EXAFS and principal component analysis : a new shell game

International Nuclear Information System (INIS)

Wasserman, S.

1998-01-01

The use of principal component (factor) analysis in the analysis EXAFS spectra is described. The components derived from EXAFS spectra share mathematical properties with the original spectra. As a result, the abstract components can be analyzed using standard EXAFS methodology to yield the bond distances and other coordination parameters. The number of components that must be analyzed is usually less than the number of original spectra. The method is demonstrated using a series of spectra from aqueous solutions of uranyl ions

PRINCIPAL COMPONENT ANALYSIS (PCA DAN APLIKASINYA DENGAN SPSS

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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.

Euler principal component analysis

NARCIS (Netherlands)

Liwicki, Stephan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja

Principal Component Analysis (PCA) is perhaps the most prominent learning tool for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2-norm employed by standard PCA is not robust to outliers. In this paper, we propose a kernel PCA method for fast and robust PCA,

Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

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Chang Liu

2014-01-01

Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.

Constrained principal component analysis and related techniques

CERN Document Server

Takane, Yoshio

2013-01-01

In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches.The book begins with four concre

Latent profile analysis and principal axis factoring of the DSM-5 dissociative subtype

Science.gov (United States)

Frewen, Paul A.; Brown, Matthew F. D.; Steuwe, Carolin; Lanius, Ruth A.

2015-01-01

Objective A dissociative subtype has been recognized based on the presence of experiences of depersonalization and derealization in relation to DSM-IV posttraumatic stress disorder (PTSD). However, the dissociative subtype has not been assessed in a community sample in relation to the revised DSM-5 PTSD criteria. Moreover, the 20-item PTSD Checklist for DSM-5 (PCL-5) currently does not assess depersonalization and derealization. Method We therefore evaluated two items for assessing depersonalization and derealization in 557 participants recruited online who endorsed PTSD symptoms of at least moderate severity on the PCL-5. Results A five-class solution identified two PTSD classes who endorsed dissociative experiences associated with either 1) severe or 2) moderate PTSD symptom severity (D-PTSD classes). Those in the severe dissociative class were particularly likely to endorse histories of childhood physical and sexual abuse. A principal axis factor analysis of the symptom list identified six latent variables: 1) Reexperiencing, 2) Emotional Numbing/Anhedonia, 3) Dissociation, 4) Negative Alterations in Cognition & Mood, 5) Avoidance, and 6) Hyperarousal. Conclusions The present results further support the presence of a dissociative subtype within the DSM-5 criteria for PTSD. PMID:25854673

Latent profile analysis and principal axis factoring of the DSM-5 dissociative subtype

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Paul A. Frewen

2015-04-01

Full Text Available Objective: A dissociative subtype has been recognized based on the presence of experiences of depersonalization and derealization in relation to DSM-IV posttraumatic stress disorder (PTSD. However, the dissociative subtype has not been assessed in a community sample in relation to the revised DSM-5 PTSD criteria. Moreover, the 20-item PTSD Checklist for DSM-5 (PCL-5 currently does not assess depersonalization and derealization. Method: We therefore evaluated two items for assessing depersonalization and derealization in 557 participants recruited online who endorsed PTSD symptoms of at least moderate severity on the PCL-5. Results: A five-class solution identified two PTSD classes who endorsed dissociative experiences associated with either 1 severe or 2 moderate PTSD symptom severity (D-PTSD classes. Those in the severe dissociative class were particularly likely to endorse histories of childhood physical and sexual abuse. A principal axis factor analysis of the symptom list identified six latent variables: 1 Reexperiencing, 2 Emotional Numbing/Anhedonia, 3 Dissociation, 4 Negative Alterations in Cognition & Mood, 5 Avoidance, and 6 Hyperarousal. Conclusions: The present results further support the presence of a dissociative subtype within the DSM-5 criteria for PTSD.

Latent profile analysis and principal axis factoring of the DSM-5 dissociative subtype.

Science.gov (United States)

Frewen, Paul A; Brown, Matthew F D; Steuwe, Carolin; Lanius, Ruth A

2015-01-01

A dissociative subtype has been recognized based on the presence of experiences of depersonalization and derealization in relation to DSM-IV posttraumatic stress disorder (PTSD). However, the dissociative subtype has not been assessed in a community sample in relation to the revised DSM-5 PTSD criteria. Moreover, the 20-item PTSD Checklist for DSM-5 (PCL-5) currently does not assess depersonalization and derealization. We therefore evaluated two items for assessing depersonalization and derealization in 557 participants recruited online who endorsed PTSD symptoms of at least moderate severity on the PCL-5. A five-class solution identified two PTSD classes who endorsed dissociative experiences associated with either 1) severe or 2) moderate PTSD symptom severity (D-PTSD classes). Those in the severe dissociative class were particularly likely to endorse histories of childhood physical and sexual abuse. A principal axis factor analysis of the symptom list identified six latent variables: 1) Reexperiencing, 2) Emotional Numbing/Anhedonia, 3) Dissociation, 4) Negative Alterations in Cognition & Mood, 5) Avoidance, and 6) Hyperarousal. The present results further support the presence of a dissociative subtype within the DSM-5 criteria for PTSD.

Physicochemical properties of different corn varieties by principal components analysis and cluster analysis

International Nuclear Information System (INIS)

Zeng, J.; Li, G.; Sun, J.

2013-01-01

Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties. The chemical compositions and some properties of corn flour which processed by drying milling were determined. The results showed that the chemical compositions and physicochemical properties were significantly different among twenty six corn varieties. The quality of corn flour was concerned with five principal components from principal component analysis and the contribution rate of starch pasting properties was important, which could account for 48.90%. Twenty six corn varieties could be classified into four groups by cluster analysis. The consistency between principal components analysis and cluster analysis indicated that multivariate analyses were feasible in the study of corn variety properties. (author)

Analysis of factors controlling soil phosphorus loss with surface runoff in Huihe National Nature Reserve by principal component and path analysis methods.

Science.gov (United States)

He, Jing; Su, Derong; Lv, Shihai; Diao, Zhaoyan; Bu, He; Wo, Qiang

2018-01-01

Phosphorus (P) loss with surface runoff accounts for the P input to and acceleration of eutrophication of the freshwater. Many studies have focused on factors affecting P loss with surface runoff from soils, but rarely on the relationship among these factors. In the present study, rainfall simulation on P loss with surface runoff was conducted in Huihe National Nature Reserve, in Hulunbeier grassland, China, and the relationships between P loss with surface runoff, soil properties, and rainfall conditions were examined. Principal component analysis and path analysis were used to analyze the direct and indirect effects on P loss with surface runoff. The results showed that P loss with surface runoff was closely correlated with soil electrical conductivity, soil pH, soil Olsen P, soil total nitrogen (TN), soil total phosphorus (TP), and soil organic carbon (SOC). The main driving factors which influenced P loss with surface runoff were soil TN, soil pH, soil Olsen P, and soil water content. Path analysis and determination coefficient analysis indicated that the standard multiple regression equation for P loss with surface runoff and each main factor was Y = 7.429 - 0.439 soil TN - 6.834 soil pH + 1.721 soil Olsen-P + 0.183 soil water content (r = 0.487, p runoff. The effect of physical and chemical properties of undisturbed soils on P loss with surface runoff was discussed, and the soil water content and soil Olsen P were strongly positive influences on the P loss with surface runoff.

Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index

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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.

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.

An easy guide to factor analysis

CERN Document Server

Kline, Paul

2014-01-01

Factor analysis is a statistical technique widely used in psychology and the social sciences. With the advent of powerful computers, factor analysis and other multivariate methods are now available to many more people. An Easy Guide to Factor Analysis presents and explains factor analysis as clearly and simply as possible. The author, Paul Kline, carefully defines all statistical terms and demonstrates step-by-step how to work out a simple example of principal components analysis and rotation. He further explains other methods of factor analysis, including confirmatory and path analysis, a

Validating the Copenhagen Psychosocial Questionnaire (COPSOQ-II) Using Set-ESEM: Identifying Psychosocial Risk Factors in a Sample of School Principals.

Science.gov (United States)

Dicke, Theresa; Marsh, Herbert W; Riley, Philip; Parker, Philip D; Guo, Jiesi; Horwood, Marcus

2018-01-01

School principals world-wide report high levels of strain and attrition resulting in a shortage of qualified principals. It is thus crucial to identify psychosocial risk factors that reflect principals' occupational wellbeing. For this purpose, we used the Copenhagen Psychosocial Questionnaire (COPSOQ-II), a widely used self-report measure covering multiple psychosocial factors identified by leading occupational stress theories. We evaluated the COPSOQ-II regarding factor structure and longitudinal, discriminant, and convergent validity using latent structural equation modeling in a large sample of Australian school principals ( N = 2,049). Results reveal that confirmatory factor analysis produced marginally acceptable model fit. A novel approach we call set exploratory structural equation modeling (set-ESEM), where cross-loadings were only allowed within a priori defined sets of factors, fit well, and was more parsimonious than a full ESEM. Further multitrait-multimethod models based on the set-ESEM confirm the importance of a principal's psychosocial risk factors; Stressors and depression were related to demands and ill-being, while confidence and autonomy were related to wellbeing. We also show that working in the private sector was beneficial for showing a low psychosocial risk, while other demographics have little effects. Finally, we identify five latent risk profiles (high risk to no risk) of school principals based on all psychosocial factors. Overall the research presented here closes the theory application gap of a strong multi-dimensional measure of psychosocial risk-factors.

Reassessing the Behavior of Principals as a Multiple-Factor in Teachers' Job Satisfaction.

Science.gov (United States)

Bogler, Ronit

This paper reports on a study that examined the effects of three factors on teacher satisfaction: principal leadership style (transformational or transactional), principal decision-making strategy (autocratic versus participative), and teachers' perceptions of their occupation. An overview of each of the three factors is provided. For the study, a…

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.

Evaluation of Parallel Analysis Methods for Determining the Number of Factors

Science.gov (United States)

Crawford, Aaron V.; Green, Samuel B.; Levy, Roy; Lo, Wen-Juo; Scott, Lietta; Svetina, Dubravka; Thompson, Marilyn S.

2010-01-01

Population and sample simulation approaches were used to compare the performance of parallel analysis using principal component analysis (PA-PCA) and parallel analysis using principal axis factoring (PA-PAF) to identify the number of underlying factors. Additionally, the accuracies of the mean eigenvalue and the 95th percentile eigenvalue criteria…

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

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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.

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.

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.

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...

Classification of peacock feather reflectance using principal component analysis similarity factors from multispectral imaging data.

Science.gov (United States)

Medina, José M; Díaz, José A; Vukusic, Pete

2015-04-20

Iridescent structural colors in biology exhibit sophisticated spatially-varying reflectance properties that depend on both the illumination and viewing angles. The classification of such spectral and spatial information in iridescent structurally colored surfaces is important to elucidate the functional role of irregularity and to improve understanding of color pattern formation at different length scales. In this study, we propose a non-invasive method for the spectral classification of spatial reflectance patterns at the micron scale based on the multispectral imaging technique and the principal component analysis similarity factor (PCASF). We demonstrate the effectiveness of this approach and its component methods by detailing its use in the study of the angle-dependent reflectance properties of Pavo cristatus (the common peacock) feathers, a species of peafowl very well known to exhibit bright and saturated iridescent colors. We show that multispectral reflectance imaging and PCASF approaches can be used as effective tools for spectral recognition of iridescent patterns in the visible spectrum and provide meaningful information for spectral classification of the irregularity of the microstructure in iridescent plumage.

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

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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.

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 ...

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

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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.

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

Analysis of factors controlling sediment phosphorus flux potential of wetlands in Hulun Buir grassland by principal component and path analysis method.

Science.gov (United States)

He, Jing; Su, Derong; Lv, Shihai; Diao, Zhaoyan; Ye, Shengxing; Zheng, Zhirong

2017-11-08

Phosphorus (P) flux potential can predict the trend of phosphorus release from wetland sediments to water and provide scientific parameters for further monitoring and management for phosphorus flux from wetland sediments to overlying water. Many studies have focused on factors affecting sediment P flux potential in sediment-water interface, but rarely on the relationship among these factors. In the present study, experiment on sediment P flux potential in sediment-water interface was conducted in six wetlands in Hulun Buir grassland, China and the relationships among sediment P flux potential in sediment-water interface, sediment physical properties, and sediment chemical characteristics were examined. Principal component analysis and path analysis were used to discuss these data in correlation coefficient, direct, and indirect effects on sediment P flux potential in sediment-water interface. Results indicated that the major factors affecting sediment P flux potential in sediment-water interface were amount of organophosphate-degradation bacterium in sediment, Ca-P content, and total phosphorus concentrations. The factors of direct influence sediment P flux potential were sediment Ca-P content, Olsen-P content, SOC content, and sediment Al-P content. The indirect influence sediment P flux potential in sediment-water interface was sediment Olsen-P content, sediment SOC content, sediment Ca-P content, and sediment Al-P content. And the standard multiple regression describing the relationship between sediment P flux potential in sediment-water interface and its major effect factors was Y = 5.849 - 1.025X 1  - 1.995X 2  + 0.188X 3  - 0.282X 4 (r = 0.9298, p < 0.01, n = 96), where Y is sediment P flux potential in sediment-water interface, X 1 is sediment Ca-P content, X 2 is sediment Olsen-P content, X 3 is sediment SOC content, and X 4 is sediment Al-P content. Therefore, future research will focus on these sediment properties to analyze the

Common Factor Analysis Versus Principal Component Analysis: Choice for Symptom Cluster Research

Directory of Open Access Journals (Sweden)

Hee-Ju Kim, PhD, RN

2008-03-01

Conclusion: If the study purpose is to explain correlations among variables and to examine the structure of the data (this is usual for most cases in symptom cluster research, CFA provides a more accurate result. If the purpose of a study is to summarize data with a smaller number of variables, PCA is the choice. PCA can also be used as an initial step in CFA because it provides information regarding the maximum number and nature of factors. In using factor analysis for symptom cluster research, several issues need to be considered, including subjectivity of solution, sample size, symptom selection, and level of measure.

Factors Affecting the Transformational Leadership Role of Principals in Implementing ICT in Schools

Science.gov (United States)

Afshari, Mojgan; Bakar, Kamariah Abu; Luan, Wong Su; Siraj, Saedah

2012-01-01

Leadership is an important factor in the effective implementation of technology in schools. This study examines the transformational leadership role of principals to determine whether transformational leadership role of principals in ICT implementation in schools is influenced by the computer competence, level of computer use, and professional…

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

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.

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.

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…

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

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.

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.

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.

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 ...

Multiple factor analysis by example using R

CERN Document Server

Pagès, Jérôme

2014-01-01

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR).The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The

Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region, Ghana.

Science.gov (United States)

Krefis, Anne Caroline; Schwarz, Norbert Georg; Nkrumah, Bernard; Acquah, Samuel; Loag, Wibke; Sarpong, Nimako; Adu-Sarkodie, Yaw; Ranft, Ulrich; May, Jürgen

2010-07-13

The socioeconomic and sociodemographic situation are important components for the design and assessment of malaria control measures. In malaria endemic areas, however, valid classification of socioeconomic factors is difficult due to the lack of standardized tax and income data. The objective of this study was to quantify household socioeconomic levels using principal component analyses (PCA) to a set of indicator variables and to use a classification scheme for the multivariate analysis of children<15 years of age presented with and without malaria to an outpatient department of a rural hospital. In total, 1,496 children presenting to the hospital were examined for malaria parasites and interviewed with a standardized questionnaire. The information of eleven indicators of the family's housing situation was reduced by PCA to a socioeconomic score, which was then classified into three socioeconomic status (poor, average and rich). Their influence on the malaria occurrence was analysed together with malaria risk co-factors, such as sex, parent's educational and ethnic background, number of children living in a household, applied malaria protection measures, place of residence and age of the child and the mother. The multivariate regression analysis demonstrated that the proportion of children with malaria decreased with increasing socioeconomic status as classified by PCA (p<0.05). Other independent factors for malaria risk were the use of malaria protection measures (p<0.05), the place of residence (p<0.05), and the age of the child (p<0.05). The socioeconomic situation is significantly associated with malaria even in holoendemic rural areas where economic differences are not much pronounced. Valid classification of the socioeconomic level is crucial to be considered as confounder in intervention trials and in the planning of malaria control measures.

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.

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.

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 ...

Exploring Technostress: Results of a Large Sample Factor Analysis

OpenAIRE

Jonušauskas, Steponas; Raišienė, Agota Giedrė

2016-01-01

With reference to the results of a large sample factor analysis, the article aims to propose the frame examining technostress in a population. The survey and principal component analysis of the sample consisting of 1013 individuals who use ICT in their everyday work was implemented in the research. 13 factors combine 68 questions and explain 59.13 per cent of the answers dispersion. Based on the factor analysis, questionnaire was reframed and prepared to reasonably analyze the respondents’ an...

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.

Factor analysis improves the selection of prescribing indicators

DEFF Research Database (Denmark)

Rasmussen, Hanne Marie Skyggedal; Søndergaard, Jens; Sokolowski, Ineta

2006-01-01

OBJECTIVE: To test a method for improving the selection of indicators of general practitioners' prescribing. METHODS: We conducted a prescription database study including all 180 general practices in the County of Funen, Denmark, approximately 472,000 inhabitants. Principal factor analysis was us...... appropriate and inappropriate prescribing, as revealed by the correlation of the indicators in the first factor. CONCLUSION: Correlation and factor analysis is a feasible method that assists the selection of indicators and gives better insight into prescribing patterns....

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.

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

Factors Affecting the Implementation of Policy 2450, Distance Education and the West Virginia Virtual School, as Perceived by Principals/Assistant Principals, Counselors, and Distance Learning Contacts and/or Course Facilitators

Science.gov (United States)

Burdette, Keith R.

2013-01-01

This study examined the factors important to the implementation of West Virginia Board of Education Policy 2450, Distance Learning and the West Virginia Virtual School. The purpose of this study was to determine the factors that facilitated and impeded implementation of the policy, as perceived by principals/assistant principals, counselors, and…

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.

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...

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.

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.

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.

Measurement Invariance of the "Servant Leadership Questionnaire" across K-12 Principal Gender

Science.gov (United States)

Xu, Lihua; Stewart, Trae; Haber-Curran, Paige

2015-01-01

Measurement invariance of the five-factor "Servant Leadership Questionnaire" between female and male K-12 principals was tested using multi-group confirmatory factor analysis. A sample of 956 principals (56.9% were females and 43.1% were males) was analysed in this study. The hierarchical multi-step measurement invariance test supported…

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...

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

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.

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...

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.

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.

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.

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),

Large Covariance Estimation by Thresholding Principal Orthogonal Complements.

Science.gov (United States)

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2013-09-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented.

Factors that Explain the Principal-Agent Relationship in Six Companies of the City of Manizales

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Tania Mackenzie Torres

2016-04-01

Full Text Available This study aims to determine the factors that explain the relationship between principal and agent in six institutions from different economic sectors (education, services, metallurgy, solidarity and financial economy in the city of Manizales. The study is based on the conceptual foundations of the neo-institutional current, which are framed in neoliberal and microeconomic models interpreted in the light of agency theory. As research strategy, the methodology consists of a multiple case study, which focuses its interest on a number of cases where each case has its own identity. The factors that determine the principal-agent relationship in these six institutions are explained by contract, incentives and controls; motivational factors, both intrinsic and extrinsic, are also identified.

Principal-vector-directed fringe-tracking technique.

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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.

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

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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.

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.

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

Determinants of Return on Assets in Romania: A Principal Component Analysis

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Sorana Vatavu

2015-03-01

Full Text Available This paper examines the impact of capital structure, as well as its determinants on the financial performance of Romanian companies listed on the Bucharest Stock Exchange. The analysis is based on cross sectional regressions and factor analysis, and it refers to a ten-year period (2003-2012. Return on assets (ROA is the performance proxy, while the capital structure indicator is debt ratio. Regression results indicate that Romanian companies register higher returns when they operate with limited borrowings. Among the capital structure determinants, tangibility and business risk have a negative impact on ROA, but the level of taxation has a positive effect, showing that companies manage their assets more efficiently during times of higher fiscal pressure. Performance is sustained by sales turnover, but not significantly influenced by high levels of liquidity. Periods of unstable economic conditions, reflected by high inflation rates and the current financial crisis, have a strong negative impact on corporate performance. Based on regression results, three factors were considered through the method of iterated principal component factors: the first one incorporates debt and size, as an indicator of consumption, the second one integrates the influence of tangibility and liquidity, marking the investment potential, and the third one is an indicator of assessed risk, integrating the volatility of earnings with the level of taxation. ROA is significantly influenced by these three factors, regardless the regression method used. The consumption factor has a negative impact on performance, while the investment and risk variables positively influence ROA.

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

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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…

A principal components analysis of the factors effecting personal exposure to air pollution in urban commuters in Dublin, Ireland.

Science.gov (United States)

McNabola, Aonghus; Broderick, Brian M; Gill, Laurence W

2009-10-01

Principal component analysis was used to examine air pollution personal exposure data of four urban commuter transport modes for their interrelationships between pollutants and relationships with traffic and meteorological data. Air quality samples of PM2.5 and VOCs were recorded during peak traffic congestion for the car, bus, cyclist and pedestrian between January 2005 and June 2006 on a busy route in Dublin, Ireland. In total, 200 personal exposure samples were recorded each comprising 17 variables describing the personal exposure concentrations, meteorological conditions and traffic conditions. The data reduction technique, principal component analysis (PCA), was used to create weighted linear combinations of the data and these were subsequently examined for interrelationships between the many variables recorded. The results of the PCA found that personal exposure concentrations in non-motorised forms of transport were influenced to a higher degree by wind speed, whereas personal exposure concentrations in motorised forms of transport were influenced to a higher degree by traffic congestion. The findings of the investigation show that the most effective mechanisms of personal exposure reduction differ between motorised and non-motorised modes of commuter transport.

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 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.

Large Covariance Estimation by Thresholding Principal Orthogonal Complements

Science.gov (United States)

Fan, Jianqing; Liao, Yuan; Mincheva, Martina

2012-01-01

This paper deals with the estimation of a high-dimensional covariance with a conditional sparsity structure and fast-diverging eigenvalues. By assuming sparse error covariance matrix in an approximate factor model, we allow for the presence of some cross-sectional correlation even after taking out common but unobservable factors. We introduce the Principal Orthogonal complEment Thresholding (POET) method to explore such an approximate factor structure with sparsity. The POET estimator includes the sample covariance matrix, the factor-based covariance matrix (Fan, Fan, and Lv, 2008), the thresholding estimator (Bickel and Levina, 2008) and the adaptive thresholding estimator (Cai and Liu, 2011) as specific examples. We provide mathematical insights when the factor analysis is approximately the same as the principal component analysis for high-dimensional data. The rates of convergence of the sparse residual covariance matrix and the conditional sparse covariance matrix are studied under various norms. It is shown that the impact of estimating the unknown factors vanishes as the dimensionality increases. The uniform rates of convergence for the unobserved factors and their factor loadings are derived. The asymptotic results are also verified by extensive simulation studies. Finally, a real data application on portfolio allocation is presented. PMID:24348088

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)

Exploring Technostress: Results of a Large Sample Factor Analysis

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Steponas Jonušauskas

2016-06-01

Full Text Available With reference to the results of a large sample factor analysis, the article aims to propose the frame examining technostress in a population. The survey and principal component analysis of the sample consisting of 1013 individuals who use ICT in their everyday work was implemented in the research. 13 factors combine 68 questions and explain 59.13 per cent of the answers dispersion. Based on the factor analysis, questionnaire was reframed and prepared to reasonably analyze the respondents’ answers, revealing technostress causes and consequences as well as technostress prevalence in the population in a statistically validated pattern. A key elements of technostress based on factor analysis can serve for the construction of technostress measurement scales in further research.

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

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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.

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

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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.

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.

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

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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 ...

Honouring Roles: The Story of a Principal and a Student

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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.

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

School Counselors and Principals: Different Perceptions of Relationship, Leadership, and Training

Science.gov (United States)

Armstrong, Stephen A.; MacDonald, Jane H.; Stillo, Sandy

2010-01-01

This study examined school counselors' and principals' perceptions of their relationship and the effectiveness of their respective professional preparation programs. An exploratory factor analysis (n = 615) revealed three salient factors: relationship quality, campus leadership and training satisfaction. Kruskal-Wallis tests revealed statistically…

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)

The Psychometric Assessment of Children with Learning Disabilities: An Index Derived from a Principal Components Analysis of the WISC-R.

Science.gov (United States)

Lawson, J. S.; Inglis, James

1984-01-01

A learning disability index (LDI) for the assessment of intellectual deficits on the Wechsler Intelligence Scale for Children-Revised (WISC-R) is described. The Factor II score coefficients derived from an unrotated principal components analysis of the WISC-R normative data, in combination with the individual's scaled scores, are used for this…

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...

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

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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.

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

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.

Manifold valued statistics, exact principal geodesic analysis and the effect of linear approximations

DEFF Research Database (Denmark)

Sommer, Stefan Horst; Lauze, Francois Bernard; Hauberg, Søren

2010-01-01

, we present a comparison between the non-linear analog of Principal Component Analysis, Principal Geodesic Analysis, in its linearized form and its exact counterpart that uses true intrinsic distances. We give examples of datasets for which the linearized version provides good approximations...... and for which it does not. Indicators for the differences between the two versions are then developed and applied to two examples of manifold valued data: outlines of vertebrae from a study of vertebral fractures and spacial coordinates of human skeleton end-effectors acquired using a stereo camera and tracking...

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

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.

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.

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)

What Motivates Principals?

Science.gov (United States)

Iannone, Ron

1973-01-01

Achievement and recognition were mentioned as factors appearing with greater frequency in principal's job satisfactions; school district policy and interpersonal relations were mentioned as job dissatisfactions. (Editor)

Delaunay algorithm and principal component analysis for 3D visualization of mitochondrial DNA nucleoids by Biplane FPALM/dSTORM

Czech Academy of Sciences Publication Activity Database

Alán, Lukáš; Špaček, Tomáš; Ježek, Petr

2016-01-01

Roč. 45, č. 5 (2016), s. 443-461 ISSN 0175-7571 R&D Projects: GA ČR(CZ) GA13-02033S; GA MŠk(CZ) ED1.1.00/02.0109 Institutional support: RVO:67985823 Keywords : 3D object segmentation * Delaunay algorithm * principal component analysis * 3D super-resolution microscopy * nucleoids * mitochondrial DNA replication Subject RIV: BO - Biophysics Impact factor: 1.472, year: 2016

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

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.

Heritable patterns of tooth decay in the permanent dentition: principal components and factor analyses.

Science.gov (United States)

Shaffer, John R; Feingold, Eleanor; Wang, Xiaojing; Tcuenco, Karen T; Weeks, Daniel E; DeSensi, Rebecca S; Polk, Deborah E; Wendell, Steve; Weyant, Robert J; Crout, Richard; McNeil, Daniel W; Marazita, Mary L

2012-03-09

Dental caries is the result of a complex interplay among environmental, behavioral, and genetic factors, with distinct patterns of decay likely due to specific etiologies. Therefore, global measures of decay, such as the DMFS index, may not be optimal for identifying risk factors that manifest as specific decay patterns, especially if the risk factors such as genetic susceptibility loci have small individual effects. We used two methods to extract patterns of decay from surface-level caries data in order to generate novel phenotypes with which to explore the genetic regulation of caries. The 128 tooth surfaces of the permanent dentition were scored as carious or not by intra-oral examination for 1,068 participants aged 18 to 75 years from 664 biological families. Principal components analysis (PCA) and factor analysis (FA), two methods of identifying underlying patterns without a priori surface classifications, were applied to our data. The three strongest caries patterns identified by PCA recaptured variation represented by DMFS index (correlation, r = 0.97), pit and fissure surface caries (r = 0.95), and smooth surface caries (r = 0.89). However, together, these three patterns explained only 37% of the variability in the data, indicating that a priori caries measures are insufficient for fully quantifying caries variation. In comparison, the first pattern identified by FA was strongly correlated with pit and fissure surface caries (r = 0.81), but other identified patterns, including a second pattern representing caries of the maxillary incisors, were not representative of any previously defined caries indices. Some patterns identified by PCA and FA were heritable (h(2) = 30-65%, p = 0.043-0.006), whereas other patterns were not, indicating both genetic and non-genetic etiologies of individual decay patterns. This study demonstrates the use of decay patterns as novel phenotypes to assist in understanding the multifactorial nature of dental caries.

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

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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.…

A Principal Component Analysis (PCA Approach to Seasonal and Zooplankton Diversity Relationships in Fishing Grounds of Mannar Gulf, India

Directory of Open Access Journals (Sweden)

Selvin J. PITCHAIKANI

2017-06-01

Full Text Available Principal component analysis (PCA is a technique used to emphasize variation and bring out strong patterns in a dataset. It is often used to make data easy to explore and visualize. The primary objective of the present study was to record information of zooplankton diversity in a systematic way and to study the variability and relationships among seasons prevailed in Gulf of Mannar. The PCA for the zooplankton seasonal diversity was investigated using the four seasonal datasets to understand the statistical significance among the four seasons. Two different principal components (PC were segregated in all the seasons homogeneously. PCA analyses revealed that Temora turbinata is an opportunistic species and zooplankton diversity was significantly different from season to season and principally, the zooplankton abundance and its dynamics in Gulf of Mannar is structured by seasonal current patterns. The factor loadings of zooplankton for different seasons in Tiruchendur coastal water (GOM is different compared with the Southwest coast of India; particularly, routine and opportunistic species were found within the positive and negative factors. The copepods Acrocalanus gracilis and Acartia erythrea were dominant in summer and Southwest monsoon due to the rainfall and freshwater discharge during the summer season; however, these species were replaced by Temora turbinata during Northeast monsoon season.

Principal components′ analysis of multifocal electroretinogram in retinitis pigmentosa

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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

Measurement Invariance of Second-Order Factor Model of the Multifactor Leadership Questionnaire (MLQ) across K-12 Principal Gender

Science.gov (United States)

Xu, Lihua; Wubbena, Zane; Stewart, Trae

2016-01-01

Purpose: The purpose of this paper is to investigate the factor structure and the measurement invariance of the Multifactor Leadership Questionnaire (MLQ) across gender of K-12 school principals (n=6,317) in the USA. Design/methodology/approach: Nine first-order factor models and four second-order factor models were tested using confirmatory…

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.

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.

[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.

Item-level factor analysis of the Self-Efficacy Scale.

Science.gov (United States)

Bunketorp Käll, Lina

2014-03-01

This study explores the internal structure of the Self-Efficacy Scale (SES) using item response analysis. The SES was previously translated into Swedish and modified to encompass all types of pain, not exclusively back pain. Data on perceived self-efficacy in 47 patients with subacute whiplash-associated disorders were derived from a previously conducted randomized-controlled trial. The item-level factor analysis was carried out using a six-step procedure. To further study the item inter-relationships and to determine the underlying structure empirically, the 20 items of the SES were also subjected to principal component analysis with varimax rotation. The analyses showed two underlying factors, named 'social activities' and 'physical activities', with seven items loading on each factor. The remaining six items of the SES appeared to measure somewhat different constructs and need to be analysed further.

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

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...

Principal component analysis of the main factors of line intensity enhancements observed in oscillating direct current plasma

International Nuclear Information System (INIS)

Stoiljkovic, Milovan M.; Pasti, Igor A.; Momcilovic, Milos D.; Savovic, Jelena J.; Pavlovic, Mirjana S.

2010-01-01

Enhancement of emission line intensities by induced oscillations of direct current (DC) arc plasma with continuous aerosol sample supply was investigated using multivariate statistics. Principal component analysis (PCA) was employed to evaluate enhancements of 34 atomic spectral lines belonging to 33 elements and 35 ionic spectral lines belonging to 23 elements. Correlation and classification of the elements were done not only by a single property such as the first ionization energy, but also by considering other relevant parameters. Special attention was paid to the influence of the oxide bond strength in an attempt to clarify/predict the enhancement effect. Energies of vaporization, atomization, and excitation were also considered in the analysis. In the case of atomic lines, the best correlation between the enhancements and first ionization energies was obtained as a negative correlation, with weak consistency in grouping of elements in score plots. Conversely, in the case of ionic lines, the best correlation of the enhancements with the sum of the first ionization energies and oxide bond energies was obtained as a positive correlation, with four distinctive groups of elements. The role of the gas-phase atom-oxide bond energy in the entire enhancement effect is underlined.

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.

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.

Confirmatory Factor Analysis of the Delirium Rating Scale Revised-98 (DRS-R98).

Science.gov (United States)

Thurber, Steven; Kishi, Yasuhiro; Trzepacz, Paula T; Franco, Jose G; Meagher, David J; Lee, Yanghyun; Kim, Jeong-Lan; Furlanetto, Leticia M; Negreiros, Daniel; Huang, Ming-Chyi; Chen, Chun-Hsin; Kean, Jacob; Leonard, Maeve

2015-01-01

Principal components analysis applied to the Delirium Rating Scale-Revised-98 contributes to understanding the delirium construct. Using a multisite pooled international delirium database, the authors applied confirmatory factor analysis to Delirium Rating Scale-Revised-98 scores from 859 adult patients evaluated by delirium experts (delirium, N=516; nondelirium, N=343). Confirmatory factor analysis found all diagnostic features and core symptoms (cognitive, language, thought process, sleep-wake cycle, motor retardation), except motor agitation, loaded onto factor 1. Motor agitation loaded onto factor 2 with noncore symptoms (delusions, affective lability, and perceptual disturbances). Factor 1 loading supports delirium as a single construct, but when accompanied by psychosis, motor agitation's role may not be solely as a circadian activity indicator.

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

Unvealing the Principal Modes of Human Upper Limb Movements through Functional Analysis

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Giuseppe Averta

2017-08-01

Full Text Available The rich variety of human upper limb movements requires an extraordinary coordination of different joints according to specific spatio-temporal patterns. However, unvealing these motor schemes is a challenging task. Principal components have been often used for analogous purposes, but such an approach relies on hypothesis of temporal uncorrelation of upper limb poses in time. To overcome these limitations, in this work, we leverage on functional principal component analysis (fPCA. We carried out experiments with 7 subjects performing a set of most significant human actions, selected considering state-of-the-art grasp taxonomies and human kinematic workspace. fPCA results show that human upper limb trajectories can be reconstructed by a linear combination of few principal time-dependent functions, with a first component alone explaining around 60/70% of the observed behaviors. This allows to infer that in daily living activities humans reduce the complexity of movement by modulating their motions through a reduced set of few principal patterns. Finally, we discuss how this approach could be profitably applied in robotics and bioengineering, opening fascinating perspectives to advance the state of the art of artificial systems, as it was the case of hand synergies.

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...

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

Science.gov (United States)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

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

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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.

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.

Dynamics and spatio-temporal variability of environmental factors in Eastern Australia using functional principal component analysis

Science.gov (United States)

Szabo, J.K.; Fedriani, E.M.; Segovia-Gonzalez, M. M.; Astheimer, L.B.; Hooper, M.J.

2010-01-01

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 19982004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types. ?? 2010 World Scientific Publishing Company.

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,…

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

Using Factor Analysis to Identify Topic Preferences Within MBA Courses

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Earl Chrysler

2003-02-01

Full Text Available This study demonstrates the role of a principal components factor analysis in conducting a gap analysis as to the desired characteristics of business alumni. Typically, gap analyses merely compare the emphases that should be given to areas of inquiry with perceptions of actual emphases. As a result, the focus is upon depth of coverage. A neglected area in need of investigation is the breadth of topic dimensions and their differences between the normative (should offer and the descriptive (actually offer. The implications of factor structures, as well as traditional gap analyses, are developed and discussed in the context of outcomes assessment.

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

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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 of NEXAFS spectra for molybdenum speciation in hydrotreating catalysts

International Nuclear Information System (INIS)

Faro Junior, Arnaldo da C.; Rodrigues, Victor de O.; Eon, Jean-G.; Rocha, Angela S.

2010-01-01

Bulk and supported molybdenum based catalysts, modified by nickel, phosphorous or tungsten were studied by NEXAFS spectroscopy at the Mo L III and L II edges. The techniques of principal component analysis (PCA) together with a linear combination analysis (LCA) allowed the detection and quantification of molybdenum atoms in two different coordination states in the oxide form of the catalysts, namely tetrahedral and octahedral coordination. (author)

Principals' Attitudes towards Risky Internet Use of Primary School Students: The Role of Occupational Factors

Science.gov (United States)

Touloupis, Thanos; Athanasiades, Christina

2018-01-01

The present study aimed to investigate primary school principals' attitudes towards risky internet use of school-aged students and how occupational factors, such as work self-efficacy, job satisfaction, and burnout, may affect these attitudes especially in a context of economic crisis, which has adversely affected working conditions and duties of…

Exploratory factor analysis of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale in people newly diagnosed with advanced cancer.

Science.gov (United States)

Bai, Mei; Dixon, Jane K

2014-01-01

The purpose of this study was to reexamine the factor pattern of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12) using exploratory factor analysis in people newly diagnosed with advanced cancer. Principal components analysis (PCA) and 3 common factor analysis methods were used to explore the factor pattern of the FACIT-Sp-12. Factorial validity was assessed in association with quality of life (QOL). Principal factor analysis (PFA), iterative PFA, and maximum likelihood suggested retrieving 3 factors: Peace, Meaning, and Faith. Both Peace and Meaning positively related to QOL, whereas only Peace uniquely contributed to QOL. This study supported the 3-factor model of the FACIT-Sp-12. Suggestions for revision of items and further validation of the identified factor pattern were provided.

Source Signals Separation and Reconstruction Following Principal Component Analysis

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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…

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 Component Analysis to Explore Climatic Variability and Dengue Outbreak in Lahore

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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 

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

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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.

Communication Factors as Predictors of Relationship Quality: A National Study of Principals and School Counselors

Science.gov (United States)

Duslak, Mark; Geier, Brett

2017-01-01

This study examined the effects of meeting frequency, structured meeting times, annual agreements, and demographic variables on school counselor perceptions of their relationship with their building principal. Results of a regression analysis indicated that meeting frequency accounted for 26.7% of the variance in school counselor-reported…

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.

Principal component analysis of cardiovascular risk traits in three generations cohort among Indian Punjabi population

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Badaruddoza

2015-09-01

Full Text Available The current study focused to determine significant cardiovascular risk factors through principal component factor analysis (PCFA among three generations on 1827 individuals in three generations including 911 males (378 from offspring, 439 from parental and 94 from grand-parental generations and 916 females (261 from offspring, 515 from parental and 140 from grandparental generations. The study performed PCFA with orthogonal rotation to reduce 12 inter-correlated variables into groups of independent factors. The factors have been identified as 2 for male grandparents, 3 for male offspring, female parents and female grandparents each, 4 for male parents and 5 for female offspring. This data reduction method identified these factors that explained 72%, 84%, 79%, 69%, 70% and 73% for male and female offspring, male and female parents and male and female grandparents respectively, of the variations in original quantitative traits. The factor 1 accounting for the largest portion of variations was strongly loaded with factors related to obesity (body mass index (BMI, waist circumference (WC, waist to hip ratio (WHR, and thickness of skinfolds among all generations with both sexes, which has been known to be an independent predictor for cardiovascular morbidity and mortality. The second largest components, factor 2 and factor 3 for almost all generations reflected traits of blood pressure phenotypes loaded, however, in male offspring generation it was observed that factor 2 was loaded with blood pressure phenotypes as well as obesity. This study not only confirmed but also extended prior work by developing a cumulative risk scale from factor scores. Till today, such a cumulative and extensive scale has not been used in any Indian studies with individuals of three generations. These findings and study highlight the importance of global approach for assessing the risk and need for studies that elucidate how these different cardiovascular risk factors

Principal component analysis of cardiovascular risk traits in three generations cohort among Indian Punjabi population.

Science.gov (United States)

Badaruddoza; Kumar, Raman; Kaur, Manpreet

2015-09-01

The current study focused to determine significant cardiovascular risk factors through principal component factor analysis (PCFA) among three generations on 1827 individuals in three generations including 911 males (378 from offspring, 439 from parental and 94 from grand-parental generations) and 916 females (261 from offspring, 515 from parental and 140 from grandparental generations). The study performed PCFA with orthogonal rotation to reduce 12 inter-correlated variables into groups of independent factors. The factors have been identified as 2 for male grandparents, 3 for male offspring, female parents and female grandparents each, 4 for male parents and 5 for female offspring. This data reduction method identified these factors that explained 72%, 84%, 79%, 69%, 70% and 73% for male and female offspring, male and female parents and male and female grandparents respectively, of the variations in original quantitative traits. The factor 1 accounting for the largest portion of variations was strongly loaded with factors related to obesity (body mass index (BMI), waist circumference (WC), waist to hip ratio (WHR), and thickness of skinfolds) among all generations with both sexes, which has been known to be an independent predictor for cardiovascular morbidity and mortality. The second largest components, factor 2 and factor 3 for almost all generations reflected traits of blood pressure phenotypes loaded, however, in male offspring generation it was observed that factor 2 was loaded with blood pressure phenotypes as well as obesity. This study not only confirmed but also extended prior work by developing a cumulative risk scale from factor scores. Till today, such a cumulative and extensive scale has not been used in any Indian studies with individuals of three generations. These findings and study highlight the importance of global approach for assessing the risk and need for studies that elucidate how these different cardiovascular risk factors interact with

The level of new science leadership behaviors of school principals: A scale development

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Akpil Şerife

2016-01-01

Full Text Available Einstein’s theory of relativity and quantum physics opened Newton physics up for discussion, thus triggering the new science at the beginning of the 20th century. Philosophy of science, which was named as the new science in the 20th century, caused fundamental changes in research methods and paradigms. The methods and set of values brought by the new science affected social sciences as well. In conjunction with this mentioned change and development, the field of education and the view of schools were influenced. In the same vein, identifying the thoughts of school principals on leadership styles based on new science was considered as a primary need and set the objective of this research. In this regard, a “The Levels of New Science Leadership Behaviors of School Principals Scale” was developed. Following the literature review, the scale with 54 items was prepared and underwent expert review. Finally it was applied to 200 school principals who were working in primary and secondary schools in the Anatolian side of Istanbul. The data acquired were analyzed through SPSS 15.0 and Lisrel 8.51 programs. The results of the analysis revealed that the scale was comprised of a total of 27 items and had 5 factors (dimensions. The reliability analysis indicated internal consistency value (Cronbach Alpha as .94. Confirmatory factor analysis was carried out in Lisrel program. According to results of confirmatory factor analysis, the X2/df ratio was calculated as 2, 24 which showed that the measurement model was in accord with the data.

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.)

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

Principal component analysis of normalized full spectrum mass spectrometry data in multiMS-toolbox: An effective tool to identify important factors for classification of different metabolic patterns and bacterial strains.

Science.gov (United States)

Cejnar, Pavel; Kuckova, Stepanka; Prochazka, Ales; Karamonova, Ludmila; Svobodova, Barbora

2018-06-15

Explorative statistical analysis of mass spectrometry data is still a time-consuming step. We analyzed critical factors for application of principal component analysis (PCA) in mass spectrometry and focused on two whole spectrum based normalization techniques and their application in the analysis of registered peak data and, in comparison, in full spectrum data analysis. We used this technique to identify different metabolic patterns in the bacterial culture of Cronobacter sakazakii, an important foodborne pathogen. Two software utilities, the ms-alone, a python-based utility for mass spectrometry data preprocessing and peak extraction, and the multiMS-toolbox, an R software tool for advanced peak registration and detailed explorative statistical analysis, were implemented. The bacterial culture of Cronobacter sakazakii was cultivated on Enterobacter sakazakii Isolation Agar, Blood Agar Base and Tryptone Soya Agar for 24 h and 48 h and applied by the smear method on an Autoflex speed MALDI-TOF mass spectrometer. For three tested cultivation media only two different metabolic patterns of Cronobacter sakazakii were identified using PCA applied on data normalized by two different normalization techniques. Results from matched peak data and subsequent detailed full spectrum analysis identified only two different metabolic patterns - a cultivation on Enterobacter sakazakii Isolation Agar showed significant differences to the cultivation on the other two tested media. The metabolic patterns for all tested cultivation media also proved the dependence on cultivation time. Both whole spectrum based normalization techniques together with the full spectrum PCA allow identification of important discriminative factors in experiments with several variable condition factors avoiding any problems with improper identification of peaks or emphasis on bellow threshold peak data. The amounts of processed data remain still manageable. Both implemented software utilities are available

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.

Factor Analysis and Modelling for Rapid Quality Assessment of Croatian Wheat Cultivars with Different Gluten Characteristics

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Želimir Kurtanjek

2008-01-01

Full Text Available Factor analysis and multivariate chemometric modelling for rapid assessment of baking quality of wheat cultivars from Slavonia region, Croatia, have been applied. The cultivars Žitarka, Kata, Monika, Ana, Demetra, Divana and Sana were grown under controlled conditions at the experimental field of Agricultural Institute Osijek during three years (2000–2002. Their quality properties were evaluated by 45 different chemical, physical and biochemical variables. The measured variables were grouped as: indirect quality parameters (6, farinographic parameters (7, extensographic parameters (5, baking test parameters (2 and reversed phase-high performance liquid chromatography (RP-HPLC of gluten proteins (25. The aim of this study is to establish minimal number (three, i.e. principal factors, among the 45 variables and to derive multivariate linear regression models for their use in simple and fast prediction of wheat properties. Selection of the principal factors based on the principal component analysis (PCA has been applied. The first three main factors of the analysis include: total glutenins (TGT, total ω-gliadins (Tω- and the ratio of dough resistance/extensibility (R/Ext. These factors account for 76.45 % of the total variance. Linear regression models gave average regression coefficients (R evaluated for the parameter groups: indirect quality R=0.91, baking test R=0.63, farinographic R=0.78, extensographic R=0.95 and RP-HPLC of gluten data R=0.90. Errors in the model predictions were evaluated by the 95 % significance intervals of the calibration lines. Practical applications of the models for rapid quality assessment and laboratory experiment planning were emphasized.

Identifying the principal driving factors of water ecosystem dependence and the corresponding indicator species in a pilot City, China

Science.gov (United States)

Zhao, C. S.; Shao, N. F.; Yang, S. T.; Xiang, H.; Lou, H. Z.; Sun, Y.; Yang, Z. Y.; Zhang, Y.; Yu, X. Y.; Zhang, C. B.; Yu, Q.

2018-01-01

The world's aquatic ecosystems yield numerous vital services, which are essential to human existence but have deteriorated seriously in recent years. By studying the mechanisms of interaction between ecosystems and habitat processes, the constraining factors can be identified, and this knowledge can be used to improve the success rate of ecological restoration initiatives. At present, there is insufficient data on the link between hydrological, water quality factors and the changes in the structure of aquatic communities to allow any meaningful study of driving factors of aquatic ecosystems. In this study, the typical monitoring stations were selected by fuzzy clustering analysis based on the spatial and temporal distribution characteristics of water ecology in Jinan City, the first pilot city for the construction of civilized aquatic ecosystems in China. The dominant species identification model was used to identify the dominant species of the aquatic community. The driving effect of hydrological and water quality factors on dominant species was analyzed by Canonical Correspondence Analysis. Then, the principal factors of aquatic ecosystem dependence were selected. The results showed that there were 10 typical monitoring stations out of 59 monitoring sites, which were representative of aquatic ecosystems, 9 dominant fish species, and 20 dominant invertebrate species. The selection of factors for aquatic ecosystem dependence in Jinan were highly influenced by its regional conditions. Chemical environmental parameters influence the temporal and spatial variation of invertebrate much more than that of fish in Jinan City. However, the methodologies coupling typical monitoring stations selection, dominant species determination and driving factors identification were certified to be a cost-effective way, which can provide in-deep theoretical and technical directions for the restoration of aquatic ecosystems elsewhere.

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

Nonlinear fitness-space-structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity analysis

International Nuclear Information System (INIS)

Tiilikainen, J; Tilli, J-M; Bosund, V; Mattila, M; Hakkarainen, T; Airaksinen, V-M; Lipsanen, H

2007-01-01

Two novel genetic algorithms implementing principal component analysis and an adaptive nonlinear fitness-space-structure technique are presented and compared with conventional algorithms in x-ray reflectivity analysis. Principal component analysis based on Hessian or interparameter covariance matrices is used to rotate a coordinate frame. The nonlinear adaptation applies nonlinear estimates to reshape the probability distribution of the trial parameters. The simulated x-ray reflectivity of a realistic model of a periodic nanolaminate structure was used as a test case for the fitting algorithms. The novel methods had significantly faster convergence and less stagnation than conventional non-adaptive genetic algorithms. The covariance approach needs no additional curve calculations compared with conventional methods, and it had better convergence properties than the computationally expensive Hessian approach. These new algorithms can also be applied to other fitting problems where tight interparameter dependence is present

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

A Blind Adaptive Color Image Watermarking Scheme Based on Principal Component Analysis, Singular Value Decomposition and Human Visual System

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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.

Selecting the Number of Principal Components in Functional Data

KAUST Repository

Li, Yehua

2013-12-01

Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select the number of principal components for both sparse and dense functional data. For dense functional data, we also develop an Akaike information criterion based on the expected Kullback-Leibler information under a Gaussian assumption. In connecting with the time series literature, we also consider a class of information criteria proposed for factor analysis of multivariate time series and show that they are still consistent for dense functional data, if a prescribed undersmoothing scheme is undertaken in the FPCA algorithm. We perform intensive simulation studies and show that the proposed information criteria vastly outperform existing methods for this type of data. Surprisingly, our empirical evidence shows that our information criteria proposed for dense functional data also perform well for sparse functional data. An empirical example using colon carcinogenesis data is also provided to illustrate the results. Supplementary materials for this article are available online. © 2013 American Statistical Association.

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 ...

Demographic, socioeconomic, and behavioral factors affecting patterns of tooth decay in the permanent dentition: principal components and factor analyses.

Science.gov (United States)

Shaffer, John R; Polk, Deborah E; Feingold, Eleanor; Wang, Xiaojing; Cuenco, Karen T; Weeks, Daniel E; DeSensi, Rebecca S; Weyant, Robert J; Crout, Richard; McNeil, Daniel W; Marazita, Mary L

2013-08-01

Dental caries of the permanent dentition is a multifactorial disease resulting from the complex interplay of endogenous and environmental risk factors. The disease is not easily quantitated due to the innumerable possible combinations of carious lesions across individual tooth surfaces of the permanent dentition. Global measures of decay, such as the DMFS index (which was developed for surveillance applications), may not be optimal for studying the epidemiology of dental caries because they ignore the distinct patterns of decay across the dentition. We hypothesize that specific risk factors may manifest their effects on specific tooth surfaces leading to patterns of decay that can be identified and studied. In this study, we utilized two statistical methods of extracting patterns of decay from surface-level caries data to create novel phenotypes with which to study the risk factors affecting dental caries. Intra-oral dental examinations were performed on 1068 participants aged 18-75 years to assess dental caries. The 128 tooth surfaces of the permanent dentition were scored as carious or not and used as input for principal components analysis (PCA) and factor analysis (FA), two methods of identifying underlying patterns without a priori knowledge of the patterns. Demographic (age, sex, birth year, race/ethnicity, and educational attainment), anthropometric (height, body mass index, waist circumference), endogenous (saliva flow), and environmental (tooth brushing frequency, home water source, and home water fluoride) risk factors were tested for association with the caries patterns identified by PCA and FA, as well as DMFS, for comparison. The ten strongest patterns (i.e. those that explain the most variation in the data set) extracted by PCA and FA were considered. The three strongest patterns identified by PCA reflected (i) global extent of decay (i.e. comparable to DMFS index), (ii) pit and fissure surface caries and (iii) smooth surface caries, respectively. The

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.

Influence factors analysis of water environmental quality of main rivers in Tianjin

Science.gov (United States)

Li, Ran; Bao, Jingling; Zou, Di; Shi, Fang

2018-01-01

According to the evaluation results of the water environment quality of main rivers in Tianjin in 1986-2015, this paper analyzed the current situation of water environmental quality of main rivers in Tianjin retrospectively, established the index system and multiple factors analysis through selecting factors influencing the water environmental quality of main rivers from the economy, industry and nature aspects with the combination method of principal component analysis and linear regression. The results showed that water consumption, sewage discharge and water resources were the main factors influencing the pollution of main rivers. Therefore, optimizing the utilization of water resources, improving utilization efficiency and reducing effluent discharge are important measures to reduce the pollution of surface water environment.

The Principals' and Teacher Counsellors' Perception of the Factors ...

African Journals Online (AJOL)

guidance and counselling services in all the 45 public secondary schools in. Laikipia District as perceived by school principals and teacher counsellors. ... the teacher counsellors and students' attitudes on delivery of guidance and counselling ...

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…

PM10 and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Science.gov (United States)

2014-01-01

Background The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it’s referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS). Results Comparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors. The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants. Conclusions The analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in

Integrating Technology: The Principals' Role and Effect

Science.gov (United States)

Machado, Lucas J.; Chung, Chia-Jung

2015-01-01

There are many factors that influence technology integration in the classroom such as teacher willingness, availability of hardware, and professional development of staff. Taking into account these elements, this paper describes research on technology integration with a focus on principals' attitudes. The role of the principal in classroom…

Constructing principals' professional identities through life stories ...

African Journals Online (AJOL)

The Life History approach was used to collect data from six ... experience as the most significant leadership factors that influence principals' ... ranging from their entry into the teaching profession to their appointment as ..... teachers. I think I learnt from my principal to be strict but accommodating ..... Teachers College Press.

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.

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.

Multi-Scale Factor Analysis of High-Dimensional Brain Signals

KAUST Repository

Ting, Chee-Ming

2017-05-18

In this paper, we develop an approach to modeling high-dimensional networks with a large number of nodes arranged in a hierarchical and modular structure. We propose a novel multi-scale factor analysis (MSFA) model which partitions the massive spatio-temporal data defined over the complex networks into a finite set of regional clusters. To achieve further dimension reduction, we represent the signals in each cluster by a small number of latent factors. The correlation matrix for all nodes in the network are approximated by lower-dimensional sub-structures derived from the cluster-specific factors. To estimate regional connectivity between numerous nodes (within each cluster), we apply principal components analysis (PCA) to produce factors which are derived as the optimal reconstruction of the observed signals under the squared loss. Then, we estimate global connectivity (between clusters or sub-networks) based on the factors across regions using the RV-coefficient as the cross-dependence measure. This gives a reliable and computationally efficient multi-scale analysis of both regional and global dependencies of the large networks. The proposed novel approach is applied to estimate brain connectivity networks using functional magnetic resonance imaging (fMRI) data. Results on resting-state fMRI reveal interesting modular and hierarchical organization of human brain networks during rest.

Estimation of physiological parameters using knowledge-based factor analysis of dynamic nuclear medicine image sequences

International Nuclear Information System (INIS)

Yap, J.T.; Chen, C.T.; Cooper, M.

1995-01-01

The authors have previously developed a knowledge-based method of factor analysis to analyze dynamic nuclear medicine image sequences. In this paper, the authors analyze dynamic PET cerebral glucose metabolism and neuroreceptor binding studies. These methods have shown the ability to reduce the dimensionality of the data, enhance the image quality of the sequence, and generate meaningful functional images and their corresponding physiological time functions. The new information produced by the factor analysis has now been used to improve the estimation of various physiological parameters. A principal component analysis (PCA) is first performed to identify statistically significant temporal variations and remove the uncorrelated variations (noise) due to Poisson counting statistics. The statistically significant principal components are then used to reconstruct a noise-reduced image sequence as well as provide an initial solution for the factor analysis. Prior knowledge such as the compartmental models or the requirement of positivity and simple structure can be used to constrain the analysis. These constraints are used to rotate the factors to the most physically and physiologically realistic solution. The final result is a small number of time functions (factors) representing the underlying physiological processes and their associated weighting images representing the spatial localization of these functions. Estimation of physiological parameters can then be performed using the noise-reduced image sequence generated from the statistically significant PCs and/or the final factor images and time functions. These results are compared to the parameter estimation using standard methods and the original raw image sequences. Graphical analysis was performed at the pixel level to generate comparable parametric images of the slope and intercept (influx constant and distribution volume)

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.

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.

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)

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.

Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

Science.gov (United States)

Santos, Nadine Correia; Costa, Patrício Soares; Amorim, Liliana; Moreira, Pedro Silva; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2015-01-01

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate. PMID:25880732

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

Principal Investigator-in-a-Box

Science.gov (United States)

Young, Laurence R.

1999-01-01

Human performance in orbit is currently limited by several factors beyond the intrinsic awkwardness of motor control in weightlessness. Cognitive functioning can be affected by such factors as cumulative sleep loss, stress and the psychological effects of long-duration small-group isolation. When an astronaut operates a scientific experiment, the performance decrement associated with such factors can lead to lost or poor quality data and even the total loss of a scientific objective, at great cost to the sponsors and to the dismay of the Principal Investigator. In long-duration flights, as anticipated on the International Space Station and on any planetary exploration, the experimental model is further complicated by long delays between training and experiment, and the large number of experiments each crew member must perform. Although no documented studies have been published on the subject, astronauts report that an unusually large number of simple errors are made in space. Whether a result of the effects of microgravity, accumulated fatigue, stress or other factors, this pattern of increased error supports the need for a computerized decision-making aid for astronauts performing experiments. Artificial intelligence and expert systems might serve as powerful tools for assisting experiments in space. Those conducting space experiments typically need assistance exactly when the planned checklist does not apply. Expert systems, which use bits of human knowledge and human methods to respond appropriately to unusual situations, have a flexibility that is highly desirable in circumstances where an invariably predictable course of action/response does not exist. Frequently the human expert on the ground is unavailable, lacking the latest information, or not consulted by the astronaut conducting the experiment. In response to these issues, we have developed "Principal Investigator-in-a-Box," or [PI], to capture the reasoning process of the real expert, the Principal

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)

Using exploratory factor analysis in personality research: Best-practice recommendations

Directory of Open Access Journals (Sweden)

Sumaya Laher

2010-11-01

Research purpose: This article presents more objective methods to determine the number of factors, most notably parallel analysis and Velicer’s minimum average partial (MAP. The benefits of rotation are also discussed. The article argues for more consistent use of Procrustes rotation and congruence coefficients in factor analytic studies. Motivation for the study: Exploratory factor analysis is often criticised for not being rigorous and objective enough in terms of the methods used to determine the number of factors, the rotations to be used and ultimately the validity of the factor structure. Research design, approach and method: The article adopts a theoretical stance to discuss the best-practice recommendations for factor analytic research in the field of psychology. Following this, an example located within personality assessment and using the NEO-PI-R specifically is presented. A total of 425 students at the University of the Witwatersrand completed the NEO-PI-R. These responses were subjected to a principal components analysis using varimax rotation. The rotated solution was subjected to a Procrustes rotation with Costa and McCrae’s (1992 matrix as the target matrix. Congruence coefficients were also computed. Main findings: The example indicates the use of the methods recommended in the article and demonstrates an objective way of determining the number of factors. It also provides an example of Procrustes rotation with coefficients of agreement as an indication of how factor analytic results may be presented more rigorously in local research. Practical/managerial implications: It is hoped that the recommendations in this article will have best-practice implications for both researchers and practitioners in the field who employ factor analysis regularly. Contribution/value-add: This article will prove useful to all researchers employing factor analysis and has the potential to set the trend for better use of factor analysis in the South African context.

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…

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 ...

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.

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.

Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults.

Science.gov (United States)

Hearty, Aine P; Gibney, Michael J

2009-02-01

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997-9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18-64 years. Cluster analysis was performed using the k-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: 'Traditional Irish'; 'Continental'; 'Unhealthy foods'; 'Light-meal foods & low-fat milk'; 'Healthy foods'; 'Wholemeal bread & desserts'. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: 'Unhealthy foods & high alcohol'; 'Traditional Irish'; 'Healthy foods'; 'Sweet convenience foods & low alcohol'. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

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)

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.

PRINCIPAL'S LEADERSHIP STYLE, AS PERCEIVED BY TEACHERS, IN RELATION TO TEACHER'S EXPERIENCE FACTOR OF SCHOOL CLIMATE IN ELEMENTARY SCHOOLS

Directory of Open Access Journals (Sweden)

Gabriel Pinkas

2017-09-01

Full Text Available The experience of the environment in which the activity is performed is a significant factor of the outcome of this activity, that is, the efficiency of the work and the degree of achieving the goal. Within the work environment, physical and social conditions can be observed. The first, which includes material and technical means, are mostly static, easily perceivable and measurable. Others, which include social relations, are much more susceptible to change, more difficult to perceive and measure, and their experience with different individuals within the same group can be more distinct. Although all members of the group participate in group dynamics and relationships, not all are equally relevant to these processes. Considering the position that carries the right and responsibility of setting up a vision and mission, setting goals, creating conditions for work, making decisions and providing feedback, the leader is in most cases crucial. This paper analyzes the role of elementary school principals in creating a school climate, as a non - material environment in which educational activity is carried out, and in this sense it is a specific group / work organization. An estimate was used to measure both variables, i.e. teacher's experience. The instruments used are Multifactor Leadership Questionnaire - MLQ (Avolio and Bass and School Level Environment Questionnaire - SLEQ (Johnson, Stevens and Zvoch. The survey was conducted in elementary schools in the wider city area of Tuzla, on a sample of 467 teachers and 25 principals. In statistical data processing, multiple regression (Ordinary least squares and direct square discriminatory analysis were applied. The obtained results point to the connection between the perceived leadership style of elementary school principals and the school climate experienced by teachers, especially in the field of innovation in teaching and mutual cooperation.

Primary School Principals' Job Satisfaction and Occupational Stress

Science.gov (United States)

Darmody, Merike; Smyth, Emer

2016-01-01

Purpose: The purpose of this paper is to explore the factors associated with occupational stress and job satisfaction among Irish primary school principals. A principal's job has become increasingly demanding and complex in recent decades. However, there is little current research into their levels of stress and job satisfaction, particularly…

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.

arXiv Principal-component analysis of two-particle azimuthal correlations in PbPb and pPb collisions at CMS

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Sirunyan, A.M.; CMS Collaboration; Adam, Wolfgang; Ambrogi, Federico; Asilar, Ece; Bergauer, Thomas; Brandstetter, Johannes; Brondolin, Erica; Dragicevic, Marko; Erö, Janos; Flechl, Martin; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Grossmann, Johannes; Hrubec, Josef; Jeitler, Manfred; König, Axel; Krammer, Natascha; Krätschmer, Ilse; Liko, Dietrich; Madlener, Thomas; Mikulec, Ivan; Pree, Elias; Rabady, Dinyar; Rad, Navid; Rohringer, Herbert; Schieck, Jochen; Schöfbeck, Robert; Spanring, Markus; Spitzbart, Daniel; Strauss, Josef; Waltenberger, Wolfgang; Wittmann, Johannes; Wulz, Claudia-Elisabeth; Zarucki, Mateusz; Chekhovsky, Vladimir; Mossolov, Vladimir; Suarez Gonzalez, Juan; De Wolf, Eddi A; Janssen, Xavier; Lauwers, Jasper; Van De Klundert, Merijn; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Van Spilbeeck, Alex; Abu Zeid, Shimaa; Blekman, Freya; D'Hondt, Jorgen; De Bruyn, Isabelle; De Clercq, Jarne; Deroover, Kevin; Flouris, Giannis; Lowette, Steven; Moortgat, Seth; Moreels, Lieselotte; Olbrechts, Annik; Python, Quentin; Skovpen, Kirill; Tavernier, Stefaan; Van Doninck, Walter; Van Mulders, Petra; Van Parijs, Isis; Brun, Hugues; Clerbaux, Barbara; De Lentdecker, Gilles; Delannoy, Hugo; Fasanella, Giuseppe; Favart, Laurent; Goldouzian, Reza; Grebenyuk, Anastasia; Karapostoli, Georgia; Lenzi, Thomas; Luetic, Jelena; Maerschalk, Thierry; Marinov, Andrey; Randle-conde, Aidan; Seva, Tomislav; Vander Velde, Catherine; Vanlaer, Pascal; Vannerom, David; Yonamine, Ryo; Zenoni, Florian; Zhang, Fengwangdong; Cimmino, Anna; Cornelis, Tom; Dobur, Didar; Fagot, Alexis; Gul, Muhammad; Khvastunov, Illia; Poyraz, Deniz; Roskas, Christos; Salva Diblen, Sinem; Tytgat, Michael; Verbeke, Willem; Zaganidis, Nicolas; Bakhshiansohi, Hamed; Bondu, Olivier; Brochet, Sébastien; Bruno, Giacomo; Caudron, Adrien; De Visscher, Simon; Delaere, Christophe; Delcourt, Martin; Francois, Brieuc; Giammanco, Andrea; Jafari, Abideh; Komm, Matthias; Krintiras, Georgios; Lemaitre, Vincent; Magitteri, Alessio; Mertens, Alexandre; Musich, Marco; Piotrzkowski, Krzysztof; Quertenmont, Loic; Vidal Marono, Miguel; Wertz, Sébastien; Beliy, Nikita; Aldá Júnior, Walter Luiz; Alves, Fábio Lúcio; Alves, Gilvan; Brito, Lucas; Correa Martins Junior, Marcos; Hensel, Carsten; Moraes, Arthur; Pol, Maria Elena; Rebello Teles, Patricia; Belchior Batista Das Chagas, Ewerton; Carvalho, Wagner; Chinellato, Jose; Custódio, Analu; Melo Da Costa, Eliza; Da Silveira, Gustavo Gil; De Jesus Damiao, Dilson; Fonseca De Souza, Sandro; Huertas Guativa, Lina Milena; Malbouisson, Helena; Melo De Almeida, Miqueias; Mora Herrera, Clemencia; Mundim, Luiz; Nogima, Helio; Santoro, Alberto; Sznajder, Andre; Tonelli Manganote, Edmilson José; Torres Da Silva De Araujo, Felipe; Vilela Pereira, Antonio; Ahuja, Sudha; Bernardes, Cesar Augusto; Tomei, Thiago; De Moraes Gregores, Eduardo; Mercadante, Pedro G; Moon, Chang-Seong; Novaes, Sergio F; Padula, Sandra; Romero Abad, David; Ruiz Vargas, José Cupertino; Aleksandrov, Aleksandar; Hadjiiska, Roumyana; Iaydjiev, Plamen; Misheva, Milena; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Shopova, Mariana; Dimitrov, Anton; Glushkov, Ivan; Litov, Leander; Pavlov, Borislav; Petkov, Peicho; Fang, Wenxing; Gao, Xuyang; Ahmad, Muhammad; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Chen, Mingshui; Chen, Ye; Jiang, Chun-Hua; Leggat, Duncan; Liu, Zhenan; Romeo, Francesco; Shaheen, Sarmad Masood; Spiezia, Aniello; Tao, Junquan; Wang, Chunjie; Wang, Zheng; Yazgan, Efe; Zhang, Huaqiao; Zhao, Jingzhou; Ban, Yong; Chen, Geng; Li, Qiang; Liu, Shuai; Mao, Yajun; Qian, Si-Jin; Wang, Dayong; Xu, Zijun; Avila, Carlos; Cabrera, Andrés; Chaparro Sierra, Luisa Fernanda; Florez, Carlos; González Hernández, Carlos Felipe; Ruiz Alvarez, José David; Courbon, Benoit; Godinovic, Nikola; Lelas, Damir; Puljak, Ivica; Ribeiro Cipriano, Pedro M; Sculac, Toni; Antunovic, Zeljko; Kovac, Marko; Brigljevic, Vuko; Ferencek, Dinko; Kadija, Kreso; Mesic, Benjamin; Susa, Tatjana; Ather, Mohsan Waseem; Attikis, Alexandros; Mavromanolakis, Georgios; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Rykaczewski, Hans; Finger, Miroslav; Finger Jr, Michael; Carrera Jarrin, Edgar; Abdelalim, Ahmed Ali; Mohammed, Yasser; Salama, Elsayed; Dewanjee, Ram Krishna; Kadastik, Mario; Perrini, Lucia; Raidal, Martti; Tiko, Andres; Veelken, Christian; Eerola, Paula; Pekkanen, Juska; Voutilainen, Mikko; Härkönen, Jaakko; Jarvinen, Terhi; Karimäki, Veikko; Kinnunen, Ritva; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Talvitie, Joonas; Tuuva, Tuure; Besancon, Marc; Couderc, Fabrice; Dejardin, Marc; Denegri, Daniel; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Ghosh, Saranya; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Kucher, Inna; Locci, Elizabeth; Machet, Martina; Malcles, Julie; Negro, Giulia; Rander, John; Rosowsky, André; Sahin, Mehmet Özgür; Titov, Maksym; Abdulsalam, Abdulla; Antropov, Iurii; Baffioni, Stephanie; Beaudette, Florian; Busson, Philippe; Cadamuro, Luca; Charlot, Claude; Davignon, Olivier; Granier de Cassagnac, Raphael; Jo, Mihee; Lisniak, Stanislav; Lobanov, Artur; Martin Blanco, Javier; Nguyen, Matthew; Ochando, Christophe; Ortona, Giacomo; Paganini, Pascal; Pigard, Philipp; Regnard, Simon; Salerno, Roberto; Sauvan, Jean-Baptiste; Sirois, Yves; Stahl Leiton, Andre Govinda; Strebler, Thomas; Yilmaz, Yetkin; Zabi, Alexandre; Zghiche, Amina; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Brom, Jean-Marie; Buttignol, Michael; Chabert, Eric Christian; Chanon, Nicolas; Collard, Caroline; Conte, Eric; Coubez, Xavier; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Jansová, Markéta; Le Bihan, Anne-Catherine; Van Hove, Pierre; Gadrat, Sébastien; Beauceron, Stephanie; Bernet, Colin; Boudoul, Gaelle; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Fay, Jean; Finco, Linda; Gascon, Susan; Gouzevitch, Maxime; Grenier, Gérald; Ille, Bernard; Lagarde, Francois; Laktineh, Imad Baptiste; Lethuillier, Morgan; Mirabito, Laurent; Pequegnot, Anne-Laure; Perries, Stephane; Popov, Andrey; Sordini, Viola; Vander Donckt, Muriel; Viret, Sébastien; Toriashvili, Tengizi; Tsamalaidze, Zviad; Autermann, Christian; Beranek, Sarah; Feld, Lutz; Kiesel, Maximilian Knut; Klein, Katja; Lipinski, Martin; Preuten, Marius; Schomakers, Christian; Schulz, Johannes; Verlage, Tobias; Albert, Andreas; Brodski, Michael; Dietz-Laursonn, Erik; Duchardt, Deborah; Endres, Matthias; Erdmann, Martin; Erdweg, Sören; Esch, Thomas; Fischer, Robert; Güth, Andreas; Hamer, Matthias; Hebbeker, Thomas; Heidemann, Carsten; Hoepfner, Kerstin; Knutzen, Simon; Merschmeyer, Markus; Meyer, Arnd; Millet, Philipp; Mukherjee, Swagata; Olschewski, Mark; Padeken, Klaas; Pook, Tobias; Radziej, Markus; Reithler, Hans; Rieger, Marcel; Scheuch, Florian; Teyssier, Daniel; Thüer, Sebastian; Flügge, Günter; Kargoll, Bastian; Kress, Thomas; Künsken, Andreas; Lingemann, Joschka; Müller, Thomas; Nehrkorn, Alexander; Nowack, Andreas; Pistone, Claudia; Pooth, Oliver; Stahl, Achim; Aldaya Martin, Maria; Arndt, Till; Asawatangtrakuldee, Chayanit; Beernaert, Kelly; Behnke, Olaf; Behrens, Ulf; Bin Anuar, Afiq Aizuddin; Borras, Kerstin; Botta, Valeria; Campbell, Alan; Connor, Patrick; Contreras-Campana, Christian; Costanza, Francesco; Diez Pardos, Carmen; Eckerlin, Guenter; Eckstein, Doris; Eichhorn, Thomas; Eren, Engin; Gallo, Elisabetta; Garay Garcia, Jasone; Geiser, Achim; Gizhko, Andrii; Grados Luyando, Juan Manuel; Grohsjean, Alexander; Gunnellini, Paolo; Harb, Ali; Hauk, Johannes; Hempel, Maria; Jung, Hannes; Kalogeropoulos, Alexis; Kasemann, Matthias; Keaveney, James; Kleinwort, Claus; Korol, Ievgen; Krücker, Dirk; Lange, Wolfgang; Lelek, Aleksandra; Lenz, Teresa; Leonard, Jessica; Lipka, Katerina; Lohmann, Wolfgang; Mankel, Rainer; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mittag, Gregor; Mnich, Joachim; Mussgiller, Andreas; Ntomari, Eleni; Pitzl, Daniel; Placakyte, Ringaile; Raspereza, Alexei; Roland, Benoit; Savitskyi, Mykola; Saxena, Pooja; Shevchenko, Rostyslav; Spannagel, Simon; Stefaniuk, Nazar; Van Onsem, Gerrit Patrick; Walsh, Roberval; Wen, Yiwen; Wichmann, Katarzyna; Wissing, Christoph; Zenaiev, Oleksandr; Bein, Samuel; Blobel, Volker; Centis Vignali, Matteo; Draeger, Arne-Rasmus; Dreyer, Torben; Garutti, Erika; Gonzalez, Daniel; Haller, Johannes; Hoffmann, Malte; Junkes, Alexandra; Klanner, Robert; Kogler, Roman; Kovalchuk, Nataliia; Kurz, Simon; Lapsien, Tobias; Marchesini, Ivan; Marconi, Daniele; Meyer, Mareike; Niedziela, Marek; Nowatschin, Dominik; Pantaleo, Felice; Peiffer, Thomas; Perieanu, Adrian; Scharf, Christian; Schleper, Peter; Schmidt, Alexander; Schumann, Svenja; Schwandt, Joern; Sonneveld, Jory; Stadie, Hartmut; Steinbrück, Georg; Stober, Fred-Markus Helmut; Stöver, Marc; Tholen, Heiner; Troendle, Daniel; Usai, Emanuele; Vanelderen, Lukas; Vanhoefer, Annika; Vormwald, Benedikt; Akbiyik, Melike; Barth, Christian; Baur, Sebastian; Butz, Erik; Caspart, René; Chwalek, Thorsten; Colombo, Fabio; De Boer, Wim; Dierlamm, Alexander; Freund, Benedikt; Friese, Raphael; Giffels, Manuel; Gilbert, Andrew; Haitz, Dominik; Hartmann, Frank; Heindl, Stefan Michael; Husemann, Ulrich; Kassel, Florian; Kudella, Simon; Mildner, Hannes; Mozer, Matthias Ulrich; Müller, Thomas; Plagge, Michael; Quast, Gunter; Rabbertz, Klaus; Schröder, Matthias; Shvetsov, Ivan; Sieber, Georg; Simonis, Hans-Jürgen; Ulrich, Ralf; Wayand, Stefan; Weber, Marc; Weiler, Thomas; Williamson, Shawn; Wöhrmann, Clemens; Wolf, Roger; Anagnostou, Georgios; Daskalakis, Georgios; Geralis, Theodoros; Giakoumopoulou, Viktoria Athina; Kyriakis, Aristotelis; Loukas, Demetrios; Topsis-Giotis, Iasonas; Kesisoglou, Stilianos; Panagiotou, Apostolos; Saoulidou, Niki; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Paradas, Evangelos; Strologas, John; Triantis, Frixos A; Csanad, Mate; Filipovic, Nicolas; Pasztor, Gabriella; Bencze, Gyorgy; Hajdu, Csaba; Horvath, Dezso; Sikler, Ferenc; Veszpremi, Viktor; Vesztergombi, Gyorgy; Zsigmond, Anna Julia; Beni, Noemi; Czellar, Sandor; Karancsi, János; Makovec, Alajos; Molnar, Jozsef; Szillasi, Zoltan; Bartók, Márton; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Choudhury, Somnath; Komaragiri, Jyothsna Rani; Bahinipati, Seema; Bhowmik, Sandeep; Mal, Prolay; Mandal, Koushik; Nayak, Aruna; Sahoo, Deepak Kumar; Sahoo, Niladribihari; Swain, Sanjay Kumar; Bansal, Sunil; Beri, Suman Bala; Bhatnagar, Vipin; Bhawandeep, Bhawandeep; Chawla, Ridhi; Dhingra, Nitish; Kalsi, Amandeep Kaur; Kaur, Anterpreet; Kaur, Manjit; Kumar, Ramandeep; Kumari, Priyanka; Mehta, Ankita; Mittal, Monika; Singh, Jasbir; Walia, Genius; Kumar, Ashok; Shah, Aashaq; Bhardwaj, Ashutosh; Chauhan, Sushil; Choudhary, Brajesh C; Garg, Rocky Bala; Keshri, Sumit; Kumar, Ajay; Malhotra, Shivali; Naimuddin, Md; Ranjan, Kirti; Sharma, Ramkrishna; Sharma, Varun; 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Chenarani, Shirin; Eskandari Tadavani, Esmaeel; Etesami, Seyed Mohsen; Khakzad, Mohsen; Mohammadi Najafabadi, Mojtaba; Naseri, Mohsen; Paktinat Mehdiabadi, Saeid; Rezaei Hosseinabadi, Ferdos; Safarzadeh, Batool; Zeinali, Maryam; Felcini, Marta; Grunewald, Martin; Abbrescia, Marcello; Calabria, Cesare; Caputo, Claudio; Colaleo, Anna; Creanza, Donato; Cristella, Leonardo; De Filippis, Nicola; De Palma, Mauro; Errico, Filippo; Fiore, Luigi; Iaselli, Giuseppe; Maggi, Giorgio; Maggi, Marcello; Miniello, Giorgia; My, Salvatore; Nuzzo, Salvatore; Pompili, Alexis; Pugliese, Gabriella; Radogna, Raffaella; Ranieri, Antonio; Selvaggi, Giovanna; Sharma, Archana; Silvestris, Lucia; Venditti, Rosamaria; Verwilligen, Piet; Abbiendi, Giovanni; Battilana, Carlo; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Campanini, Renato; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Chhibra, Simranjit Singh; Codispoti, Giuseppe; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Guiducci, Luigi; Marcellini, Stefano; Masetti, Gianni; Navarria, Francesco; Perrotta, Andrea; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gian Piero; Tosi, Nicolò; Albergo, Sebastiano; Costa, Salvatore; Di Mattia, Alessandro; Giordano, Ferdinando; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Chatterjee, Kalyanmoy; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Lenzi, Piergiulio; Meschini, Marco; Paoletti, Simone; Russo, Lorenzo; Sguazzoni, Giacomo; Strom, Derek; Viliani, Lorenzo; Benussi, Luigi; Bianco, Stefano; Fabbri, Franco; Piccolo, Davide; Primavera, Federica; Calvelli, Valerio; Ferro, Fabrizio; Robutti, Enrico; Tosi, Silvano; Brianza, Luca; Brivio, Francesco; Ciriolo, Vincenzo; Dinardo, Mauro Emanuele; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Govoni, Pietro; Malberti, Martina; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pauwels, Kristof; Pedrini, Daniele; Pigazzini, Simone; Ragazzi, Stefano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Cavallo, Nicola; Di Guida, Salvatore; Fabozzi, Francesco; Fienga, Francesco; Iorio, Alberto Orso Maria; Khan, Wajid Ali; Lista, Luca; Meola, Sabino; Paolucci, Pierluigi; Sciacca, Crisostomo; Thyssen, Filip; Azzi, Patrizia; Bacchetta, Nicola; Benato, Lisa; Bisello, Dario; Boletti, Alessio; Checchia, Paolo; Dall'Osso, Martino; De Castro Manzano, Pablo; Dorigo, Tommaso; Dosselli, Umberto; Gasparini, Fabrizio; Gozzelino, Andrea; Lacaprara, Stefano; Margoni, Martino; Meneguzzo, Anna Teresa; Michelotto, Michele; Montecassiano, Fabio; Pantano, Devis; Pozzobon, Nicola; Ronchese, Paolo; Rossin, Roberto; Simonetto, Franco; Torassa, Ezio; Zanetti, Marco; Zotto, Pierluigi; Zumerle, Gianni; Braghieri, Alessandro; Fallavollita, Francesco; Magnani, Alice; Montagna, Paolo; Ratti, Sergio P; Re, Valerio; Ressegotti, Martina; Riccardi, Cristina; Salvini, Paola; Vai, Ilaria; Vitulo, Paolo; Alunni Solestizi, Luisa; Bilei, Gian Mario; Ciangottini, Diego; Fanò, Livio; Lariccia, Paolo; Leonardi, Roberto; Mantovani, Giancarlo; Mariani, Valentina; Menichelli, Mauro; Saha, Anirban; Santocchia, Attilio; Spiga, Daniele; Androsov, Konstantin; Azzurri, Paolo; Bagliesi, Giuseppe; Bernardini, Jacopo; Boccali, Tommaso; Borrello, Laura; Castaldi, Rino; Ciocci, Maria Agnese; Dell'Orso, Roberto; Fedi, Giacomo; Giassi, Alessandro; Grippo, Maria Teresa; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Rizzi, Andrea; Savoy-Navarro, Aurore; Spagnolo, Paolo; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Cipriani, Marco; Daci, Nadir; Del Re, Daniele; Diemoz, Marcella; Gelli, Simone; Longo, Egidio; Margaroli, Fabrizio; Marzocchi, Badder; Meridiani, Paolo; Organtini, Giovanni; Paramatti, Riccardo; Preiato, Federico; Rahatlou, Shahram; Rovelli, Chiara; Santanastasio, Francesco; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Bartosik, Nazar; Bellan, Riccardo; Biino, Cristina; Cartiglia, Nicolo; Cenna, Francesca; Costa, Marco; Covarelli, Roberto; Degano, Alessandro; Demaria, Natale; Kiani, Bilal; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Monteil, Ennio; Monteno, Marco; Obertino, Maria Margherita; Pacher, Luca; Pastrone, Nadia; Pelliccioni, Mario; Pinna Angioni, Gian Luca; Ravera, Fabio; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Shchelina, Ksenia; Sola, Valentina; Solano, Ada; Staiano, Amedeo; Traczyk, Piotr; Belforte, Stefano; Casarsa, Massimo; Cossutti, Fabio; Della Ricca, Giuseppe; Zanetti, Anna; Kim, Dong Hee; Kim, Gui Nyun; Kim, Min Suk; Lee, Jeongeun; Lee, Sangeun; Lee, Seh Wook; Oh, Young Do; Sekmen, Sezen; Son, Dong-Chul; Yang, Yu Chul; Lee, Ari; Kim, Hyunchul; Moon, Dong Ho; Oh, Geonhee; Brochero Cifuentes, Javier Andres; Goh, Junghwan; Kim, Tae Jeong; Cho, Sungwoong; Choi, Suyong; Go, Yeonju; Gyun, Dooyeon; Ha, Seungkyu; Hong, Byung-Sik; Jo, Youngkwon; Kim, Yongsun; Lee, Kisoo; Lee, Kyong Sei; Lee, Songkyo; Lim, Jaehoon; Park, Sung Keun; Roh, Youn; Almond, John; Kim, Junho; Kim, Jae Sung; Lee, Haneol; Lee, Kyeongpil; Nam, Kyungwook; Oh, Sung Bin; Radburn-Smith, Benjamin Charles; Seo, Seon-hee; Yang, Unki; Yoo, Hwi Dong; Yu, Geum Bong; Choi, Minkyoo; Kim, Hyunyong; Kim, Ji Hyun; Lee, Jason Sang Hun; Park, Inkyu; Ryu, Geonmo; Choi, Young-Il; Hwang, Chanwook; Lee, Jongseok; Yu, Intae; Dudenas, Vytautas; Juodagalvis, Andrius; Vaitkus, Juozas; Ahmed, Ijaz; Ibrahim, Zainol Abidin; Md Ali, Mohd Adli Bin; Mohamad Idris, Faridah; Wan Abdullah, Wan Ahmad Tajuddin; Yusli, Mohd Nizam; Zolkapli, Zukhaimira; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-De La Cruz, Ivan; Lopez-Fernandez, Ricardo; Mejia Guisao, Jhovanny; Sánchez Hernández, Alberto; Carrillo Moreno, Salvador; Oropeza Barrera, Cristina; Vazquez Valencia, Fabiola; Pedraza, Isabel; Salazar Ibarguen, Humberto Antonio; Uribe Estrada, Cecilia; Morelos Pineda, Antonio; Krofcheck, David; Butler, Philip H; Ahmad, Ashfaq; Ahmad, Muhammad; Hassan, Qamar; Hoorani, Hafeez R; Saddique, Asif; Shah, Mehar Ali; Shoaib, Muhammad; Waqas, Muhammad; Bialkowska, Helena; Bluj, Michal; Boimska, Bozena; Frueboes, Tomasz; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Zalewski, Piotr; Bunkowski, Karol; Byszuk, Adrian; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Misiura, Maciej; Olszewski, Michal; Pyskir, Andrzej; Walczak, Marek; Bargassa, Pedrame; Beirão Da Cruz E Silva, Cristóvão; Calpas, Betty; Di Francesco, Agostino; Faccioli, Pietro; Gallinaro, Michele; Hollar, Jonathan; Leonardo, Nuno; Lloret Iglesias, Lara; Nemallapudi, Mythra Varun; Seixas, Joao; Toldaiev, Oleksii; Vadruccio, Daniele; Varela, Joao; Afanasiev, Serguei; Bunin, Pavel; Gavrilenko, Mikhail; Golutvin, Igor; Gorbunov, Ilya; Kamenev, Alexey; Karjavin, Vladimir; Lanev, Alexander; Malakhov, Alexander; Matveev, Viktor; Palichik, Vladimir; Perelygin, Victor; Shmatov, Sergey; Shulha, Siarhei; Skatchkov, Nikolai; Smirnov, Vitaly; Voytishin, Nikolay; Zarubin, Anatoli; Ivanov, Yury; Kim, Victor; Kuznetsova, Ekaterina; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Karneyeu, Anton; Kirsanov, Mikhail; Krasnikov, Nikolai; Pashenkov, Anatoli; Tlisov, Danila; Toropin, Alexander; Epshteyn, Vladimir; Gavrilov, Vladimir; Lychkovskaya, Natalia; Popov, Vladimir; Pozdnyakov, Ivan; Safronov, Grigory; Spiridonov, Alexander; Stepennov, Anton; Toms, Maria; Vlasov, Evgueni; Zhokin, Alexander; Aushev, Tagir; Bylinkin, Alexander; Chadeeva, Marina; Popova, Elena; Rusinov, Vladimir; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Terkulov, Adel; Baskakov, Alexey; Belyaev, Andrey; Boos, Edouard; Demiyanov, Andrey; Ershov, Alexander; Gribushin, Andrey; Kodolova, Olga; Korotkikh, Vladimir; Lokhtin, Igor; Miagkov, Igor; Obraztsov, Stepan; Petrushanko, Sergey; Savrin, Viktor; Snigirev, Alexander; Vardanyan, Irina; Blinov, Vladimir; Skovpen, Yuri; Shtol, Dmitry; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Elumakhov, Dmitry; Kachanov, Vassili; Kalinin, Alexey; Konstantinov, Dmitri; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Cirkovic, Predrag; Devetak, Damir; Dordevic, Milos; Milosevic, Jovan; Rekovic, Vladimir; Alcaraz Maestre, Juan; Barrio Luna, Mar; Cerrada, Marcos; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Escalante Del Valle, Alberto; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Pérez-Calero Yzquierdo, Antonio María; Puerta Pelayo, Jesus; Quintario Olmeda, Adrián; Redondo, Ignacio; Romero, Luciano; Senghi Soares, Mara; Álvarez Fernández, Adrian; de Trocóniz, Jorge F; Missiroli, Marino; Moran, Dermot; Cuevas, Javier; Erice, Carlos; Fernandez Menendez, Javier; Gonzalez Caballero, Isidro; González Fernández, Juan Rodrigo; Palencia Cortezon, Enrique; Sanchez Cruz, Sergio; Suárez Andrés, Ignacio; Vischia, Pietro; Vizan Garcia, Jesus Manuel; Cabrillo, Iban Jose; Calderon, Alicia; Chazin Quero, Barbara; Curras, Esteban; Fernandez, Marcos; Garcia-Ferrero, Juan; Gomez, Gervasio; Lopez Virto, Amparo; Marco, Jesus; Martinez Rivero, Celso; Martinez Ruiz del Arbol, Pablo; Matorras, Francisco; Piedra Gomez, Jonatan; Rodrigo, Teresa; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Trevisani, Nicolò; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Baillon, Paul; Ball, Austin; Barney, David; Bianco, Michele; Bloch, Philippe; Bocci, Andrea; Botta, Cristina; Camporesi, Tiziano; Castello, Roberto; Cepeda, Maria; Cerminara, Gianluca; Chapon, Emilien; Chen, Yi; D'Enterria, David; Dabrowski, Anne; Daponte, Vincenzo; David Tinoco Mendes, Andre; De Gruttola, Michele; De Roeck, Albert; Di Marco, Emanuele; Dobson, Marc; Dorney, Brian; Du Pree, Tristan; Dünser, Marc; Dupont, Niels; Elliott-Peisert, Anna; Everaerts, Pieter; Franzoni, Giovanni; Fulcher, Jonathan; Funk, Wolfgang; Gigi, Dominique; Gill, Karl; Glege, Frank; Gulhan, Doga; Gundacker, Stefan; Guthoff, Moritz; Harris, Philip; Hegeman, Jeroen; Innocente, Vincenzo; Janot, Patrick; Karacheban, Olena; Kieseler, Jan; Kirschenmann, Henning; Knünz, Valentin; Kornmayer, Andreas; Kortelainen, Matti J; Krammer, Manfred; Lange, Clemens; Lecoq, Paul; Lourenco, Carlos; Lucchini, Marco Toliman; Malgeri, Luca; Mannelli, Marcello; Martelli, Arabella; Meijers, Frans; Merlin, Jeremie Alexandre; Mersi, Stefano; Meschi, Emilio; Milenovic, Predrag; Moortgat, Filip; Mulders, Martijn; Neugebauer, Hannes; Orfanelli, Styliani; Orsini, Luciano; Pape, Luc; Perez, Emmanuel; Peruzzi, Marco; Petrilli, Achille; Petrucciani, Giovanni; Pfeiffer, Andreas; Pierini, Maurizio; Racz, Attila; Reis, Thomas; Rolandi, Gigi; Rovere, Marco; Sakulin, Hannes; Schäfer, Christoph; Schwick, Christoph; Seidel, Markus; Selvaggi, Michele; Sharma, Archana; Silva, Pedro; Sphicas, Paraskevas; Steggemann, Jan; Stoye, Markus; Tosi, Mia; Treille, Daniel; Triossi, Andrea; Tsirou, Andromachi; Veckalns, Viesturs; Veres, Gabor Istvan; Verweij, Marta; Wardle, Nicholas; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; Kotlinski, Danek; Langenegger, Urs; Rohe, Tilman; Wiederkehr, Stephan Albert; Bachmair, Felix; Bäni, Lukas; Berger, Pirmin; Bianchini, Lorenzo; Casal, Bruno; Dissertori, Günther; Dittmar, Michael; Donegà, Mauro; Grab, Christoph; Heidegger, Constantin; Hits, Dmitry; Hoss, Jan; Kasieczka, Gregor; Klijnsma, Thomas; Lustermann, Werner; Mangano, Boris; Marionneau, Matthieu; Meinhard, Maren Tabea; Meister, Daniel; Micheli, Francesco; Musella, Pasquale; Nessi-Tedaldi, Francesca; Pandolfi, Francesco; Pata, Joosep; Pauss, Felicitas; Perrin, Gaël; Perrozzi, Luca; Quittnat, Milena; Rossini, Marco; Schönenberger, Myriam; Shchutska, Lesya; Starodumov, Andrei; Tavolaro, Vittorio Raoul; Theofilatos, Konstantinos; Vesterbacka Olsson, Minna Leonora; Wallny, Rainer; Zagozdzinska, Agnieszka; Zhu, De Hua; Aarrestad, Thea Klaeboe; Amsler, Claude; Caminada, Lea; Canelli, Maria Florencia; De Cosa, Annapaola; Donato, Silvio; Galloni, Camilla; Hinzmann, Andreas; Hreus, Tomas; Kilminster, Benjamin; Ngadiuba, Jennifer; Pinna, Deborah; Rauco, Giorgia; Robmann, Peter; Salerno, Daniel; Seitz, Claudia; Zucchetta, Alberto; Candelise, Vieri; Doan, Thi Hien; Jain, Shilpi; Khurana, Raman; Konyushikhin, Maxim; Kuo, Chia-Ming; Lin, Willis; Pozdnyakov, Andrey; Yu, Shin-Shan; Kumar, Arun; Chang, Paoti; Chao, Yuan; Chen, Kai-Feng; Chen, Po-Hsun; Fiori, Francesco; Hou, George Wei-Shu; Hsiung, Yee; Liu, Yueh-Feng; Lu, Rong-Shyang; Miñano Moya, Mercedes; Paganis, Efstathios; Psallidas, Andreas; Tsai, Jui-fa; Asavapibhop, Burin; Kovitanggoon, Kittikul; Singh, Gurpreet; Srimanobhas, Norraphat; Adiguzel, Aytul; Bakirci, Mustafa Numan; Boran, Fatma; Cerci, Salim; Damarseckin, Serdal; Demiroglu, Zuhal Seyma; Dozen, Candan; Dumanoglu, Isa; Girgis, Semiray; Gokbulut, Gul; Guler, Yalcin; Hos, Ilknur; Kangal, Evrim Ersin; Kara, Ozgun; Kayis Topaksu, Aysel; Kiminsu, Ugur; Oglakci, Mehmet; Onengut, Gulsen; Ozdemir, Kadri; Tali, Bayram; Turkcapar, Semra; Zorbakir, Ibrahim Soner; Zorbilmez, Caglar; Bilin, Bugra; Karapinar, Guler; Ocalan, Kadir; Yalvac, Metin; Zeyrek, Mehmet; Gülmez, Erhan; Kaya, Mithat; Kaya, Ozlem; Tekten, Sevgi; Yetkin, Elif Asli; Nazlim Agaras, Merve; Atay, Serhat; Cakir, Altan; Cankocak, Kerem; Grynyov, Boris; Levchuk, Leonid; Sorokin, Pavel; Aggleton, Robin; Ball, Fionn; Beck, Lana; Brooke, James John; Burns, Douglas; Clement, Emyr; Cussans, David; Flacher, Henning; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Jacob, Jeson; Kreczko, Lukasz; Lucas, Chris; Newbold, Dave M; Paramesvaran, Sudarshan; Poll, Anthony; Sakuma, Tai; Seif El Nasr-storey, Sarah; Smith, Dominic; Smith, Vincent J; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Calligaris, Luigi; Cieri, Davide; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Olaiya, Emmanuel; Petyt, David; Shepherd-Themistocleous, Claire; Thea, Alessandro; Tomalin, Ian R; Williams, Thomas; Baber, Mark; Bainbridge, Robert; Breeze, Shane; Buchmuller, Oliver; Bundock, Aaron; Casasso, Stefano; Citron, Matthew; Colling, David; Corpe, Louie; Dauncey, Paul; Davies, Gavin; De Wit, Adinda; Della Negra, Michel; Di Maria, Riccardo; Dunne, Patrick; Elwood, Adam; Futyan, David; Haddad, Yacine; Hall, Geoffrey; Iles, Gregory; James, Thomas; Lane, Rebecca; Laner, Christian; Lyons, Louis; Magnan, Anne-Marie; Malik, Sarah; Mastrolorenzo, Luca; Matsushita, Takashi; Nash, Jordan; Nikitenko, Alexander; Pela, Joao; Pesaresi, Mark; Raymond, David Mark; Richards, Alexander; Rose, Andrew; Scott, Edward; Seez, Christopher; Shtipliyski, Antoni; Summers, Sioni; Tapper, Alexander; Uchida, Kirika; Vazquez Acosta, Monica; Virdee, Tejinder; Winterbottom, Daniel; Wright, Jack; Zenz, Seth Conrad; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Borzou, Ahmad; Call, Kenneth; Dittmann, Jay; Hatakeyama, Kenichi; Liu, Hongxuan; Pastika, Nathaniel; Bartek, Rachel; Dominguez, Aaron; Buccilli, Andrew; Cooper, Seth; Henderson, Conor; Rumerio, Paolo; West, Christopher; Arcaro, Daniel; Avetisyan, Aram; Bose, Tulika; Gastler, Daniel; Rankin, Dylan; Richardson, Clint; Rohlf, James; Sulak, Lawrence; Zou, David; Benelli, Gabriele; Cutts, David; Garabedian, Alex; Hakala, John; Heintz, Ulrich; Hogan, Julie Managan; Kwok, Ka Hei Martin; Laird, Edward; Landsberg, Greg; Mao, Zaixing; Narain, Meenakshi; Pazzini, Jacopo; Piperov, Stefan; Sagir, Sinan; Syarif, Rizki; Yu, David; Band, Reyer; Brainerd, Christopher; Burns, Dustin; Calderon De La Barca Sanchez, Manuel; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Erbacher, Robin; Flores, Chad; Funk, Garrett; Gardner, Michael; Ko, Winston; Lander, Richard; Mclean, Christine; Mulhearn, Michael; Pellett, Dave; Pilot, Justin; Shalhout, Shalhout; Shi, Mengyao; Smith, John; Squires, Michael; Stolp, Dustin; Tos, Kyle; Tripathi, Mani; Wang, Zhangqier; Bachtis, Michail; Bravo, Cameron; Cousins, Robert; Dasgupta, Abhigyan; Florent, Alice; Hauser, Jay; Ignatenko, Mikhail; Mccoll, Nickolas; Saltzberg, David; Schnaible, Christian; Valuev, Vyacheslav; Bouvier, Elvire; Burt, Kira; Clare, Robert; Ellison, John Anthony; Gary, J William; Ghiasi Shirazi, Seyyed Mohammad Amin; Hanson, Gail; Heilman, Jesse; Jandir, Pawandeep; Kennedy, Elizabeth; Lacroix, Florent; Long, Owen Rosser; Olmedo Negrete, Manuel; Paneva, Mirena Ivova; Shrinivas, Amithabh; Si, Weinan; Wei, Hua; Wimpenny, Stephen; Yates, Brent; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Derdzinski, Mark; Gerosa, Raffaele; Hashemi, Bobak; Holzner, André; Klein, Daniel; Kole, Gouranga; Krutelyov, Vyacheslav; Letts, James; Macneill, Ian; Masciovecchio, Mario; Olivito, Dominick; Padhi, Sanjay; Pieri, Marco; Sani, Matteo; Sharma, Vivek; Simon, Sean; Tadel, Matevz; Vartak, Adish; Wasserbaech, Steven; Wood, John; Würthwein, Frank; Yagil, Avraham; Zevi Della Porta, Giovanni; Amin, Nick; Bhandari, Rohan; Bradmiller-Feld, John; Campagnari, Claudio; Dishaw, Adam; Dutta, Valentina; Franco Sevilla, Manuel; George, Christopher; Golf, Frank; Gouskos, Loukas; Gran, Jason; Heller, Ryan; Incandela, Joe; Mullin, Sam Daniel; Ovcharova, Ana; Qu, Huilin; Richman, Jeffrey; Stuart, David; Suarez, Indara; Yoo, Jaehyeok; Anderson, Dustin; Bendavid, Joshua; Bornheim, Adolf; Lawhorn, Jay Mathew; Newman, Harvey B; Nguyen, Thong; Pena, Cristian; Spiropulu, Maria; Vlimant, Jean-Roch; Xie, Si; Zhang, Zhicai; Zhu, Ren-Yuan; Andrews, Michael Benjamin; Ferguson, Thomas; Mudholkar, Tanmay; Paulini, Manfred; Russ, James; Sun, Menglei; Vogel, Helmut; Vorobiev, Igor; Weinberg, Marc; Cumalat, John Perry; Ford, William T; Jensen, Frank; Johnson, Andrew; Krohn, Michael; Leontsinis, Stefanos; Mulholland, Troy; Stenson, Kevin; Wagner, Stephen Robert; Alexander, James; Chaves, Jorge; Chu, Jennifer; Dittmer, Susan; Mcdermott, Kevin; Mirman, Nathan; Patterson, Juliet Ritchie; Rinkevicius, Aurelijus; Ryd, Anders; Skinnari, Louise; Soffi, Livia; Tan, Shao Min; Tao, Zhengcheng; Thom, Julia; Tucker, Jordan; Wittich, Peter; Zientek, Margaret; Abdullin, Salavat; Albrow, Michael; Apollinari, Giorgio; Apresyan, Artur; Apyan, Aram; Banerjee, Sunanda; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Bolla, Gino; Burkett, Kevin; Butler, Joel Nathan; Canepa, Anadi; Cheung, Harry; Chlebana, Frank; Cremonesi, Matteo; Duarte, Javier; Elvira, Victor Daniel; Freeman, Jim; Gecse, Zoltan; Gottschalk, Erik; Gray, Lindsey; Green, Dan; Grünendahl, Stefan; Gutsche, Oliver; Harris, Robert M; Hasegawa, Satoshi; Hirschauer, James; Hu, Zhen; Jayatilaka, Bodhitha; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Klima, Boaz; Kreis, Benjamin; Lammel, Stephan; Lincoln, Don; Lipton, Ron; Liu, Miaoyuan; Liu, Tiehui; Lopes De Sá, Rafael; Lykken, Joseph; Maeshima, Kaori; Magini, Nicolo; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Merkel, Petra; Mrenna, Stephen; Nahn, Steve; O'Dell, Vivian; Pedro, Kevin; Prokofyev, Oleg; Rakness, Gregory; Ristori, Luciano; Schneider, Basil; Sexton-Kennedy, Elizabeth; Soha, Aron; Spalding, William J; Spiegel, Leonard; Stoynev, Stoyan; Strait, James; Strobbe, Nadja; Taylor, Lucas; Tkaczyk, Slawek; Tran, Nhan Viet; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vernieri, Caterina; Verzocchi, Marco; Vidal, Richard; Wang, Michael; Weber, Hannsjoerg Artur; Whitbeck, Andrew; Acosta, Darin; Avery, Paul; Bortignon, Pierluigi; Brinkerhoff, Andrew; Carnes, Andrew; Carver, Matthew; Curry, David; Das, Souvik; Field, Richard D; Furic, Ivan-Kresimir; Konigsberg, Jacobo; Korytov, Andrey; Kotov, Khristian; Ma, Peisen; Matchev, Konstantin; Mei, Hualin; Mitselmakher, Guenakh; Rank, Douglas; Sperka, David; Terentyev, Nikolay; Thomas, Laurent; Wang, Jian; Wang, Sean-Jiun; Yelton, John; Joshi, Yagya Raj; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Ackert, Andrew; Adams, Todd; Askew, Andrew; Hagopian, Sharon; Hagopian, Vasken; Johnson, Kurtis F; Kolberg, Ted; Perry, Thomas; Prosper, Harrison; Santra, Arka; Yohay, Rachel; Baarmand, Marc M; Bhopatkar, Vallary; Colafranceschi, Stefano; Hohlmann, Marcus; Noonan, Daniel; Roy, Titas; Yumiceva, Francisco; Adams, Mark Raymond; Apanasevich, Leonard; Berry, Douglas; Betts, Russell Richard; Cavanaugh, Richard; Chen, Xuan; Evdokimov, Olga; Gerber, Cecilia Elena; Hangal, Dhanush Anil; Hofman, David Jonathan; Jung, Kurt; Kamin, Jason; Sandoval Gonzalez, Irving Daniel; Tonjes, Marguerite; Trauger, Hallie; Varelas, Nikos; Wang, Hui; Wu, Zhenbin; Zhang, Jingyu; Bilki, Burak; Clarida, Warren; Dilsiz, Kamuran; Durgut, Süleyman; Gandrajula, Reddy Pratap; Haytmyradov, Maksat; Khristenko, Viktor; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Ogul, Hasan; Onel, Yasar; Ozok, Ferhat; Penzo, Aldo; Snyder, Christina; Tiras, Emrah; Wetzel, James; Yi, Kai; Blumenfeld, Barry; Cocoros, Alice; Eminizer, Nicholas; Fehling, David; Feng, Lei; Gritsan, Andrei; Maksimovic, Petar; Roskes, Jeffrey; Sarica, Ulascan; Swartz, Morris; Xiao, Meng; You, Can; Al-bataineh, Ayman; Baringer, Philip; Bean, Alice; Boren, Samuel; Bowen, James; Castle, James; Khalil, Sadia; Kropivnitskaya, Anna; Majumder, Devdatta; Mcbrayer, William; Murray, Michael; Royon, Christophe; Sanders, Stephen; Schmitz, Erich; Stringer, Robert; Tapia Takaki, Daniel; Wang, Quan; Ivanov, Andrew; Kaadze, Ketino; Maravin, Yurii; Mohammadi, Abdollah; Saini, Lovedeep Kaur; Skhirtladze, Nikoloz; Toda, Sachiko; Rebassoo, Finn; Wright, Douglas; Anelli, Christopher; Baden, Drew; Baron, Owen; Belloni, Alberto; Calvert, Brian; Eno, Sarah Catherine; Ferraioli, Charles; Hadley, Nicholas John; Jabeen, Shabnam; Jeng, Geng-Yuan; Kellogg, Richard G; Kunkle, Joshua; Mignerey, Alice; Ricci-Tam, Francesca; Shin, Young Ho; Skuja, Andris; Tonwar, Suresh C; Abercrombie, Daniel; Allen, Brandon; Azzolini, Virginia; Barbieri, Richard; Baty, Austin; Bi, Ran; Brandt, Stephanie; Busza, Wit; Cali, Ivan Amos; D'Alfonso, Mariarosaria; Demiragli, Zeynep; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Hsu, Dylan; Iiyama, Yutaro; Innocenti, Gian Michele; Klute, Markus; Kovalskyi, Dmytro; Lai, Yue Shi; Lee, Yen-Jie; Levin, Andrew; Luckey, Paul David; Maier, Benedikt; Marini, Andrea Carlo; Mcginn, Christopher; Mironov, Camelia; Narayanan, Siddharth; Niu, Xinmei; Paus, Christoph; Roland, Christof; Roland, Gunther; Salfeld-Nebgen, Jakob; Stephans, George; Tatar, Kaya; Velicanu, Dragos; Wang, Jing; Wang, Ta-Wei; Wyslouch, Bolek; Benvenuti, Alberto; Chatterjee, Rajdeep Mohan; Evans, Andrew; Hansen, Peter; Kalafut, Sean; Kubota, Yuichi; Lesko, Zachary; Mans, Jeremy; Nourbakhsh, Shervin; Ruckstuhl, Nicole; Rusack, Roger; Turkewitz, Jared; Acosta, John Gabriel; Oliveros, Sandra; Avdeeva, Ekaterina; Bloom, Kenneth; Claes, Daniel R; Fangmeier, Caleb; Gonzalez Suarez, Rebeca; Kamalieddin, Rami; Kravchenko, Ilya; Monroy, Jose; Siado, Joaquin Emilo; Snow, Gregory R; Stieger, Benjamin; Alyari, Maral; Dolen, James; Godshalk, Andrew; Harrington, Charles; Iashvili, Ia; Nguyen, Duong; Parker, Ashley; Rappoccio, Salvatore; Roozbahani, Bahareh; Alverson, George; Barberis, Emanuela; Hortiangtham, Apichart; Massironi, Andrea; Morse, David Michael; Nash, David; Orimoto, Toyoko; Teixeira De Lima, Rafael; Trocino, Daniele; Wang, Ren-Jie; Wood, Darien; Bhattacharya, Saptaparna; Charaf, Otman; Hahn, Kristan Allan; Mucia, Nicholas; Odell, Nathaniel; Pollack, Brian; Schmitt, Michael Henry; Sung, Kevin; Trovato, Marco; Velasco, Mayda; Dev, Nabarun; Hildreth, Michael; Hurtado Anampa, Kenyi; Jessop, Colin; Karmgard, Daniel John; Kellams, Nathan; Lannon, Kevin; Loukas, Nikitas; Marinelli, Nancy; Meng, Fanbo; Mueller, Charles; Musienko, Yuri; Planer, Michael; Reinsvold, Allison; Ruchti, Randy; Smith, Geoffrey; Taroni, Silvia; Wayne, Mitchell; Wolf, Matthias; Woodard, Anna; Alimena, Juliette; Antonelli, Louis; Bylsma, Ben; Durkin, Lloyd Stanley; Flowers, Sean; Francis, Brian; Hart, Andrew; Hill, Christopher; Ji, Weifeng; Liu, Bingxuan; Luo, Wuming; Puigh, Darren; Winer, Brian L; Wulsin, Howard Wells; Benaglia, Andrea; Cooperstein, Stephane; Driga, Olga; Elmer, Peter; Hardenbrook, Joshua; Hebda, Philip; Lange, David; Luo, Jingyu; Marlow, Daniel; Mei, Kelvin; Ojalvo, Isabel; Olsen, James; Palmer, Christopher; Piroué, Pierre; Stickland, David; Svyatkovskiy, Alexey; Tully, Christopher; Malik, Sudhir; Norberg, Scarlet; Barker, Anthony; Barnes, Virgil E; Folgueras, Santiago; Gutay, Laszlo; Jha, Manoj; Jones, Matthew; Jung, Andreas Werner; Khatiwada, Ajeeta; Miller, David Harry; Neumeister, Norbert; Schulte, Jan-Frederik; Sun, Jian; Wang, Fuqiang; Xie, Wei; Cheng, Tongguang; Parashar, Neeti; Stupak, John; Adair, Antony; Akgun, Bora; Chen, Zhenyu; Ecklund, Karl Matthew; Geurts, Frank JM; Guilbaud, Maxime; Li, Wei; Michlin, Benjamin; Northup, Michael; Padley, Brian Paul; Roberts, Jay; Rorie, Jamal; Tu, Zhoudunming; Zabel, James; Bodek, Arie; de Barbaro, Pawel; Demina, Regina; Duh, Yi-ting; Ferbel, Thomas; Galanti, Mario; Garcia-Bellido, Aran; Han, Jiyeon; Hindrichs, Otto; Khukhunaishvili, Aleko; Lo, Kin Ho; Tan, Ping; Verzetti, Mauro; Ciesielski, Robert; Goulianos, Konstantin; Mesropian, Christina; Agapitos, Antonis; Chou, John Paul; Gershtein, Yuri; Gómez Espinosa, Tirso Alejandro; Halkiadakis, Eva; Heindl, Maximilian; Hughes, Elliot; Kaplan, Steven; Kunnawalkam Elayavalli, Raghav; Kyriacou, Savvas; Lath, Amitabh; Montalvo, Roy; Nash, Kevin; Osherson, Marc; Saka, Halil; Salur, Sevil; Schnetzer, Steve; Sheffield, David; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Thomassen, Peter; Walker, Matthew; Foerster, Mark; Heideman, Joseph; Riley, Grant; Rose, Keith; Spanier, Stefan; Thapa, Krishna; Bouhali, Othmane; Castaneda Hernandez, Alfredo; Celik, Ali; Dalchenko, Mykhailo; De Mattia, Marco; Delgado, Andrea; Dildick, Sven; Eusebi, Ricardo; Gilmore, Jason; Huang, Tao; Kamon, Teruki; Mueller, Ryan; Pakhotin, Yuriy; Patel, Rishi; Perloff, Alexx; Perniè, Luca; Rathjens, Denis; Safonov, Alexei; Tatarinov, Aysen; Ulmer, Keith; Akchurin, Nural; Damgov, Jordan; De Guio, Federico; Dudero, Phillip Russell; Faulkner, James; Gurpinar, Emine; Kunori, Shuichi; Lamichhane, Kamal; Lee, Sung Won; Libeiro, Terence; Peltola, Timo; Undleeb, Sonaina; Volobouev, Igor; Wang, Zhixing; Greene, Senta; Gurrola, Alfredo; Janjam, Ravi; Johns, Willard; Maguire, Charles; Melo, Andrew; Ni, Hong; Sheldon, Paul; Tuo, Shengquan; Velkovska, Julia; Xu, Qiao; Arenton, Michael Wayne; Barria, Patrizia; Cox, Bradley; Hirosky, Robert; Ledovskoy, Alexander; Li, Hengne; Neu, Christopher; Sinthuprasith, Tutanon; Sun, Xin; Wang, Yanchu; Wolfe, Evan; Xia, Fan; Clarke, Christopher; Harr, Robert; Karchin, Paul Edmund; Sturdy, Jared; Zaleski, Shawn; Buchanan, James; Caillol, Cécile; Dasu, Sridhara; Dodd, Laura; Duric, Senka; Gomber, Bhawna; Grothe, Monika; Herndon, Matthew; Hervé, Alain; Hussain, Usama; Klabbers, Pamela; Lanaro, Armando; Levine, Aaron; Long, Kenneth; Loveless, Richard; Pierro, Giuseppe Antonio; Polese, Giovanni; Ruggles, Tyler; Savin, Alexander; Smith, Nicholas; Smith, Wesley H; Taylor, Devin; Woods, Nathaniel

2017-12-05

For the first time a principal-component analysis is used to separate out different orthogonal modes of the two-particle correlation matrix from heavy ion collisions. The analysis uses data from $\\sqrt{s_{\\mathrm{NN}}} = $ 2.76 TeV PbPb and $\\sqrt{s_{\\mathrm{NN}}} = $ 5.02 TeV pPb collisions collected by the CMS experiment at the LHC. Two-particle azimuthal correlations have been extensively used to study hydrodynamic flow in heavy ion collisions. Recently it has been shown that the expected factorization of two-particle results into a product of the constituent single-particle anisotropies is broken. The new information provided by these modes may shed light on the breakdown of flow factorization in heavy ion collisions. The first two modes ("leading" and "subleading") of two-particle correlations are presented for elliptical and triangular anisotropies in PbPb and pPb collisions as a function of $ p_{\\mathrm{T}} $ over a wide range of event activity. The leading mode is found to be essentially equivalent to...

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.

Principals' Perceived Supervisory Behaviors Regarding Marginal Teachers in Two States

Science.gov (United States)

Range, Bret; Hewitt, Paul; Young, Suzie

2014-01-01

This descriptive study used an online survey to determine how principals in two states viewed the supervision of marginal teachers. Principals ranked their own evaluation of the teacher as the most important factor when identifying marginal teachers and relied on informal methods to diagnose marginal teaching. Female principals rated a majority of…

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

Science.gov (United States)

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C

2004-09-08

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins. Copyright 2004 American Institute of Physics

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…

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...

Three-Dimensional X-Ray Photoelectron Tomography on the Nanoscale: Limits of Data Processing by Principal Component Analysis

DEFF Research Database (Denmark)

Hajati, S.; Walton, J.; Tougaard, S.

2013-01-01

In a previous article, we studied the influence of spectral noise on a new method for three-dimensional X-ray photoelectron spectroscopy (3D XPS) imaging, which is based on analysis of the XPS peak shape [Hajati, S., Tougaard, S., Walton, J. & Fairley, N. (2008). Surf Sci 602, 3064-3070]. Here, we...... study in more detail the influence of noise reduction by principal component analysis (PCA) on 3D XPS images of carbon contamination of a patterned oxidized silicon sample and on 3D XPS images of Ag covered by a nanoscale patterned octadiene layer. PCA is very efficient for noise reduction, and using...... acquisition time. A small additional amount of information is obtained by using up to five PCA factors, but due to the increased noise level, this information can only be extracted if the intensity of the start and end points for each spectrum are obtained as averages over several energy points....

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

Science.gov (United States)

Li, Can; Joiner, Joanna; Krotkov, A.; Bhartia, Pawan K.

2013-01-01

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Honouring Roles: The Story of a Principal and a Student

Science.gov (United States)

Cranston, Jerome

2012-01-01

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…

Determinants of job stress in chemical process industry: A factor analysis approach.

Science.gov (United States)

Menon, Balagopal G; Praveensal, C J; Madhu, G

2015-01-01

Job stress is one of the active research domains in industrial safety research. The job stress can result in accidents and health related issues in workers in chemical process industries. Hence it is important to measure the level of job stress in workers so as to mitigate the same to avoid the worker's safety related problems in the industries. The objective of this study is to determine the job stress factors in the chemical process industry in Kerala state, India. This study also aims to propose a comprehensive model and an instrument framework for measuring job stress levels in the chemical process industries in Kerala, India. The data is collected through a questionnaire survey conducted in chemical process industries in Kerala. The collected data out of 1197 surveys is subjected to principal component and confirmatory factor analysis to develop the job stress factor structure. The factor analysis revealed 8 factors that influence the job stress in process industries. It is also found that the job stress in employees is most influenced by role ambiguity and the least by work environment. The study has developed an instrument framework towards measuring job stress utilizing exploratory factor analysis and structural equation modeling.

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

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.

Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF

International Nuclear Information System (INIS)

Alfeld, Matthias; Falkenberg, Gerald; Wahabzada, Mirwaes; Bauckhage, Christian; Kersting, Kristian; Wellenreuther, Gerd

2014-01-01

Stacks of elemental distribution images acquired by XRF can be difficult to interpret, if they contain high degrees of redundancy and components differing in their quantitative but not qualitative elemental composition. Factor analysis, mainly in the form of Principal Component Analysis (PCA), has been used to reduce the level of redundancy and highlight correlations. PCA, however, does not yield physically meaningful representations as they often contain negative values. This limitation can be overcome, by employing factor analysis that is restricted to non-negativity. In this paper we present the first application of the Python Matrix Factorization Module (pymf) on XRF data. This is done in a case study on the painting Saul and David from the studio of Rembrandt van Rijn. We show how the discrimination between two different Co containing compounds with minimum user intervention and a priori knowledge is supported by Non-Negative Matrix Factorization (NMF).

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.

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...

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,.

Design of experiments and principal component analysis as approaches for enhancing performance of gas-diffusional air-breathing bilirubin oxidase cathode

Science.gov (United States)

Babanova, Sofia; Artyushkova, Kateryna; Ulyanova, Yevgenia; Singhal, Sameer; Atanassov, Plamen

2014-01-01

Two statistical methods, design of experiments (DOE) and principal component analysis (PCA) are employed to investigate and improve performance of air-breathing gas-diffusional enzymatic electrodes. DOE is utilized as a tool for systematic organization and evaluation of various factors affecting the performance of the composite system. Based on the results from the DOE, an improved cathode is constructed. The current density generated utilizing the improved cathode (755 ± 39 μA cm-2 at 0.3 V vs. Ag/AgCl) is 2-5 times higher than the highest current density previously achieved. Three major factors contributing to the cathode performance are identified: the amount of enzyme, the volume of phosphate buffer used to immobilize the enzyme, and the thickness of the gas-diffusion layer (GDL). PCA is applied as an independent confirmation tool to support conclusions made by DOE and to visualize the contribution of factors in individual cathode configurations.

Factor analysis of Wechsler Adult Intelligence Scale-Revised in developmentally disabled persons.

Science.gov (United States)

Di Nuovo, Santo F; Buono, Serafino

2006-12-01

The results of previous studies on the factorial structure of Wechsler Intelligence Scales are somewhat inconsistent across normal and pathological samples. To study specific clinical groups, such as developmentally disabled persons, it is useful to examine the factor structure in appropriate samples. A factor analysis was carried out using the principal component method and the Varimax orthogonal rotation on the Wechsler Adult Intelligence Scale (WAIS-R) in a sample of 203 developmentally disabled persons, with a mean age of 25 years 4 months. Developmental disability ranged from mild to moderate. Partially contrasting with previous studies on normal samples, results found a two-factor solution. Wechsler's traditional Verbal and Performance scales seems to be more appropriate for this sample than the alternative three-factor solution.

Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

International Nuclear Information System (INIS)

Lamboni, Matieyendou; Monod, Herve; Makowski, David

2011-01-01

Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006 ) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.

Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models

Energy Technology Data Exchange (ETDEWEB)

Lamboni, Matieyendou [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Monod, Herve, E-mail: herve.monod@jouy.inra.f [INRA, Unite MIA (UR341), F78352 Jouy en Josas Cedex (France); Makowski, David [INRA, UMR Agronomie INRA/AgroParisTech (UMR 211), BP 01, F78850 Thiverval-Grignon (France)

2011-04-15

Many dynamic models are used for risk assessment and decision support in ecology and crop science. Such models generate time-dependent model predictions, with time either discretised or continuous. Their global sensitivity analysis is usually applied separately on each time output, but Campbell et al. (2006) advocated global sensitivity analyses on the expansion of the dynamics in a well-chosen functional basis. This paper focuses on the particular case when principal components analysis is combined with analysis of variance. In addition to the indices associated with the principal components, generalised sensitivity indices are proposed to synthesize the influence of each parameter on the whole time series output. Index definitions are given when the uncertainty on the input factors is either discrete or continuous and when the dynamic model is either discrete or functional. A general estimation algorithm is proposed, based on classical methods of global sensitivity analysis. The method is applied to a dynamic wheat crop model with 13 uncertain parameters. Three methods of global sensitivity analysis are compared: the Sobol'-Saltelli method, the extended FAST method, and the fractional factorial design of resolution 6.

Principal coordinate analysis of genotype × environment interaction for grain yield of bread wheat in the semi-arid regions

Directory of Open Access Journals (Sweden)

Sabaghnia Naser

2013-01-01

Full Text Available Multi-environmental trials have significant main effects and significant multiplicative genotype × environment (GE interaction effect. Principal coordinate analysis (PCOA offers a more appropriate statistical analysis to deal with such situations, compared to traditional statistical methods. Eighteen bread wheat genotypes were grown in four semi-arid regions over three year seasons to study the GE interaction and yield stability and obtained data on grain yield were analyzed using PCOA. Combined analysis of variance indicated that all of the studied effects including the main effects of genotype and environments as well as the GE interaction were highly significant. According to grand means and total mean yield, test environments were grouped to two main groups as high mean yield (H and low mean yield (L. There were five H test environments and six L test environments which analyzed in the sequential cycles. For each cycle, both scatter point diagram and minimum spanning tree plot were drawn. The identified most stable genotypes with dynamic stability concept and based on the minimum spanning tree plots and centroid distances were G1 (3310.2 kg ha-1 and G5 (3065.6 kg ha-1, and therefore could be recommended for unfavorable or poor conditions. Also, genotypes G7 (3047.2 kg ha-1 and G16 (3132.3 kg ha-1 were located several times in the vertex positions of high cycles according to the principal coordinates analysis. The principal coordinates analysis provided useful and interesting ways of investigating GE interaction of barley genotypes. Finally, the results of principal coordinates analysis in general confirmed the breeding value of the genotypes, obtained on the basis of the yield stability evaluation.

Principal component analysis to assess the composition and fate of impurities in a large river-embedded reservoir: Qingcaosha Reservoir.

Science.gov (United States)

Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun

2013-08-01

Qingcaosha Reservoir (QR) is the largest river-embedded reservoir in east China, which receives its source water from the Yangtze River (YR). The temporal and spatial variations in dissolved organic matter (DOM), chromophoric DOM (CDOM), nitrogen, phosphorus and phytoplankton biomass were investigated from June to September in 2012 and were integrated by principal component analysis (PCA). Three PCA factors were identified: (1) phytoplankton related factor 1, (2) total DOM related factor 2, and (3) eutrophication related factor 3. Factor 1 was a lake-type parameter which correlated with chlorophyll-a and protein-like CDOM (r = 0.793 and r = 0.831, respectively). Factor 2 was a river-type parameter which correlated with total DOC and humic-like CDOM (r = 0.668 and r = 0.726, respectively). Factor 3 correlated with total nitrogen and phosphorus (r = 0.864 and r = 0.621, respectively). The low flow speed, self-sedimentation and nutrient accumulation in QR resulted in increases in PCA factor 1 scores (phytoplankton biomass and derived CDOM) in the spatial scale, indicating a change of river-type water (YR) to lake-type water (QR). In summer, the water temperature variation induced a growth-bloom-decay process of phytoplankton combined with the increase of PCA factor 2 (humic-like CDOM) in the QR, which was absent in the YR.

The Factor Structure in Equity Options

DEFF Research Database (Denmark)

Christoffersen, Peter; Fournier, Mathieu; Jacobs, Kris

Principal component analysis of equity options on Dow-Jones firms reveals a strong factor structure. The first principal component explains 77% of the variation in the equity volatility level, 77% of the variation in the equity option skew, and 60% of the implied volatility term structure across...... equities. Furthermore, the first principal component has a 92% correlation with S&P500 index option volatility, a 64% correlation with the index option skew, and a 80% correlation with the index option term structure. We develop an equity option valuation model that captures this factor structure...

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…

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

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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.

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.

[Priorization of facilitators for the implementation of medication review with follow-up service in Spanish community pharmacies through exploratory factor analysis].

Science.gov (United States)

Gil, Modesta Inmaculada; Benrimoj, Shalom Isaac; Martínez-Martínez, Fernando; Cardero, Manuel; Gastelurrutia, Miguel Ángel

2013-01-01

to prioritize previously identified in Spain facilitators for the implementation of new Pharmaceutical Services that allow designing strategies for the implementation of Medication Review with follow-up (MRFup) service. Exploratory factor analysis (EFA). A draft of a questionnaire was performed based on a previous literature review and following the RAND/UCLA methodology. An expert panel worked with it and generated a definitive questionnaire which, after piloting, was used with a representative sample of pharmacists, owners or staff members, who were working in community pharmacy, using computer-assisted telephone interviewing (CATI) methodology. To understand underlying constructs in the questionnaire an EFA was performed. Different approaches were tested such as principal components factor analysis and principal axis factoring method. The best interpretability was achieved using the Factorization of Principal axis method with Direct Oblimin rotation, which explained the 40.0% of total variance. This produced four factors defined as: «Incentives», «External campaigns», «Expert in MRFup» and «Professionalism of the pharmacist». It can be stated that for implementation and sustainability of MRFup Service it is necessary being paid; also it must be explained to health professional and society in general. Practice of MRFup service demands pharmacists receiving a more clinical education and assuming more responsibilities as health professionals. Copyright © 2012 Elsevier España, S.L. All rights reserved.

Factor analysis of the Hamilton Depression Rating Scale in Parkinson's disease.

Science.gov (United States)

Broen, M P G; Moonen, A J H; Kuijf, M L; Dujardin, K; Marsh, L; Richard, I H; Starkstein, S E; Martinez-Martin, P; Leentjens, A F G

2015-02-01

Several studies have validated the Hamilton Depression Rating Scale (HAMD) in patients with Parkinson's disease (PD), and reported adequate reliability and construct validity. However, the factorial validity of the HAMD has not yet been investigated. The aim of our analysis was to explore the factor structure of the HAMD in a large sample of PD patients. A principal component analysis of the 17-item HAMD was performed on data of 341 PD patients, available from a previous cross sectional study on anxiety. An eigenvalue ≥1 was used to determine the number of factors. Factor loadings ≥0.4 in combination with oblique rotations were used to identify which variables made up the factors. Kaiser-Meyer-Olkin measure (KMO), Cronbach's alpha, Bartlett's test, communality, percentage of non-redundant residuals and the component correlation matrix were computed to assess factor validity. KMO verified the sample's adequacy for factor analysis and Cronbach's alpha indicated a good internal consistency of the total scale. Six factors had eigenvalues ≥1 and together explained 59.19% of the variance. The number of items per factor varied from 1 to 6. Inter-item correlations within each component were low. There was a high percentage of non-redundant residuals and low communality. This analysis demonstrates that the factorial validity of the HAMD in PD is unsatisfactory. This implies that the scale is not appropriate for studying specific symptom domains of depression based on factorial structure in a PD population. Copyright © 2014 Elsevier Ltd. All rights reserved.

Factor Analysis of the Spanish Version of the WAIS: The Escala de Inteligencia Wechsler para Adultos (EIWA).

Science.gov (United States)

Gomez, Francisco C., Jr.; And Others

1992-01-01

The standardization of the Escala de Inteligencia Wechsler para Adultos (EIWA) and the original Wechsler Adult Intelligence Scale (WAIS) were subjected to principal components analysis to examine their comparability for 616 EIWA subjects and 800 WAIS subjects. Similarity of factor structures of both scales is supported. (SLD)

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.

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

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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

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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

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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.

Factor analysis of financial and operational performance measures of non-profit hospitals.

Science.gov (United States)

Das, Dhiman

2009-01-01

To understand the important dimensions of the financial and operational performance of non-profit hospitals. Secondary data for non-profit US hospitals between 1996 and 2004. I use iterative principal factor analysis of hospitals' financial and operational ratios for each year of the study. For factor interpretation, I use oblique rotation. Financial ratios were created using cost report data from HCRIS 2552-96 available from the Centers for Medicaid & Medicare Services (CMS). I identify five factors--capital structure, profitability, activity, liquidity, and an operational factor--that explain most of the variation in the performance of non-profit hospitals. I also find that capital structure is more important than profitability in determining the performance of these hospitals. The importance of capital structure highlights a significant shift in the organization of the non-profit hospitals' finances.

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 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.

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

Information technology portfolio in supply chain management using factor analysis

Directory of Open Access Journals (Sweden)

Ahmad Jaafarnejad

2013-11-01

Full Text Available The adoption of information technology (IT along with supply chain management (SCM has become increasingly a necessity among most businesses. This enhances supply chain (SC performance and helps companies achieve the organizational competitiveness. IT systems capture and analyze information and enable management to make decisions by considering a global scope across the entire SC. This paper reviews the existing literature on IT in SCM and considers pertinent criteria. Using principal component analysis (PCA of factor analysis (FA, a number of related criteria are divided into smaller groups. Finally, SC managers can develop an IT portfolio in SCM using mean values of few extracted components on the relevance –emergency matrix. A numerical example is provided to explain details of the proposed method.

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....

Use of principal components analysis (PCA) on estuarine sediment datasets: The effect of data pre-treatment

International Nuclear Information System (INIS)

Reid, M.K.; Spencer, K.L.

2009-01-01

Principal components analysis (PCA) is a multivariate statistical technique capable of discerning patterns in large environmental datasets. Although widely used, there is disparity in the literature with respect to data pre-treatment prior to PCA. This research examines the influence of commonly reported data pre-treatment methods on PCA outputs, and hence data interpretation, using a typical environmental dataset comprising sediment geochemical data from an estuary in SE England. This study demonstrated that applying the routinely used log (x + 1) transformation skewed the data and masked important trends. Removing outlying samples and correcting for the influence of grain size had the most significant effect on PCA outputs and data interpretation. Reducing the influence of grain size using granulometric normalisation meant that other factors affecting metal variability, including mineralogy, anthropogenic sources and distance along the salinity transect could be identified and interpreted more clearly. - Data pre-treatment can have a significant influence on the outcome of PCA.

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.

Tracking polychlorinated biphenyls (PCBs) congener patterns in Newark Bay surface sediment using principal component analysis (PCA) and positive matrix factorization (PMF).

Science.gov (United States)

Saba, Tarek; Su, Steave

2013-09-15

PCB congener data for Newark Bay surface sediments were analyzed using PCA and PMF, and relationships between the outcomes from these two techniques were explored. The PCA scores plot separated the Lower Passaic River Mouth samples from North Newark Bay, thus indicating dissimilarity. Although PCA was able to identify subareas in the Bay system with specific PCB congener patterns (e.g., higher chlorinated congeners in Elizabeth River), further conclusions reading potential PCB source profiles or potential upland source areas were not clear for the PCA scores plot. PMF identified five source factors, and explained the Bay sample congener profiles as a mix of these Factors. This PMF solution was equivalent to (1) defining an envelope that encompasses all samples on the PCA scores plot, (2) defining source factors that plot on that envelope, and (3) explaining the congener profile for each Bay sediment sample (inside the scores plot envelope) as a mix of factors. PMF analysis allowed identifying characteristic features in the source factor congener distributions that allowed tracking of source factors to shoreline areas where PCB inputs to the Bay may have originated. The combined analysis from PCA and PMF showed that direct discharges to the Bay are likely the dominant sources of PCBs to the sediment. Review of historical upland activities and regulatory files will be needed, in addition to the PCA and PMF analysis, to fully reconstruct the history of operations and PCB releases around the Newark Bay area that impacted the Bay sediment. Copyright © 2013 Elsevier B.V. All rights reserved.

High School Principals as Leaders: Styles and Sources of Power

Science.gov (United States)

Brinia, Vasiliki; Papantoniou, Eva

2016-01-01

Purpose: The purpose of this paper is to present the characteristics of leadership (style adopted, sources of power exercised and factors affecting leadership) of high school principals in Greece. Design/Methodology/Approach: In total, 235 school principals were surveyed using questionnaires. These questionnaires assessed how often they adopted…

Biogen water of the Irtysh river - the principal population health factor suffered from the nuclear test on the Semipalatinsk test site

International Nuclear Information System (INIS)

Inyushin, V.M.; Yurenkov, V.V.

2001-01-01

Authors notes, that Semipalatinsk test site activity together with other factors changes a water quality. The principal consequence of nuclear tests is build up in new cells by the 'pathogenous' water in plants, animals and human . 'Pathogenous water' is generating at steady changes of electronic strictures (spin performances) at ionizing radiation effect, radionuclides and other factors of nuclear explosions. These factors were did not known to a world-wide science. The second of very important phenomenon of nuclear explosions consequences at the Semipalatinsk test site is reduce of the hydro-plasma in the water sources the Irtysh River basin. This was proved with direct studies microcurrents density in the water as well as analysis of electronic and ionic structures with the plasma-graphic help. The water having the pathogenous memory - building up in the cells - reduces the immune status, negatively reflects on the vital functions, decreasing the resistance to unfavorable effects. It is offered for remediation of the 'pathogenous water' to use the bio-genized water as most adequate one to endogenous water of living cells. Mass usage of the bio-genized water takes out genetic consequences of nuclear explosions and increase of an immunity level. The 'Bio-genization' of the Irtysh River water is concludes a few stages

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

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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.

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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.

The principal component analysis method used with polynomial Chaos expansion to propagate uncertainties through critical transport problems

Energy Technology Data Exchange (ETDEWEB)

Rising, M. E.; Prinja, A. K. [Univ. of New Mexico, Dept. of Chemical and Nuclear Engineering, Albuquerque, NM 87131 (United States)

2012-07-01

A critical neutron transport problem with random material properties is introduced. The total cross section and the average neutron multiplicity are assumed to be uncertain, characterized by the mean and variance with a log-normal distribution. The average neutron multiplicity and the total cross section are assumed to be uncorrected and the material properties for differing materials are also assumed to be uncorrected. The principal component analysis method is used to decompose the covariance matrix into eigenvalues and eigenvectors and then 'realizations' of the material properties can be computed. A simple Monte Carlo brute force sampling of the decomposed covariance matrix is employed to obtain a benchmark result for each test problem. In order to save computational time and to characterize the moments and probability density function of the multiplication factor the polynomial chaos expansion method is employed along with the stochastic collocation method. A Gauss-Hermite quadrature set is convolved into a multidimensional tensor product quadrature set and is successfully used to compute the polynomial chaos expansion coefficients of the multiplication factor. Finally, for a particular critical fuel pin assembly the appropriate number of random variables and polynomial expansion order are investigated. (authors)

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.

Estimation of Leakage Ratio Using Principal Component Analysis and Artificial Neural Network in Water Distribution Systems

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Dongwoo Jang

2018-03-01

Full Text Available Leaks in a water distribution network (WDS constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA and artificial neural network (ANN were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

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.

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.

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

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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.

Meta-analysis Number of Plants Drugs Used by Characteristics Socioeconomic Factors, Environmental and Geographic

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Febiola Diah Pratiwi

2017-09-01

Full Text Available Ethnobotany is the study of public relations with the use of plants. Use of plants by people influenced by several factors, such as social, cultural, socioeconomic, and geographic. Most of the ethnicities in Indonesia has a high dependence on plants medicine for survival. However, the factors that influence the use of medicinal plants by people in Indonesia have not been studied, so that research is needed to optimize the use of medicinal plants to sustainability benefits. The purpose of this study is to analyze the number of species of plants medicine used by the influence of socio-economic, environmental, and geographic factors using principal component analysis and analyzing patterns of use of plants medicine. The results showed that the economy and infrastructure components (access to electricity, means of education, income level, health facilities, distance from the highway, remoteness, and the fastest time toward the road and the number of people graduating from elementary school affect the number of medicinal plant species used. Based on the results of the study of literature and field observations, the pattern of use of plants medicine in addition to be used as medicine, the plant is used for food, building materials, plant ornamental, ceremonial, wood, wicker and crafts, coloring agents, animal feed, ingredients aromatic, and pesticide. The usage patterns in each region or village has the distinction of which is influenced by the remoteness factor due to the differences in the social, economic, environmental, and geographic.  Keywords: ethnobotany, plants medicine, principal component analysis

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

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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.

Principals' Leadership Styles and Student Achievement

Science.gov (United States)

Harnish, David Alan

2012-01-01

Many schools struggle to meet No Child Left Behind's stringent adequate yearly progress standards, although the benchmark has stimulated national creativity and reform. The purpose of this study was to explore teacher perceptions of principals' leadership styles, curriculum reform, and student achievement to ascertain possible factors to improve…

Anomalous decrease in X-ray diffraction intensities of Cu-Ni-Al-Co-Cr-Fe-Si alloy systems with multi-principal elements

International Nuclear Information System (INIS)

Yeh, J.-W.; Chang, S.-Y.; Hong, Y.-D.; Chen, S.-K.; Lin, S.-J.

2007-01-01

With an aim to understand the great reduction in the X-ray diffraction (XRD) intensities of high-entropy alloys, a series of Cu-Ni-Al-Co-Cr-Fe-Si alloys with systematic addition of principal elements from pure element to seven elements was investigated for quantitative analysis of XRD intensities. The variation of XRD peak intensities of the alloy system is similar to that caused by thermal effect, but the intensities further drop beyond the thermal effect with increasing number of incorporated principal elements. An intrinsic lattice distortion effect caused by the addition of multi-principal elements with different atomic sizes is expected for the anomalous decrease in XRD intensities. The mathematical factor of this distortion effect for the modification of XRD structure factor is formulated analogue to that of thermal effect

School Principals' Perceptions of Ethically Just Responses to a Teacher Sexting Vignette: Severity of Administrator Response, Principals' Personality, and Offender Gender and Race

Science.gov (United States)

Wagner, Kenneth

2012-01-01

Site level administrators make decisions during the course of a day that have ethical dimensions that challenge their personal values and ethics. This study examined the extent to which particular factors would affect principals' and vice principals' judgments of the ethicality of sanctions given a teacher who had been sending sexually…

Instructional, Transformational, and Managerial Leadership and Student Achievement: High School Principals Make a Difference

Science.gov (United States)

Valentine, Jerry W.; Prater, Mike

2011-01-01

This statewide study examined the relationships between principal managerial, instructional, and transformational leadership and student achievement in public high schools. Differences in student achievement were found when schools were grouped according to principal leadership factors. Principal leadership behaviors promoting instructional and…

The Audit of Principal Effectiveness: Instrumentation for Principalship Research. A Research Project Report.

Science.gov (United States)

Valentine, Jerry W.; Bowman, Michael L.

Using the literature and research on principal effectiveness as a foundation, the Audit of Principal Effectiveness was developed. Initially, 162 items forming 12 theoretical factors describing effective principal behavior were identified and sorted into two documents. The documents, each containing 81 items, were mailed to a total of 3,660…

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.

Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

Science.gov (United States)

Gutha, Rajasekar; Yarrappagaari, Suresh; Thopireddy, Lavanya; Reddy, Kesireddy Sathyavelu; Saddala, Rajeswara Reddy

2018-03-01

In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present study is aimed at identifying the effect of abiotic and biotic stress factors, and it is found that several stress responsive genes are common for both abiotic and biotic stress factors in zebrafish. The meta-analysis of micro-array studies revealed that almost 4.7% i.e., 108 common DEGs are differentially regulated between abiotic and biotic stresses. This shows that there is a global coordination and fine-tuning of gene regulation in response to these two types of challenges. We also performed dimension reduction methods, principal component analysis, and partial least squares discriminant analysis which are able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of DEGs. Beside these methods, the random forests decision tree model classified five out of 8 stress conditions with high accuracy. Finally, Functional enrichment analysis revealed the different gene ontology terms, transcription factors and miRNAs factors in the regulation of stress responses. Copyright © 2017 Elsevier Inc. All rights reserved.

Principals' Transformational Leadership in School Improvement

Science.gov (United States)

Yang, Yingxiu

2013-01-01

Purpose: This paper aims to contribute experience and ideas of the transformational leadership, not only for the principal want to improve leadership himself (herself), but also for the school at critical period of improvement, through summarizing forming process and the problem during the course and key factors that affect the course.…

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

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...

Principal coordinate analysis assisted chromatographic analysis of bacterial cell wall collection: A robust classification approach.

Science.gov (United States)

Kumar, Keshav; Cava, Felipe

2018-04-10

In the present work, Principal coordinate analysis (PCoA) is introduced to develop a robust model to classify the chromatographic data sets of peptidoglycan sample. PcoA captures the heterogeneity present in the data sets by using the dissimilarity matrix as input. Thus, in principle, it can even capture the subtle differences in the bacterial peptidoglycan composition and can provide a more robust and fast approach for classifying the bacterial collection and identifying the novel cell wall targets for further biological and clinical studies. The utility of the proposed approach is successfully demonstrated by analysing the two different kind of bacterial collections. The first set comprised of peptidoglycan sample belonging to different subclasses of Alphaproteobacteria. Whereas, the second set that is relatively more intricate for the chemometric analysis consist of different wild type Vibrio Cholerae and its mutants having subtle differences in their peptidoglycan composition. The present work clearly proposes a useful approach that can classify the chromatographic data sets of chromatographic peptidoglycan samples having subtle differences. Furthermore, present work clearly suggest that PCoA can be a method of choice in any data analysis workflow. Copyright © 2018 Elsevier Inc. All rights reserved.

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

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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

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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.

Factor analysis of the Mayo-Portland Adaptability Inventory: structure and validity.

Science.gov (United States)

Bohac, D L; Malec, J F; Moessner, A M

1997-07-01

Principal-components (PC) factor analysis of the Mayo-Portland Adaptability Inventory (MPAI) was conducted using a sample of outpatients (n = 189) with acquired brain injury (ABI) to evaluate whether outcome after ABI is multifactorial or unifactorial in nature. An eight-factor model was derived which explained 64-4% of the total variance. The eight factors were interpreted as representing Activities of Daily Living, Social Initiation, Cognition, Impaired-Self-awareness/Distress, Social Skills/ Support, Independence, Visuoperceptual, and Psychiatric, respectively. Validation of the Cognition factor was supported when factor scores were correlated with various neuropsychological measures. In addition, 117 patient self-rating total scores were used to evaluate the Impaired Self-awareness/Distress factor. An inverse relationship was observed, supporting this factor's ability to capture the two-dimensional phenomena of diminished self-awareness or enhanced emotional distress. A new subscale structure is suggested, that may allow greater clinical utility in understanding how ABI manifests in patients, and may provide clinicians with a better structure for implementing treatment strategies to address specific areas of impairment and disability for specific patients. Additionally, more precise measurement of treatment outcomes may be afforded by this reorganization.

Analysis of factors that influencing the interest of Bali State Polytechnic’s students in entrepreneurship

Science.gov (United States)

Ayuni, N. W. D.; Sari, I. G. A. M. K. K.

2018-01-01

The high rate of unemployment results the economic growth to be hampered. To solve this situation, the government try to change the students’ mindset from becoming a job seeker to become a job creator or entrepreneur. One real action that usually been held in Bali State Polytechnic is Student Entrepreneurial Program. The purpose of this research is to identify and analyze the factors that influence the interest of Bali State Polytechnic’s Students in entrepreneurship, especially in the Entrepreneurial Student Program. Method used in this research is Factor Analysis including Bartlett Test, Kaiser-Mayer Olkin (KMO), Measure of Sampling Adequacy (MSA), factor extraction using Principal Component Analysis (PCA), factor selection using eigen value and scree plot, and factor rotation using orthogonal rotation varimax. Result shows that there are four factors that influencing the interest of Bali State Polytechnic’s Students in Entrepreneurship which are Contextual Factor (including Entrepreneurship Training, Academic Support, Perceived Confidence, and Economic Challenge), Self Efficacy Factor (including Leadership, Mental Maturity, Relation with Entrepreneur, and Authority), Subjective Norm Factor (including Support of Important Relative, Support of Friends, and Family Role), and Attitude Factor (including Self Realization).

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.

Climate change adaptation: Uncovering constraints to the use of adaptation strategies among food crop farmers in South-west, Nigeria using principal component analysis (PCA

Directory of Open Access Journals (Sweden)

Moradeyo Adebanjo Otitoju

2016-12-01

Full Text Available This study focused on the constraints to the use of climate variability/change adaptation strategies in South-west Nigeria. Multistage random technique was employed to select the location and the respondents. Descriptive statistics and principal component analysis (PCA were the analytical tools engaged in this study. The constraints to climate variability and change examined before did not use PCA but generalized factor analysis. Hence, there is need to examine these constraints extensively using PCA. Uncovering the constraints to the use of climate variability/change adaptation strategies among crop framers is important to give a realistic direction in the development of farmer-inclusive climate policies in Nigeria. The PCA result showed that the principal constraints that the farmers faced in climate change adaptation were public, institutional and labour constraint; land, neighbourhood norms and religious beliefs constraint; high cost of inputs, technological and information constraint; farm distance, access to climate information, off-farm job and credit constraint; and poor agricultural programmes and service delivery constraint. These findings pointed out the need for both the government and non-government organizations to intensify efforts on institutional, technological and farmers’ friendly land tenure and information systems as effective measures to guide inclusive climate change adaptation policies and development in South-west Nigeria.

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.

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.

Foundations of factor analysis

CERN Document Server

Mulaik, Stanley A

2009-01-01

Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Model Example of Factor AnalysisMathematical Foundations for Factor Analysis Introduction Scalar AlgebraVectorsMatrix AlgebraDeterminants Treatment of Variables as Vectors Maxima and Minima of FunctionsComposite Variables and Linear Transformations Introduction Composite Variables Unweighted Composite VariablesDifferentially Weighted Composites Matrix EquationsMulti

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.

High School Principals Who Stay: Stability in a Time of Change

Science.gov (United States)

Luebke, Patricia A.

2013-01-01

This qualitative study explored the institutional factors, personal characteristics, and work-related relationships of high school principals that led to their longer than usual tenure in their positions. Data were gathered from interviews with ten high school principals who had served in their positions for a range of 8 to 23 years, much longer…

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)

Contact- and distance-based principal component analysis of protein dynamics

Energy Technology Data Exchange (ETDEWEB)

Ernst, Matthias; Sittel, Florian; Stock, Gerhard, E-mail: stock@physik.uni-freiburg.de [Biomolecular Dynamics, Institute of Physics, Albert Ludwigs University, 79104 Freiburg (Germany)

2015-12-28

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

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 ...

Motivational factors influencing the homeowners’ decisions between residential heating systems: An empirical analysis for Germany

International Nuclear Information System (INIS)

Michelsen, Carl Christian; Madlener, Reinhard

2013-01-01

Heating demand accounts for a large fraction of the overall energy demand of private households in Germany. A better understanding of the adoption and diffusion of energy-efficient and renewables-based residential heating systems (RHS) is of high policy relevance, particularly against the background of climate change, security of energy supply and increasing energy prices. In this paper, we explore the multi-dimensionality of the homeowners’ motivation to decide between competing RHS. A questionnaire survey (N=2440) conducted in 2010 among homeowners who had recently installed a RHS provides the empirical foundation. Principal component analysis shows that 25 items capturing different adoption motivations can be grouped around six dimensions: (1) cost aspects, (2) general attitude towards the RHS, (3) government grant, (4) reactions to external threats (i.e., environmental or energy supply security considerations), (5) comfort considerations, and (6) influence of peers. Moreover, a cluster analysis with the identified motivational factors as segmentation variables reveals three adopter types: (1) the convenience-oriented, (2) the consequences-aware, and (3) the multilaterally-motivated RHS adopter. Finally, we show that the influence of the motivational factors on the adoption decision also differs by certain characteristics of the homeowner and features of the home. - Highlights: ► Study of the multi-dimensionality of the motivation to adopt residential heating systems (RHS). ► Principal component and cluster analysis are applied to representative survey data for Germany. ► Motivation has six dimensions, including rational decision-making and emotional factors. ► Adoption motivation differs by certain characteristics of the homeowner and of the home. ► Many adopters are driven by existing habits and perceptions about the convenience of the RHS

Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

Science.gov (United States)

Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

2018-02-12

Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

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.

[Habitat factor analysis for Torreya grandis cv. Merrillii based on spatial information technology].

Science.gov (United States)

Wang, Xiao-ming; Wang, Ke; Ao, Wei-jiu; Deng, Jin-song; Han, Ning; Zhu, Xiao-yun

2008-11-01

Torreya grandis cv. Merrillii, a tertiary survival plant, is a rare tree species of significant economic value and expands rapidly in China. Its special habitat factor analysis has the potential value to provide guide information for its planting, management, and sustainable development, because the suitable growth conditions for this tree species are special and strict. In this paper, the special habitat factors for T. grandis cv. Merrillii in its core region, i.e., in seven villages of Zhuji City, Zhejiang Province were analyzed with Principal Component Analysis (PCA) and a series of data, such as IKONOS image, Digital Elevation Model (DEM), and field survey data supported by the spatial information technology. The results showed that T. grandis cv. Merrillii exhibited high selectivity of environmental factors such as elevation, slope, and aspect. 96.22% of T. grandis cv. Merrillii trees were located at the elevation from 300 to 600 m, 97.52% of them were found to present on the areas whose slope was less than 300, and 74.43% of them distributed on sunny and half-sunny slopes. The results of PCA analysis indicated that the main environmental factors affecting the habitat of T. grandis cv. Merrillii were moisture, heat, and soil nutrients, and moisture might be one of the most important ecological factors for T. grandis cv. Merrillii due to the unique biological and ecological characteristics of the tree species.

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.

On the Factor Structure of a Reading Comprehension Test

Science.gov (United States)

Salehi, Mohammad

2011-01-01

To investigate the construct validly of a section of a high stakes test, an exploratory factor analysis using principal components analysis was employed. The rotation used was varimax with the suppression level of 0.30. Eleven factors were extracted out of 35 reading comprehension items. The fact that these factors emerged speak to the construct…

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...

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...

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.

Factor analysis and Mokken scaling of the Organizational Commitment Questionnaire in nurses.

Science.gov (United States)

Al-Yami, M; Galdas, P; Watson, R

2018-03-22

To generate an Arabic version of the Organizational Commitment Questionnaire that would be easily understood by Arabic speakers and would be sensitive to Arabic culture. The nursing workforce in Saudi Arabia is undergoing a process of Saudization but there is a need to understand the factors that will help to retain this workforce. No organizational commitment tools exist in Arabic that are specifically designed for health organizations. An Arabic version of the organizational commitment tool could aid Arabic speaking employers to understand their employees' perceptions of their organizations. Translation and back-translation followed by factor analysis (principal components analysis and confirmatory factor analysis) to test the factorial validity and item response theory (Mokken scaling). A two-factor structure was obtained for the Organizational Commitment Questionnaire comprising Factor 1: Value commitment; and Factor 2: Commitment to stay with acceptable reliability measured by internal consistency. A Mokken scale was obtained including items from both factors showing a hierarchy of items running from commitment to the organization and commitment to self. This study shows that the Arabic version of the OCQ retained the established two-factor structure of the original English-language version. Although the two factors - 'value commitment' and 'commitment to stay' - repudiate the original developers' single factor claim. A useful insight into the structure of the Organizational Commitment Questionnaire has been obtained with the novel addition of a hierarchical scale. The Organizational Commitment Questionnaire is now ready to be used with nurses in the Arab speaking world and could be used a tool to measure the contemporary commitment of nursing employees and in future interventions aimed at increasing commitment and retention of valuable nursing staff. © 2018 International Council of Nurses.

The principal radionuclides in high level radioactive waste management

International Nuclear Information System (INIS)

Mulyanto

1998-01-01

The principal radionuclides in high level radioactive waste management. The selection of the principal radionuclides in the high level waste (HLW) management was developed in order to improve the disposal scenario of HLW. In this study the unified criteria for selection of the principal radionuclides were proposed as; (1) the value of hazard index estimated by annual limit of intake (ALI) for long-term tendency,(2) the relative dose factor related to adsorbed migration rate transferred by ground water, and (3) heat generation in the repository. From this study it can be concluded that the principal radionuclides in the HLW management were minor actinide (MA=Np, Am, Cm, etc), Tc, I, Cs and Sr, based on the unified basic criteria introduced in this study. The remaining short-lived fission product (SLFPs), after the selected nuclides are removed, should be immobilized and solidified in a glass matrix. Potential risk due to the remaining SLFPs can be lower than that of uranium ore after about 300 year. (author)

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

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.

Road transport-related energy consumption: Analysis of driving factors in Tunisia

International Nuclear Information System (INIS)

Mraihi, Rafaa; Abdallah, Khaled ben; Abid, Mehdi

2013-01-01

The rapid growth of urban population and the development of road infrastructures in Tunisian cities have brought about many environmental and economic problems, including the rise scored in energy consumption and the increase in the quantity of gas emissions arising from road transport. Despite the critical nature of such problems, no policies have yet been adopted to improve energy efficiency in the transport sector. This paper aims to determine driving factors of energy consumption change for the road mode. It uses decomposition analysis to discuss the effects of economic, demographic and urban factors on the evolution of transport energy consumption. The main result highlighted in the present work is that vehicle fuel intensity, vehicle intensity, GDP per capita, urbanized kilometers and national road network are found to be the main drivers of energy consumption change in the road transport sector during 1990–2006 period. Consequently, several strategies can be elaborated to reduce road transport energy. Economic, fiscal and regulatory instruments can be applied in order to make road transport more sustainable. -- Highlights: •We are interested in determining driving factors of transport energy consumption growth in Tunisia. •We use decomposition analysis approach. •Vehicle fuel and road vehicle intensities are found to be principal factors. •Motorization and urbanization are also found to be responsible

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.

Análisis del fracaso empresarial por sectores: factores diferenciadores = Cross-industry analysis of business failure: differential factors

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María Jesús Mures Quintana

2012-12-01

Full Text Available El objetivo de este trabajo se centra en el análisis del fracaso empresarial por sectores, a fin de identificar los factores explicativos y predictivos de este fenómeno que son diferentes en tres de los principales sectores que se distinguen en toda economía: industria, construcción y servicios. Para cada uno de estos sectores, seguimos el mismo procedimiento. En primer lugar, aplicamos un análisis de componentes principales con el que identificamos los factores explicativos del fracaso empresarial en los tres sectores. A continuación, consideramos dichos factores como variables independientes en un análisis discriminante, que aplicamos para predecir el fracaso de una muestra de empresas, utilizando no sólo información financiera en forma de ratios, sino también otras variables no financieras relativas a las empresas, así como información externa a las mismas que refleja las condiciones macroeconómicas bajo las que desarrollan su actividad. This paper focuses on a cross-industry analysis of business failure, in order to identify the explanatory and predictor factors of this event that are different in three of the main industries in every economy: manufacturing, building and service. For each one of these industries, the same procedure is followed. First, a principal components analysis is applied in order to identify the explanatory factors of business failure in the three industries. Next, these factors are considered as independent variables in a discriminant analysis, so as to predict the firms’ failure, using not only financial information expressed by ratios, but also other non-financial variables related to the firms, as well as external information that reflects macroeconomic conditions under which they develop their activity.

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.

A novel framework of ERP implementation in Indian SMEs: Kernel principal component analysis and intuitionistic Fuzzy TOPSIS driven approach

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Indranil Ghosh

2016-04-01

Full Text Available Over the years, organizations have witnessed a transformational change at global market place. Integration of operations and partnership have become the key success factors for organizations. In order to achieve inclusive growth while operating in a dynamic uncertain environment, organizations irrespective of the scale of business need to stay connected across the entire value chain. The purpose of this paper is to analyze Enterprise Resource Planning (ERP implementation process for Small and Medium Enterprises (SMEs in India to identify the key enablers. Exhaustive survey of existing literature as a part of secondary research work, has been conducted in order to identify the critical success factors and usefulness of ERP implementation in different industrial sectors initially and examines the impact of those factors in Indian SMEs. Kernel Principal Component Analysis (KPCA has been applied on survey response to recognize the key constructs related to Critical Success Factors (CSFs and tangible benefits of ERP implementation. Intuitionistic Fuzzy set theory based Technique of Order Preference by Similarity to Ideal Solution (TOPSIS method is then used to rank the respective CSFs by mapping their contribution to the benefits realized through implementing ERP. Overall this work attempts to present a guideline for ERP adoption process in the said sector utilizing the framework built upon KPCA and Intuitionistic Fuzzy TOPSIS. Findings of this work can act as guidelines for monitoring the entire ERP implementation project.

Optimal pattern synthesis for speech recognition based on principal component analysis

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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)

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

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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.

Real-time dynamic MR image reconstruction using compressed sensing and principal component analysis (CS-PCA): Demonstration in lung tumor tracking.

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Dietz, Bryson; Yip, Eugene; Yun, Jihyun; Fallone, B Gino; Wachowicz, Keith

2017-08-01

This work presents a real-time dynamic image reconstruction technique, which combines compressed sensing and principal component analysis (CS-PCA), to achieve real-time adaptive radiotherapy with the use of a linac-magnetic resonance imaging system. Six retrospective fully sampled dynamic data sets of patients diagnosed with non-small-cell lung cancer were used to investigate the CS-PCA algorithm. Using a database of fully sampled k-space, principal components (PC's) were calculated to aid in the reconstruction of undersampled images. Missing k-space data were calculated by projecting the current undersampled k-space data onto the PC's to generate the corresponding PC weights. The weighted PC's were summed together, and the missing k-space was iteratively updated. To gain insight into how the reconstruction might proceed at lower fields, 6× noise was added to the 3T data to investigate how the algorithm handles noisy data. Acceleration factors ranging from 2 to 10× were investigated using CS-PCA and Split Bregman CS for comparison. Metrics to determine the reconstruction quality included the normalized mean square error (NMSE), as well as the dice coefficients (DC) and centroid displacement of the tumor segmentations. Our results demonstrate that CS-PCA performed superior than CS alone. The CS-PCA patient averaged DC for 3T and 6× noise added data remained above 0.9 for acceleration factors up to 10×. The patient averaged NMSE gradually increased with increasing acceleration; however, it remained below 0.06 up to an acceleration factor of 10× for both 3T and 6× noise added data. The CS-PCA reconstruction speed ranged from 5 to 20 ms (Intel i7-4710HQ CPU @ 2.5 GHz), depending on the chosen parameters. A real-time reconstruction technique was developed for adaptive radiotherapy using a Linac-MRI system. Our CS-PCA algorithm can achieve tumor contours with DC greater than 0.9 and NMSE less than 0.06 at acceleration factors of up to, and including, 10×. The

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

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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.

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

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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

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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,…

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

Science.gov (United States)

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

2014-01-01

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

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

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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.

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…

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.…

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…

Factors Affecting Turkish Students' Achievement in Mathematics

Science.gov (United States)

Demir, Ibrahim; Kilic, Serpil; Depren, Ozer

2009-01-01

Following past researches, student background, learning strategies, self-related cognitions in mathematics and school climate variables were important for achievement. The purpose of this study was to identify a number of factors that represent the relationship among sets of interrelated variables using principal component factor analysis and…

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

The Perfect Match: A Case Study of a First Year Woman Principal.

Science.gov (United States)

Duncan, P. Kay; Seguin, Cynthia Anast; Spaulding, Wendy

This paper presents a case study illustrating the experiences of a first-year elementary-school principal. It follows her through her 18 months on the job, and analyzes the factors contributing to her ouster. The data for the study were gathered through two interviews with the principal and interviews with five other persons in her school…

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

A Principal Components Analysis of the Rathus Assertiveness Schedule.

Science.gov (United States)

Law, H. G.; And Others

1979-01-01

Investigated the adequacy of the Rathus Assertiveness Schedule (RAS) as a global measure of assertiveness. Analysis indicated that the RAS does not provide a unidimensional index of assertiveness, but rather measures a number of factors including situation-specific assertive behavior, aggressiveness, and a more general assertiveness. (Author)

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...

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 component analysis to assess the efficiency and mechanism for enhanced coagulation of natural algae-laden water using a novel dual coagulant system.

Science.gov (United States)

Ou, Hua-Se; Wei, Chao-Hai; Deng, Yang; Gao, Nai-Yun; Ren, Yuan; Hu, Yun

2014-02-01

A novel dual coagulant system of polyaluminum chloride sulfate (PACS) and polydiallyldimethylammonium chloride (PDADMAC) was used to treat natural algae-laden water from Meiliang Gulf, Lake Taihu. PACS (Aln(OH)mCl3n-m-2k(SO4)k) has a mass ratio of 10 %, a SO4 (2-)/Al3 (+) mole ratio of 0.0664, and an OH/Al mole ratio of 2. The PDADMAC ([C8H16NCl]m) has a MW which ranges from 5 × 10(5) to 20 × 10(5) Da. The variations of contaminants in water samples during treatments were estimated in the form of principal component analysis (PCA) factor scores and conventional variables (turbidity, DOC, etc.). Parallel factor analysis determined four chromophoric dissolved organic matters (CDOM) components, and PCA identified four integrated principle factors. PCA factor 1 had significant correlations with chlorophyll-a (r=0.718), protein-like CDOM C1 (0.689), and C2 (0.756). Factor 2 correlated with UV254 (0.672), humic-like CDOM component C3 (0.716), and C4 (0.758). Factors 3 and 4 had correlations with NH3-N (0.748) and T-P (0.769), respectively. The variations of PCA factors scores revealed that PACS contributed less aluminum dissolution than PAC to obtain equivalent removal efficiency of contaminants. This might be due to the high cationic charge and pre-hydrolyzation of PACS. Compared with PACS coagulation (20 mg L(-1)), the removal of PCA factors 1, 2, and 4 increased 45, 33, and 12 %, respectively, in combined PACS-PDADMAC treatment (0.8 mg L(-1) +20 mg L(-1)). Since PAC contained more Al (0.053 g/1 g) than PACS (0.028 g/1 g), the results indicated that PACS contributed less Al dissolution into the water to obtain equivalent removal efficiency.

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.

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 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.

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.

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.

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

Science.gov (United States)

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

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.)

[School principals--too ill for healthy schools?].

Science.gov (United States)

Weber, A; Weltle, D; Lederer, P

2004-03-01

School principals on the one hand play an important role in maintaining the performance and health of teachers, but on the other hand often feel over-burdened themselves and suffer from illnesses which not only impair their health-promoting function, but also lead to limitations in their fitness for the occupation. The aim of our study was therefore, using objective parameters and larger numbers of cases, to obtain a differentiated insight into the type and extent of morbidity spectrum and the health-related early retirement of school principals. In a prospective total assessment (the whole of Bavaria in the period 1997-1999), all the reports about the premature unfitness for work of school directors were evaluated. The analysis included for example socio-demographic/occupational factors, diagnoses, assessment of performance and rehabilitation. The answers given in a standardised, anonymous questionnaire provided the database. Evaluation was carried out by means of descriptive statistics. The median age of the 408 school principals included in the evaluation (heads and vice-heads, 30% of whom were women) was 58 (min: 41 years old, max: 64 years old). The most frequent workplaces were primary schools (38%) and secondary schools (25%). 84% (n=342) of the headmasters were assessed to be unfit for work. The main reasons for early retirement were psychic/psychosomatic illnesses (F-ICD 10) which made up 45% of the cases. The relative frequency was higher in women than in men. Depressive disorders and exhaustion syndromes (burnout) dominated among the psychiatric diagnoses (proportion: 57%). The most frequent somatic diseases were cardio-vascular diseases (I-ICD10) in 19% of cases, then muscular/skeletal diseases (M-ICD10) in 10% and malignant tumours (C-ICD 10) in 9% of cases. Cardio-vascular diseases, in particular arterial hypertonia and ischaemic heart disease, were, in addition, found in headmasters significantly more frequently than in teachers without a leadership

Increasing Principal Effectiveness: A Strategic Investment for ESEA

Science.gov (United States)

Center for American Progress, 2011

2011-01-01

School principals are second only to teachers among school-based factors that influence student achievement and they are critical to attracting and retaining effective teachers and other school staff. Yet in the past, federal policymakers haven't given school leadership much attention. This reauthorization of the Elementary and Secondary Education…

3-Way characterization of soils by Procrustes rotation, matrix-augmented principal components analysis and parallel factor analysis

Czech Academy of Sciences Publication Activity Database

Andrade, J.M.; Kubista, Mikael; Carlosena, A.; Prada, D.

2007-01-01

Roč. 603, č. 1 (2007), s. 20-29 ISSN 0003-2670 Institutional research plan: CEZ:AV0Z50520514 Keywords : PCA * heavy metals * soil Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.186, year: 2007

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.)

Principal factors of soil spatial heterogeneity and ecosystem services at the Central Chernozemic Region of Russia

Science.gov (United States)

Vasenev, Ivan; Valentini, Riccardo

2013-04-01

The essential spatial heterogeneity is mutual feature for most natural and man-changed soils at the Central Chernozemic Region of Russia which is not only one of the biggest «food baskets» in RF but very important regulator of ecosystem principal services at the European territory of Russia. The original spatial heterogeneity of dominated here forest-steppe and steppe Chernozems and the other soils has been further complicated by a specific land-use history and different-direction soil successions due to environmental changes and more than 1000-year history of human impacts. The carried out long-term researches of representative natural, rural and urban landscapes in Kursk, Orel, Tambov and Voronezh oblasts give us the regional multi-factorial matrix of elementary soil cover patterns (ESCP) with different land-use practices and history, soil-geomorphologic features, environmental and microclimate conditions. The validation and ranging of the limiting factors of ESCP regulation and development, ecosystem principal services, land functional qualities and agroecological state have been done for dominating and most dynamical components of ESCP regional-typological forms - with application of regional and local GIS, soil spatial patterns mapping, traditional regression kriging, correlation tree models. The outcomes of statistical modeling show the essential amplification of erosion, dehumification and CO2 emission, acidification and alkalization, disaggregation and overcompaction processes due to violation of agroecologically sound land-use systems and traditional balances of organic matter, nutrients, Ca and Na in agrolandscapes. Due to long-term intensive and out-of-balance land-use practices the famous Russian Chernozems begin to lose not only their unique natural features of (around 1 m of humus horizon, 4-6% of Corg and favorable agrophysical features), but traditional soil cover patterns, ecosystem services and agroecological functions. Key-site monitoring

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…

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…

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.

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.