter-cell interference. In this regard, coordinated beamforming techniques are considered as a viable solution to counteract the arising interference. The goal of this work is to analyze the efficiency of coordinated beamforming techniques in mitigating both intra-cell and inter-cell interference. In particular, we consider the downlink of a Time-division duplexing (TDD) massive multiple-input-multiple-output (MIMO) tier-HetNet and analyze different beamforming schemes together with different degrees of coordination between the BSs. We exploit random matrix theory tools in order to provide, in explicit form, deterministic equivalents for the average achievable rates in the macro-cell and the micro-cells. We prove that our theoretical derivations allow us to draw some conclusions regarding the role played by coordination strategies in reducing the inter-cell interference. These findings are finally validated by a selection of some numerical results. © 2016 IEEE.

Principal variance component analysis of crop composition data: a case study on herbicide-tolerant cotton.

Science.gov (United States)

Harrison, Jay M; Howard, Delia; Malven, Marianne; Halls, Steven C; Culler, Angela H; Harrigan, George G; Wolfinger, Russell D

2013-07-03

Compositional studies on genetically modified (GM) and non-GM crops have consistently demonstrated that their respective levels of key nutrients and antinutrients are remarkably similar and that other factors such as germplasm and environment contribute more to compositional variability than transgenic breeding. We propose that graphical and statistical approaches that can provide meaningful evaluations of the relative impact of different factors to compositional variability may offer advantages over traditional frequentist testing. A case study on the novel application of principal variance component analysis (PVCA) in a compositional assessment of herbicide-tolerant GM cotton is presented. Results of the traditional analysis of variance approach confirmed the compositional equivalence of the GM and non-GM cotton. The multivariate approach of PVCA provided further information on the impact of location and germplasm on compositional variability relative to GM.

RE Rooted in Principal's Biography

NARCIS (Netherlands)

ter Avest, Ina; Bakker, C.

2017-01-01

Critical incidents in the biography of principals appear to be steering in their innovative way of constructing InterReligious Education in their schools. In this contribution, the authors present the biographical narratives of 4 principals: 1 principal introducing interreligious education in a

Association test based on SNP set: logistic kernel machine based test vs. principal component analysis.

Directory of Open Access Journals (Sweden)

Yang Zhao

Full Text Available GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM and principal components analysis based approach (PCA using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.

Time-space coordination of mining operations for protection of the surface. [Poland

Energy Technology Data Exchange (ETDEWEB)

Stranz, B

1975-01-01

In Polish mines, more than 41 percent of coal resources beneath built-up areas can be extracted. In 1973 an analysis of the mining and geological conditions was conducted in one of the mines, principally from the point of view of suitably coordinated mining advance with caving. Various possible systems of extraction were analyzed for three time periods up to 1985. A detailed inventory was prepared of surface structures in the whole concession area, particular attention being paid to industrial and social or communal areas. Preliminary and final predictions were made of deformation indices for various time periods, including predicted subsidences, and dynamic and static horizontal strains. The optimum variant was chosen, and capital expenditure and economic effects were taken into account. Solutions worked out for various sectors of the overall problem were presented to the mine management in the form of programmes for advancing the mining face in individual panels and seams so as to obtain maximum possible production with roof caving, under protected buildings.

Accountability Systems: A Comparative Analysis of Superintendent, Principal, and Teacher Perceptions

Directory of Open Access Journals (Sweden)

KERRYENGLERT,PH.D.

2007-04-01

Full Text Available A key assumption of NCLB appears to be that assessment data in and of itself can foster or promote change. Specifically, the supposition is that by requiring assessment data to be reported yearly, schools will be motivated - and will have the ability - to address those areas where student achievement is lagging. This assumption rests on the notion that educator competence in understanding and utilizing such data will result in academic success. Testing this assumption with empirical evidence is an important component of researching the efficacy of current accountability policies and practices in general. Over the past three years we have been involved in a series of empirical examinations of accountability. Each of these studies has been aimed at gathering varied perspectives on and about accountability, ranging from superintendents to principals to teachers. Our research examines education accountability at three interconnected layers: district administrators, principals, and teachers. This nested data set (superintendents were surveyed, as were their principals, and their principals’ teachers allows for not only an examination of the perceptions and reflections of the members of each group but also for an evaluation of the consistency of those beliefs across the members of the educational community. This study will present findings from research projects that speak to each of these levels, focusing on how each understands education accountability and how those meanings are consistent across groups and to what degree.

A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

Directory of Open Access Journals (Sweden)

M. Imran

2017-09-01

Full Text Available A blind adaptive color image watermarking scheme based on principal component analysis, singular value decomposition, and human visual system is proposed. The use of principal component analysis to decorrelate the three color channels of host image, improves the perceptual quality of watermarked image. Whereas, human visual system and fuzzy inference system helped to improve both imperceptibility and robustness by selecting adaptive scaling factor, so that, areas more prone to noise can be added with more information as compared to less prone areas. To achieve security, location of watermark embedding is kept secret and used as key at the time of watermark extraction, whereas, for capacity both singular values and vectors are involved in watermark embedding process. As a result, four contradictory requirements; imperceptibility, robustness, security and capacity are achieved as suggested by results. Both subjective and objective methods are acquired to examine the performance of proposed schemes. For subjective analysis the watermarked images and watermarks extracted from attacked watermarked images are shown. For objective analysis of proposed scheme in terms of imperceptibility, peak signal to noise ratio, structural similarity index, visual information fidelity and normalized color difference are used. Whereas, for objective analysis in terms of robustness, normalized correlation, bit error rate, normalized hamming distance and global authentication rate are used. Security is checked by using different keys to extract the watermark. The proposed schemes are compared with state-of-the-art watermarking techniques and found better performance as suggested by results.

Nonlinear analysis of renal autoregulation in rats using principal dynamic modes

DEFF Research Database (Denmark)

Marmarelis, V Z; Chon, K H; Holstein-Rathlou, N H

1999-01-01

This article presents results of the use of a novel methodology employing principal dynamic modes (PDM) for modeling the nonlinear dynamics of renal autoregulation in rats. The analyzed experimental data are broadband (0-0.5 Hz) blood pressure-flow data generated by pseudorandom forcing and colle......This article presents results of the use of a novel methodology employing principal dynamic modes (PDM) for modeling the nonlinear dynamics of renal autoregulation in rats. The analyzed experimental data are broadband (0-0.5 Hz) blood pressure-flow data generated by pseudorandom forcing...... and collected in normotensive and hypertensive rats for two levels of pressure forcing (as measured by the standard deviation of the pressure fluctuation). The PDMs are computed from first-order and second-order kernel estimates obtained from the data via the Laguerre expansion technique. The results...

The Reflexive Adaptations of School Principals in a "Local" South African Space

Science.gov (United States)

Fataar, Aslam

2009-01-01

This paper is an analysis of the work of three principals in an impoverished black township in post-apartheid South Africa. Based on qualitative approaches, it discusses the principals' entry into the township, and their navigation of their schools' surrounding social dynamics. It combines the lenses of "space" and…

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

Science.gov (United States)

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.

A coordinate descent MM algorithm for fast computation of sparse logistic PCA

KAUST Repository

Lee, Seokho; Huang, Jianhua Z.

2013-01-01

Sparse logistic principal component analysis was proposed in Lee et al. (2010) for exploratory analysis of binary data. Relying on the joint estimation of multiple principal components, the algorithm therein is computationally too demanding

Geographic distribution of suicide and railway suicide in Belgium, 2008-2013: a principal component analysis.

Science.gov (United States)

Strale, Mathieu; Krysinska, Karolina; Overmeiren, Gaëtan Van; Andriessen, Karl

2017-06-01

This study investigated the geographic distribution of suicide and railway suicide in Belgium over 2008--2013 on local (i.e., district or arrondissement) level. There were differences in the regional distribution of suicide and railway suicides in Belgium over the study period. Principal component analysis identified three groups of correlations among population variables and socio-economic indicators, such as population density, unemployment, and age group distribution, on two components that helped explaining the variance of railway suicide at a local (arrondissement) level. This information is of particular importance to prevent suicides in high-risk areas on the Belgian railway network.

A New Model for Birth Weight Prediction Using 2- and 3-Dimensional Ultrasonography by Principal Component Analysis: A Chinese Population Study.

Science.gov (United States)

Liao, Shuxin; Wang, Yunfang; Xiao, Shufang; Deng, Xujie; Fang, Bimei; Yang, Fang

2018-03-30

To establish a new model for birth weight prediction using 2- and 3-dimensional ultrasonography (US) by principal component analysis (PCA). Two- and 3-dimensional US was prospectively performed in women with normal singleton pregnancies within 7 days before delivery (37-41 weeks' gestation). The participants were divided into a development group (n = 600) and a validation group (n = 597). Principal component analysis and stepwise linear regression analysis were used to develop a new prediction model. The new model's accuracy in predicting fetal birth weight was confirmed by the validation group through comparisons with previously published formulas. A total of 1197 cases were recruited in this study. All interclass and intraclass correlation coefficients of US measurements were greater than 0.75. Two principal components (PCs) were considered primary in determining estimated fetal birth weight, which were derived from 9 US measurements. Stepwise linear regression analysis showed a positive association between birth weight and PC1 and PC2. In the development group, our model had a small mean percentage error (mean ± SD, 3.661% ± 3.161%). At least a 47.558% decrease in the mean percentage error and a 57.421% decrease in the standard deviation of the new model compared with previously published formulas were noted. The results were similar to those in the validation group, and the new model covered 100% of birth weights within 10% of actual birth weights. The birth weight prediction model based on 2- and 3-dimensional US by PCA could help improve the precision of estimated fetal birth weight. © 2018 by the American Institute of Ultrasound in Medicine.

What Are the Different Types of Principals across the United States? A Latent Class Analysis of Principal Perception of Leadership

Science.gov (United States)

Urick, Angela; Bowers, Alex J.

2014-01-01

Purpose: Effective styles of principal leadership can help address multiple issues in struggling schools, such as low student achievement and high rates of teacher attrition. Although the literature has nominated certain "idealized" leadership styles as being more or less effective, such as transformational, instructional, and shared…

Redesigning Principal Internships: Practicing Principals' Perspectives

Science.gov (United States)

Anast-May, Linda; Buckner, Barbara; Geer, Gregory

2011-01-01

Internship programs too often do not provide the types of experiences that effectively bridge the gap between theory and practice and prepare school leaders who are capable of leading and transforming schools. To help address this problem, the current study is directed at providing insight into practicing principals' views of the types of…

Bare coordination: the semantic shift

OpenAIRE

de Swart, Henriette; Le Bruyn, Bert

2014-01-01

This paper develops an analysis of the syntax-semantics interface of two types of split coordination structures. In the first type, two bare singular count nouns appear as arguments in a coordinated structure, as in bride and groom were happy. We call this the N&N construction. In the second type, the determiner shows agreement with the first conjunct, while the second conjunct is bare, as in the Spanish example el hornero y hornera cobraban en panes (‘thesg.m bakersg.m and bakersg.f werepl p...

Artificial neural network combined with principal component analysis for resolution of complex pharmaceutical formulations.

Science.gov (United States)

Ioele, Giuseppina; De Luca, Michele; Dinç, Erdal; Oliverio, Filomena; Ragno, Gaetano

2011-01-01

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China.

Science.gov (United States)

Zhang, Yuji; Li, Xiaoju; Mao, Lu; Zhang, Mei; Li, Ke; Zheng, Yinxia; Cui, Wangfei; Yin, Hongpo; He, Yanli; Jing, Mingxia

2018-01-01

The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis. A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ 2 -test and a binary logistic regression model. This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications. Community management plays an important role in improving the patients' medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers.